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Research Results Section – Writing Guide and Examples

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Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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How to write the results section of a research paper

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Table of Contents

At its core, a research paper aims to fill a gap in the research on a given topic. As a result, the results section of the paper, which describes the key findings of the study, is often considered the core of the paper. This is the section that gets the most attention from reviewers, peers, students, and any news organization reporting on your findings. Writing a clear, concise, and logical results section is, therefore, one of the most important parts of preparing your manuscript.

Difference between results and discussion

Before delving into how to write the results section, it is important to first understand the difference between the results and discussion sections. The results section needs to detail the findings of the study. The aim of this section is not to draw connections between the different findings or to compare it to previous findings in literature—that is the purview of the discussion section. Unlike the discussion section, which can touch upon the hypothetical, the results section needs to focus on the purely factual. In some cases, it may even be preferable to club these two sections together into a single section. For example, while writing  a review article, it can be worthwhile to club these two sections together, as the main results in this case are the conclusions that can be drawn from the literature.

Structure of the results section

Although the main purpose of the results section in a research paper is to report the findings, it is necessary to present an introduction and repeat the research question. This establishes a connection to the previous section of the paper and creates a smooth flow of information.

Next, the results section needs to communicate the findings of your research in a systematic manner. The section needs to be organized such that the primary research question is addressed first, then the secondary research questions. If the research addresses multiple questions, the results section must individually connect with each of the questions. This ensures clarity and minimizes confusion while reading.

Consider representing your results visually. For example, graphs, tables, and other figures can help illustrate the findings of your paper, especially if there is a large amount of data in the results.

Remember, an appealing results section can help peer reviewers better understand the merits of your research, thereby increasing your chances of publication.

Practical guidance for writing an effective results section for a research paper

• Always use simple and clear language. Avoid the use of uncertain or out-of-focus expressions.
• The findings of the study must be expressed in an objective and unbiased manner. While it is acceptable to correlate certain findings in the discussion section, it is best to avoid overinterpreting the results.
• If the research addresses more than one hypothesis, use sub-sections to describe the results. This prevents confusion and promotes understanding.
• Ensure that negative results are included in this section, even if they do not support the research hypothesis.
• Wherever possible, use illustrations like tables, figures, charts, or other visual representations to showcase the results of your research paper. Mention these illustrations in the text, but do not repeat the information that they convey.
• For statistical data, it is adequate to highlight the tests and explain their results. The initial or raw data should not be mentioned in the results section of a research paper.

The results section of a research paper is usually the most impactful section because it draws the greatest attention. Regardless of the subject of your research paper, a well-written results section is capable of generating interest in your research.

For detailed information and assistance on writing the results of a research paper, refer to Elsevier Author Services.

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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE:   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on 27 October 2016 by Bas Swaen . Revised on 25 October 2022 by Tegan George.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean – any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs discussion vs conclusion, checklist: research results, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analysed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like ‘appears’ or ‘implies’.
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe shop: first discuss the shoes as a whole, then the trainers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualise trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarise or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

Figure 1: Intention to donate to environmental organisations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

‘I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.’

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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How to Write an APA Results Section

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Nusha Ashjaee 

What to Include in an APA Results Section

• Justify Claims
• Summarize Results

Report All Relevant Results

• Report Statistical Findings

Include Tables and Figures

What not to include in an apa results section.

Psychology papers generally follow a specific structure. One important section of a paper is known as the results section. An APA results section of a psychology paper summarizes the data that was collected and the statistical analyses that were performed. The goal of this section is to report the results of your study or experiment without any type of subjective interpretation.

At a Glance

The results section is a vital part of an APA paper that summarizes a study's findings and statistical analysis. This section often includes descriptive text, tables, and figures to help summarize the findings. The focus is purely on summarizing and presenting the findings and should not include any interpretation, since you'll cover that in the subsequent discussion section.

This article covers how to write an APA results section, including what to include and what to avoid.

The results section is the third section of a psychology paper. It will appear after the introduction and methods sections and before the discussion section.

The results section should include:

• A summary of the research findings.
• Information about participant flow, recruitment, retention, and attrition. If some participants started the study and later left or failed to complete the study, then this should be described. 
• Information about any reasons why some data might have been excluded from the study. 
• Statistical information including samples sizes and statistical tests that were used. It should report standard deviations, p-values, and other measures of interest.

Results Should Justify Your Claims

Report data in order to sufficiently justify your conclusions. Since you'll be talking about your own interpretation of the results in the discussion section, you need to be sure that the information reported in the results section justifies your claims.

When you start writing your discussion section, you can then look back on your results to ensure that all the data you need are there to fully support your conclusions. Be sure not to make claims in your discussion section that are not supported by the findings described in your results section.

Summarize Your Results

Remember, you are summarizing the results of your psychological study, not reporting them in full detail. The results section should be a relatively brief overview of your findings, not a complete presentation of every single number and calculation.

If you choose, you can create a supplemental online archive where other researchers can access the raw data if they choose.

How long should a results section be?

The length of your results section will vary depending on the nature of your paper and the complexity of your research. In most cases, this will be the shortest section of your paper.

Just as the results section of your psychology paper should sufficiently justify your claims, it should also provide an accurate look at what you found in your study. Be sure to mention all relevant information.

Don't omit findings simply because they failed to support your predictions.

Your hypothesis may have expected more statistically significant results or your study didn't support your hypothesis , but that doesn't mean that the conclusions you reach are not useful. Provide data about what you found in your results section, then save your interpretation for what the results might mean in the discussion section.

While your study might not have supported your original predictions, your finding can provide important inspiration for future explorations into a topic.

How is the results section different from the discussion section?

The results section provides the results of your study or experiment. The goal of the section is to report what happened and the statistical analyses you performed. The discussion section is where you will examine what these results mean and whether they support or fail to support your hypothesis.

Report Your Statistical Findings

Always assume that your readers have a solid understanding of statistical concepts. There's no need to explain what a t-test is or how a one-way ANOVA works. Your responsibility is to report the results of your study, not to teach your readers how to analyze or interpret statistics.

Include Effect Sizes

The Publication Manual of the American Psychological Association recommends including effect sizes in your results section so that readers can appreciate the importance of your study's findings.

Your results section should include both text and illustrations. Presenting data in this way makes it easier for readers to quickly look at your results.

Structure your results section around tables or figures that summarize the results of your statistical analysis. In many cases, the easiest way to accomplish this is to first create your tables and figures and then organize them in a logical way. Next, write the summary text to support your illustrative materials.

Only include tables and figures if you are going to talk about them in the body text of your results section.

In addition to knowing what you should include in the results section of your psychology paper, it's also important to be aware of things that you should avoid putting in this section:

Cause-and-Effect Conclusions

Don't draw cause-effect conclusions. Avoid making any claims suggesting that your result "proves" that something is true. 

Interpretations

Present the data without editorializing it. Save your comments and interpretations for the discussion section of your paper. 

Statistics Without Context

Don't include statistics without narration. The results section should not be a numbers dump. Instead, you should sequentially narrate what these numbers mean.

Don't include the raw data in the results section. The results section should be a concise presentation of the results. If there is raw data that would be useful, include it in the appendix .

Don't only rely on descriptive text. Use tables and figures to present these findings when appropriate. This makes the results section easier to read and can convey a great deal of information quickly.

Repeated Data

Don't present the same data twice in your illustrative materials. If you have already presented some data in a table, don't present it again in a figure. If you have presented data in a figure, don't present it again in a table.

All of Your Findings

Don't feel like you have to include everything. If data is irrelevant to the research question, don't include it in the results section.

But Don't Skip Relevant Data

Don't leave out results because they don't support your claims. Even if your data does not support your hypothesis, including it in your findings is essential if it's relevant.

More Tips for Writing a Results Section

If you are struggling, there are a few things to remember that might help:

• Use the past tense . The results section should be written in the past tense.
• Be concise and objective . You will have the opportunity to give your own interpretations of the results in the discussion section.
• Use APA format . As you are writing your results section, keep a style guide on hand. The Publication Manual of the American Psychological Association is the official source for APA style.
• Visit your library . Read some journal articles that are on your topic. Pay attention to how the authors present the results of their research.
• Get a second opinion . If possible, take your paper to your school's writing lab for additional assistance.

What This Means For You

Remember, the results section of your paper is all about providing the data from your study. This section is often the shortest part of your paper, and in most cases, the most clinical.

Be sure not to include any subjective interpretation of the results. Simply relay the data in the most objective and straightforward way possible. You can then provide your own analysis of what these results mean in the discussion section of your paper.

Bavdekar SB, Chandak S. Results: Unraveling the findings . J Assoc Physicians India . 2015 Sep;63(9):44-6. PMID:27608866.

Snyder N, Foltz C, Lendner M, Vaccaro AR. How to write an effective results section .  Clin Spine Surg . 2019;32(7):295-296. doi:10.1097/BSD.0000000000000845

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

Purdue Online Writing Lab. APA sample paper: Experimental psychology .

Berkeley University. Reviewing test results .

Tuncel A, Atan A. How to clearly articulate results and construct tables and figures in a scientific paper ? Turk J Urol . 2013;39(Suppl 1):16-19. doi:10.5152/tud.2013.048

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Writing a scientific paper.

• Writing a lab report
• INTRODUCTION

Writing a "good" results section

Figures and Captions in Lab Reports

"Results Checklist" from: How to Write a Good Scientific Paper. Chris A. Mack. SPIE. 2018.

Additional tips for results sections.

• LITERATURE CITED
• Bibliography of guides to scientific writing and presenting
• Peer Review
• Presentations
• Lab Report Writing Guides on the Web

This is the core of the paper. Don't start the results sections with methods you left out of the Materials and Methods section. You need to give an overall description of the experiments and present the data you found.

• Factual statements supported by evidence. Short and sweet without excess words
• Present representative data rather than endlessly repetitive data
• Discuss variables only if they had an effect (positive or negative)
• Use meaningful statistics
• Avoid redundancy. If it is in the tables or captions you may not need to repeat it

A short article by Dr. Brett Couch and Dr. Deena Wassenberg, Biology Program, University of Minnesota

• Present the results of the paper, in logical order, using tables and graphs as necessary.
• Explain the results and show how they help to answer the research questions posed in the Introduction. Evidence does not explain itself; the results must be presented and then explained. 
• Avoid: presenting results that are never discussed;  presenting results in chronological order rather than logical order; ignoring results that do not support the conclusions; 
• Number tables and figures separately beginning with 1 (i.e. Table 1, Table 2, Figure 1, etc.).
• Do not attempt to evaluate the results in this section. Report only what you found; hold all discussion of the significance of the results for the Discussion section.
• It is not necessary to describe every step of your statistical analyses. Scientists understand all about null hypotheses, rejection rules, and so forth and do not need to be reminded of them. Just say something like, "Honeybees did not use the flowers in proportion to their availability (X2 = 7.9, p<0.05, d.f.= 4, chi-square test)." Likewise, cite tables and figures without describing in detail how the data were manipulated. Explanations of this sort should appear in a legend or caption written on the same page as the figure or table.
• You must refer in the text to each figure or table you include in your paper.
• Tables generally should report summary-level data, such as means ± standard deviations, rather than all your raw data.  A long list of all your individual observations will mean much less than a few concise, easy-to-read tables or figures that bring out the main findings of your study.  
• Only use a figure (graph) when the data lend themselves to a good visual representation.  Avoid using figures that show too many variables or trends at once, because they can be hard to understand.

From:  https://writingcenter.gmu.edu/guides/imrad-results-discussion

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How to Write the Results Section of a Research Paper

Table of Contents

Laura Moro-Martin, freelance scientific writer on Kolabtree, provides expert tips on how to write the results section of a research paper . 

You have prepared a detailed −but concise− Methods section . Now it is time to write the Results of your research article. This part of the paper reports the findings of the experiments that you conducted to answer the research question(s). The Results can be considered the nucleus of a scientific article because they justify your claims, so you need to ensure that they are clear and understandable. You are telling a story −of course, a scientific story− and you want the readers to picture that same story in their minds. Let’s see how to avoid that your message ends up as in the ‘telephone game’.

The Results Section: Goals and Structure

Depending on the discipline, journal, and the nature of the study, the structure of the article can differ. We will focus on articles were the Results and Discussion appear in two separate sections, but it is possible in some cases to combine them.

In the Results section, you provide an overall description of the experiments and present the data that you obtained in a logical order, using tables and graphs as necessary. The Results section should simply state your findings without bias or interpretation. For example, in your analysis, you may have noticed a significant correlation between two variables never described before. It is correct to explain this in the Results section. However, speculation about the reasons for this correlation should go in the Discussion section of your paper.

In general, the Results section includes the following elements:

• A very short introductory context that repeats the research question and helps to understand your results.
• Report on data collection, recruitment, and/or participants. For example, in the case of clinical research, it is common to include a first table summarizing the demographic, clinical, and other relevant characteristics of the study participants.
• A systematic description of the main findings in a logical order (generally following the order of the Methods section), highlighting the most relevant results.
• Other important secondary findings, such as secondary outcomes or subgroup analyses (remember that you do not need to mention any single result).
• Visual elements, such as, figures, charts, maps, tables, etc. that summarize and illustrate the findings. These elements should be cited in the text and numbered in order. Figures and tables should be able to stand on its own without the text, which means that the legend should include enough information to understand the non-textual element.

How to Write the Results Section of a Research Paper: Tips

The first tip −applicable to other sections of the paper too− is to check and apply the requirements of the journal to which you are submitting your work.

In the Results section, you need to write concisely and objectively, leaving interpretation for the Discussion section. As always, ‘learning from others’ can help you. Select a few papers from your field, including some published in your target journal, which you consider ‘good quality’ and well written. Read them carefully and observe how the Results section is structured, the type and amount of information provided, and how the findings are exposed in a logical order. Keep an eye on visual elements, such as figures, tables, and supplementary materials. Understand what works well in those papers to effectively convey their findings, and apply it to your writing.

Your Results section needs to describe the sequence of what you did and found, the frequency of occurrence of a particular event or result, the quantities of your observations, and the causality (i.e. the relationships or connections) between the events that you observed.

To organize the results, you can try to provide them alongside the research questions. In practice, this means that you will organize this section based on the sequence of tables and figures summarizing the results of your statistical analysis. In this way, it will be easier for readers to look at and understand your findings. You need to report your statistical findings, without describing every step of your statistical analysis. Tables and figures generally report summary-level data (for example, means and standard deviations), rather than all the raw data.

Following, you can prepare the summary text to support those visual elements. You need not only to present but also to explain your findings, showing how they help to address the research question(s) and how they align with the objectives that you presented in the Introduction . Keep in mind that results do not speak for themselves, so if you do not describe them in words, the reader may perceive the findings differently from you. Build coherence along this section using goal statements and explicit reasoning (guide the reader through your reasoning, including sentences of this type: ‘In order to…, we performed….’; ‘In view of this result, we ….’, etc.).

In summary, the general steps for writing the Results section of a research article are:

• Check the guidelines of your target journal and read articles that it has published in similar topics to your study.
• Catalogue your findings in relation to the journal requirements, and design figures and tables to organize your data.
• Write the Results section following the order of figures and tables.
• Edit and revise your draft and seek additional input from colleagues or experts.

The Style of the Results Section

‘If you are out to describe the truth, leave elegance to the tailor’, Austrian physicist Ludwig Boltzmann said. Although the scope of the Results section −and of scientific papers in general− is eminently functional, this does not mean that you cannot write well. Try to improve the rhythm to move the reader along, use transitions and connectors between different sections and paragraphs, and dedicate time to revise your writing.

The Results section should be written in the past tense. Although writing in the passive voice may be tempting, the use of the active voice makes the action much more visualizable. The passive voice weakens the power of language and increases the number of words needed to say the same thing, so we recommend using the active voice as much as possible. Another tip to make your language visualizable and reduce sentence length is the use of verbal phrases instead of long nouns. For example, instead of writing ‘As shown in Table 1, there was a significant increase in gene expression’, you can say ‘As shown in Table 1, gene expression increased significantly’.

Get a Second (And Even Third) Opinion

Writing a scientific article is not an individual work. Take advantage of your co-authors by making them check the Results section and adding their comments and suggestions. Not only that, but an external opinion will help you to identify misinterpretations or errors. Ask a colleague that is not directly involved in the work to review your Results and then try to evaluate what your colleague did or did not understand. If needed, seek additional help from a qualified expert.

Common Errors to Avoid While Writing the Results Section

Several mistakes frequently occur when you write the Results section of a research paper. Here we have collected a few examples:

• Including raw results and/or endlessly repetitive data. You do not need to present every single number and calculation, but a summary of the results. If relevant, raw data can be included in supplementary materials.
• Including redundant information. If data are contained in the tables or figures, you do not need to repeat all of them in the Results section. You will have the opportunity to highlight the most relevant results in the Discussion .
• Repeating background information or methods , or introducing several sentences of introductory information (if you feel that more background information is necessary to present a result, consider inserting that information in the Introduction ).
• Results and Methods do not match . You need to explain the methodology used to obtain all the experimental observations.
• Ignoring negative results or results that do not support the conclusions. In addition to posing potential ethical concerns on your work, reviewers will not like it. You need to mention all relevant findings, even if they failed to support your predictions or hypotheses. Negative results are useful and will guide future studies on the topic. Provide your interpretation for negative results in the Discussion .
• Discussing or interpreting the results . Leave that for the Discussion , unless your target journal allows preparing one section combining Results and Discussion .
• Errors in figures/tables are varied and common . Examples of errors include using an excessive number of figures/tables (it is a good idea to select the most relevant ones and move the rest to supplementary materials), very complex figures/tables (hard-to-read figures with many subfigures or enormous tables may confuse your readers; think how these elements will be visualized in the final format of the article), difficult to interpret figures/tables (cryptic abbreviations; inadequate use of colors, axis, scales, symbols, etc.), and figures/tables that are not self-standing (figures/tables require a caption, all abbreviations used need to be explained in the legend or a footnote, and statistical tests applied are frequently reported). Do not include tables and figures that are not mentioned in the body text of your Results .

In summary, the Results section is the nucleus of your paper that justifies your claims. Take time to adequately organize it and prepare understandable figures and tables to convey your message to the reader. Good writing!

• The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. https://abacus.bates.edu/~ganderso/biology/resources/writing/HTWsections.html – methods (accessed on 30th September 2020)
• Organizing Academic Research Papers: 7. The Results. https://library.sacredheart.edu/c.php?g=29803&p=185931 (accessed on 30th September 2020)
• Kendra Cherry. How to Write an APA Results Section. https://www.verywellmind.com/how-to-write-a-results-section-2795727 (accessed on 30th September 2020)
• Chapin Rodríguez. Empowering your scientific language by making it “visualizable”. http://creaducate.eu/wp-content/uploads/2019/11/tipsheet36_visualizable-lang-tip-sheet.pdf (accessed on 1st October 2020)
• IMRaD Results Discussion. https://writingcenter.gmu.edu/guides/imrad-results-discussion (accessed on 1st October 2020)
• Writing the Results Section for a Research Paper. https://wordvice.com/writing-the-results-section-for-a-research-paper/ (accessed on 1st October 2020)
• Scott L. Montgomery. The Chicago Guide to Communicating Science , Chapter 9. Second edition, The University of Chicago Press, 2017.
• Hilary Glasman-Deal . Science Research Writing for Non-Native Speakers of English, Unit 2 . Imperial College Press, 2010.

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Organizing Academic Research Papers: 7. The Results

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The results section of the research paper is where you report the findings of your study based upon the information gathered as a result of the methodology [or methodologies] you applied. The results section should simply state the findings, without bias or interpretation, and arranged in a logical sequence. The results section should always be written in the past tense. A section describing results [a.k.a., "findings"] is particularly necessary if your paper includes data generated from your own research.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Research results can only confirm or reject the research problem underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise, using non-textual elements, such as figures and tables, if appropriate, to present results more effectively. In deciding what data to describe in your results section, you must clearly distinguish material that would normally be included in a research paper from any raw data or other material that could be included as an appendix. In general, raw data should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good rule is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper].

Bates College; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Structure and Writing Style

I. Structure and Approach

For most research paper formats, there are two ways of presenting and organizing the results .

• Present the results followed by a short explanation of the findings . For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is correct to point this out in the results section. However, speculating as to why this correlation exists, and offering a hypothesis about what may be happening, belongs in the discussion section of your paper.
• Present a section and then discuss it, before presenting the next section then discussing it, and so on . This is more common in longer papers because it helps the reader to better understand each finding. In this model, it can be helpful to provide a brief conclusion in the results section that ties each of the findings together and links to the discussion.

NOTE: The discussion section should generally follow the same format chosen in presenting and organizing the results.

II.  Content

In general, the content of your results section should include the following elements:

• An introductory context for understanding the results by restating the research problem that underpins the purpose of your study.
• A summary of your key findings arranged in a logical sequence that generally follows your methodology section.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate the findings, if appropriate.
• In the text, a systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation [remember that not all results that emerge from the methodology that you used to gather the data may be relevant].
• Use of the past tense when refering to your results.
• The page length of your results section is guided by the amount and types of data to be reported. However, focus only on findings that are important and related to addressing the research problem.

Using Non-textual Elements

• Either place figures, tables, charts, etc. within the text of the result, or include them in the back of the report--do one or the other but never do both.
• In the text, refer to each non-textual element in numbered order [e.g.,  Table 1, Table 2; Chart 1, Chart 2; Map 1, Map 2].
• If you place non-textual elements at the end of the report, make sure they are clearly distinguished from any attached appendix materials, such as raw data.
• Regardless of placement, each non-textual element must be numbered consecutively and complete with caption [caption goes under the figure, table, chart, etc.]
• Each non-textual element must be titled, numbered consecutively, and complete with a heading [title with description goes above the figure, table, chart, etc.].
• In proofreading your results section, be sure that each non-textual element is sufficiently complete so that it could stand on its own, separate from the text.

III. Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save all this for the next section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings ; this should have been done in your Introduction section, but don't panic! Often the results of a study point to the need to provide additional background information or to explain the topic further, so don't think you did something wrong. Revise your introduction as needed.
• Ignoring negative results . If some of your results fail to support your hypothesis, do not ignore them. Document them, then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, often provides you with the opportunity to write a more engaging discussion section, therefore, don't be afraid to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater or lesser than..." or "demonstrates promising trends that...."
• Presenting the same data or repeating the same information more than once . If you feel the need to highlight something, you will have a chance to do that in the discussion section.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. If you are not sure, look up the term in a dictionary.

Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers . Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results . Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in social science journals where the author(s) have combined a description of the findings from the study with a discussion about their implications. You could do this. However, if you are inexperienced writing research papers, consider creating two sections for each element in your paper as a way to better organize your thoughts and, by extension, your  paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret your data and answer the "so what?" question. As you become more skilled writing research papers, you may want to meld the results of your study with a discussion of its implications.

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How to Write an Effective Results Section

Affiliation.

• 1 Rothman Orthopaedics Institute, Philadelphia, PA.
• PMID: 31145152
• DOI: 10.1097/BSD.0000000000000845

Developing a well-written research paper is an important step in completing a scientific study. This paper is where the principle investigator and co-authors report the purpose, methods, findings, and conclusions of the study. A key element of writing a research paper is to clearly and objectively report the study's findings in the Results section. The Results section is where the authors inform the readers about the findings from the statistical analysis of the data collected to operationalize the study hypothesis, optimally adding novel information to the collective knowledge on the subject matter. By utilizing clear, concise, and well-organized writing techniques and visual aids in the reporting of the data, the author is able to construct a case for the research question at hand even without interpreting the data.

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Training videos   |   Faqs

Results Section Examples and Writing Tips

Abstract | Introduction | Literature Review | Research question | Materials & Methods | Results | Discussion | Conclusion

In this blog, we look at how to write the results section of a research paper. We will go through plenty of results examples and understand how to construct a great results section for your research paper.

1. What is the purpose of the results section?

The authors report their findings in the results section. This is a relatively easy section to write. You simply have to organize your results and write them up in a logical sequence. You must present your findings without evaluating or interpreting them. You must try to illustrate your data using figures and tables to make it more accessible to the readers.

2. How should I structure my results section?

The results section of a research paper typically contains the following components:

Data pre-processing You can talk about any difficulties you encountered while collecting or processing the data.

Main findings You should present your main findings (both positive and negative) in a logical sequence.

Statistics You must use descriptive statistics and inferential statistics to present your data (mean, SD, CI etc.)

Figures and tables Use figures and tables to summarize large amounts of data in a visually pleasing way.

Trends and patterns Trends and patterns describe the general change in some variable in relation to another variable.

Reanalysis You can talk about any reanalysis of data you had to perform in order to reconfirm your findings.

3. Examples of results section

Let’s look at some examples of the results section. We will be looking at results section examples from different fields and of different formats. We have split this section into multiple components so that it is easy for you to understand.

3.1. An example of a pre-processing passage in the results section

Here is an example of the results section from an engineering research paper where the authors talk about pre-processing.  The authors are saying that they performed a series of steps before conducting the actual analysis.  They are saying that they filled in the missing data using linear interpolation. And then, they transformed and normalized the data. Finally, they applied data smoothing to remove any noise in the data. As you can see the authors are very transparent and are detailing everything they did to the data before the actual analysis.

The following is a brief summary of the preprocessing steps applied prior to analysis. The data were screened for outliers and such data points were set to missing. Subsequently, the missing data were filled by linear interpolation. The data were transformed using the Box method and then normalized to zero mean and unit standard deviation. The smoothing was applied at the final step to remove the noise in the data. _ Missing data   _  Missing data fix _  Data normalization _  Data smoothing

3.2. An example of main findings passage in the results section

While presenting your findings you must clearly explain the following:

This is an example of the results section from a psychology research paper where authors are outlining their main findings. In this paper, the authors are investigating the effects of different types of music on people. The authors say that they found significant differences between classical and pop music in terms of memory recall.  And then, they are saying that they did not find any differences in terms of emotional response. Finally, they are saying that they were quite surprised to find that both types of music fatigued the listeners at the same rate.

The results indicate significant differences between classical music and pop music in terms of their effects on memory recall and cognition (p<0.05). However, there was no significant difference across the groups for the emotional response to the music (p>0.05). It was surprising to find that both types of music elicited similar levels of fatigue in both groups (p>0.05). _ What was found?   _  What was not found? _  Unexpected results

It is very clear from their tone that the last result was a bit unexpected. You can see that the authors have presented their findings in an unbiased manner without any interpretation. They have listed, the positive, the negative, and the neutral results logically in the text. This is how it should be done.

3.3. An example of using statistics in the results section

The use of relevant statistics is very important while writing your results section. It offers two benefits, number one, it will help you summarize the data in a meaningful way, and number two, it will make your text sound more credible. This results example is from a social sciences research paper investigating the relationship between social media and mental health. I want you to pay specific attention to the descriptive and inferential statistics used throughout the text.

The results of this study indicate significant differences in anxiety levels between high social media usage and moderate social media usage (p<0.05). The average time spent by high social media users (5.2 ± 2.2) was considerably higher than that of moderate social media users (3.2 ± 2.2). The odds of sleep disturbance were significantly greater for high social media users (odds ratio, 2.12; 95% CI, 1.81-2.17) compared to moderate social media users (odds ratio, 1.14; 95% CI, 1.07-1.18). _ p-values   _  Standard deviation _  Odds ratio & confidence interval

The authors say that there is a significant difference in anxiety levels between high social media users and moderate social media users. Since the authors have used the word significant, they have specified the p-value of the statistical test they used to ascertain this. Then they are providing the actual values of the amount of time spent by both groups on social media. They have presented the data in the form, mean plus or minus standard deviation, which is the standard scientific way to represent this type of data. In the final statement, they talk about the odds of both groups experiencing sleep disturbances. They have provided the odds ratio along with the confidence intervals.

3.4. An example of using figures and tables in the results section

One of the important components of the results section is figures and tables. Try to present your data in figures and tables wherever necessary.

Here is a results example where authors are using figures and tables to describe their results. In the first couple of lines, they are talking about a trend in their data that relates to the change in temperature over time. They are constantly referring to the figure to get their point across to the readers. And finally, in the last sentence, they are telling the readers that the actual numerical data is provided in the table, and they can refer to it if they want. This is a standard way to use figures and tables in your research paper.

In Figure 1.1, the values are plotted as a function of time. The two peaks in the plot correspond to the maximum and minimum temperature values. The specific values obtained for each experiment are given in Table 2. _ Figure   _  Figure info _  Table

3.5. An example of elaborating trends/patterns in the results section

As a researcher, it is your job to identify trends, patterns, and relationships between different variables in your data, and tell your readers about them. Because they can reveal very important evidence that you can use to answer your research questions or prove your hypothesis.

The temperature value increases until it reaches a peak value, then decreases rapidly to zero. This effect was 10 times larger at room temperature. There appears to be a positive association between temperature and time. _ Trend   _  Pattern _  Interpreting the evidence

In this example, the author talks about his observation that the temperature changes over time in a certain pattern. Then the author talks about noticing the same behaviour under different conditions. Then based on the evidence, the author concludes that there is a positive association between time and temperature. The passage flows very well. You can clearly understand what the author is trying to say here.

4. Frequently Asked Questions

Most journals require separate results and discussion sections. So, it is very important that you are just reporting and describing your results without interpreting them in your results section. The interpretation of the results must be done in the discussion section.

Do not suppress negative results in your paper. Don’t worry if your experiments did not yield the results that you were expecting. Don’t try to ignore or downplay the result just because it doesn’t support your hypothesis. It doesn’t mean that your research is a failure. Negative results are as good as positive results. Actually, you have discovered a useful piece of evidence that your experiments don’t work

Here is an example of an author reporting negative and moderate results in the paper. This is perfectly fine. The authors have reported their results in the paper with full transparency and honesty. And that is how it should be.

The performance did not improve significantly with the new approach, though some marginal improvement was still achieved in terms of speed. These findings are in contrary to our original hypothesis. _  Moderate results  _  Negative results

Most people skim through the paper just going through figures and tables without reading any text in the paper. So, captions should be as short as possible but detailed enough for the readers to understand the figure or table without having to read the text.

The best way to answer this question is if you want to illustrate the trends and patterns in the data, then a figure is the best option. If you want to show the actual values or present a lot of numerical information in your paper. Then, a table might be the best way to go.

The best way to answer this question is if you cannot present your data in your text in one or two lines, then you should consider putting it in a figure or a table.

You should write your results section in the past tense, because you are reporting the results of an experiment that you conducted in the past.

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Writing a Results Section

The next stage of any research paper: writing the results section, announcing your findings to the world.

This article is a part of the guide:

• Outline Examples
• Example of a Paper
• Write a Hypothesis
• Introduction

Browse Full Outline

• 1 Write a Research Paper
• 2 Writing a Paper
• 3.1 Write an Outline
• 3.2 Outline Examples
• 4.1 Thesis Statement
• 4.2 Write a Hypothesis
• 5.2 Abstract
• 5.3 Introduction
• 5.4 Methods
• 5.5 Results
• 5.6 Discussion
• 5.7 Conclusion
• 5.8 Bibliography
• 6.1 Table of Contents
• 6.2 Acknowledgements
• 6.3 Appendix
• 7.1 In Text Citations
• 7.2 Footnotes
• 7.3.1 Floating Blocks
• 7.4 Example of a Paper
• 7.5 Example of a Paper 2
• 7.6.1 Citations
• 7.7.1 Writing Style
• 7.7.2 Citations
• 8.1.1 Sham Peer Review
• 8.1.2 Advantages
• 8.1.3 Disadvantages
• 8.2 Publication Bias
• 8.3.1 Journal Rejection
• 9.1 Article Writing
• 9.2 Ideas for Topics

In theory, this is the easiest part to write, because it is a straightforward commentary of exactly what you observed and found. In reality, it can be a little tricky, because it is very easy to include too much information and bury the important findings.

Too Much Information?

The results section is not for interpreting the results in any way; that belongs strictly in the discussion section. You should aim to narrate your findings without trying to interpret or evaluate them, other than to provide a link to the discussion section.

For example, you may have noticed an unusual correlation between two variables during the analysis of your results. It is correct to point this out in the results section.

Speculating why this correlation is happening, and postulating about what may be happening, belongs in the discussion section .

It is very easy to put too much information into the results section and obscure your findings underneath reams of irrelevance.

If you make a table of your findings, you do not need to insert a graph highlighting the same data. If you have a table of results, refer to it in the text, but do not repeat the figures - duplicate information will be penalized.

One common way of getting around this is to be less specific in the text. For example, if the result in table one shows 23.9%, you could write….

Table One shows that almost a quarter of…..

Tips for Writing a Results Section

Perhaps the best way to use the results section is to show the most relevant information in the graphs, figures and tables.

The text, conversely, is used to direct the reader to those, also clarifying any unclear points. The text should also act as a link to the discussion section, highlighting any correlations and findings and leaving plenty of open questions.

For most research paper formats , there are two ways of presenting and organizing the results. The first method is to present the results and add a short discussion explaining them at the end, before leading into the discussion proper.

This is very common where the research paper is straightforward, and provides continuity. The other way is to present a section and then discuss it, before presenting the next section with a short discussion. This is common in longer papers, and your discussion part of the paper will generally follow the same structure.

Be sure to include negative results - writing a results section without them not only invalidate the paper, but it is extremely bad science. The negative results, and how you handle them, often gives you the makings of a great discussion section, so do not be afraid to highlight them.

Using an Appendix to Streamline Writing the Results Section

If you condense your raw data down, there is no need to include the initial findings in the results, because this will simply confuse the reader.

If you are in doubt about how much to include, you can always insert your raw data into the appendix section, allowing others to follow your calculations from the start. This is especially useful if you have used many statistical manipulations, so that people can check your calculations and ensure that you have not made any mistakes.

In the age of spreadsheets, where the computer program prepares all of the calculations for you, this is becoming less common, although you should specify the program that you used and the version. On that note, it is unnecessary show your working - assume that the reader understands what a Chi Squared test, or a Students t-test is, and can perform it themselves.

Once you have a streamlined and informative results section, you can move onto the discussion section, where you begin to elaborate your findings.

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Martyn Shuttleworth (Mar 2, 2009). Writing a Results Section. Retrieved Jun 01, 2024 from Explorable.com: https://explorable.com/writing-a-results-section

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Results Section Of A Research Paper: How To Write It Properly

The results section of a research paper refers to the part that represents the study’s core findings from the methods that the researcher used to collect and analyze data. This section presents the results logically without interpretation or bias from the author.

Thus, this part of a research paper sets up the read for evaluation and analysis of the findings in the discussion section. Essentially, this section breaks down the information into several sentences, showing its importance to the research question. Writing results section in a research paper entails summarizing the gathered data and the performed statistical analysis. That way, the author presents or reports the results without subjective interpretation.

What Is The Results Section Of A Research Paper?

In its simplest definition, a research paper results section is where the researcher reports the findings of a study based on the applied methodology for gathering information. It’s the part where the author states the research findings in a logical sequence without interpreting them. If the research paper has data from actual research, this section should feature a detailed description of the results.

When writing a dissertation, a thesis, or any other academic paper, the result section should come third in sections’ sequence. It should follow the Methods and Materials presentation and the Discussion section comes after it. But most scientific papers present the Results and Discussion sections together. However, the results section answers the question, “What did your research uncover?”

Ideally, this section allows you to report findings in research paper, creating the basis for sufficiently justified conclusions. After writing the study findings in the results section, you interpret them in the subsequent discussion part. Therefore, your results section should report information that will justify your claims. That way, you can look back on the results section when writing the discussion part to ensure that your report supports your conclusions.

What Goes in the Results Section of a Research Paper?

This section should present results in research paper. The findings part of a research paper can differ in structure depending on the study, discipline, and journal. Nevertheless, the results section presents a description of the experiment while presenting the research results. When writing this part of your research paper, you can use graphs and tables if necessary.

However, state the findings without interpreting them. For instance, you can find a correlation between variables when analyzing data. In that case, your results section can explain this correlation without speculating about the causes of this correlation.

Here’s what to include in the results section of research paper:

A brief introductory of the context, repeating the research questions to help the readers understand the results A report about information collection, participants, and recruitment: for instance, you can include a demographic summary with the participants’ characteristics A systematic findings’ description, with a logical presentation highlighting relevant and crucial results A contextual data analysis explaining the meaning in sentences Information corresponding to the primary research questions Secondary findings like subgroup analysis and secondary outcomes Visual elements like charts, figures, tables, and maps, illustrating and summarizing the findings

Ensure that your results section cites and numbers visual elements in an orderly manner. Every table or figure should stand alone without text. That means visual elements should have adequate non-textual content to enable the audiences to understand their meanings.

If your study has a broad scope, several variables, or used methodologies that yielded different results, state the most relevant results only based on the research question you presented in your Introduction section.

The general rule is to leave out any data that doesn’t present your study’s direct outcome or findings. Unless the professor, advisor, university faulty, or your target journal requests you to combine the Results and Discussion sections, omit the interpretations and explanations of the results in this section.

How Long Should A Results Section Be?

The findings section of a research paper ranges between two and three pages, with tables, text, and figures. In most cases, universities and journals insist that this section shouldn’t exceed 1,000 words over four to nine paragraphs, usually with no references.

But a good findings section occupies 5% of the entire paper. For instance, this section should have 500 words if a dissertation has 10,000 words. If the educator didn’t specify the number of words to include in this chapter, use the data you collect to determine its length. Nevertheless, be as concise as possible by featuring only relevant results that answer your research question.

How To Write Results Section Of Research Paper

Perhaps, you have completed researching and writing the preceding sections, and you’re now wondering how to write results. By the time you’re composing this section, you already have findings or answers to your research questions. However, you don’t even know how to start a results section. And your search for guidelines landed you on this page.

Well, every research project is different and unique. That’s why researchers use different strategies when writing this section of their research papers. The scientific or academic discipline, specialization field, target journal, and the author are factors influencing how you write this section. Nevertheless, there’s a general way of writing this section, although it might differ slightly between disciplines. Here’s how to write results section in a research paper.

Check the instructions or guidelines. Check their instructions or guidelines first, whether you’re writing the research paper as part of your coursework or for an academic journal. These guidelines outline the requirements for presenting results in research papers. Also, check the published articles to know how to approach this section. When reviewing the procedures, check content restrictions and length. Essentially, learn everything you can about this section from the instructions or guidelines before you start writing. Reflect on your research findings. With instructions and guidelines in mind, reflect on your research findings to determine how to present them in your research paper. Decide on the best way to show the results so that they can answer the research question. Also, strive to clarify and streamline your report, especially with a complex and lengthy results section. You can use subheadings to avoid peripheral and excessive details. Additionally, consider breaking down the content to make it easy for the readers to understand or remember. Your hypothesis, research question, or methodologies might influence the structure of the findings sections. Nevertheless, a hierarchy of importance, chronological order, or meaningful grouping of categories or themes can be an effective way of presenting your findings. Design your visual presentations. Visual presentations improve the textual report of the research findings. Therefore, decide on the figures and styles to use in your tables, graphs, photos, and maps. However, check the instructions and guidelines of your faculty or journal to determine the visual aids you can use. Also, check what the guidelines say about their formats and design elements. Ideally, number the figures and tables according to their mention in the text. Additionally, your figures and tables should be self-explanatory. Write your findings section. Writing the results section of a research paper entails communicating the information you gathered from your study. Ideally, be as objective and factual as possible. If you gathered complex information, try to simplify and present it accurately, precisely, and clearly. Therefore, use well-structured sentences instead of complex expressions and phrases. Also, use an active voice and past tense since you’ve already done the research. Additionally, use correct spelling, grammar, and punctuation. Take your time to present the findings in the best way possible to focus your readers on your study objectives while preparing them for the coming speculations, interpretations, and recommendations. Edit Your Findings Section. Once you’ve written the results part of your paper, please go through it to ensure that you’ve presented your study findings in the best way possible. Make sure that the content of this section is factual, accurate, and without errors. You’ve taken a considerable amount of time to compose the results scientific paper audiences will find interesting to read. Therefore, take a moment to go through the draft and eliminate all errors.

Practical Tips on How to Write a Results Section of a Research Paper

The results part of a research paper aims to present the key findings objectively in a logical and orderly sequence using text and illustrative materials. A common mistake that many authors make is confusing the information in the discussion and the results sections. To avoid this, focus on presenting your research findings without interpreting them or speculating about them.

The following tips on how to write a results section should make this task easier for you:

Summarize your study results: Instead of reporting the findings in full detail, summarize them. That way, you can develop an overview of the results. Present relevant findings only: Don’t report everything you found during your research. Instead, present pertinent information only. That means taking time to analyze your results to know what your audiences want to know. Report statistical findings: When writing this section, assume that the audiences understand statistical concepts. Therefore, don’t try to explain the nitty-gritty in this section. Remember that your work is to report your study’s findings in this section. Be objective and concise: You can interpret the findings in the discussion sections. Therefore, focus on presenting the results objectively and concisely in this section. Use the suitable format: Use the correct style to present the findings depending on your study field.

Get Professional Help with the Research Section

Maybe you’re pursuing your graduate or undergraduate studies but cannot write the results part of your paper. Perhaps, you’re done researching and analyzing information, but this section proves too tricky for you to write. Well, you’re not alone because many students across the world struggle to present their research findings.

Luckily, our highly educated, talented, and experienced writers are always ready to assist such learners. If you are stuck with the results part of your paper, our professionals can help you . We offer high-quality, custom writing help online. We’re a reliable team of experts with a sterling reputation for providing comprehensive assistance to college, high school, and university learners. We deliver highly informative academic papers after conducting extensive and in-depth research. Contact us saying something like, “please do my thesis” to get quality help with your paper!

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Research Paper Writing: 6. Results / Analysis

• 1. Getting Started
• 2. Abstract
• 3. Introduction
• 4. Literature Review
• 5. Methods / Materials
• 6. Results / Analysis
• 7. Discussion
• 8. Conclusion
• 9. Reference

Writing about the information

There are two sections of a research paper depending on what style is being written. The sections are usually straightforward commentary of exactly what the writer observed and found during the actual research. It is important to include only the important findings, and avoid too much information that can bury the exact meaning of the context.

The results section should aim to narrate the findings without trying to interpret or evaluate, and also provide a direction to the discussion section of the research paper. The results are reported and reveals the analysis. The analysis section is where the writer describes what was done with the data found.  In order to write the analysis section it is important to know what the analysis consisted of, but does not mean data is needed. The analysis should already be performed to write the results section.

Written explanations

How should the analysis section be written?

• Should be a paragraph within the research paper
• Consider all the requirements (spacing, margins, and font)
• Should be the writer’s own explanation of the chosen problem
• Thorough evaluation of work
• Description of the weak and strong points
• Discussion of the effect and impact
• Includes criticism

How should the results section be written?

• Show the most relevant information in graphs, figures, and tables
• Include data that may be in the form of pictures, artifacts, notes, and interviews
• Clarify unclear points
• Present results with a short discussion explaining them at the end
• Include the negative results
• Provide stability, accuracy, and value

How the style is presented

Analysis section

• Includes a justification of the methods used
• Technical explanation

Results section

• Purely descriptive
• Easily explained for the targeted audience
• Data driven

Example of a Results Section

Publication Manual of the American Psychological Association Sixth Ed. 2010

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How to Write a Results Section for a Dissertation or Research Paper: Guide & Examples

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A results section is a crucial part of a research paper or dissertation, where you analyze your major findings. This section goes beyond simply presenting study outcomes. You should also include a comprehensive statistical analysis and interpret the collected data in detail.

Without dissertation research results, it is impossible to imagine a scientific work. Your task here is to present your study findings. What are qualitative or quantitative indicators? How to use tables and diagrams? How to describe data? Our article answers all these questions and many more. So, read further to discover how to analyze and describe your research indexes or contact or professionals for dissertation help from StudyCrumb.

What Is a Results Section of Dissertation?

The results section of a dissertation is a data statement from your research. Here you should present the main findings of your study to your readers. This section aims to show information objectively, systematically, concisely. It is allowed using text supplemented with illustrations.  In general, this section's length is not limited but should include all necessary data. Interpretations or conclusions should not be included in this section. Therefore, in theory, this is one of your shortest sections. But it can also be one of the most challenging sections.  The introduction presents a research topic and answers the question "why?". The Methods section explains the data collection process and answers "how?". Meanwhile, the result section shows actual data gained from experiments and tells "what?" Thus, this part plays a critical role in highlighting study's relevance. This chapter gives reader study relevance with novelty. So, you should figure out how to write it correctly. Here are main tasks that you should keep in mind while writing:

• Results answer the question "What was found in your research?"
• Results contain only your study's outcome. They do not include comments or interpretations.
• Results must always be presented accurately & objectively.
• Tables & figures are used to draw readers' attention. But the same data should never be presented in the form of a table and a figure. Don't repeat anything from a table also in text.

Dissertation: Results vs Discussion vs Conclusion

Results and discussion sections of a dissertation are often confused among researchers. Sometimes both these parts are mixed up with a conclusion for thesis . Figured out what is covered in each of these important chapters. Your readers should see that you notice how different they are. A clear understanding of differences will help you write your dissertation more effectively. 5 differences between Results VS Discussion VS Conclusion:

Wanna figure out the actual difference between discussion vs conclusion? Check out our helpful articles about Dissertation Discussion or Dissertation Conclusion.

Present Your Findings When Writing Results Section of Dissertation

Now it's time to understand how to arrange the results section of the dissertation. First, present most general findings, then narrow it down to a more specific one. Describe both qualitative & quantitative results. For example, imagine you are comparing the behavior of hamsters and mice. First, say a few words about the behavioral type of mammals that you studied. Then, mention rodents in general. At end, describe specific species of animals you carried out an experiment on.

Qualitative Results Section in Dissertation

In your dissertation results section, qualitative data may not be directly related to specific sub-questions or hypotheses. You can structure this chapter around main issues that arise when analyzing data. For each question, make a general observation of what data show. For example, you may recall recurring agreements or differences, patterns, trends. Personal answers are the basis of your research. Clarify and support these views with direct quotes. Add more information to the thesis appendix if it's needed.

Quantitative Results Section in a Dissertation

The easiest way to write a quantitative dissertation results section is to build it around a sub-question or hypothesis of your research. For each subquery, provide relevant results and include statistical analysis . Then briefly evaluate importance & reliability. Notice how each result relates to the problem or whether it supports the hypothesis. Focus on key trends, differences, and relationships between data. But don't speculate about their meaning or consequences. This should be put in the discussion vs conclusion section. Suppose your results are not directly related to answering your questions. Maybe there is additional information that helps readers understand how you collect data. In that case, you can include them in the appendix. It is often helpful to include visual elements such as graphs, charts, and tables. But only if they accurately support your results and add value.

Tables and Figures in Results Section in Dissertation

We recommend you use tables or figures in the dissertation results section correctly. Such interpretation can effectively present complex data concisely and visually. It allows readers to quickly gain a statistical overview. On the contrary, poorly designed graphs can confuse readers. That will reduce the effectiveness of your article.  Here are our recommendations that help you understand how to use tables and figures:

• Make sure tables and figures are self-explanatory. Sometimes, your readers may look at tables and figures before reading the entire text. So they should make sense as separate elements.
• Do not repeat the content of tables and figures in text. Text can be used to highlight key points from tables and figures. But do not repeat every element.
• Make sure that values ​​or information in tables and text are consistent. Make sure that abbreviations, group names, interpretations are the same as in text.
• Use clear, informative titles for tables and figures. Do not leave any table or figure without a title or legend. Otherwise, readers will not be able to understand data's meaning. Also, make sure column names, labels, figures are understandable.
• Check accuracy of data presented in tables and figures. Always double-check tables and figures to make sure numbers converge.
• Tables should not contain redundant information. Make sure tables in the article are not too crowded. If you need to provide extensive data, use Appendixes.
• Make sure images are clear. Make sure images and all parts of drawings are precise. Lettering should be in a standard font and legible against the background of the picture.
• Ask for permission to use illustrations. If you use illustrations, be sure to ask copyright holders and indicate them.

Tips on How to Write a Results Section

We have prepared several tips on how to write the results section of the dissertation!  Present data collected during study objectively, logically, and concisely. Highlight most important results and organize them into specific sections. It is an excellent way to show that you have covered all the descriptive information you need. Correct usage of visual elements effectively helps your readers with understanding. So, follow main 3 rules for writing this part:

• State only actual results. Leave explanations and comments for Discussion.
• Use text, tables, and pictures to orderly highlight key results.
• Make sure that contents of tables and figures are not repeated in text.

In case you have questions about a  conceptual framework in research , you will find a blog dedicated to this issue in our database.

What to Avoid When Writing the Results Section of a Dissertation

Here we will discuss how NOT to write the results section of a dissertation. Or simply, what points to avoid:

• Do not make your research too complicated. Your paper, tables, and graphs should be clearly marked and follow order. So that they can exist independently without further explanation.
• Do not include raw data. Remember, you are summarizing relevant results, not reporting them in detail. This chapter should briefly summarize your findings. Avoid complete introduction to each number and calculation.
• Do not contradict errors or false results. Explain these errors and contradictions in conclusions. This often happens when different research methods have been used.
• Do not write a conclusion or discussion. Instead, this part should contain summaries of findings.
• Do not tend to include explanations and inferences from results. Such an approach can make this chapter subjective, unclear, and confusing to the reader.
• Do not forget about novelty. Its lack is one of the main reasons for the paper's rejection.

Dissertation Results Section Example

Let's take a look at some good results section of dissertation examples. Remember that this part shows fundamental research you've done in detail. So, it has to be clear and concise, as you can see in the sample.

Final Thoughts on Writing Results Section of Dissertation

When writing a results section of a dissertation, highlight your achievements by data. The main chapter's task is to convince the reader of conclusions' validity of your research. You should not overload text with too detailed information. Never use words whose meanings you do not understand. Also, oversimplification may seem unconvincing for readers. But on the other hand, writing this part can even be fun. You can directly see your study results, which you'll interpret later. So keep going, and we wish you courage!

Writing any academic paper is long and thorough work. But StudyCrumb got you back! Our professional writers will deliver any type of work quickly and excellently! 

Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing.

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Research Tips and Infromation

How to Write Results Section of Your Research Paper

Introduction

How to summarize the data preprocessing steps in the results section, how to summarize the research findings in the results section, common phrasal verbs used in results section, what are common mistakes observed in the results section, how long should a results section be of a research paper, should the results of a research paper be given in the introduction or in another section.

• What is the difference between the "discussion" and the "results" section of a research paper?

Does the summary be part of the result section in the research article?

Why do some scientific papers not include a ‘methods and results’ section, how do you introduce a results section, why do researchers need to avoid making speculations in the results section of a research paper.

The result section is the third major part of the research paper and it’s probably the most important part because it contains actual outcomes about your experiment. The other sections contain a plan, hope and interpretations but the result section is the actual truth of your study.

In the result section, one should aim to narrate his/her finding without trying to interpret or evaluate them. Basically, the result section explains any issues you faced during your data collection, the main results of the experiment and any other interesting trends in the data.

With the results, we want to convey our data in the most accessible way, so we usually use visual elements like graphs and tables to make it easier to understand. The facts, figures, and findings are to be presented in a logical manner leading to the hypothesis and following the sequence of the method section. Mention must be made for the negative results as it would substantiate the discussion section later on. Interpretation of the meaning of the results section is done in the discussion section .

How Results Section is Structured?

When structuring the results section, it is important that your information is presented in a logical order. 

Now, when it comes to the organization of the result section, as a generic rule

• Always start with textual content, not a Table or Figure
• Make sure you show the Tables and Figures after they are mentioned in the text
• Explain any missing data or problems you had while collecting the data.

The results section gives you the opportunity to:

• Summarize the  Data Preprocessing Steps

2. Report on the Findings 

3. Summarize the Research Findings

At the beginning of the result section, you can discuss how you have collected, transformed and analyzed your data. This step is usually known as data preprocessing.

The data collection step may involve collecting data from various hardware, software or internet sources.

If your research requires data cleaning, then explain the steps and procedures used for data cleaning. Here, the researchers can describe how they transformed data to facilitate analysis (e.g. converting data from one format to another format). If there was missing data, explain how you have substituted missing values and with what techniques you have substituted your data.

You can mention what software or statistical procedures you have used to analyze and interpret the data.  Demonstrate with the help of charts or tables the cleansed data ready to be used for getting results.   In a few research papers, you may find these steps appearing at the end of the method section. 

How to Present your Research Findings in Research Section?

Second, present your findings in a structured way (such as thematically or chronologically), bringing the readers’ attention to any important, interesting, or significant findings.

Be sure to include a combination of text and visuals. Data illustrations should not be used to substitute or replace text, but to enhance the narrative of your findings.  

Resultant data are to be presented either through text, figures, graphs or tables or in a combination of all of the best suited for leading to the hypothesis. Care should be taken to prevent any duplication of the text, figures, graphs, and tables. If any result is presented in figures or graphs, it need not be explained through text. Similarly, any data presented through the graph should not be repeated in the table.

Each table and graph should be clearly labelled and titled. Each different finding should be made in a separate sub-section under the proper sub-heading following the sequence adopted in Method Section.

If you are not comfortable with data analysis then you can take professional services for research data analysis .

Figures 

 Identify and list the figures which are relevant to your results. For example, if you are working on the problem statement of ” Identifying the pathological issues with pomegranate fruits”, then you can add the figures of pomegranate fruits with good quality and bad quality along with their stage of infection. If you are working on pomegranate cultivar-related issues, put the figures of pomegranate fruits belonging to different cultivars. 

The key takeaway here is not to add any figures which may not directly contribute to results. These diagrams may include generic block diagrams, and images conveying generic information like farm fields, plantations etc.

While putting the figures, as much as possible use grayscale images as many users take the photocopies in black and white mode. In certain scenarios you are 

 In the case of figures, the captions should come below, called Fig. 1, Fig. 2 and so on. 

You can visit my article on The Power of Images in Research Papers: How They Enhance the Quality of Your Paper? . This article will help you how images or figures enhances the possibility of selection of your paper to top quality journals and conferences.

Tables are good for showing the exact values or showing much different information in one place. Graphs are good for showing overall trends and are much easier to understand quickly. It also depends on your data.

Tables are labelled at the top as Table 1,  Table 2 and so on.  Every table must have a caption. It’s good if one can put independent variable conditions on the left side vertically, and the things you have measured horizontally so one can easily compare the measurements across the categories. But you need to decide for each table you make, what is easiest to understand, and what fits on the paper.

Visit article on Best Practices for Designing and Formatting Tables in Research Papers for further details on proper representation of tables at proper places.

You can use various types of graphs in your results like a line graph, bar graph, scatter plot, a line graph with colours, a box with whiskers plot and a histogram.

In general, continuous variables like temperature, growth, age, and time can be better displayed in a line graph on a scatter plot or maybe on histograms.

If you have comparative data that you would like to represent through a chart then a bar chart would be the best option. This type of chart is one of the more familiar options as it is easy to interpret.

These charts are useful for displaying data that is classified into nominal or ordinal categories. In any case, you need to decide which is the best option for each particular example you have,  but never put a graph and a table with the same data in your paper.

In the case of graphs, the captions should come below, called Fig. 1, Fig. 2 and so on. 

A limited number of professional tools provide you the chance to add some life to your graphs, charts, and figures and present your data in a way that will astound your audience as much as your astounding results.

My article on Maximizing the Impact of Your Research Paper with Graphs and Charts will help you in drawing eye catching and informative graphs and charts for your research paper.

The results section should include a closing paragraph that clearly summarizes the key findings of the study. This paves the way for the discussion section of the research paper, wherein the results are interpreted and put in conversation with existing literature.

Any unusual correlation observed between variables should be noted in the result section. But any speculation about the reason for such an unusual correlation should be avoided. Such speculations are the domains of the discussion section.

Comparisons between samples or controls are to be clearly defined by specifically mentioning the common quality and the degree of difference between the comparable samples or controls. Results should always be presented in the past tense.

Common academic phrases that can be used in the results section of a paper or research article. I have included a table with examples to illustrate how these phrases might be used:

Let’s look at some of the common mistakes which can be observed in the result section.

• One should not include raw data which are not directly related to your objectives. Readers will not be able to interpret your intentions and may unnecessarily collect unwanted data while replicating your experiments.
• Do not just tell the readers to look at the Table and Figure and figure it out by themselves, e.g “The results are shown in the following Tables and Graphs”.
• Do not give too much explanation about Figures and Tables.

“An Optimized Fuzzy Based Short Term Object Motion Prediction for Real-Life Robot Navigation Environment”  ( Paper Link )

Object motions with different motion patterns are generated by a simulator in different directions to generate the initial rule base. The rules generated are clustered based on the direction of the motion pattern into the directional space clusters. Table 1 shows the number of rules that remained in each directional space after removing inconsistencies and redundancies.

Our predictor algorithm is tested for a real-life benchmark dataset (EC Funded CAVIAR project/IST 2001 37540) to check for relative error. The data set consists of different human motion patterns observed at INRIA Lab at Grenoble, France and Shop Centre. These motion patterns consist of frames captured at 25 frames/second. A typical scenario of the INRIA Lab and the Shop Centre is shown in the Figure below.

                                                      Fig.1: A typical scenario of the INRIA Lab and the Shop Centre

For each test case, the average response time is calculated to find its suitability for a real-life environment. The prediction algorithm is tested by processing the frame data of moving human patterns stored in the database at intervals of 50 frames (02 Seconds).

The navigation environment is presented in the form of a Prediction graph where the x-axis represents the Range parameter and the y-axis represents the Angle parameter. The predicted Angle and Range values are compared with actual values obtained from the real-life environment.

The performance of the predictor is tested when more than one object is sensed by the sensor. The tests are carried out assuming at most 6-8 objects can be visible and can affect the decisions to be made regarding robot traversal.

The results section is an essential component of any research paper, as it provides readers with a detailed understanding of the study’s findings. In this blog post, we discussed three important steps for writing a results section: summarizing the data preprocessing steps, reporting on the findings, and summarizing the research findings.

Firstly, summarizing the data preprocessing steps is crucial in the results section, as it provides readers with an understanding of how the raw data was processed and transformed. This step includes data cleaning, data transformation, and data reduction techniques. By summarizing the data preprocessing steps, readers can understand how the data was prepared for analysis, which is critical for interpreting the study’s findings accurately.

Secondly, reporting on the findings is an important step in the results section. It involves presenting the study’s results in a clear and concise manner, using tables, graphs, and statistical analyses where necessary. This step should be focused on answering the research question or hypothesis and should present the findings in a way that is easily understood by the reader. Reporting on the findings can also include providing detailed interpretations of the results, as well as any potential limitations of the study.

Finally, summarizing the research findings is crucial in the results section, as it provides readers with a concise summary of the study’s main results and conclusions. This step should be written in a clear and straightforward manner, highlighting the most important findings and explaining their significance. Additionally, it should relate the study’s findings to the research question or hypothesis and provide a conclusion that is well-supported by the results.

Overall, the results section of a research paper is a critical component that requires careful attention to detail. By following the guidelines discussed in this blog post, researchers can present their findings in a clear and concise manner, helping readers to understand the research process and the resulting conclusions.

Frequently Asked Questions

An IMRaD paper format suggests around 35% of the text should be dedicated to the results and discussion section. For a research paper of length 10 pages, the results and discussion section should occupy 3-4 pages.

The results of a research paper should be given in a separate section. However, the highlights of the results can be discussed in the introduction section.

What is the difference between the “discussion” and the “results” section of a research paper?

The results section only depicts the results obtained by implementing the methodology used. The results will be in the form of figures, tables, charts or graphs. The discussion section elaborates the analysis of the results obtained in the results section.

The summary can be part of the results section of a research paper. However, the results obtained can be summarized in the form of a table in results section of a research paper.

Survey papers and papers which are focussed on theoretical proofs do not involve separate methods and results sections.

The results section is introduced by the data collection steps and the setting up of equipment in different scenarios for obtaining the results.

Making speculations in the results section may lead to wrong interpretations by the researcher who is planning to replicate the methodology used for obtaining the results. This may further lead to wrong comparative analysis.

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• Published: 28 May 2024

Gut microbiome remodeling and metabolomic profile improves in response to protein pacing with intermittent fasting versus continuous caloric restriction

• Alex E. Mohr   ORCID: orcid.org/0000-0001-5401-3702 1 , 2 ,
• Karen L. Sweazea 1 , 2 , 3 ,
• Devin A. Bowes   ORCID: orcid.org/0000-0001-9819-2503 2 ,
• Paniz Jasbi 4 , 5 ,
• Corrie M. Whisner   ORCID: orcid.org/0000-0003-3888-6348 1 , 2 ,
• Dorothy D. Sears   ORCID: orcid.org/0000-0002-9260-3540 1 ,
• Rosa Krajmalnik-Brown   ORCID: orcid.org/0000-0001-6064-3524 2 ,
• Yan Jin 6 ,
• Haiwei Gu 1 , 6 ,
• Judith Klein-Seetharaman   ORCID: orcid.org/0000-0002-4892-6828 1 , 4 ,
• Karen M. Arciero 7 ,
• Eric Gumpricht 8 &
• Paul J. Arciero   ORCID: orcid.org/0000-0001-7445-6164 7 , 9  

Nature Communications volume  15 , Article number:  4155 ( 2024 ) Cite this article

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The gut microbiome (GM) modulates body weight/composition and gastrointestinal functioning; therefore, approaches targeting resident gut microbes have attracted considerable interest. Intermittent fasting (IF) and protein pacing (P) regimens are effective in facilitating weight loss (WL) and enhancing body composition. However, the interrelationships between IF- and P-induced WL and the GM are unknown. The current randomized controlled study describes distinct fecal microbial and plasma metabolomic signatures between combined IF-P ( n  = 21) versus a heart-healthy, calorie-restricted (CR, n  = 20) diet matched for overall energy intake in free-living human participants (women = 27; men = 14) with overweight/obesity for 8 weeks. Gut symptomatology improves and abundance of Christensenellaceae microbes and circulating cytokines and amino acid metabolites favoring fat oxidation increase with IF-P (p < 0.05), whereas metabolites associated with a longevity-related metabolic pathway increase with CR (p < 0.05). Differences indicate GM and metabolomic factors play a role in WL maintenance and body composition. This novel work provides insight into the GM and metabolomic profile of participants following an IF-P or CR diet and highlights important differences in microbial assembly associated with WL and body composition responsiveness. These data may inform future GM-focused precision nutrition recommendations using larger sample sizes of longer duration. Trial registration, March 6, 2020 (ClinicalTrials.gov as NCT04327141), based on a previous randomized intervention trial.

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Introduction.

As a principal modulator of the gut microbiome (GM) and weight status, nutritional input holds great therapeutic promise for addressing a wide range of metabolic dysregulation 1 . Dependent on the host for nutrients and fluid, one of the main processes by which the GM affects host physiology is producing bioactive metabolites from the gastrointestinal (GI) contents. Nutrient composition, feeding frequency, and meal timing impact this dependency 2 , 3 . To maintain a stable community and ecosystem, the GM must regulate its growth rate and diversity in response to nutrient availability and population density 4 . Such maintenance is affected by caloric restriction (CR) coupled with periods of feeding and intermittent fasting (IF) 5 . Moreover, we’ve recently shown the nutritional composition and meal frequency during these periods alter the metabolizable energy for the host 6 . The current study incorporates protein pacing (P), defined as four meals/day consumed evenly spaced every 4 h, consisting of 25–50 g of protein/meal 7 , 8 , 9 . Indeed, we have previously characterized a dietary approach of calorie-restricted IF-P combined and P alone 7 , 8 . These studies included nutrient-dense meal replacement shakes, along with whole foods, to quantitatively examine beneficial changes in body composition and cardiometabolic, inflammatory, and toxin-related outcomes in healthy and overweight individuals 7 , 8 , 10 , 11 , 12 . Further, recent preclinical work in mice has identified dietary protein as having anti-obesity effects after CR that are partially modulated through the GM 13 . Thus, the need to examine this in humans is warranted.

In this current work, we compare the effects of two low-calorie dietary interventions matched for weekly energy intake and expenditure; continuous caloric restriction on a heart-healthy diet (CR) aligned with current United States (US) dietary recommendations 14 versus our calorie-restricted IF-P diet 8 , 15 , in forty-one individuals with overweight or obesity, over an 8-week intervention. We hypothesize an IF-P diet may favorably influence the GM and metabolome to a greater extent than a calorie-matched CR alone. This exploratory investigation utilizes data and samples from a randomized controlled trial (NCT04327141) that compares the effects of the CR versus IF-P diet on anthropometric and cardiometabolic outcomes, as previously published 15 . As an additional analysis, we select “high” and “low” responders based on relative weight loss (WL) for a subgroup examination of the IF-P diet to better elucidate potential differential responses to intermittent fasting and protein pacing. Of special note, one individual lost 15% of their initial body weight over the 8-week intervention; this individual is followed longitudinally for a year to explore the dynamics of their GM and fecal metabolome. Novel findings from the current study shows an IF-P regimen results in improved gut symptomatology, a more pronounced community shift, and greater divergence of the gut microbiome, including microbial families and genera, such as Christensenellaceae , Rikenellaceae , and Marvinbryantia , associated with favorable metabolic profiles, compared to CR. Furthermore, IF-P significantly increases cytokines linked to lipolysis, weight loss, inflammation, and immune response. These findings shed light on the differential effects of IF-P as a promising dietary intervention for obesity management and microbiotic and metabolic health.

Intermittent fasting - protein pacing (IF-P) significantly influences gut microbiome (GM) dynamics compared to calorie restriction (CR)

We compared an IF-P vs. a CR per-protocol dietary intervention (matched for total energy intake and expenditure) over eight weeks to compare changes in weight, cardiometabolic outcomes, and the GM in men and women with overweight/obesity (IF-P: n  = 21; CR: n  = 20). One participant in each group were lost to follow-up due to non-compliance with dietary intervention (Fig.  1a ; CONSORT flow diagram: Supplementary Fig.  S1a ). The primary outcomes of dietary intake, body weight and composition responses, cardiometabolic outcomes, and hunger ratings after both dietary interventions are provided in our companion paper 15 . Briefly, after a one-week run-in period consuming their usual dietary intake (baseline diet), with no differences between groups at baseline for any dietary intake variable 15 , both dietary interventions significantly reduced total fat, carbohydrate, sodium, sugar, and energy intake by approximately 40% (~1000 kcals/day) from baseline levels (Fig.  1b ; Supplementary Data  1 ). By design, IF-P increased protein intake greater than CR during the intervention. The IF-P regimen consisted of 35% carbohydrate, 30% fat, and 35% protein for five to six days per week and a weekly extended modified fasting period (36–60 h) consisting of 350–550 kcals per day using randomization, as detailed previously 7 , 8 , 9 , 10 , 15 . In comparison, the CR regimen consisted of 41% carbohydrate, 38% fat, and 21% protein in accordance with current US dietary recommendations (Supplementary Table  S1 ) 14 , 16 . Using two-way factorial mixed model analysis of variance (ANOVA), significant macronutrient decreases drove energy reduction from dietary fat and carbohydrate ( p  < 0.001), with increased protein in the IF-P compared to CR ( p  < 0.001; Supplementary Fig.  S1b ; Supplementary Data  1 ). Regarding GI functioning and GM modulation, IF-P significantly decreased sugar and increased dietary fiber relative to CR (IF-P; pre, 20 ± 2 vs. post, 26 ± 2: CR; pre, 24 ± 3 vs. 24 ± 2 g/day; p  < 0.05). Despite similar average weekly energy intake (~9000 kcals/week) and physical activity energy expenditure (~350 kcals/day; p  = 0.260) during the intervention, participants following the IF-P regimen lost significantly more body weight (−8.81 ± 0.71% vs. −5.40 ± 0.67%; p  = 0.003; Fig.  1c ; Supplementary Data  1 ) and total, abdominal, and visceral fat mass and increased fat-free mass percentage (~2×; p  ≤ 0.030), as previously reported 15 . In addition, within-group analyses revealed a significant decrease in the reported frequency of total and lower-moderate GI symptoms (GI symptom rating score [GSRS] ≥4) over time for both IF-P and CR participants. However, when comparing the two dietary interventions at each time point, a more substantial reduction was observed in IF-P participants compared to CR participants (i.e., −9.3% vs. −5.4% and −13.2% vs. −3.9%, respectively; Table  1 ). The increased protein and lower sugar intake in IF-P compared to CR may have favorably mediated the GM and symptomatology.

a Study design with baseline participant characteristics. A registered dietitian counseled individuals from both groups each week. Time points with data collection are shown for both IF-P and CR participants. Icons created using BioRender.com. b Total daily caloric intake at each time point was not significantly different between IF-P and CR diet groups (two-sided Student’s t -test, p  < 0.05). Adjusted values are displayed by dividing total weekly intake by seven, to account for the fasting periods of IF-P. c IF-P participants lost significantly more weight over time versus CR participants. Points connected by line represent percent of weight compared to baseline weight for each participant. d Overall gut microbial colonization, as demonstrated by qPCR-based quantification of 16S rRNA gene copies per gram wet weight was unaffected by time or intervention (linear-mixed effects [LME] model, two-sided p  > 0.05). Alpha diversity metrics, e observed amplicon sequence variants (ASVs), and f Phylogenetic diversity at the ASV level significantly increased over time, independent of the intervention. g Intra-individual changes in GM community structure from baseline to weeks four and eight in IF-P participants shifted significantly throughout the IF-P intervention compared to CR as measured by the Bray-Curtis dissimilarity index (two-sided Wilcoxon rank-sum test). All box and whiskers plots display the box ranging from the first to the third quartile, and the center the median value, while the whiskers extend from each quartile to the minimum or maximum values. Heatmap of significant changes in h family- and i genus-level bacter i a by intervention. Colors indicate the within-group change beta coefficients over time for each cell, and asterisks denote significance. Black-white annotations on the bottom denote the significance of between-group change difference (by MaAsLin2 group × time interactions; p -values were corrected to produce adjusted values [ p .adj] using the Benjamini–Hochberg method). For all panels, IF-P: n  = 20, CR: n  = 19. Source data are provided as a Source Data file.

The substantial reduction in calorie intake of both groups (~40% from baseline) led us to investigate its potential impact on transient microbial colonization in the gut, as estimated by 16S rRNA gene copies (linear-mixed effects model [LME] time effect, p  = 0.114; Fig.  1d ; Supplementary Data  2 ). While it might be expected that a significant reduction in calorie intake could influence gut microbial colonization, our findings indicate that this reduction did not reach statistical significance within the timeframe of our study. This result contrasts with previous research that imposed more substantial energy restriction, such as a four-week regimen of ~800 kcal/day in participants with overweight/obesity, where overall gut microbial colonization notably decreased 4 . In addition to assessing microbial colonization, we also investigated whether the calorie reduction significantly influenced principal stool characteristics, including wet stool weight, Bristol stool scale (BSS), and fecal pH ( p  ≥ 0.066; Table  1 ). However, we did not observe statistically significant changes in these parameters over the course of the study. Moreover, there were no significant differences between the two dietary intervention groups over time (interaction effect, p  ≥ 0.051). In contrast, there were significant time effects for observed amplicon sequence variants (ASVs) and phylogenetic diversity (LME time effect, p  ≤ 0.023; Fig.  1e, f ; Supplementary Data  2 ), with values increasing at weeks four and eight compared to baseline (pairwise comparisons, p  ≤ 0.048); however, no interaction was observed for either alpha diversity metric (group × time effect, p  ≥ 0.925). To rule out the potential confounding effects of GI transit time 17 , BSS (as a surrogate marker) and stool pH were not significantly correlated with alpha diversity (Spearman correlations, p  ≥ 0.210). In relation to community composition, much of the intervention variance could be attributed to individual response upon testing nested permutational analysis of variance (PERMANOVA; R 2  = 0.749, p  = 0.001; Supplementary Table  S2 ), showcasing the highly individualistic landscape of the human GM in response to dietary intervention. However, a significant 1.8% of the variance was accounted for by the group × time interaction ( p  = 0.001). Moreover, individual responses over time showed variance between the two dietary interventions (PERMANOVA, R 2  = 0.123, p  = 0.003). This variability was apparent by assessing intra-individual differences, where a pronounced increase in Bray-Curtis dissimilarity was observed in the IF-P compared to the CR group after four (median Bray-Curtis dissimilarity, 0.53 [IQR: 0.47–0.61] vs. 0.38 [IQR: 0.33–0.47]) and eight weeks (0.50 [IQR: 0.41–0.55] vs. 0.39 [IQR: 0.33–0.45]; Fig.  1g ; Wilcoxon rank-sum test, p  ≤ 0.005).

To understand the taxa driving this GM variation from baseline to weeks four and eight between the two dietary interventions, we constructed MaAsLin2 linear-mixed models with the individual participant as a random factor 18 . We observed differential abundance patterns at the family and genus level in response to the IF-P but not the CR intervention. Of the 28 family and 69 genus-level features captured after filtering, a respective total of six and 18 taxa displayed significant interaction effects, with all significant time effects occurring from IF-P ( p .adj ≤ 0.10; Fig. 1h, i ; Supplementary Data  3 , 4 ). Notably, the changes observed at the four-week mark were more pronounced compared to those at eight weeks. These early alterations may signify an initial adaptation phase during which microbial populations respond to the modified substrate availability and nutrient composition, suggesting a degree of community resilience 19 . Increases were sustained to the third fecal collection for the family Christensenellaceae and the genera Incertae Sedis ( Ruminococcaceae family), Christensenellaceae R-7 group , and UBA1819 ( Ruminococcaceae family) (effect size > 2.0). Christensenellaceae is well regarded as a marker of a lean (anti-obesity) phenotype 20 and is associated with higher protein intake 21 . Other notable increases included Rikenellaceae , which, like Christensenellaceae , has been linked to reduced visceral adipose tissue and healthy metabolic profiles 22 , and Marvinbryantia , a candidate marker for predicting long-term weight loss success in individuals with obesity 23 . In addition, IF-P increased Ruminococcaceae , which has been noted to have an increased proteolytic and lipolytic capacity 24 . This shift in IF-P participants likely represents a change in GM substrate fermentation preferences as the diet regimen (relative protein and carbohydrate) and energy restriction is expected to increase the proteolytic: saccharolytic potential ratio 25 . In contrast, all taxa that decreased in IF-P participants were butyrate producers. These included the family Butyricicoccaceae and several genera such as Butyricicoccus (week four), Eubacterium ventriosum group (weeks four and eight), and Agathobacter (week four) (effect size < −2.0). When comparing monozygotic twin pairs, Eubacterium ventriosum group and another reduced genus, Roseburia , were more abundant in the higher body mass index (BMI) siblings 26 . Others, such as the mucosa-associated Butyricicoccus and Erysipelotricaceae UCG-003, have been positively correlated with insulin resistance and speculated to contribute to impaired glycolipid metabolism 27 .

Despite these changes in GM composition and increased fiber intake (+30% vs. baseline) of the IF-P participants 15 , we did not detect a significant shift in the abundance of the principal fecal short-chain fatty acids (SCFAs), acetate, propionate, butyrate, or valerate, as assessed by gas chromatography-mass spectrometry (GC–MS) (LME, p  ≥ 0.470; Supplementary Fig.  S1c ; Supplementary Data  5 ). Several factors likely contribute to this finding. For example, the distinct physical-chemical properties of fiber sources between IF-P and CR are inherently different. Participants adhering to the IF-P diet consumed most of their dietary fiber as liquid meal replacements (shakes) that are rich in non-digestible, oligosaccharide dietary-resistant starch 5 (RS5). In contrast, subjects on the CR regimen consumed their fiber from whole food sources such as vegetables, whole grains, and legumes. These fiber sources provided a mixture of soluble and insoluble fibers and a more complex fiber profile than IF-P participants. Moreover, even similar fiber profiles may function differently due to differences in food matrices and/or food preparation (cooking, raw consumption, etc.). Also of relevance is the timing of their fiber consumption. IF-P participants’ fiber intake was concentrated in fiber-rich shakes, offering immediate availability of fiber to the GI tract. In contrast, CR participants consumed fiber through whole foods, leading to a slower digestion and absorption process influenced by individual digestive transit times and enzymatic profiles. Interestingly, our results parallel recent work where participants more than doubled their fiber intake without affecting fecal SCFAs 28 . The disparate findings may be due to the type of dietary-resistant starch (RS) as a component of the nutrition regimen. In the current study, RS5 was included in the meal replacement shakes (eight grams/shake, two shakes/day, 16 g/day total). Prior research supports resistant starch intakes of >20 g/day favorably modulate SCFA production, primarily butyrate, over four to 12-week interventions 29 , 30 . Moreover, this lack of response in fecal SCFAs in both groups may have been further compounded by the significant reduction in energy intake in both groups, where the epithelia of the GI tract may have absorbed any potential increase in SCFAs from the dietary shift. It is worth noting that stool analysis may not be the most reliable biological surrogate for capturing SCFA flux over time 28 . Nevertheless, the changes in nutrient quality, timing, ratios, and the observed shift toward proteolytic activity suggest that the luminal matrix of digesta in the IF-P group impacted substrate availability for GM. This effect appears to be an influencing force in driving the observed beneficial shifts in microbial communities, such as Christensenellaceae and Incertae Sedis , as well as improvements in GI symptomatology in IF-P compared to CR. These results underscore the complexity of dietary influences on GM and highlight the need for further research to explore the impact of liquid meal replacements versus whole food sources on GM changes and SCFA status.

IF-P modulates circulating cytokines and gut microbiome taxa compared to CR

Caloric restriction and WL have been well known to positively influence inflammatory cytokine expression, with GM now emerging as an important modulator 31 . Surveying a panel of 14 plasma cytokines, we noted significant interaction (group × time) effects for IL-4, IL-6, IL-8, and IL-13 (LME, p  ≤ 0.034; Fig.  2a–d ; Supplementary Table  S3 ; Supplementary Data  6 ). These cytokines exhibited increases at weeks four and/or eight compared to baseline exclusively in the IF-P group (pairwise comparisons, p .adj ≤ 0.098), while no significant changes were observed in the CR group ( p .adj ≥ 0.562). Notably, IL-4 has been reported to display lipolytic effects 32 , and IL-8 has been positively associated with weight loss and maintenance 33 . Regarded as a proinflammatory myokine, IL-6 can acutely increase lipid mobilization in adipose tissue under fasting or exercise conditions 34 , 35 , 36 . IL-13 may be important for gut mucosal immune responses and is a stimulator of mucus production from goblet cells 37 , which has been recently reported to be influenced during a two-day-a-week fasting regimen in mice 38 . These results were of note considering the significant total body weight, fat, and visceral fat loss in the IF-P compared to the CR group. Surprisingly, correlational analysis with change (post – pre) in anthropometric and select plasma biomarker values with the cytokine profile did not reveal any significant associations after correcting for multiple testing effects ( p .adj ≥ 0.476; Supplementary Data  7 ). Plasma cytokines were, however, correlated with microbial composition for samples collected in the IF-P group during the intervention period (weeks four and eight) using graph-guided fused least absolute shrinkage and selection operator (GFLASSO) regression, revealing associations between cytokine-taxa pairs (Supplementary Fig.  S2a ). Of the four cytokines that increased in IF-P participants, we identified multiple significant correlations: Colidextribacter (rho = −0.55, p .adj = 0.015), Ruminococcus gauvreauii group (rho = 0.50, p .adj = 0.036), and Intestinibacter (rho = 0.45, p .adj = 0.086) with IL-4 (Supplementary Fig.  S2b ) and an unclassified genus from Oscillospiraceae (rho = −0.53, p .adj = 0.019), Colidextribacter (rho = −0.52, p .adj = 0.019), and Ruminoccus gauvreauii group (rho = 0.51, p .adj = 0.019) with IL-13 (Supplementary Fig.  S2c ).

a IL-4, b IL-6, c IL-8, and d IL-13: Each panel shows the cytokine concentration levels. Significant time effects and interaction effects (group × time) were detected using linear-mixed effects models (LME, two-sided p  < 0.05), indicating differential changes over the intervention period. IF-P participants exhibited significant increases in cytokine levels compared to baseline, as evidenced by pairwise comparisons adjusted for multiple testing using the Benjamini–Hochberg method (two-sided p .adj < 0.10). All box and whiskers plots display the box ranging from the first to the third quartile, and the center the median value, while the whiskers extend from each quartile to the minimum or maximum values. For all panels, IF-P: n  = 20, CR: n  = 19. Source data are provided as a Source Data file.

Displaying negative correlations for IL-4 and IL-13, Colidextribacter has been shown to be positively correlated to fat accumulation, insulin, and triglyceride levels in mice fed a high-fat diet 39 and positively correlated with products of lipid peroxidation, suggesting its potential role in promoting oxidative stress 40 . Conversely, Ruminoccus gauvreauii group was positively correlated with IL-4 and IL-13. Although limited information is available regarding the host interactions of this microbe, this genus is considered a commensal part of the core human GM and able to convert complex polysaccharides into a variety of nutrients for their hosts 41 . While these findings highlight the potential interplay between specific microbes and cytokine profiles, the directional influence—whether microbial changes drive cytokine alterations or vice versa—cannot be determined in this study setting. Furthermore, despite the change in cytokine profiles in the IF-P group, we did not detect any significant time or group × time effects when measuring lipopolysaccharide-binding protein (LBP; Δ pre/post, IF-P: 0.24 ± 0.31 vs CR: −0.93 ± 0.49 μg/mL; p  ≥ 0.254), a surrogate marker for gut permeability 42 . While the GM plays a crucial role in modulating the gut-immune axis, the observed cytokine fluctuations and microbial associations might also involve other factors. These include the production of specific metabolites due to shifts in microbial composition as well as the influence of the dietary regimen itself, which may have a central role in shaping these interactions.

IF-P and CR yield distinct circulating metabolite signatures and convergence of multiple metabolic pathways

To understand the potential differential impact of IF-P versus CR on the host, we surveyed the plasma metabolome, reliably detecting 136 plasma metabolites across 117 samples (i.e., QC CV < 20% and relative abundance > 1000 in 80% of samples). Based on outlier examination (random forest [RF] and principal component analysis [PCA]), no samples were categorized as outliers, and all data were retained for subsequent analysis. Metabolomic profile shifts were observed in both IF-P and CR groups compared with baseline (Canberra distance), however, these did not differ significantly by group or time (weeks four and eight; Wilcoxon rank-sum test, p  ≥ 0.087; Supplementary Fig.  S3a ). We prepared a general linear model (GLM) with age, sex, and time as covariates and corrected for false discovery rate (FDR). When controlling for these relevant covariates, we observed significant differences between IF-P and CR for 15 metabolites (Fig.  3a , Supplementary Table  S4 ): 2,3-dihydroxybenzoic acid, malonic acid, choline, agmatine, protocatechuic acid, myoinositol, oxaloacetic acid, xylitol, dulcitol, asparagine, n-acetylglutamine, sorbitol, cytidine, acetylcarnitine, and urate ( p .adj ≤ 0.089). To estimate the univariate classification performance of the 15 significant metabolites, we performed a receiver operating characteristic (ROC) analysis. Ten metabolites demonstrated a moderate area under the curve (AUC) (0.718–0.819), while five metabolites had an AUC < 0.70. Therefore, to improve classification performance, we constructed a supervised PLS-DA model using levels of the 15 significant metabolites ( p .adj ≤ 0.089) and analyzed variable importance in projection (VIP) scores (Supplementary Fig.  S3b ). Five metabolites with a VIP > 1.0 (2,3-dihydroxybenzoic acid, malonic acid, protocatechuic acid, agmatine, and myoinositol) were retained to construct an enhanced orthogonal projection to latent structures discriminant analysis (OPLS-DA) model. In contrast, the model fit was assessed with 100-fold leave-one-out cross-validation (LOOCV; see “Methods” section). Permutation testing showed the refined OPLS-DA model to have an acceptable fit to data ( Q 2  = 0.460, p  < 0.001), with appreciable explanatory capacity ( R 2  = 0.506, p  < 0.001; Supplementary Fig.  S3c ). The ROC analysis produced an area under the curve (AUC) of 0.929 (95% CI: 0.868–0.973, sensitivity = 0.8, specificity = 0.9; Supplementary Fig.  S3d ) between the CR and IF-P groups showing good accuracy of the GLM and providing strong support for the differential expression of these 15 metabolites between groups.

a Abundance and log fold-change of significant plasma metabolites between IF-P and CR groups as determined by a general linear model (GLM) adjusted for age, sex, and time. All GLM analyses utilized two-sided p -values, with multiple testing corrections applied using the Benjamini–Hochberg method ( p .adj). Metabolome pathway analysis was conducted for b IF-P and c CR using all reliably detected metabolites showing significantly altered pathways ( p .adj < 0.10) with moderate and above impact (>0.10). Impact scores were calculated using a hypergeometric test, while significance was assessed via a test of relative betweenness centrality, emphasizing the changes in metabolic network connectivity. For all panels, IF-P: n  = 20, CR: n  = 19. Source data are provided as a Source Data file.

Two metabolites, malonic acid, and acetylcarnitine, increased compared to the CR intervention. Several other investigators have noted the increase in acetylcarnitine via fasting protocols 43 , 44 . This increase is consistent with free fatty acid mobilization and increased transportation of these fatty acids via carnitine acylation into the mitochondria for fatty acid oxidation. These results would also be consistent with the expected ketogenesis, although not documented in our study, but noted by similar fasting interventions 44 . Relatedly, malonic acid, a naturally occurring organic acid, is a key regulatory molecule in fatty acid synthesis via its conversion to acetoacetate; hence, our results may reflect this increased synthesis in response to the mobilization and oxidation of fatty acids occurring during fasting. Other metabolites that decreased with IF-P include several sugar alcohols (myoinositol, dulcitol, and xylitol). Dulcitol (galactitol) is a sugar alcohol derived from galactose. It is possible that during fasting, levels of dulcitol decrease as glucose (initially) and free fatty acids (after 24–36 h of fasting) are preferentially utilized as energy substrates. One amino acid (asparagine) and one amino acid analog (N-acetylglutamine, associated with consumption of a Mediterranean diet 45 ) also decreased with IF-P relative to CR. Finally, 2,3-dihydroxybenzoic acid significantly decreased with IF-P. This metabolite is formed during the metabolism of flavonoids, as it is found abundantly in fruits, vegetables, and some spices. At the cellular level, this hydroxybenzoic acid functions as a cell signaling agent and has been speculated as a potentially protective molecule in various cancers 46 . It is unclear whether this metabolite decreased due to either dietary intake or metabolic processes related to high-protein intake or the fasting protocol. Collectively, the metabolic responses to these dietary regimens reflect the interrelationships of several anabolic and catabolic physiologic responses to three key components of these interventions: (a) the WL process itself, (b) changes in amount (and type) of macronutrient distribution (i.e., meal replacement shakes vs. whole food diet approach; higher vs. normal protein intakes), and (c) the adherence to fasting (IF-P only).

To determine the significantly impacted pathways of the dietary interventions, we grouped participant samples according to baseline or intervention period (weeks four and eight), with IF-P and CR assessed separately. A total of 14 pathways were significant in the IF-P group ( p .adj < 0.10; Fig.  3b ), with three displaying large impact coefficients (>0.5): (1) Glycine, serine, and threonine metabolism, (2) alanine, aspartate, and glutamate metabolism, and (3) ascorbate and aldarate metabolism. In comparison, 24 pathways were significant for the CR group (Fig.  3c ), with four showing large impact coefficients (>0.5): (1) Phenylalanine, tyrosine, and tryptophan biosynthesis, (2) alanine, aspartate, and glutamate metabolism, (3) citrate cycle (TCA cycle), and (4) glycine, serine and threonine metabolism. Notably, the glycine, serine, and threonine pathway has recently been found in preclinical models to play a pivotal role in longevity and related life-sustaining mechanisms independent of diet, though heavily impacted by fasting time and caloric restriction 47 . This may be partially related to the ability of glycine to increase tissue glutathione 48 , 49 and protect against oxidative stress 50 . In our analysis, this pathway was significant in both diet groups and is biochemically and topologically related to the additionally captured amino acid pathway, alanine, aspartate, and glutamate metabolism, as well as the energy-releasing pathway, the citrate cycle (TCA cycle). Notably, in the CR group, phenylalanine, tyrosine, and tryptophan biosynthesis, are important for neurotransmitter production and reported to be suppressed (tryptophan) in obesity 51 . This representation may have also been attributed to the differences in protein intake 52 or differences in dietary diversity 53 , yet to be determined. Regardless, we noted similar representations of pathway impact between IF-P and CR, with metabolic response centered on utilization of amino acids in addition to lipid turnover and energy pathways.

Gut microbiome and plasma metabolome latent factors indicate differential multi-omic signatures between IF-P and CR regimens

As the plasma metabolome has been suggested as a bidirectional mediator of GM influence on the host 54 , we performed a multi-omics factor analysis (MOFA) 55 to identify potential patterns of covariation and co-occurrence between the microbiome and circulating metabolites. Operating in a probabilistic Bayesian framework, MOFA simultaneously performs unsupervised matrix factorization to obtain overall sources of variability via a limited number of inferred factors and identifies shared versus exclusive variation across multiple omic data sets 55 . Eight latent factors were identified (minimum explained variance ≥2%; see “Methods” section), with the plasma metabolome and GM explaining 37.12% and 17.49% of the overall sample variability, respectively (Fig.  4a ). Based on significance and the proportion of total variance explained by individual factors for each omic assay, Factors 1 ( R 2  = 11.98) and 6 ( R 2  = 5.28) captured the greatest covariation between the two omic layers (Fig.  4a ; Supplementary Table  S5 ). In contrast, Factors 2 and 5 were nearly exclusive to the metabolome, and factors 3 and 4 to the GM. Interestingly, Factor 1 was significantly negatively correlated to dietary protein intake (Spearman rho = −0.270, p.adj = 0.021; Fig.  4b ) and captured the variation associated with the CR diet (Wilcoxon rank-sum test, p .adj = 3.2e-04; Fig.  4c ). Factor 6 had the greatest number of significant correlations, including negative associations with visceral adipose tissue, waist circumference, body weight, BMI, fat mass, android fat, subcutaneous adipose tissue, dietary sodium, carbohydrate, fat, energy intake (kcal), and sugar (Spearman rho ≤ −0.220, p .adj ≤ 0.075) and captured the variation associated with IF-P (Wilcoxon rank-sum test, p .adj = 0.007).

a The cumulative proportion of total variance explained ( R 2 ) and proportion of total variance explained by eight individual latent factors for each omic layer. b Spearman correlation matrix of the eight latent factors and clinical anthropometric and dietary covariates. Each circle represents a separate association, with the size indicating the significance (-log10 ( p -values)) and the color representing the effect size (hue) with its direction (red: positive; blue: negative). All correlations are calculated using two-sided tests. Asterisks within a circle denote significance after adjustment with the Benjamini–Hochberg method. c Scatter plot of Factors 1 and 6, with each dot representing a sample colored by intervention. Box and whisker plots illustrate significant differences between groups after adjusting for multiple testing using the Benjamini–Hochberg method (Wilcoxon rank-sum test; top = Factor 1, p .adj = 3.2e-04; right = Factor 6, p .adj = 0.007). The plots show boxes ranging from the first to the third quartile and the median at the center, with whiskers extending to the minimum and maximum values. d Factor 1 and 6 loadings of genera and metabolites with the largest weights annotated. Symbols: * p .adj < 0.10, ** p .adj < 0.01, *** p .adj < 0.001, **** p .adj < 1.0e-04. For all panels, IF-P: n  = 20, CR: n  = 19. Source data are provided as a Source Data file.

Assessing the positive weights (feature importance) of Factor 1 revealed a microbial and metabolomic signature linked with CR, including the taxa Faecalibacterium , Romboutsia , and Roseburia , and the plasma metabolites myoinositol, agmatine, N-acetylglutamine, erythrose, and mucic acid (Fig.  4d ). Previous dietary restriction studies have reported co-occurrence of gut microbial taxa and plasma metabolites that span a wide variety of applications and investigations 56 . The specific co-occurrences observed in Factor 1 exhibited an abundance of butyrate-producing bacterial taxa that utilize carbohydrates as their predominant substrate and plasma metabolites that are generally involved in carbohydrate metabolism, such as erythrose, an intermediate in the pentose phosphate pathway (PPP), and mucic acid which is derived from galactose and/or galactose-containing compounds (i.e., lactose). These co-occurrence patterns biologically cohere considering the nutritional profile of the CR group and the large contribution of fiber-rich, unrefined carbohydrates and reduction in sugar (~50% kcal from sugar). Indeed, these nutritional changes may have influenced the GM to accommodate changes in dietary substrate more efficiently. One interesting co-occurrence was the genus Romboutsia and metabolite N-acetylglutamine. Romboutsia has been shown to produce several SCFAs and ferment certain amino acids, including glutamate 57 . N-acetylglutamine is biosynthesized from glutamate; thus, its co-occurrence with the abundance of Romboutsia encourages further exploration into this interaction 58 .

Factor 6 captured the signature associated with IF-P, with positive contributions from the taxa Incertae Sedis ( Ruminococcaceae family), Erysipelatoclostridium , Christensenellaceae R-7 group , Oscillospiraceae UCG-002, and Alistipes , and the plasma metabolites malonic acid, adipic acid, succinate, methylmalonic acid, and mucic acid (Fig.  4d ). Prior work has established that Alistipes increases from diets rich in protein and fat, and contributes to the highest number of putrefaction pathways (i.e., fermentation of undigested proteins in the GI tract) over the other commensals 59 . This could explain the co-occurrence of plasma metabolites from protein catabolism, such as 2-aminoadipid acid, adipic acid, and glutamic acid 22 , 59 . Oscillospiraceae has recently been viewed with next-generation probiotic potential, harboring positive regulatory effects in areas related to obesity and chronic inflammation 60 . Mentioned prior, recent studies have reported on the role of Christensenellaceae on human health, participating in host amino acid and lipid metabolism as well as fiber fermentation 20 , with Christensenellaceae R-7 group notably evidenced to correlate with visceral adipose tissue reduction 22 . As such, the elevated abundance of microbes in the GM of IF-P participants observed in this study in tandem with the co-occurrence of metabolites indicative of protein degradation and mobilization and oxidation of fatty acids, such as methylmalonic acid, malonic acid, and succinate, presents a nascent multi-omic signature of IF-P. In addition, and more pronounced in the IF-P vs CR group, participants decreased sugar intake by ~75% (kcals) compared to baseline levels. Considering the other regimental components of IF-P, the differences in multi-omic signatures likely display the selective pressures of these two interventions.

Gut microbiome (GM) composition is associated with weight loss (WL) responsiveness to IF-P diet

The IF-P intervention produced a microbiome and metabolomic response; however, the loss in body weight and fat across individuals varied (Fig.  5a ). To provide deeper characterization and explore differential features of WL responsiveness, we performed a GM-focused subgroup analysis by employing shotgun metagenomic and untargeted fecal metabolomic surveys in 10 individuals that either achieved ≥10% loss in body weight or bordered on clinically important WL (i.e., >5% BW; herein, ‘High’ and ‘Low’ responders) 61 . Importantly, baseline characteristics between WL responder classification did not differ significantly (baseline body weight: High, 108.9 ± 30.8 vs. Low, 81.9 ± 18.1 kg, p  = 0.117; Supplementary Table  S6 ). Assessing the GM at the fundamental taxonomic rank, species composition showed significant separation by weight loss response evaluated by Bray-Curtis dissimilarity (group × time: R 2  = 0.114, p  = 0.001; Fig.  5b ; Supplementary Table  S7 ), with most of the variation explained by the individual ( R 2  = 0.711, p  = 0.001). In comparison, species level alpha diversity did not differ significantly between classifications (group × time: p  ≥ 0.674; Fig.  5c, d ). Identifying 212 species after filtering, we noted significant differences in bacterial abundances between groups over time (Fig.  5e ; Supplementary Data  8 ). A total of 10 features increased in the High-responder group relative to the Low-response group over the eight-week study period, including Collinsella SGB14861 , Clostridium leptum , Blautia hydrogenotrophica , and less typified species; GGB74510 SGB47635 (unclassified Firmicutes), GGB3511 SGB4688 (unclassified Firmicutes), Faecalicatena contorta , Lachnospiraceae bacterium NSJ-29 , Phascolarctobacterium SGB4573 , GGB38744 SGB14842 (unclassified Oscillospiraceae ), and Massiliimalia timonensis (effect size ≥ 1.163, p .adj ≤ 0.092). The increase in Collinsella , a less characterized anaerobic pathobiont that produces lactate and has been associated with low-fiber intakes 62 , 63 and lipid metabolism 64 , may have been related to the periods of CR and IF, in conjunction with the greater influx of host-released fatty acids in the High-responder group. Relatedly, Clostridium leptum growth has been linked with increases in monounsaturated fat intake, reductions in blood cholesterol 65 , and stimulation of Treg induction (i.e., anti-inflammatory) 66 . The latter association is relevant to the SCFA-promoting (primarily butyrate) qualities of Clostridium leptum 67 . Blautia hydrogenotrophica , an acetogen with bidirectional metabolic cross-feeding properties (e.g., transfer of hydrogen and acetate), is also important for butyrate formation 68 . Taxa that decreased relative to the Low-responder group; Eubacterium ventriosum , Streptococcus salivarius , Eubacterium rectale , Anaerostipes hadrus , Roseburia inulinivorans , Mediterraneibacter glycyrrhizinilyticus , and Blautia massiliensis (effect size ≤ −1.690, p .adj ≤ 0.078), included butyrate producers, Eubacterium ventriosum , Eubacterium rectale , Roseburia inulinivorans , and others, such as Streptococcus salivarius , a nuclear factor kappa B (NF-κB) activity repressor 69 and Peroxisome proliferator-activated receptor gamma (PPARγ) inhibitor potentially influencing lipid and glucose metabolism 70 . Investigating monozygotic (MZ) twin pairs, Eubacterium ventriosum was more abundant in the higher BMI siblings 26 , with enhanced scavenging fermentation capabilities 71 . Roseburia inulinivorans is a mobile firmicute (flagella) that harbors a wide-ranging enzymatic repertoire able to act on various dietary polysaccharide substrates suggestive of the ability to respond to the availability of alternative dietary substrates 72 . While we noted a more variable shift in fecal total SCFAs, acetate, propionate, butyrate, or valerate (via targeted GC–MS), in the Low weight loss responders, there was no significant difference when compared to High weight loss responders (Wilcoxon rank-sum test, p  ≥ 0.210; Supplementaryl Fig.  S4a ; Supplementary Data  9 ).

a Relative weight loss over the eight-week intervention for each participant in the IF-P group. b NMDS ordination showed the personalized trajectories of participants’ microbiomes over time. Dotted lines connect the same individual and point toward the final sample collection. No significant time or group × time interaction effects for alpha diversity metrics, c observed species, and d the Shannon index. Box and whiskers plots display the box ranging from the first to the third quartile, and the center the median value, while the whiskers extend from each quartile to the minimum or maximum values. Volcano plots displaying differential abundance between High and Low weight loss responders for e microbial species and f functional pathways. Significant features were more enriched in High and Low weight loss responders colored orange and light blue, respectively. g Alluvial plot displaying the fecal metabolite profile at the subclass level (Human Microbiome Database). Most abundant metabolite subclasses displayed (i.e., ≥1%). Metabolome pathway analysis for h High and i Low weight loss responders using all reliably detected fecal metabolites showing altered pathways with moderate and above impact (>0.10). Impact was calculated using a hypergeometric test, while significance was determined using a test of relative betweenness centrality. j Grid-fused least absolute shrinkage and selection operator (GFLASSO) regression of species from differential abundance analysis displayed correlative relationships with fecal metabolites. Species with greater abundance in High (High > Low) and Low (Low > High) weight loss responders are separate‘. For all panels, High: n  = 5, Low: n  = 5. Source data are provided as a Source Data file.

Less affected compared to taxonomic features were the 275 microbial-affiliated metabolic pathways identified after filtering, of which gluconeogenesis III and guanosine ribonucleotides de novo biosynthesis were increased (effect size ≥ 0.108, p .adj = 0.079), while super pathway of L-alanine biosynthesis, sucrose degradation IV (sucrose phosphorylase), sucrose degradation III (sucrose invertase), super pathway of thiamine diphosphate biosynthesis III, and flavin biosynthesis I (bacteria and plants) were decreased in the High relative to the Low weight loss responder group (effect size ≤ −0.247, p .adj ≤ 0.079; Fig.  5f ; Supplementary Data  10 )

As the difference in microbial shifts versus function is well established, we also tracked the fecal metabolome to better understand metabolic modification/production and identify potential microbial metabolic targets for future weight loss interventions. Overall, we reliably detected (QC relative standard deviation > 20% and mean intensity value > 1000 in 80% of samples) and annotated 607 (Human Metabolome Database) compounds across fecal samples. Notably, we found the fecal metabolite profile of both subgroups abundant in amino acids, peptides, and analogs, with decreases in sulfates, furanones, and quaternary ammonium salts and increases in cholestane steroids, carboxylic acid derivatives, and imidazoles (Fig.  5g ). Assessing metabolite changes between groups did not yield significance when comparing logFC values (Wilcoxon rank-sum test, p .adj > 0.10; Supplementary Fig.  S4b ). Pathway analysis of High weight loss responders revealed prominent metabolic signatures relevant to lipid metabolism (glycerolipid and arachidonic metabolism), nucleotide turnover (pyrimidine metabolism), and aromatic amino acid formation (phenylalanine, tyrosine, and tryptophan biosynthesis; Fig.  5h , Supplementary Data  11 ). In comparison, the more prominent enriched pathways for Low weight loss responders included those related to amino acid and peptide metabolism (glycine, serine, and threonine, d-glutamine and d-glutamate, and tyrosine metabolism and arginine biosynthesis; Fig.  5i , Supplementary Data  12 ).

Finally, species captured by our differential abundance analysis were channeled into a GFLASSO model with the fecal metabolome library to select metabolically relevant compounds best predicted by microbial abundances. Restricting taxa and metabolites displaying stronger co-occurrence signals (GFLASSO coefficients > 0.02), we noted several patterns (Fig.  5j ). This included positive associations between GGB3511 SGB4688 (unclassified Firmicute) and malonic acid (important to fatty acid metabolism), as well as Roseburia inulinivorans and 3-Hydroxy-2-oxo-1H-indole-3-acetic acid. Negative associations included Phascolarctobacterium SGB4573 with the fatty acid ester, methyl sorbate, and Streptococcus salivarius (anti-inflammatory) with leukotriene B4 dimethylamide.

Differences detected in our subgroup analysis suggest that the GM composition plays a role in WL responsiveness during IF-P interventions. Notable differences in taxa and fecal metabolites suggest differing substrate utilization capabilities and nutrient-acquiring pathways between High and Low responders, despite being on the same dietary regimen. Although differences between High and Low responders were statistically significant for the microbiome data, the magnitude of differences varied, suggesting further research is needed to clarify these differences.

Long-term IF-P remodels the gut microbiome after substantial weight loss – A case study

Considering the microbiomic and metabolic importance of sustained WL, we additionally performed a longitudinal, exploratory case study analysis on the participant who lost the most body weight during the eight-week WL period (−15.3% BW, −24.9 kg). Under rigorous clinical supervision, this individual was guided through and comprehensively tracked over 52 weeks, strictly adhering to an IF-P regimen, including WL (0–16 weeks) and maintenance (16–52 weeks) periods, which included adjusting the calorie intake to maintain energy balance. Microbial richness and evenness at the species level displayed a general inverse trend with body weight reduction, although they converged at 52 weeks (Fig.  6a, b ). Species dissimilarity peaked at weeks four and 16, after which it plateaued, but remained consistently higher in comparison to baseline over the 52-week period (Fig.  6c ). Examining positive linear coefficients of a PERMANOVA model, constructed to detect variation between community compositions over time, dominant influences included several species within the Lachnospiraceae family such as Fusicatenibacter saccharivorans , Blautia wexlerae , Blautia massillensis , Anaerostipes hadrus , and Coprococcus comes and others like Akkermansia muciniphila (Fig.  6d ). Negative contributions included species from the Oscillospiraceae family, such as Ruminococcus bromii and Ruminococcus torques . Indeed, visualizing community composition over the sampling time points suggested specific GM remodeling (Fig.  6e ; Supplementary Data  13 ). Many keystone taxa prominent over time in the microbiome are highly relevant to the significant reduction in body weight and metabolic improvement of the case-study participant. For example, Blautia wexlerae , a commensal bacterium recently reported to confer anti-adipogenesis and anti-inflammatory properties to adipocytes 73 became visually more prominent over time. This association was also the case for the health-associated microbe, Anaerostipes hadrus , which converts inositol stereoisomers (including myoinositol) to propionate and acetate, apt to improve insulin sensitivity and reduce serum triglyceride levels 74 , translating to reduced host metabolic disease risk 75 . Other elevated taxa, like the mucin-degrading Akkermansia muciniphila and Bacteroides faecis , are negatively correlated with markers for insulin resistance 76 . There was also a notable bloom of Collinsella SGB14861 (anaerobic pathobiont producing lactate) 63 and suppression of Eubacterium rectale , Ruminococcus torques (associated with circadian rhythm disruption in mice) 77 , and Ruminococcus bromii (an exceptional starch degrader) 78 .

Change in alpha diversity metrics a observed species and b Shannon index with percentage of baseline body weight. c Bray-Curtis dissimilarity at the species level with d top PERMANOVA model coefficients (analysis: species~time). e Alluvial plot displaying the variation in abundance of the 20 most prevalent bacteria over time. For visual clarity, the less abundant taxa are not displayed. f Canberra distance of fecal metabolome with g top PERMANOVA model coefficients (analysis: pathway~time). h Pathway analysis of fecal metabolites comparing baseline to subsequent sample collections. Data are plotted as -log10(p) versus pathway impact. Node size corresponds to the proportion of metabolites captured in each pathway set, while node color signifies significance. Impact was calculated using a hypergeometric test, while significance was determined using a test of relative betweenness centrality. No p -value adjustments were made. Source data are provided as a Source Data file.

Compared to the more pronounced shifts in the GM, an inspection of Bray-Curtis dissimilarity at the microbial metabolic pathway level was much less affected (Supplementary Fig.  S5a ). Though positive contributions in multiple biosynthesis pathways were noted, as well as reductions in the superpathway of UDP-glucose-derived O-antigen building blocks biosynthesis and glucose and glucose-1-phosphate degradation (Supplementary Fig.  S5b ; Supplementary Data  14 ). We also tracked the fecal metabolome concordance with the GM to corroborate potential metabolic output. Shifts in metabolites captured by calculating the Canberra distance were prominent (Fig.  6f ), with positive influences from agrocybin (possessing antifungal activity 79 ), nicotinic acid (nicotinamide adenine dinucleotide precursor), and sulfate, and reductions in cadaverine (involved in the inhibition of intestinal motility 80 ), maltitol, acetohydroxamic acid (a urease inhibitor), and hypoxanthine, after removing the dominant amino acid subclass (Fig.  6g ; Supplementary Fig.  S5c ). At the chemical class level, we observed apparent shifts in chemical subclasses; cholestane steroids, amines, purines, and purine derivatives, and amino acids, peptides, and analogs (Supplementary Fig.  S5d ). Given our case-study approach, we performed a pathway analysis using all reliably detected fecal metabolites at each collection point over 52 weeks. Pathway analysis (Fig.  6h ) identified primary bile acid biosynthesis ( p  = 0.014) and cysteine and methionine metabolism ( p  = 0.096) as having the greatest significance, while the greatest impact (I) was observed in phenylalanine, tyrosine, and tryptophan biosynthesis and linoleic acid metabolism ( I  = 1.0). Alanine, aspartate, and glutamate metabolism ( I  = 0.756), vitamin B6 metabolism ( I  = 0.647), sulfur metabolism ( I  = 0.532), phenylalanine metabolism (I =  0.357), and nicotinate and nicotinamide metabolism ( I  = 0.194) also displayed marked pathway impacts (Supplementary Fig.  S5e ; Supplementary Data  15 ). Together, these integrated findings from the group comparisons (IF-P vs. CR), high vs. low responders, and the case study, suggest that the remodeling of the gut microbiome through sustained weight loss on an IF-P regimen not only alters the microbial composition but also influences key metabolic pathways and output, reflective of fat mobilization and metabolic improvement.

Our study demonstrates distinct effects of IF-P on gut symptomatology and microbiome, as well as circulating metabolites compared to continuous CR. We observed significant changes in the GM response to both interventions; however, the IF-P group exhibited a more pronounced community shift and greater divergence from baseline (i.e., intra-individual Bray-Curtis dissimilarities). This shift was characterized by increased specific microbial families and genera, such as Christensenellaceae , Rikenellaceae , and Marvinbryantia , associated with favorable metabolic profiles. Furthermore, IF-P significantly increased circulating cytokine concentrations of IL-4, IL-6, IL-8, and IL-13. These cytokines have been linked to lipolysis, WL, inflammation, and immune response. The plasma metabolome analysis revealed distinct metabolite signatures in IF-P and CR groups, with the convergence of multiple metabolic pathways. These findings shed light on the differential effects of IF regimens, including IF-P as a promising dietary intervention for obesity management and microbiotic and metabolic health.

While acknowledging individual contributions of WL, protein pacing, and IF, we propose that the beneficial shifts observed may be best characterized as the culmination of features inherent in our IF-P approach. For example, it is possible that microbial competition is leveraged during reduced and intermittent nutritional input periods, emphasizing nutrient composition and food matrix type (combination of whole food and meal replacements vs. primarily whole food), affecting available substrates for gut microbes. IF-P participants’ fiber intake was concentrated in fiber-rich (RS5 type) shakes, offering immediate availability of fiber to the GI tract. In contrast, CR participants consumed fiber through whole foods, leading to a slower digestion and absorption process influenced by individual digestive transit times and enzymatic profiles. This nutritional environment may create ecological niches that support symbiont microbial communities. In this investigation, we provide support of such remodeling, with intentional fasting and increased relative protein (protein pacing) consumption well-validated to improve body composition and metabolism during weight loss 7 , 8 , 15 . Our results align with previous studies on CR, where greater relative protein intake was associated with an increased abundance of Christensenella 81 . This increase is likely a result of increased amino acid-derived metabolites 21 . We also observed increased signatures of amino acid metabolism in the GM of IF-P participants, which may be attributed to increased nitrogen availability, prompting de novo amino acid biosynthesis. The liquid format of two of the daily meals and precise timing of high-quality protein consumption (Protein Pacing) in the IF-P regimen may have influenced these results, as amino acids play essential roles in microbial communities, acting as energy and nitrogen sources and essential nutrients for amino acid auxotrophs.

In addition to the differences in nutrient composition, the IF-P group exhibited a profound reduction (33%) in visceral fat 15 . This reduction is significant because visceral fat is highly correlated with GM. While the specific influence of GM on fat depots in our study remains unclear, the shift in cytokine profile and metabolic pathways suggests an interaction between GM and fat metabolism. Regarding GM-host interaction, we did not detect changes in gut permeability assaying LBP. However, correlations were found with cytokines IL-4 and IL-13 and microbes Colidextribacter (negative association) and Ruminoccus gauveauii group (positive association). These associations may reflect the direct impact of the dietary intervention, yet they also hint at a deeper crosstalk within the gut-immune axis. This crosstalk is known to play a pivotal role in modulating host inflammation and influencing adipose tissue signaling pathways 42 . Furthermore, the observed microbial shifts, including changes in populations of Christensenella , suggest a nuanced role for certain microbes in regulating metabolic health. Notably, certain strains of Christensenella have been implicated in the regulation of key metabolic markers, such as glycemia and leptin levels, and in promoting hepatic fat oxidation 82 .

Our findings also underscore that GM composition plays a role in WL responsiveness during IF-P interventions. Subgroup analysis based on WL responsiveness revealed significant differences in species composition at the taxonomic level. The High-responder group showed an increased abundance of certain bacteria associated with metabolic benefits and anti-inflammatory effects. In contrast, the Low-responder group exhibited an increased abundance of butyrate-producing and nutritionally adaptive species (e.g., Eubacterium ventriosum 71 and Roseburia inulinivorans 72 ). Fecal metabolome analysis further highlighted differences between the two subgroups, with distinct metabolic signatures and enrichment in specific metabolic pathways. Notably, the High WL responders displayed enrichment of fecal metabolites involved in lipid metabolism. In contrast, Low responders were more prominent in pathways related to the metabolism of amino acids and peptides, including glycine, serine, and threonine, d-glutamine, and d-glutamate, as well as tyrosine metabolism and arginine biosynthesis. The latter metabolic signature has been reported in individuals with severe obesity undergoing high-protein, low-calorie diets 83 . As both High and Low WL responders were consuming the same diet, our results suggest differences in GM composition and metabolism, which could play a role in determining the success of an IF-P regimen. Though, as these enrichment analyses were performed in an exploratory manner, we acknowledge the need for a more systematic approach to validate these findings.

Finally, we provide evidence of long-term GM stabilization from these changes by following one individual over 12 months. Dietary restriction is widely used to reduce fat mass and weight in individuals with or without obesity; however, weight regain after such periods presents a critical challenge, and the underlying homeostatic mechanisms remain largely elusive. Notably, keystone taxa that became more prominent over time were associated with anti-adipogenesis, improved insulin sensitivity, and reduced metabolic disease risk. The microbial shifts were accompanied by noticeable changes in the fecal metabolome, with shifts in various metabolites and chemical subclasses. Pathway analysis identified impacts on primary bile acid biosynthesis, cysteine and methionine metabolism, and other fat mobilization and metabolic improvement pathways. These shifts were accompanied by noticeable changes in the fecal metabolome, particularly in metabolites and chemical subclasses related to lipid metabolism, nucleotide turnover, and aromatic amino acid formation.

Despite the valuable insights from our study on the complex interactions between intermittent fasting, higher protein intake using protein pacing, the GM, and circulating metabolites in obese individuals, several limitations should be acknowledged. First, our reliance on fecal samples to represent the GM may have overlooked potential microbial populations in the upper GI tract. Including samples from proximal regions in future studies would provide a more comprehensive understanding of the gut microbiome’s response to IF-P and CR. In addition, the sample size for our study was determined based on the primary outcomes related to body weight and composition from the parent study 15 . This sample size may have reduced statistical power and potentially amplified individual variability among participants. However, it is important to note that the smaller RCT design allowed for more precise control over diet and lifestyle factors, minimizing potential confounding influences on the study outcomes. Furthermore, the study’s duration was limited to eight weeks, which prevented potential insights into the differential long-term effects between the two interventions. However, we were able to extend the follow-up duration and conduct periodic assessments for a year in our case-study participant, offering a more comprehensive understanding of the sustainability of the observed changes and the potential for weight regain for IF-P. The current study compared a combination of whole food and supplements (shakes and bars; IF-P) versus primarily whole food (CR), which together with variations in protein and fiber content and type may have influenced the gut symptomatology and nutrient absorption between groups. Additionally, study participants self-reported dietary intake daily, although there was close monitoring of intake through the return of empty food packaging/containers of consumed food and daily monitoring by investigators and weekly meetings with a registered dietitian. Overall, knowledge gaps are present in this research, including how the microbiome is rebuilt after food reintroduction and how overall caloric restriction and specific macronutrients contribute to this process. However, considering the multifactorial nature of weight loss and metabolic health, our work represents an important precedent for future work. Future investigators should consider integrating these factors to provide a more comprehensive understanding of the underlying mechanisms. Additional research is warranted to characterize the metabolic signature of IF-P, the time relationship between these fasting periods, and the analysis of these metabolic changes. A strength of our High-Low-responder and case-study analyses is the hypothesis-driving nature of the findings, from which targeted microbiome and/or precision nutrition interventions can be designed and tested.

In conclusion, our study provides valuable insights into the complex interactions among intermittent fasting and protein pacing, the GM, and circulating metabolites in individuals with obesity. Specifically, intermittent fasting - protein pacing significantly reduces gut symptomatology and increases gut microbes associated with a lean phenotype ( Christensenella ) and circulating cytokines mediating total body weight and fat loss. These findings highlight the importance of personalized approaches in tailoring dietary interventions for optimal weight management and metabolic health outcomes. Further research is necessary to elucidate the underlying mechanisms driving these associations and to explore the therapeutic implications for developing personalized strategies in obesity management. Additionally, future studies should consider investigating microbial populations in upper GI sections and potential intestinal tissue remodeling to gain a more comprehensive understanding of the gut microbiome’s role in these interventions.

Study design and participants

The protocol of the clinical trial was registered on March 6, 2020 (Clinicaltrials.gov; NCT04327141), and the results of the primary analysis have been published previously 15 . Briefly, participants were recruited from Saratoga Springs, NY, and were provided informed written consent in accordance with the Skidmore College Human Subjects Institutional Review Board before participation (IRB#: 1911-859), including consent for the use of samples and data from the current study. Each procedure performed was in adherence with New York state regulations and the Federal Wide Assurance, which follows the National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, and in agreement with the Helsinki Declaration (revised in 1983). Their physicians performed a comprehensive medical examination/history assessment to rule out any current cardiovascular or metabolic disease. For at least six months before the start of the study, all eligible participants were either sedentary or lightly active (<30 min, two days/week of organized physical activity), with overweight or obesity (BMI > 27.5 kg/m2; % body fat > 30%), weight stable (±2 kg), and middle-aged (30–65 years). In addition, participants taking antibiotics, antifungals, or probiotics within the previous two months were excluded. Enrolled participants were matched for body weight, BMI, and body fat and randomly assigned to one of two groups: (a) IF-P ( n  = 21; 14 women; 7 men) or (b) CR ( n  = 20; 12 women; 8 men) for eight weeks. During a one-week run-in period, subjects maintained a stable body weight by consuming a similar caloric intake as their pre-enrollment caloric intake while maintaining their sedentary lifestyle. This was confirmed by matching their pre-enrollment dietary intake to the one-week run-in diet period 15 . Following baseline testing, participants were provided detailed instructions on their weight loss dietary regimen (Supplementary Table  S1 ) and received weekly dietary counseling and compliance/adherence monitoring from the research team via daily food records, and weekly registered dietitian meetings, along with weekly visits to the Human Nutrition and Metabolism laboratory at Skidmore College (Saratoga Springs, NY) for meal distribution and empty packet/container returns. All outcome variables were assessed pre (week 0), mid (week 4), and post (week 8). All participants were compensated $100 for successful completion of the study and received an additional monthly stipend of $75 for groceries (CR group only) or up to two meals per day of food supplements and meal replacements (IF-P only).

IF days consisted of ~350–550 kcals per day, in which participants were provided a variety of supplements and snacks. Protein pacing (P) days for IF-P consisted of four and five meals/day for women and men, respectively, two of which (breakfast and one other meal) were liquid meal replacement shakes with added whole foods (Whole Blend IsaLean® Shakes, 350/400 kcals, 30/36 g of protein/meal, 9 g of fiber); a whole food evening dinner meal (450/500 kcals men), an afternoon snack (200 kcals, men only), and an evening protein snack (IsaLean® or IsaPro® Shake or IsaLean Whole Blend® Bar; 200–250 kcals). This dietary regimen provided 1350–1500 and 1700–1850 kcals/day for women and men, respectively, and a macronutrient distribution targeting 35% protein, 35% carbohydrate, 20–30 g/day of fiber, and 30% fat. Isagenix International, LLC (Gilbert, AZ, USA) provided all meal replacement shakes, bars, beverages, and supplements. In comparison, participants assigned to the CR diet followed specific guidelines of the National Cholesterol Education Program Therapeutics Lifestyle Changes (TLC) diet of the American Heart Association with a strong Mediterranean diet influence of a variety of fresh vegetables, fruits, nuts, and legumes. The specific macronutrient distribution recommended was <35% of kcal as fat; 50%–60% of kcal as carbohydrates; 15% kcal as protein; <200 mg/dL of dietary cholesterol; and 20–30 g/day of fiber. The total calorie intake was 1200 and 1500 calories per day for women and men, respectively, during the 8-week weight loss intervention. In addition to weekly meetings with the registered dietitian and daily contact with research team members, subjects were provided detailed written instructions for their meal plans. They were closely monitored through daily participant-researcher communication (e.g., email, text, and mobile phone), two-day food diary analysis, weekly dietary intake journal inspections, weekly meal/supplement container distribution, and returning empty packets and containers.

Gastrointestinal (GI) symptom rating scale

Participants completed the 15-question GI symptom rating scale (GSRS) 84 at baseline, week four, and week eight. Briefly, each question is rated on a 7-point Likert scale (1 = absent; 2 = minor; 3 = mild; 4 = moderate; 5 = moderately severe; 6 = severe and 7 = very severe) and recalled from the previous week. Questions include symptoms related to upper abdominal pain, heartburn, regurgitation (acid reflux), empty feeling in the stomach, nausea, abdominal rumbling, bloating, belching, flatulence, and questions on defecation. The GSRS questionnaire provides explanations of each symptom, is understandable, and has reproducibility for measuring the presence of GI symptoms 85 . In our analysis, a score of ≥2 (minor) was defined as symptom presence, and a score ≥ 4 (moderate) was defined as moderate symptom presence. Furthermore, to better categorize symptom location, bloating, flatulence, constipation, diarrhea, stool consistency, defecation urgency, and sensation of not completely emptying bowels were classified as lower GI symptoms, and nausea, heartburn, regurgitation, upper abdominal pain, empty feeling in the stomach, stomach rumbling, and belching was classified as upper GI symptoms. Total scores were also generated for overall symptom and moderate symptom presence.

Fecal sample collection and DNA extraction

Participants were instructed to provide stool samples at baseline, week four, and week eight of the intervention. The case-study participant additionally provided samples at weeks 12, 16, 32, and 52. The entire bowel movement was collected and transported within 24 h of defecation to the Skidmore College Human Nutrition and Metabolism (Saratoga Springs, NY) laboratory using a cooler and ice packs and frozen at −80 °C. Samples were then sent to ASU (Phoenix, AZ) overnight on dry ice for analysis, where they were thawed at 4 °C and processed. Wet weight was recorded to the nearest 0.01 g after subtracting the weight of fecal collection materials. Stool samples were then rated according to the BSS 86 , homogenized in a stomacher bag, and the pH was measured (Symphony SB70P, VWR International, LLC., Radnor, PA, USA). Next, the extraction of DNA was performed using the DNeasy PowerSoil Pro Kit (Cat. No. 47016, Qiagen, Germantown, MD) per the manufacturer’s instructions. DNA concentration and quality were quantified using the NanoDrop™ OneC Microvolume UV-Vis Spectrophotometer (Thermo Scientific™, Waltham, MA) according to manufacturer instructions. The OD 260 /OD 280 ratio of all samples was ≥1.80 (demonstrating DNA purity).

Quantification of bacterial 16S rRNA genes

To estimate total bacterial biomass per sample (16S rRNA gene copies per gram of wet stool), DNA extracted from the fecal collections was assessed via quantitative polymerase chain reaction (qPCR) based on previously published methods 87 , 88 . Briefly, all 20 μL qPCR reactions contained 10 uL of 2X SYBR Premix Ex Taq ™ (Tli RNase H Plus) (Takara Bio USA, Inc., San Jose, CA, USA), 0.3 μM (0.6 μL) of each primer (926 F: AAACTCAAAKGAATTGACGG; 1062 R: CTCACRRCACGAGCTGAC), 2 μL DNA template (or PCR-grade water as negative control), and 6.8 μL nuclease-free water (Thermo Fisher Scientific, Waltham, MA, USA). PCR thermal cycling conditions were as follows: 95 °C for 5 min, followed by 35 cycles of 95 °C for 15 s, 61.5 °C for 15 s, and 72 °C for 20 s, then hold at 72 °C for 5 min, along with a melt curve of 95 °C for 15 s, 60 °C for 1 min, then 95 °C for 1 s. Quantification was performed using a QuantStudio3™ Real-Time PCR System by Applied Biosystems with QuantStudio Design and Analysis Software 1.2 from Thermo Fisher Scientific (Waltham, MA, USA). All samples were analyzed in technical replicates. For quality assurance and quality control, molecular negative template controls (NTC) consisting of PCR-grade water (Invitrogen, Waltham, MA, USA) and positive controls created by linearized plasmids were run on every qPCR plate. Standard curves were run-in triplicate and used for sample quantification, ranging from 10 7 to 10 1 copies/μL with a cycle threshold (CT) detection limit cutoff of 33. Reaction efficiency was approximately 101%, with a slope of −3.29 and R 2  ≥ 0.99.

Fecal microbiome analysis

Amplification of the 16S rRNA gene sequence was completed in triplicate PCRs using 96-well plates. Barcoded universal forward 515 F primers and 806 R reverse primers containing Illumina adapter sequences, which target the highly conserved V4 region, were used to amplify microbial DNA 89 , 90 . PCR, amplicon cleaning, and quantification were performed as previously outlined 90 . Equimolar ratios of amplicons from individual samples were pooled together before sequencing on the Illumina platform (Illumina MiSeq instrument, Illumina, Inc., San Diego, CA). Raw Illumina microbial data were cleaned by removing short and long sequences, sequences with primer mismatches, uncorrectable barcodes, and ambiguous bases using the Quantitative Insights into Microbial Ecology 2 (QIIME2) software, version 2021.8 91 .

16S rRNA sequencing produced 7,366,128 reads with a median of 53,776 per sample (range: 9512–470,848). Paired-end, demultiplexed data were imported and analyzed using QIIME2 software. Upon examination of sequence quality plots, base pairs were trimmed at position 20 and truncated at position 240 and were run through DADA2 to remove low-quality regions and construct a feature table using ASVs. Next, the ASV feature table was passed through the feature-classifier plugin 92 , which was implemented using a naive Bayes machine-learning classifier, pre-trained to discern taxonomy mapped to the latest version of the rRNA database SILVA (138.1; 99% ASVs from 515 F/806 R region of sequences) 93 . Based on an assessment of alpha rarefaction, a threshold of 6500 sequences/sample was established, retaining all samples for downstream analysis. A phylogenic tree was then constructed using the fragment-insertion plugin with SILVA at a p-sampling depth of the rarefaction threshold to impute high-quality reads and normalize for uneven sequencing depth between samples 94 . Alpha diversity (intra-community diversity) was measured using observed ASVs and the Phylogenetic diversity index. Additionally, the Shannon index was calculated for the subgroup and case-study analyses to capture richness and evenness at the species level. Beta diversity (inter-community diversity) was measured using Bray-Curtis dissimilarity.

For shotgun metagenomics, DNA was sequenced on the Illumina NextSeq 500 platform (Illumina, CA, USA) to generate 2 × 150 bp paired-end reads at greater sequencing depth with a minimum of 10 million reads. Raw Illumina sequencing reads underwent standard quality control with FastQC. Adapters were trimmed using TrimGalore. DNA sequences were aligned to Hg38 using bowtie2 95 . DNA sequences were then analyzed via the bio bakery pipeline 96 for taxonomic composition and potential functional content with MetaPhlAn4 and HUMAnN 3.0 (UniRef90 gene-families and MetaCyc metabolic pathways), using standard parameters. Functional profiling resulted in 8528 distinct Kyoto Encyclopedia of Genes and Genomes Orthology (KO) groups and 511 metabolic pathways, which align with previous human gut microbiome studies 96 .

Blood sample collection and biochemical analyses

All participants were tested between the hours of 6:00 a.m. and 9:00 a.m., after an overnight fast for body composition assessments (height, body weight, and total body composition) at weeks 0, 4, and 8. 12-h fasted venous blood samples (~20 mL) were collected into EDTA-coated vacutainer tubes and centrifuged (Hettich Rotina 46R5) for 15 min at 4000 ×  g at −4 °C. After separation, plasma was stored at −80 °C until analyzed. Undiluted plasma samples were sent to Eve Technologies (Calgary, Alberta, Canada) for assessment of inflammatory cytokines [Granulocyte-macrophage colony-stimulating factor [GM-CSF], interferon-γ (IFNγ), interleukin (IL)-β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-13, IL-17A, IL-23, and Tumor necrosis factor-α (TNFα)] using a high human sensitivity 14-plex cytokine assay (Millipore, Burlington, MA). Circulating LBP concentrations were quantified in duplicate using 1000x diluted plasma samples. A commercially available kit was used per the manufacturer’s protocol (Cat No. EH297RB, Thermo Fisher Scientific, Inc, Waltham, MA; intra-assay coefficient variation [CV] <10%).

Targeted plasma metabolomic analysis

For the plasma metabolomic analysis, a 12-h fasted venous blood sample (~20 mL) was collected into EDTA-coated vacutainer tubes and centrifuged (Hettich Rotina 46R5) for 15 min at 4000 ×  g at 4 °C. After separation, 2 mL of plasma was aliquoted and stored at −80 °C at the Biochemistry Laboratory at Skidmore College (Saratoga Springs, NY, USA). Samples were then sent to the Arizona Metabolomics Laboratory at ASU (Phoenix, AZ, USA) overnight on dry ice for analysis, where they were thawed at 4 °C and processed. Briefly, 50 μL of plasma from each sample was processed to precipitate proteins and extract metabolites by adding 500 μL MeOH and 50 μL internal standard solution (containing 1810.5 μM 13 C 3 -lactate and 142 μM 13 C 5 -glutamic acid). The mixture was vortexed (10 s) and stored for 30 min at –20 °C, then centrifuged at 224,000 ×  g for 10 min at 4 °C. Supernatants (450 μL) were extracted, transferred to new Eppendorf vials, and dried (CentriVap Concentrator; Labconco, Fort Scott, KS, USA). Samples were then reconstituted in 150 μL of 40% phosphate-buffered saline (PBS)/60% acetonitrile (ACN) and centrifuged again at 22,000 ×  g at 4 °C for 10 min. Supernatants (100 µL) were transferred to an LC autosampler vial for subsequent analysis. Quality control (QC) was performed by creating a pooled sample from all plasma samples and injecting once every ten experimental samples to monitor system performance.

The highly-reproducible targeted LC–MS/MS method used in the current investigation was modeled after previous studies 97 , 98 , 99 . The specific metabolites included in our targeted detection panel are representative of more than 35 biological pathways most essential to biological metabolism and have been successfully leveraged for the sensitive and broad detection of effects related to diet 100 , diseases 101 , drug treatment 102 , environmental contamination 103 , and lifestyle factors 104 . Briefly, LC–MS/MS experiments were performed on an Agilent 1290 UPLC-6490 QQQ-MS system (Santa Clara, CA, USA). Each sample was injected twice for analysis, 10 µL using negative and 4 µL using positive ionization modes. Chromatographic separations were performed in hydrophilic interaction chromatography (HILIC) mode on a Waters Xbridge BEH Amide column (150 × 2.1 mm, 2.5 µm particle size, Waters Corporation, Milford, MA, USA). The flow rate was 0.3 mL/min, the autosampler temperature was maintained at 4 °C, and the column compartment was set at 40 °C. The mobile phase system was composed of Solvents A (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% H 2 O/5% ACN) and B (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% ACN/5% H 2 O). After the initial 1 min isocratic elution of 90% Solvent B, the percentage of Solvent B decreased to 40% at t  = 11 min. The composition of Solvent B was maintained at 40% for 4 min ( t  = 15 min).

The mass spectrometer was equipped with an electrospray ionization (ESI) source. Targeted data acquisition was performed in multiple-reaction monitoring (MRM) mode. The LC–MS system was controlled by Agilent MassHunter Workstation software (Santa Clara, CA, USA), and extracted MRM peaks were integrated using Agilent MassHunter Quantitative Data Analysis software (Santa Clara, CA, USA).

GC–MS fecal short-chain fatty acid analysis

Before GC–MS analysis of SCFAs, frozen fecal samples were first thawed overnight under 4 °C. Then, 20 mg of each sample was homogenized with 5 μL hexanoic acid—6,6,6-d 3 (internal standard; 200 µM in H 2 O), 15 μL sodium hydroxide (NaOH [0.5 M]), and 500 μL MeOH. Samples were stored at −20 °C for 20 min and centrifuged at 22,000 ×  g for 10 min afterward. Next, 450 μL of supernatant was collected, and the sample pH was adjusted to 10 by adding 30 μL of NaOH:H 2 O (1:4, v-v). Samples were then dried, and the residues were initially derivatized with 40 µL of 20 mg/mL MeOX solution in pyridine under 60 °C for 90 min. Subsequently, 60 µL of MTBSTFA containing d 27 -mysristic acid was added, and the mixture was incubated at 60 °C for 30 min. The samples were then vortexed for 30 s and centrifuged at 22,000 ×  g for 10 min. Finally, 70 µL of supernatant was collected from each sample and injected into new glass vials for GC–MS analysis.

GC–MS conditions used here were adopted from a previously published protocol 105 . Briefly, GC–MS experiments were performed on an Agilent 7820 A GC-5977B MSD system (Santa Clara, CA); all samples were analyzed by injecting 1 µL of prepared samples. Helium was the carrier gas with a constant flow rate of 1.2 mL/min. Separation of metabolites was achieved using an Agilent HP-5 ms capillary column (30 m × 250 µm × 0.25 µm). Ramping parameters were as follows: column temperature was maintained at 60 °C for 1 min, increased at a rate of 10 °C/min to 325 °C, and then held at this temperature for 10 min. Mass spectral signals were recorded at an m/z range of 50–600, and data extraction was performed using Agilent Quantitative Analysis software. Following peak integration, metabolites were filtered for reliability. Only those with QC CV < 20% and a relative abundance of 1000 in > 80% of samples were retained for statistical analysis.

Untargeted fecal metabolomic analysis

Briefly, each fecal sample (~20 mg) was homogenized in 200 µL MeOH:PBS (4:1, v-v, containing 1810.5 μM 13 C 3 -lactate and 142 μM 13 C 5 -glutamic Acid) in an Eppendorf tube using a Bullet Blender homogenizer (Next Advance, Averill Park, NY). Then 800 µL MeOH:PBS (4:1, v-v, containing 1810.5 μM 13 C 3 -lactate and 142 μM 13 C 5 -glutamic Acid) was added, and after vortexing for 10 s, the samples were stored at −20 °C for 30 min. The samples were then sonicated in an ice bath for 30 min. The samples were centrifuged at 22,000 ×  g for 10 min (4 °C), and 800 µL supernatant was transferred to a new Eppendorf tube. The samples were then dried under vacuum using a CentriVap Concentrator (Labconco, Fort Scott, KS). Prior to MS analysis, the obtained residue was reconstituted in 150 μL 40% PBS/60% ACN. A quality control (QC) sample was pooled from all the study samples.

The untargeted LC–MS metabolomics method used here was modeled after that developed and used in a growing number of studies 106 , 107 , 108 . Briefly, all LC–MS experiments were performed on a Thermo Vanquish UPLC-Exploris 240 Orbitrap MS instrument (Waltham, MA). Each sample was injected twice, 10 µL for analysis using negative ionization mode and 4 µL for analysis using positive ionization mode. Both chromatographic separations were performed in hydrophilic interaction chromatography (HILIC) mode on a Waters XBridge BEH Amide column (150 × 2.1 mm, 2.5 µm particle size, Waters Corporation, Milford, MA). The flow rate was 0.3 mL/min, autosampler temperature was kept at 4 °C, and the column compartment was set at 40 °C. The mobile phase was composed of Solvents A (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% H 2 O/5% ACN) and B (10 mM ammonium acetate, 10 mM ammonium hydroxide in 95% ACN/5% H 2 O). After the initial 1 min isocratic elution of 90% B, the percentage of Solvent B decreased to 40% at t  = 11 min. The composition of Solvent B maintained at 40% for 4 min ( t  = 15 min), and then the percentage of B gradually went back to 90%, to prepare for the next injection. Using mass spectrometer equipped with an electrospray ionization (ESI) source, we collected untargeted data from 70 to 1050 m/z.

To identify peaks from the MS spectra, we made extensive use of the in-house chemical standards (~600 aqueous metabolites), and in addition, we searched the resulting MS spectra against the HMDB library, Lipidmap database, METLIN database, as well as commercial databases including mzCloud, Metabolika, and ChemSpider. The absolute intensity threshold for the MS data extraction was 1000, and the mass accuracy limit was set to 5 ppm. Identifications and annotations used available data for retention time (RT), exact mass (MS), MS/MS fragmentation pattern, and isotopic pattern. We used the Thermo Compound Discoverer 3.3 software for aqueous metabolomics data processing. The untargeted data were processed by the software for peak picking, alignment, and normalization. To improve rigor, only the signals/peaks with CV < 20% across quality control (QC) pools, and the signals showing up in >80% of all the samples were included for further analysis. To ensure the robustness of our model validation, we employed an enhanced validation approach by repeating the LOOCV process 100 times. Each iteration involves excluding one sample from the dataset to serve as the test set, with the model being trained on the remaining samples. This approach, referred to as ‘repeated LOOCV’, was adopted to mitigate bias and provide a thorough validation of our model’s predictive capability. The method signifies the number of repetitions of the LOOCV process, rather than splitting the dataset into 100 equal parts.

Multi-omics data analysis

For MOFA, bacterial 16S rRNA ASVs and plasma metabolites were integrated using the MOFA2 package 55 . Before integration, ASV sequences were filtered (minimum of 5 ASV in greater than 10% of all samples), collapsed to the genus level, and scaled using a centralized-log-ratio, as described previously 109 . Plasma metabolites were scaled and normalized as described in the metabolome analysis. The inputs for MOFA model training comprised 53 taxa and 138 metabolites. The latent factors and feature loadings were extracted from the best-trained model with the built-in functions of MOFA2. After model fitting, the number of factors was estimated by requiring a minimum of 2% variance explained across all microbiome modalities.

Integrating microbial taxa with the same filtration as stated above (at the genus level from 16S amplicon sequencing and species level from metagenomic sequencing) and cytokine data and fecal metabolomic data, respectively, was conducted with GFLASSO (R package: GFLASSO, v0.0.0.9000). This correlation-based network solution can handle multiple response variables for a given set of predictors (in this case: 1. cytokine abundances predicted by microbial taxa response; and 2. fecal metabolite response predicted by microbial taxa). Solution parsimony was determined by an unweighted (i.e., presence or absence of association by imposing a correlation threshold) network structure. The regularization and fusion parameters were determined from the smallest root mean squared error (RMSE) estimate via cross-validation, accounting for interdependencies among microbial features. The tested parameters encompassed all combinations between λ and γ with values ranging from 0 to 1 (inclusive) in step increments of 0.1. GFLASSO coefficient matrices were constructed using a threshold coefficient of >0.02 to discern the strongest associative signals.

Statistical analysis

Gastrointestinal symptom scores were on the low end of the GSRS scale and not normally distributed; therefore, nonparametric statistical tests were applied. Symptom prevalence (number of scores ≥ 2) and moderate symptom prevalence (≥4) for total, upper, and lower GI GSRS clusters were analyzed using contingency tables. Specifically, differences between IF-P and CR GI symptoms at baseline were compared using a Fisher’s Exact test, whereas baseline vs. weeks four and eight values were compared with McNemar’s test. Stool weight, BSS, fecal pH, plasma cytokines and LBP, and SCFAs were assessed for normality with Q-Q plots and Shapiro-Wilk tests and log-transformed where appropriate. These were then tested for time and interaction (group × time) effects using linear-mixed effect (LME) models, with each participant included as a random effect.

For analysis and visualization of the microbiome data, artifacts generated in QIIME2 were imported into the R environment (v4.2.2) using the phyloseq package (v1.42.0) 110 . Before conducting downstream analyses, sequences were filtered to remove all non-bacterial sequences, including archaea, mitochondria, and chloroplasts. After assessing normality (Shapiro-Wilk’s tests), LME models were used to test the effect of time and the interaction of group and time with the covariates of age and sex with each participant included as a random effect on the alpha diversity metrics using the nLME package (v3.1.160). For beta diversity, a nested permutational analysis of variance (PERMANOVA) was conducted on Bray-Curtis dissimilarities using the Adonis test in the vegan package (v2.6.2) with 999 permutations. The PERMANOVA model incorporated the factors of time, individual, interaction (group × time), and participant (nested factor). A permutation test for homogeneity in multivariate dispersion (PERMDISP) was conducted using the ‘betadisper’ function in the vegan package to compare dispersion. To support the Adonis analysis, intra-individual differences were also compared between groups, as previously described 111 , by calculating the within-subject distance for paired samples (baseline vs. weeks four and eight) and testing for group distances (Wilcoxon rank-sum test). Differential abundance analysis was performed using MaAsLin2 (v1.12.0) 18 . To detect changes in microbial features between groups over time, we built linear-mixed models that include group, time, and their interaction, with age and sex as covariates and the participant as a random factor. Before analysis, raw counts from the ASV table were filtered for any sequence not present five times in at least 30% of all samples. A significant p-value for the product term indicates that changes in microbial features differed over time between groups. The Benjamini–Hochberg (BH) procedure was used to correct for multiple testing at ≤0.10. To assess the correlation between changes in specific taxa and biomarkers over the eight-week intervention, Spearman correlation tests were performed.

Univariate and multivariate analyses of plasma metabolites and metabolic ontology analysis were performed, and results were visualized using the MetaboAnalystR 5.0 112 . Human metabolomic data were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) human pathway library to analyze predicted states 113 . The data were log 10 -transformed, and Pareto scaled to approximate normality before all analyses. A GLM was constructed with age, sex, and time as covariates to determine significantly affected metabolites by group intervention. Levene’s test was performed to detect significant homogeneity. The BH procedure was used to correct for multiple testing at ≤0.10. Fecal metabolomic analysis for the subgroup comparison was performed by assessing logFC values between groups with a Wilcoxon rank-sum test with BH adjustment. For pathway analysis, the impact was calculated using a hypergeometric test, while significance was determined using a test of relative betweenness centrality. Importantly, the BH procedure was not applied to pathway and enzyme enrichment analyses for the subgroup assessment since these analyses involve testing the significance of multiple related hypotheses rather than independent hypotheses, which is too conservative, resulting in false negative results.

For MOFA, latent factors explaining ≥2.0% of model variance from the plasma metabolomic and amplicon microbiome data were used to perform Spearman correlations on anthropometric and nutritional data and compared between IF-P and CR groups using Wilcoxon rank-sum tests. The highest beta coefficients (>0.3) detected from GFLASSO models were further assessed by performing Spearman correlations of select microbial features with the response variables (i.e., cytokines and fecal metabolites). All statistical tests were performed with a significance level of p  < 0.05 and BH correction of p .adj < 0.10. In addition, we present data in this study in accordance with the ‘Strengthening The Organization and Reporting of Microbiome Studies’ (STORMS) guidelines for human microbiome research 114 .

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The microbiome sequencing data generated in this study have been deposited in the BioProject Database of National Centre for Biotechnology Information database under accession code PRJNA847971 . The metadata data linking the microbiome sequences with the appropriate sample ID and intervention in this study are provided in Supplementary Data  1 . The processed data are available at https://github.com/Alex-E-Mohr/GM-Remodeling-IF-ProteinPacing-vs-CaloricRestriction .  Source data are provided with this paper.

Code availability

The R code used for analysis and figure generation for reproducibility purposes are available at: https://github.com/Alex-E-Mohr/GM-Remodeling-IF-ProteinPacing-vs-CaloricRestriction . 115

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Acknowledgements

We thank the trial volunteers for their dedication and commitment to the study protocol. We are grateful for the research assistants from Skidmore College who provided valuable assistance with study protocol design, scheduling, recruitment, data testing, collection, entry, and statistical analysis, and preparation of manuscripts: Molly Boyce, Jenny Zhang, Melissa Haas, Olivia Furlong, Emma Valdez, Jessica Centore, Annika Smith, Kaitlyn Judd, Aaliyah Yarde, Katy Ehnstrom, Dakembay Hoyte, Sheriden Beard, Heather Mak, and Monique Dudar. We are grateful for the extensive guidance and counseling provided by the registered dietitian Jaime Martin. We thank research coordinator Michelle Poe for her superior dedication to all aspects of the study. This study was primarily funded by an unrestricted grant from Isagenix International LLC to P.J.A. (grant #:1911-859), with secondary funding provided to K.L.S.

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Contributions

Study conceived and designed: P.J.A. Manuscript preparation with input from all authors: A.E.M., K.L.S., D.A.B., P.J., C.M.W., D.D.S., R.K.-B., H.G., J.K.-S., K.M.A., E.G., and P.J.A. Randomized study design and execution: K.M.A., and P.J.A. Microbiome analysis: A.E.M., D.A.B., C.M.W., and R.K.-B. Blood analyte analysis: A.E.M., K.L.S., and P.J.A. Metabolomic analysis: A.E.M., Y.J., H.G., and P.J. Statistical analysis and data presentation: A.E.M., C.M.W., D.D.S., R.K.-B., and P.J.A. Supervision and funding: K.L.S., E.G., and P.J.A.

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P.J.A. is a consultant for Isagenix International LLC, the study’s sponsor, he is an advisory board member of the International Protein Board (iPB), and he receives financial compensation for books and keynote presentations on protein pacing ( www.paularciero.com ). Eric Gumpricht is employed by Isagenix International, LLC, the funding source for this research. Isagenix International, LLC had no role in the study design, data collection, analysis, or decision to publish. No authors have financial interests regarding the outcomes of this investigation. The other authors declare no competing interests.

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Mohr, A.E., Sweazea, K.L., Bowes, D.A. et al. Gut microbiome remodeling and metabolomic profile improves in response to protein pacing with intermittent fasting versus continuous caloric restriction. Nat Commun 15 , 4155 (2024). https://doi.org/10.1038/s41467-024-48355-5

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Can Online Music Platforms Be Fair? An Interdisciplinary Research Manifesto

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• Volume 55 , pages 249–279, ( 2024 )

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In this article we present a manifesto for research into the complex interplay between social media, music streaming services, and their algorithms, which are reshaping the European music industry – a sector that has transitioned from ownership to access-based models. Our focus is to assess whether the current digital economy supports a fair and sustainable development for cultural and creative industries. The manifesto is designed to pave the way for a comprehensive analysis. We begin with the context of our research by briefly examining the de-materialisation of the music industry and the critical role of proprietary algorithms in organising and ranking creative works. We then scrutinise the notion of “fairness” within digital markets, a concept that is attracting increasing policy interest in the EU. We believe that, for “fairness” to be effective, the main inquiry around this concept – especially as regards remuneration of music creators – must be necessarily interdisciplinary. This presupposes collaboration across complementary fields to address gaps and inconsistencies in the understanding of how these platforms influence music creation and consumption and whether these environments and technologies should be regulated. We outline how interdisciplinary expertise (political science, law, economics, and computer science) can enhance the current understanding of “fairness” within Europe’s cultural policies and help address policy challenges. The article details how our research plan will unfold across various disciplinary hubs of a Horizon Europe project ( Fair MusE ) that aims to explore the challenges and opportunities of today’s digital music landscape. The plan culminates in the integration of these hubs’ findings to deliver “key exploitable results”.

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1 Introduction

The exponential growth of social media and streaming services, and the fast-growing influence of their algorithms and data infrastructures, raise questions as to whether today’s digital economy will allow the cultural and creative industries (CCIs), and especially the music ecosystem, to develop in a fair and sustainable way, at least for most authors and performers. The digital revolution has done much more than just simplify content dissemination and enable content production to reach unprecedented scales. Digital technologies have broadened the notion of “creation” itself, which ranges from traditional works of composers, performers, record labels, and broadcasters to new forms of musical and music-based creativity that digital settings, social media, and artificial intelligence (AI) have enabled. These new forms and trends include the streaming of live music events and home-made creations that became even more appealing and diffused due to the COVID-19 health emergency and the ensuing long-term restrictions on the performing arts. In this scenario, the commercial power of a handful of very large tech companies increased significantly. These companies can be identified, at least in part, with the owners of the “very large online platforms” (VLOPs) Footnote 1 under Art. 33 of the DSA and with providers of core platform services according to the notion embodied in the DMA. Footnote 2 The ability of these companies to control access to unprecedented volumes of creative works and, at the same time, creators’ ability to reach and develop potential audiences raises existential questions for Europe’s policymakers and the CCIs, including players such as radio, TV broadcasters, and the market for live performance exploitations.

This paper takes the form of a manifesto to advocate a new, interdisciplinary research approach that can remedy the shortcomings of a purely doctrinal and scientifically segregated (i.e. “silo-like”) analysis of EU cultural and industrial policies in the music sector and of their effective impact in today’s platform- and algorithm-dominated economy. In our view, only a well-designed combination of distinct and complementary disciplines can test methodologically and verify empirically whether the EU’s policy changes in copyright law and recent EU regulations (Digital Services Act – DSA – and Digital Markets Act – DMA) seeking to curb the exceptional power of VLOPs are justified and suitable for today’s internet. To this end, we authored a research proposal and built an EU-wide interdisciplinary group of academics and industry partners whose consortium – Fair MusE Footnote 3 – received funding from the EC/REA’s Horizon Europe program. The group’s principal investigators are experts in the fields of law, economics, political science, and computer science and have a consolidated leadership in developing projects of international relevance and solid connections with policymakers and industry.

The predominantly academic character of this consortium Footnote 4 aims at guaranteeing the highest quality and independence of the proposed research. The consortium composition seeks to prevent conflicts of interests which would inevitably arise in our view if, due to the project’s mission, the consortium incorporated industry partners (such as a major record publisher or label, an online music service provider, or a social media platform owner) that would pursue their own corporate interests. This could hinder, or even distort, the results of the empirical research concerning data and confidential information Fair MusE has envisaged. To prevent this risk while still being able to engage in “co-creation” of tools for policymakers and the music industry together with CCIs, our consortium incorporates industry partners which have an interest in promoting fairness in music ecosystems: (i) an Italian composers’ collecting society (SIAE), which is broadly representative of Italian composers and whose repertoire is strong at the local level but not mainstream at the international level; Footnote 5 and (ii) a UK-based company (Verifi Media Ltd) that is currently leading the market development of rights data management services for the music industry, including data collaboration and sharing, which are a prerequisite for market transparency for both creators and exploiters of digital music. Footnote 6

Our manifesto is based on Fair MusE’s main research proposal and puts forward a novel approach to address the European idea of a “fair” digital society and of fair digital markets in the music sector in an extensive and integrated manner. Such a necessity is even more compelling at the European level if we consider that the notion of “fairness” is currently being used in several policy areas. Footnote 7 Considering that fairness is designed to support cultural creation in today’s fast-changing, very broad, and increasingly AI-dominated music ecosystems, independent research should give this concept a more tangible and measurable dimension. Our manifesto and its potential outcomes aim at pursuing this goal and making policymakers, stakeholders, and the general public more aware of the risks that creators’ lack of appropriate remuneration as well as platforms’ algorithm-based and non-transparent exploitations of creative works pose to music’s sustainability and diversity.

The manifesto is organised as follows. Section 2 briefly summarises how the music industry has progressively de-materialised over the past three decades and gone from ownership-based to access-centred business models where streaming services and social media platforms organise and rank sound recordings on the grounds of their (secret) algorithms. Section 3 lists complementary disciplines and methods that are necessary to perform effective and independent research activities focused on whether music platforms can function fairly, especially for music creators. This section identifies gaps in the literature and shows how interdisciplinary research can go beyond the state of the art and help resolve persisting policy dilemmas in this field. Section 4 describes the main contents and purposes of our manifesto while drawing on the emerging notion of fairness in EU music policymaking and other policy fields. Section 5 details how we see our ideas being put into practice in Fair MusE’s research proposal and concrete set of activities.

2 Evolution of the Music Industry and Its Current Dependence on Platforms

The music industry, more than other sectors, has gone through radical changes in the past two decades. These have been even more difficult to face because of the extreme fragmentation of the rights, business interests, and artistic prerogatives that characterise the related creative communities. When the internet first emerged in the mid-1990s, the end-to-end architecture of this new medium and the fast development of file-sharing software enabled internet users to access and exchange large amounts of recorded music without intermediation. Free and uncompensated file sharing threatened the survival of the music industry for almost a decade, given that it had the potential to replace physical formats like CDs, which were the core business of the industry. Footnote 8 Since the early 2000s, proprietary online platforms have dramatically changed content distribution models and made music materials ubiquitous in the online environment. Although unauthorised file-sharing continued, becoming even more efficient and sophisticated, an unstoppable evolution of the internet infrastructure in terms of bandwidth and connectivity enabled companies to launch on-demand music stores, such as iTunes, which Apple released in 2001. iTunes was the first service that made digital music marketable by successfully creating its own ecosystem based on proprietary technologies for computers and portable devices. Streaming services like Spotify and Deezer as well as social media platforms like YouTube emerged at a later stage, which consolidated both a trajectory of music consumption from an ownership to an access model as well as a process of online re-intermediation for the whole internet and, even more so, for digital music distribution. This platform-centred environment has allowed music right-holders to start licensing their works and earn remuneration from the technology companies that exploited their music. Despite this evolution, music right-holders’ communities claim not only that the value of their works has been disrupted by a platform-dominated economy but also that a “value gap” exists between the remuneration they earn from music streaming services and social media platforms. Footnote 9

Our interdisciplinary research agenda seeks to understand and illustrate, in an autonomous and evidence-based way, the consequences that the various business models deployed by the largest digital music platforms have had as far as music production, distribution, and consumption processes are concerned. These complex environments are deeply influencing the economic and social value of this art form, in ways which are often contradictory from a public policy perspective. On the one hand, platforms have effectively enabled new forms of music production and home-made creations that empower amateur, early career, or disenfranchised categories of authors (“professionalising amateurs”) to gain online exposure and eventually establish themselves as music professionals. Footnote 10 On the other hand, these algorithm-dominated businesses seem to have induced a significant impoverishment of creators, especially those of niche or marginal repertoires that are penalised by the logic of filter bubbles and recommender systems.

The above-mentioned scenario has led to significant reforms of legal and regulatory frameworks that aim to govern and shape European music ecosystems. The most significant among these adaptations are embodied in Directive 2019/790: Footnote 11

This directive seeks to protect the commercial value of copyright works – in particular recorded music – by making providers of online sharing content services directly liable for works their users make available. Footnote 12 This policy change represents a turn away from the legal principle of platform neutrality that EU lawmakers maintained for nearly two decades to stimulate the growth of a robust internet infrastructure. In reversing this principle, the legal provision aimed at obliging social media companies to obtain licences and to implement content identification technologies that can either restrict access to unauthorised works or help copyright holders to be remunerated for online exploitation of their works.

A second, potentially very impactful change is condensed into Chapter 3 of the directive, where the law codifies a principle of fair and proportionate remuneration for authors and performers, in particular with regard to online music exploitations, Footnote 13 and a right to receive – on a regular basis – timely, accurate, relevant, and comprehensive information on modes of exploitation of their works, direct and indirect revenues generated, as well as any remuneration due. Footnote 14

We believe that both these policy changes constitute a turning point or even a “big bang” in the European history of copyright and artists’ rights, whose real effects are yet to be evaluated in a non-doctrinal and evidence-based way. This has not happened yet because of the very slow transposition of these provisions into national laws and an approach to academic research on these reforms that we find incomplete, too abstract and ideological, and discipline-segregated (“silo-like”).

3 Advancing Complementary Disciplinary Expertise to Go Beyond the State of the Art

Our research presupposes the identification of disciplines that can eventually enable independent scholars to fully understand the consequences of market-driven and legislative changes in Europe’s music ecosystem, going beyond the state of the art in measuring and enhancing the impact of the main EU policymaking initiatives in this field. While the music industry has been analysed from an economic perspective, Footnote 15 we believe that these analyses should be strongly connected to political, legal, and technical investigations and a thorough empirical exploration of the societal impact of music platforms on European music creators and audiences. In the following subsections we seek to identify gaps in the literature and illustrate how research can produce new knowledge to the benefit of policymakers, stakeholders, and society at large.

3.1 Politics: The EU as a Policymaker in the Music Industry

Despite a series of thoughtful studies on EU cultural-media policies, Footnote 16 there has so far been no comprehensive attempt to examine and critically assess the ways in which EU policy and law have sought to cope with the notion and the goal of fairness in the music sector, the values underpinning the policy instruments introduced (market vs. non-market values), and the objectives pursued. We believe that the first pillar of an effective research agenda in this field should be a comprehensive policy analysis of different EU initiatives that relate to the music sector. We need such an exhaustive analysis to understand the origin, nature, breadth, and degree of policy changes towards the governance of online platforms in Europe and the implications for the music ecosystem. This endeavour shall consist in scrutinising several policy instruments, proposals, and reports, including key documents related to “Music Moves Europe”, that the EU has issued in the past three decades. Footnote 17

Our analysis will focus predominantly on three issues that have dominated debates on online platforms and EU music governance in the past few years: (i) the availability and prominence of local and national music content online; (ii) the rights for creators in relation to the use of their music works by online service providers; and (iii) a fair and proportionate remuneration of music creators. Our team will engage in a historical analysis covering a span of 30 years of EU policy initiatives in this sector to understand the nature and breadth of policy changes towards the governance of music streaming and social media platforms, including the latest tweaks that specifically regard fairness and transparency. This analysis will also help us address the way EU governance rules seek to promote fairness in an economy where platforms’ dominance was exacerbated by the COVID-19 pandemic. We believe that an in-depth understanding of these changes is essential for policymakers as well as key digital industry players and music associations to assess the pros and cons of an increasingly pervasive dimension of EU law where copyright, contract law, and various forms of platform regulation are used to govern the extended landscape of business models and music professionals that characterises the platform economy. This unprecedented policy analysis can produce, in our view, new knowledge on the impact of online platforms and of phenomena such as the COVID-19 pandemic on music production and dissemination and thus contribute to finding solutions with a clear potential to bolster fairness.

3.2 Law: Copyright, Contract Law, and Platform Liability

Despite the adoption of the DSA and its broad attempt to introduce new obligations for VLOPs, the most important form of regulation aimed at helping music right-holders exercise their rights in the social media landscape is Art. 17 of the 2019 Copyright Directive. Footnote 18 This provision aims at setting a new standard of copyright liability applicable to social media platforms and at excluding the (previously uncertain) application of liability exemptions embodied in Directive 2000/31 (e-Commerce Directive). Footnote 19 Since it was included (as Art. 13) in the EU Commission’s directive proposal in September 2016, this provision has been the target of an endless number of academic articles, studies, parliament interrogations, open letters, popular petitions, and other initiatives that aimed at flagging the “negatives” of the complex legal mechanism it incorporates, especially for the protection of freedom of expression and “internet freedom”. Footnote 20 The volume and the strength of this critical movement increased, and became even more apparent, as soon as the EU Member States started transposing this provision in a rather inhomogeneous, scattered, and (mostly) untimely manner. Footnote 21 Such a broadly shared and vehement attack on this provision found its point of sublimation in the appeal brought by the Republic of Poland against Art. 17 before the European Court of Justice (ECJ), which the Court eventually rejected. Footnote 22

Our research agenda, while duly considering the controversial aspects of this provision, as reflected in an exceptionally abundant literature, aims mostly at identifying its “positives”. We believe that only a fairness-centred reading and an evidence-based analysis of Art. 17 and its national implementations can tell whether this legislative reform strikes a suitable balance between antagonistic interests. A literature review shows that, from a constitutional perspective, many European legal scholars tend to place copyright and the rights of authors at a level that is lower than that of other fundamental rights. Several scholars write as if internet users’ freedom of expression and the tech companies’ freedom to run their online businesses should systematically prevail over the authors’ expectation to enforce their rights and to receive fair remuneration for the exploitations of their work. Footnote 23 Despite the relevance of these remarks, this conclusion cannot be justified on the grounds of the European human rights framework if we consider that even the European Court of Human Rights (ECtHR), in several judgments, held that copyright, as a form of “property”, prevailed over other fundamental rights. Footnote 24 This conclusion is even clearer and stronger under EU law, considering the constitutionalisation of the EU Charter of Fundamental Rights. As recently held by the ECJ on the grounds of the Charter, the complex provision of Art. 17 of the 2019 Copyright Directive can be viewed as a proportionate and legitimate attempt to ensure a fair balance between the protection of users’ and online intermediaries’ interests, on the one hand, and creators’ rights on the other. Footnote 25 In the social media industry, the ECJ’s reasoning in Poland v. Parliament and Council emphasised that, although not inviolable and absolute, the right to intellectual property embodied in Art. 17(2) of the EU Charter on Fundamental Rights is a human right whose high level of protection justifies the complex regulation embodied in Art. 17 of the 2019 Copyright Directive and supports its adoption and EU-wide enforcement. Footnote 26 This opinion is perfectly consistent with the continental European approach to copyright and authors’ rights as personality rights and human rights that give rise to moral and economic prerogatives. Footnote 27

Currently, the implementation of a principle of “fair balance” based on the EU Charter of Fundamental Rights clearly shows that (i) the protection of authors’ rights can prevail over other fundamental rights, and (ii) the resolution of disputes in this field, especially in online environments, requires the ECJ to engage in a case-by-case assessment of the various interests at stake. Footnote 28 Our research seeks to provide more than just a doctrinal analysis of the effective impact of the 2019 Copyright Directive in the European music sector. In doing so, we intend to embrace an evidence-based and neutral approach to Art. 17, which only a minority of European scholars seem to have pursued, at least in the literature available in English. Footnote 29 To fill this gap, we will involve stakeholders and experts in empirical investigations to ascertain whether platform obligations, on the one hand, and copyright exceptions and the remedies embodied in Art. 17 to protect media and artistic freedoms, on the other hand, are being effectively implemented across EU Member States. Moreover, to assess more objectively the impact of content filtering measures, we will scrutinise music licensing practices, the use of content-recognition technologies, and other forms of content moderation before and after the entry into force of Art. 17’s national transpositions. This is relevant, in our view, also to understand whether these practices are well-established policies of social media services even in jurisdictions where a provision like Art. 17 and a brand-new legal infrastructure such as the DSA do not exist.

An equally relevant research gap in legal scholarship exists regarding the interplay of Art. 17 with other principles, rights, and obligations embodied in Chapter 3 of the 2019 Copyright Directive. Our research project assumes that, without empirical investigations, it is impossible to assess the effects of these joint measures on the businesses of legacy music producers and new generations of music creators. As things stand, the above-mentioned legal principles of fairness, proportionate remuneration, and transparency are likely to remain empty promises without the development of a new, data-driven approach to creators’ rights. This approach can only be based on the availability of large volumes of data enabling music creators, their representatives, and online exploiters to negotiate and conclude licensing agreements in a smooth, nuanced, machine-readable, transparent, and thus fair manner. Footnote 30 Our research proposal assumes that, in data-analytics businesses like digital platforms, even subscription-based services that choose and curate their repertoires (negotiating and paying royalties to creators) cannot ensure fair and proportionate remuneration without using reliable, standardised, and unequivocal copyright ownership and management information coming from the music sector. Footnote 31 The research we advocate in this field goes beyond the state of the art by providing a cross-country empirical analysis of the impact of recent copyright and contract law provisions embodied in the 2019 Copyright Directive and, at an earlier stage, Directive 2014/26 on the collective management of copyright on the music industry, broadly defined. Footnote 32 Our research includes an evaluation of how EU competition law and EU regulations (including the DSA, DMA, and upcoming legislation such as the EU Artificial Intelligence Act Footnote 33 and the EU Data Act Footnote 34 ) can apply and have an impact in the domain of online music platforms. This will allow us not only to produce evidence-based policy recommendations, but also to formulate a law-data-and-technology concept – built on the grounds of “co-creation” with stakeholders – to identify and rank solutions to the problem of information asymmetry across online platforms in Europe.

3.3 Economics and Business: Music Professionals and Value Networks

The music industry has been at the forefront of CCIs when it comes to the impact of technological advancements and related business model innovations. Currently, streaming platforms and social media are dominating the market, relying on their crucial position as intermediaries Footnote 35 and benefiting from winner-takes-all effects. Footnote 36 Their new business models, favouring access over ownership Footnote 37 and relying on the availability of vast amounts of (real-time) data, are accused of altering the value of content, particularly music. The music industry and its business models have constantly evolved with digitalisation and the growing domination of platforms. Footnote 38 Economists can contribute to interdisciplinary research by integrating the latest advancements in their analysis of value networks, of music professionals’ perspectives, and of innovative business models and by offering a longitudinal perspective on ecosystems, extensive surveys, and the use of quick-scan analysis to map large numbers of companies’ business models. Footnote 39 This will notably allow the integration of the role of “professionalising amateurs”, Footnote 40 a new category of content creators who act as YouTube, TikTok, or other social media’s partners, with growing economic and cultural relevance. After YouTube’s launch of its creator partnerships and programmatic advertising in 2006, these social media platforms started signing creators for the purpose of maximising value from their content and communities. More generally, the economic and business analysis of the music industry will consider the role played by data. A major disruption emerged from the availability of vast amounts of (real-time) data for music platforms. By translating data on user’s music consumption into relevant metrics, some authors argue, the business model of the industry was reshaped from music as a product to music as a service. Footnote 41 This is the case for services relying on advertising (content-sharing services like YouTube and Spotify’s free service) since data allow for the personalisation of advertising. This is, however, also the case with licensed services. For example, Spotify’s freemium model has been strongly supported by the platform’s focus on personalised content, which has been key in converting users to premium subscriptions. Footnote 42 Curated user-specific playlists are part of their product offering and perceived value. Footnote 43

The economic analysis we advocate addresses the notion of fairness notably in relation to value networks. While there is an increasing policy interest in ensuring that music streaming platforms are fair, there is a research gap regarding the industry’s and music professionals’ perspectives on fairness in the music platform market. Footnote 44 Since online platforms have become major enablers of music content flow, with unparalleled gatekeeping powers, Footnote 45 the remuneration of creators deeply depends on monetisation practices of platforms and on the ways through which algorithms expose information and cultural content. Footnote 46 However, to properly define this notion, there is an empirical gap regarding the industry’s and music professionals’ understandings of fairness in the music platform economy at both the European and national levels. Footnote 47 This task is even more complex if we consider that the impact of COVID-19 on culture and the performing arts has led to re-evaluations of the power of these platforms, paving the way for in-depth research into how industry representatives from the tech and music sectors conceptualise the fairness of music streaming platforms and social media. Footnote 48 The dramatic consequences of the recent pandemic for the performing arts encouraged several countries to start public inquiries into the power of global platforms, whose consequences are yet to be seen. Footnote 49

3.4 Computer Science: Influence of Algorithms on Music Consumption

Despite being presented as easing consumer choice, Footnote 50 platforms’ recommender algorithms are accused of lacking transparency, Footnote 51 threatening the exposure of content diversity and thereby challenging democracies Footnote 52 as well as violating consumers’ rights and citizens’ freedom of expression. Footnote 53 Algorithms have been accused of bias, Footnote 54 reinforcing discrimination in the real world, notably linked to race and gender, Footnote 55 and further increasing the popularity of superstars, blockbusters, and best-sellers at the expense of minority perspectives, local content, and emerging artists. Footnote 56 Our research project will highlight the effective influence of algorithms and aim to understand the way algorithms are being designed and implemented by different platforms. Footnote 57 Data are part of the algorithmic systems (especially recommender systems) that build this crucial personalisation process. Technological and economic developments have led to the availability of overwhelming quantities of digital content, notably music. Footnote 58 While some physical limitations have disappeared (for instance: space for storing, time for scheduling), others remain, notably users’ attention and what can be displayed to users (for instance: what a Spotify or YouTube user sees when connecting to the platform). Because of “overchoice”, Footnote 59 item selection can become cumbersome and complicated. Footnote 60 This makes it crucial, especially for media content providers, to incorporate algorithms that allow for a flexible and immediate response and adjustment to personal preferences of consumers. Such algorithms automatically filter, rank, and recommend content. Footnote 61 They influence the display or recommendation of content. Hence, algorithms are not neutral, and they raise questions as to how they are designed and implemented, who decides such matters, and on which basis. Beyond platform providers, all stakeholders in the music industry develop strategies and business models to cope with algorithms and adapt them to their own objectives.

Our research aims to produce new knowledge on the way platforms are affecting music diversity across the consortium members’ countries. In extending the work by Snickars and Mähler to detect and map patterns in algorithmic auditing by Spotify’s recommendation service, Footnote 62 we will account for a shortcoming of their work: access to data. Instead of using fictitious, stereotypical bots acting as users, we believe that this research would be more meaningful and fit for its purpose with the recruitment of a sufficiently broad and diverse number of real users (for instance: +1000). These users can donate their playlist data on the grounds of their right to access personal data collected and stored by streaming services and social media companies under Art. 20 of the General Data Protection Regulation (GDPR). Footnote 63 Although the recruitment of users as personal data donors can be difficult, their involvement can be spurred by a data donation campaign across EU countries to fund symbolic or little monetary rewards, so that users have both a financial and an ethical motivation to participate.

User data are very useful for measuring the influence of algorithms on music consumption because patterns in personal playlists can be compared against one another and with curated playlists obtained from several radio broadcast channels in each of the countries where the investigation takes place. Moreover, in-depth qualitative interviews on music habits, perception of bias, diversity, and serendipity with 100 users can add a qualitative dimension to the interpretation of the playlist data. An important contribution here can be the development of fairness indicators for online platforms’ algorithmic systems based on the analysis of the data collected. To do this analysis, Fair MusE’s data scientists can rely on the use of Human-Num Footnote 64 and Dataiku, Footnote 65 a free software platform to analyse machine-learning algorithms, predictive models, and big data. Indeed, with the data and related statistics, this research can lead to an in-depth data analysis of the way platforms and their algorithms function and influence consumers. With that input, this new research can go further than previous research Footnote 66 by addressing the concept of fairness in a broader way through the development of indicators related to several dimensions.

4 Our Ethos

Disciplinary expertise is core to our work; its interdisciplinary deployment is what makes our research and its empirical investigations meaningful and promising. We believe that to address a multi-faceted concept such as fairness and to use it as an effective and desirable policy and legal instrument in the music sector, the approach shall necessarily be interdisciplinary. New criteria, methodologies, and tools are required.

4.1 The Concept of “Fairness” and Its Special Function in the CCIs

Recent developments in EU law and policymaking clearly show a strong and fast-growing policy interest in the notion of “fairness” in digital markets and ecosystems. Although this notion has various, conflicting facets, EU policy and legislative initiatives through which the European Commission is currently exploring the function of “fairness” clearly aim to promote awareness of how certain structural factors can radically reduce economic output and social welfare in several industries. Footnote 67 Especially in the CCIs, the principle of fairness is expected to reduce financial losses for content creators, whose work is significant not only in terms of economic growth but also in terms of the sustainability of Europe’s cultural and linguistic diversity. Footnote 68 From this angle, the music sector is exceptionally relevant and complex considering its vastness as a cultural and commercial phenomenon and the fact that music is created and enjoyed everywhere, including low-income areas and communities where more expensive and complex types of creative works cannot be produced.

Our research seeks to shed light on the economic, cultural, societal, and technical context of EU music ecosystems, where a great variety of composers, performers, record labels, and platform artists target very different audiences in terms of size and geographical scope without knowing how the main digital music gatekeepers treat, promote, and commercially exploit their works. In this regard, the notion of fairness stands not only as a prerequisite for the pursuit of goals such as sustainability and competitiveness of an entire industry but also as a guarantee of consistency and compliance with the EU’s constitutional obligation to preserve and promote the cultural diversity of artistic productions. An important assumption of our research agenda is that EU lawmakers believe that a genuinely diverse music ecosystem can thrive only on the grounds of contractual and economic fairness. This presupposes much greater transparency in collective rights management, data collection, and proportionate remuneration of individual authors and performers. Yet these values, which have been recently embodied in EU legislative measures, are far from materialising in either market realities or in the day-to-day activities of music creators and their commercial and cultural partners.

4.2 Interdisciplinary Effort to Elaborate New Criteria, Methodologies and Tools

Our investigation entails considering a broad variety of online and offline environments where music professionals are involved, and assessing contemporary uncertainties around music’s economic and societal value and how they challenge creators’ opportunities to thrive and make a living. We believe that, notwithstanding the exceptional challenges that platformisation poses to a more transparent, competitive, and sustainable music sector in Europe, the current state of digitalisation holds the potential to help a great variety of music creators gain recognition beyond local or national borders and to overcome physical limitations. To investigate the impact of platforms on CCIs, a truly interdisciplinary team and approach are needed to connect media production, dissemination, and use on the one hand, and the legal conditions that are expected to achieve public policy goals on the other. Where our research seeks to innovate the most concerns tackling “fairness” from a conceptual perspective, considering it as a complex concept that requires interdisciplinarity and the analysis of several stakeholders’ perspectives and points of view. In a digital media economy where the largest gatekeepers are data-analytics businesses, appealing content such as music (in both audio and audiovisual formats) is used to attract and keep users active on the gatekeepers’ platforms for as long as possible.

Our approach to the notion of fairness from policy, legal, economic, and technical perspectives considers the various challenges raised by the advent and domination of platforms such as YouTube, Spotify, and, more recently, TikTok. Our research project is designed to unveil how today’s music industry can significantly improve and evolve in terms of transparency and access to relevant data. So far, the digital music sector has been dominated by trade secrecy, which has made it very difficult for policymakers to intervene by developing appropriate policy measures. Footnote 69 Our assumption is that greater transparency in the music sector and broader societal participation can help fight some phenomena that systematically penalise the majority of performing artists, music composers, and content producers. These phenomena include the implementation of unfair algorithmic systems and a race to the bottom that leads to the degradation of the commercial value of professionally created music and unfair remuneration. Our research also assumes that there is an exceptionally complex problem of data asymmetry across different stakeholders in the value chains, insofar as online platforms treat data about artists’ and content producers’ compensation and modes of content supply, exploitation, and consumption as a trade secret, claiming they need to protect data from industrial competition. The restricted access to data raises major issues in terms of accountability and of establishing a level playing field in the music sector. Lack of transparency also prevents the development of policy measures to promote fairness and diversity in a post-COVID-19 context.

In Fair MusE, we aim to investigate whether and how platforms have effectively enabled new forms of music production and home-made creations that empower amateur, early career, or disenfranchised categories of authors (“professionalising amateurs”) to gain online exposure, build and curate new audiences, and eventually become well-established music professionals. Footnote 70 At the same time, this type of analysis will enable the consortium to assess whether content platforms have induced a significant impoverishment of creators of niche or marginal repertoires that seem to be penalised by the logic and functioning of algorithms. Footnote 71

4.3 Our Agenda’s Major Obstacles

In designing our research project and building on the experience of the consortium partners, we have tried to identify potential challenges, the biggest of which is certainly the secrecy of the data our research is expected to collect and draw upon. Our project deals with issues that are very sensitive – commercially and technically – for major economic and political stakeholders at the European and global levels. We are aware of the difficulties this might raise, especially when liaising with the tech companies that own very large platforms and music services. For this reason, our research plan relies on multiple data collection sources and seeks to take advantage of duties of data disclosure that, under certain conditions, EU law imposes on data controllers and processors.

Another difficulty for research dealing with exceptionally large corporate interests such as those that exist in the music sector and, even more so, in the tech industry is that of developing normative recommendations on the EU policy and legal frameworks towards creators, business strategies, or large media environments while facing the risk of capture and lack of neutrality, which could weigh upon each research or communication initiative. Research that takes copyright and creators’ rights as one of its main pillars is subject to a lot of – not necessarily justified – criticism. We know that scientists cannot avoid being drawn into the controversies they are investigating. Footnote 72 In any case, while acknowledging that it can be difficult, especially for social scientists, to ensure neutrality and objectivity when investigating issues that touch upon their values, groups, and cultures, Footnote 73 our objective is to take a balanced approach that relies on critical thinking without ever transforming it into activism.

Another set of challenges comes from the strongly interdisciplinary nature of our research. Public research funding agencies promote and identify interdisciplinarity, but organisational constraints can restrict their capacity to fully embrace novel ways of interdisciplinary collaboration and investigation. Footnote 74 More generally, researchers from different disciplines and different countries work in different contexts, share different objectives, and may simply differ in terms of vocabulary used. Regarding the context, Friedman argues that institutional structures and funding patterns (among other things) make interdisciplinary research difficult. Footnote 75 One could simply add that researchers working in the social sciences in labs or under remote working arrangements (by necessity or by choice) have a totally different experience from their fellows working in biological labs. Moreover, different objectives can be illustrated by the fact that while there are “few more familiar aphorisms in the academic community than ‘publish or perish’”, Footnote 76 the length, the type of outlet (e.g. journal vs. conference proceedings or monographs), the usual number of authors, etc. can vary greatly from one discipline to another. As regards different vocabularies, they are at the core of our work on the multi-faceted notion of fairness. More generally, this challenge relates to the fact that sector-specific differences in methodologies can quickly emerge during interdisciplinary research efforts. Footnote 77 Rogers et al. even suggest that interdisciplinary research can be difficult to achieve due to incommensurable positions adopted by different disciplines. Footnote 78 Cultural differences – as one may find in large European research projects – may add to the difficulty to understand each other. Arguably, some of the interdisciplinary collaborations envisaged in Fair MusE are more common than others (for instance: between law and economics), but our mix is more peculiar. Finally, one challenge could be that interdisciplinary research potentially detracts from researchers’ expertise. While learning from others, researchers may end up spending less time developing their disciplinary expertise. This is largely because interdisciplinary research involves negotiating conflicts. Footnote 79 Sanz-Menéndez therefore finds that interdisciplinary research can lead to both specialisation and fragmentation, depending on the research area. Footnote 80

5 Putting Our Research Agenda into Practice

From a methodological perspective, we believe that a two-phase structure can allow us to pursue our research agenda and put our idea of integrating different disciplinary elements into practice.

5.1 Phase 1

Phase 1 (M1–M24, where “M” stands for “Month”) is designed essentially as a two-year mapping exercise in which four research hubs (which include industry partners) will split into two groups: (i) Law and Political Science, on the one hand, and (ii) Economics and Computer/Data Science on the other. The former focuses on the role of EU regulation, assessing the impact of new or recent policy or lawmaking initiatives targeting online platforms in the existing law and policy scenario (as detailed in Section 5.1.1 ). The latter analyses the complexities of music platforms from the perspectives of music professionals and their business models (see Section 5.1.2 ) and of consumers, where our computer scientists analyse the influence of algorithms on music diversity (Section 5.1.3 ).

5.1.1 Assessing the Role of Regulation

A. Analysis of the normative and policy framework

Our project explores, among others, the domain of music policy and lawmaking through an in-depth critical analysis of EU instruments, reports, and proposals. Footnote 81 The consortium will pay special attention to the 2019 Copyright Directive and to the overarching framework for the EU Commission’s actions in support of the European music sector: “Music Moves Europe”. Footnote 82 Both instruments are exceptionally important pillars of the EU music sector policy, seeking to address key concerns of this industry and professionals in terms of financial aid, intellectual property rights regulation and subsidies. Considering that fairness has been a key driver for rethinking the sector-specific objectives of EU policy initiatives, Footnote 83 it is crucial for our project to explore the role of policymaking over the past few decades and to understand the evolution of this field and how (and when) “fairness” became a priority.

B. Music creators’ rights under EU law

This part of our work focuses mainly on the rights and other prerogatives originating from the implementation of Directive 2001/29 (the so-called “Information Society” Directive), Footnote 84 the 2014 Collective Rights Management (CRM) Directive, and the 2019 Copyright Directive. We will investigate the practical implications of authors’ and performers’ rights for transparency, fair remuneration, and contractual adjustments (and, possibly, revocation) of their copyright transfers, as laid down in Chapter 3 of the 2019 Copyright Directive. This will be done by analysing the standard “Terms of Service” of each of the aforementioned platforms because they play an essential function from a copyright point of view, granting social media companies a free, global, perpetual, and non-exclusive licence which covers the original work each user-creator uploads. This analytical exercise will have long-term utility, as the DSA imposes more stringent obligations on VLOPs. Footnote 85

C. Copyright liability of social media platforms

This section focuses on the scope and implications of Art. 17 of the 2019 Copyright Directive and of its national transpositions. Footnote 86 We will verify how social media companies seek to obtain licences for all works uploaded by their users and how they eventually restrict access to unauthorised works without infringing on users’ fundamental rights and freedoms. For this task, academics and experts from the consortium’s industry partners, authors’ collecting societies, and music right-holders’ representatives who are members of Fair MusE’s Advisory Board will cooperate closely. Footnote 87

D. Collective rights management in Europe

One of our research assumptions is that the global reach of social media and their multi-territorial distribution of music has been at odds with collective rights management, which has traditionally been fragmented from a territorial perspective, ultimately on the grounds of copyright’s territoriality. Footnote 88 Fair MusE aims to analyse the governance and licensing practices of EU collecting societies, especially for digital uses, as a result of the implementations of the CRM Directive. This analysis is essential to evaluate whether EU law has paved the way for an adequate music metadata infrastructure and the emergence of music data collection standards. Footnote 89 From a music licensing perspective, our main goal is that of ascertaining whether the EU has succeeded in reducing the very high transaction costs that, until the adoption of this directive in 2014, made fair remuneration of various music right-holders very difficult if not impossible. Footnote 90

E. EU competition law

We believe that traditional competition law remedies and the European Commission’s investigations in this field have a significant role to play in targeting potentially anticompetitive practices of dominant music platforms and social media. Footnote 91 This work includes a comparative analysis of the US and EU legal and music market scenarios. For several reasons, US federal antitrust law seems unfit (at least until recently) to remedy the extreme corporate power that the largest platform owners have acquired. Footnote 92 This situation sharply contrasts with that of the EU, where competition law has been widely used against tech companies’ abuses of their dominant position and where policymakers are trying to prevent these abuses through ex ante regulation.

F. Platform regulation and soft law instruments

Fair MusE’s team will consider the interplay between copyright-specific rules in the 2019 Copyright Directive and general obligations of digital platforms arising from regulations such as the DSA and the DMA. Considering that some of the largest online music platforms qualify, under the above-mentioned regulations, as “very large online platforms” and/or “gatekeepers”, we will map and evaluate how data access rights and protection mechanisms enshrined in these regulations impact on music right-holders’ effective participation and business on platforms. This work presupposes an analysis of automated decision-making procedures and music platforms’ content moderation policies, also to understand how many of these activities rely on standardisation, certification procedures, or human review. Our analysis includes soft law instruments, such as codes of conduct and best practices, which might prove essential to promote fairness towards music creators by enhancing data transparency and facilitating fair and proportionate remuneration.

5.1.2 Platforms, Business Models, and Professionals in the Music Industry

A. From value networks in the music industry to new music ecosystems

Our research project analyses evolutions in the music industry considering the implications of dematerialisation, of the dominance of platforms and their increasing reliance on algorithmic systems to filter and recommend content. To do so, based on a methodology applied in previous research, Footnote 93 we will map “value networks” and the inter-relations between actors. To this end, our researchers will identify: (i) the value chains and related activities; (ii) the different stages in the value chains that compose the value networks (including content creation, content production, distribution and placement, support environment, and support industries); (iii) the different actors (both generic names and actual examples of key players) in a process of stakeholder mapping. At the same time, our researchers will analyse relations between the different actors and possible schematic relations with other value networks, mapping inter-relations among, and multi-directional flows of value between, the actors and the process of value creation.

Our research will go beyond the deployment of a “value network” analysis by incorporating business perspectives that are targeted at platform-centred and platform-led networks and ecosystems. The added value of also applying “ecosystem” theories Footnote 94 consists in being able to address a wider range of factors (including regulation, music education, live performances, etc.) that determine how value is being created in the music industry.

The above-mentioned analysis will allow us to observe the impact of online music platforms beyond online streaming consumption. This impact is primarily in the online realm, between uses on different platforms (for instance: how the use of a track excerpt on TikTok can lead to an increase in this track’s exposure on streaming platforms), but also in the interactions between online and more traditional offline uses, such as the cross-effects between live performances and online consumption. Our analysis will finally address fairness from an economic perspective, especially in relation to the “value gap” debates, and more generally issues of creators’ remuneration, Footnote 95 in close connection with the project’s legal analysis (see Section 5.1.1 . supra ).

B. Conflictual and consensual aspects of fairness that digital industry and music professionals consider relevant for platforms

Our project will investigate what “fairness” actually is, not only for music professionals but also for the online platform providers themselves. At the European level, the focus will be on six key European associations: DIGITALEUROPE, DOT Europe, European live music association, European Music Council, European Composer and Songwriter Alliance, and IMPALA. Footnote 96 Data collection will draw on desk research (notably grey literature documents coming from the six associations) and will further be gathered by conducting semi-structured interviews.

At the Member State level, the goal is to explore (i) whether fairness is related to the remuneration of music composers and the rights for authors in relation to the use of their works by platforms, and (ii) whether fairness is perceived as connected with additional aspects, such as the role of online platforms in fostering cultural diversity, the creation of a level playing field for independent digital distribution platforms, etc. We will place special emphasis on the perception and use of algorithms (for instance: recommender systems) by authors and music professionals, seeking to explore how they understand algorithms’ influence and whether they adapt their works to fit the platforms’ expectations. Data collection will draw on an online panel survey involving participants from the digital industry and music associations in Fair MusE’s eight countries of investigation (Portugal, Austria, Belgium, Denmark, Estonia, France, Greece, and Italy). Potential differences between Member States deriving from the size of the music market and their different systems of subsidies to the music sector will meaningfully enrich the analysis.

C. Online music platforms from a business model angle

Our analysis will finally map business models, combining research methods including desk research, expert interviews, and case studies. Our framework for mapping innovative business models will be based to a large extent on the Business Model Matrix Footnote 97 and the Business Model Canvas. Footnote 98 Based on the main types of actors identified previously, this work will produce a two-step business model analysis. First, based on a quick-scan analysis, Footnote 99 we will map all the main business model features of all the main types of stakeholders. It is expected that these main stakeholder categories are authors, distributors, and (playlist) curators. Second, we will conduct an in-depth analysis of at least six platforms with innovative models that are active in the EU. While online platforms have already been largely defined and researched, an in-depth analysis of online music platforms from a business model angle is still missing. We will conduct semi-structured interviews with selected organisations and companies to produce in-depth case studies.

5.1.3 Consumers, Platforms, and Music Diversity

A. In-depth assessment of the influence of algorithms on music consumption

Finally, Phase 1 of our research will include the consumer side of platforms, trying to analyse how these platforms and their algorithms impact consumers and, conversely, the strategies end-users may deploy to access, discover, and remain informed about music thanks to, or despite, platforms. This is also crucial for EU policymakers to effectively promote a fair and sustainable ecosystem. This work will help us make a synthesis of the various issues that have been encountered in research so far, especially as regards the practical effects of algorithms’ design (including recommender systems and playlists) on internet users.

B. Quantitative approach and data analysis

Our team will examine the effective influence of algorithms in the context of music recommender systems by using a quantitative approach and data analysis. We will rely on existing methods in the analysis of recommender systems, Footnote 100 extending Snickars and Mähler’s Footnote 101 analysis of algorithms beyond Spotify. Footnote 102 We will apply a broader and innovative approach to the collection of playlist data by replacing stereotypical fictitious users with +1000 real users who will donate their platform-derived data. Footnote 103 We will compare the +1000 anonymised playlists against each other and against playlists from 80 broadcast radio channels (i.e. ten from each of the eight EU countries within the consortium). This way we will be able to map playlist patterns; characterise diversity and bias in personalised playlists – which represents actual listening – with the curated playlists coming from broadcast radios. Qualitative in-depth interviews on music habits, perceptions of bias, Footnote 104 diversity, and serendipity with approximately 100 users (selected among those who donate their historical playlist data) will add a qualitative dimension to the interpretation of the playlist data. Interviews with broadcast editors responsible for playlists, curation, editorial profile, and rotation policies, and with representatives of online music platforms, will add an interpretative dimension to the analysis of broadcast music programming.

C. Fairness indicators

Finally, based on our previous work, our research team will produce fairness indicators in terms of platform transparency Footnote 105 and bias in recommender systems – as in Htun Footnote 106 and Mehrotra Footnote 107 – regarding algorithmic systems that are currently being used by the online platforms under scrutiny. By characterising the mechanics of the music recommender system algorithms as well the programming policies of many broadcast channels, our research team will highlight effective variables that indicate whether a given platform is fair and gives rise to a sustainable music business, while further suggesting a predictive model that can mitigate the adverse effects of these algorithms from a music diversity perspective.

5.2 Phase 2

In Phase 2 (M25–M36) we envisage the delivery of research outcomes to policymakers and stakeholders (Sections. 5.2.1 , 5.2.2 , and 5.2.3 ) alongside a comprehensive set of policy recommendations embedded in a White Paper on fairness in Europe’s music ecosystems (Section 5.2.4 ).

5.2.1 Music Copyright Infrastructure

In order to be fair, the increasingly platform-dominated music ecosystem needs to address the current lack of adequate data infrastructures through standardisation and sharing of content identifiers and music repertoire information, without which online music exploitations cannot be rewarded in a fair and proportionate way. To this end, we intend to develop a pilot named “Music Copyright Infrastructure”, the main goal of which is to help stakeholders target and solve the problem of information asymmetry across online platforms and right-holders – an asymmetry that is detrimental to all parties, including consumers interested in the diversity of music. We know that online music exploiters have turned data into their main asset (namely: massive, real-time data about their users, music consumption, and hence online music revenues). Considering prior efforts to solve these data asymmetries and their failures, due to participant concerns about the control of data and costs, we will provide a model agreement (and a set of guidelines) to help right-holders and licensees such as online platforms conclude music data-sharing agreements. In our view, these model agreements can help prioritise disclosure over enclosure (or secrecy) and can be directly tested by Fair MusE’s industry partners during the last year of project development.

5.2.2 Music Data Dashboard

The consortium will develop a demo of a Music Data Dashboard of statistical indicators for the European music sector to serve the information needs of policymakers, music professionals, and other stakeholders in this sector. This Dashboard will enable users to get a better understanding of evolutions related to the digitalisation and platformisation of the European music sectors by proposing or identifying indicators and data collection methods. Moreover, the Dashboard will incorporate a link to national statistical institutes, where appropriate. In short, we will (i) review current statistical sources of data on music at the EU and national levels, thus analysing statistical shortcomings in current sources, particularly regarding online music consumption and revenues; (ii) validate the data identified as well as the structure and the objectives of this tool during a “co-creation” policy workshop that involves policymakers; and, eventually; and (iii) deliver a demo for the Dashboard.

5.2.3 Fairness Score

The consortium partners will use the result of the business models analysis and of platform algorithms to set up a tool to assess music services and social media. A “Fairness Score” can become an effective tool to evaluate how online music platforms concretely deal with the criteria and goals EU policymakers intend to foster in the digital media environment. Each criterion, in its definition and assessment, will rely on the work performed in Phase 1 and will be reflected in the White Paper’s recommendations.

The Fairness Score will include the following indicative list of criteria: (i) governance in platform/social media; Footnote 108 (ii) market/non-market values; Footnote 109 (iii) local and national music in content online; Footnote 110 (iv) rights for creators, including access to data regarding their works and the exploitation thereof; Footnote 111 (v) fair and proportionate remuneration; Footnote 112 (vi) business model of the platform/social media; Footnote 113 (vii) gender equality; Footnote 114 (viii) small and medium-size producers vis-à-vis “superstars”; Footnote 115 and (ix) promotion of diversity in the algorithm. Footnote 116

Our Score will either be shaped as an industry-led solution or – on the grounds of data disclosure obligations that arise under EU law (cf. GDPR, DSA, DMA) Footnote 117 – as a soft-law policy instrument or a proper legislative instrument. We assume that this instrument could help EU policymakers influence platform/social media’s practices and conduct at various levels: legal (for instance, in terms of compliance with EU artists’ rights and copyright contract law); economic (for example, as regards fair and transparent remuneration); and social (promotion of cultural and gender diversity); and technical (algorithmic transparency).

5.2.4 Policy Recommendations: White Paper on Fairness in the Music Sector

Our policy recommendations will draw upon the above-mentioned research results, especially the in-depth analysis of new EU law measures aimed at promoting fairness and transparency towards music creators. On the grounds of an interdisciplinary analysis of the consequences of recent EU legislative measures, and of the related national transpositions, our Policy Recommendations will detail tools and actions to facilitate the exercise of creators’ rights through adequate data infrastructures. More precisely, we will include recommendations on the main objectives of Fair MusE: (i) whether and how today’s music industry can significantly improve and evolve in terms of transparency and fairness; (ii) whether and how, from both a legal and technological standpoint, the music sector can develop reliable, standardised, and unequivocal rights ownership information to be able to remunerate individual creators in a fair and proportionate way; and (iii) how legislative or industry-led solutions can reduce or minimise risks created by the enhanced dominance of the largest online music platforms.

6 Conclusion

In this manifesto , we advocate a new, interdisciplinary research approach that can remedy the shortcomings of a purely “silo-like” analysis of EU cultural and industrial policies in the music sector and of their effective impact in today’s platform- and algorithm-dominated economy. The music industry is an interesting case to apply this approach to, as it has gone through radical changes in the past two decades because of the extreme fragmentation of the rights, business interests, and artistic prerogatives that characterise the related creative communities. This has led to significant reforms of the legal and regulatory frameworks governing and shaping European music ecosystems, particularly those embodied in the 2019 Copyright Directive. This directive constitutes a “big bang” in the European history of copyright and artists’ rights, whose real effects are yet to be evaluated in a non-doctrinal and evidence-based way.

Approaching such changes, and in particular the multi-faceted concept of fairness, requires interdisciplinary expertise. This should include policy, legal, economic, and computer science perspectives. In Fair MusE, we analyse the EU as a policymaker in the music industry; we examine the legal framework regarding copyright, contract law, and platform liability; we study music professionals and how value networks have evolved; we assess how algorithms influence music consumption. We involve the music industry, notably via industry partners, members of our Advisory Board and other experts representing the tech and music industries, as well as the community of independent legal practitioners in several European countries. This does not go without challenges: overcoming data secrecy; dealing with opposing interests that govern strategic decisions in the music sector; and ensuring a harmonious collaboration between the diverse disciplines combined in Fair MusE. The last section describes briefly how we will do it, with a quick overview of the tasks and the main expected outcomes.

One point we are especially interested in is the EU’s policy responses. The 2019 Copyright Directive, with its provisions on the copyright liability of social media platforms (Art. 17), the fair and proportionate remuneration of authors and performers (Art. 18), and the codification of a right to transparency and access to data on the earnings generated by creative works (Art. 19), has an exceptional potential to strengthen the bargaining power of individual right-holders and their respective collecting societies in digital markets. The above-mentioned policy changes can become even more effective if we consider the entry into force of other instruments embodied in EU regulations, such as the DSA and the DMA, which are designed to significantly increase the level of responsiveness, internal risk assessment, and accountability of VLOPs and gatekeepers. This new array of EU law provisions targeted at the platform economy can certainly help address some of the existential questions raised by the largest online intermediaries’ ability to control consumers’ access to music repertoires and, at the same time, creators’ content distribution strategies and remuneration opportunities.

We argue that a proper evaluation of these recent developments in EU law should be supported by clear evidence. Such evidence can be built only through interdisciplinary efforts by independent researchers. We know that, to be effective and desirable as a policy instrument, the multi-faceted – and somehow open-ended – notion of “fairness” (used in key EU law provisions, and in many judgments of the ECJ in the copyright law sphere) needs to be dissected and analysed from a legal, policy, economic, and technological perspective, embracing a simultaneously balanced and multi-stakeholder viewpoint. That is the main reason why we promoted the creation of a consortium like Fair MusE, and why we intend to involve several categories of music professionals as well as representatives of industry and civil society in the co-creation of the project’s outcomes. Beyond the music-specific character of our interdisciplinary analysis, we are confident that our research results can also be very useful for other creative industries and media environments – including the news publishing sector – where data-driven exploitations and artificial intelligence have become pervasive and are inevitably changing the processes of content value creation and control and re-shaping ecosystems.

Change history

26 february 2024.

A Correction to this paper has been published: https://doi.org/10.1007/s40319-024-01435-x

Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market For Digital Services and amending Directive 2000/31/EC [2022] OJ L277/1 (“DSA”). See Chapter 3, Section 5.

Regulation (EU) 2022/1925 of the European Parliament and of the Council of 14 September 2022 on contestable and fair markets in the digital sector and amending Directives (EU) 2019/1937 and (EU) 2020/1828 [2022] OJ L265/1 (“DMA”): see Art. 2 and Art. 3.

Promoting Fairness of the Music Ecosystem in a Platform-Dominated and Post-Pandemic Europe (“Fair MusE”), Grant agreement ID: 101095088, https://cordis.europa.eu/project/id/101095088 , accessed on 2 November 2023.

The academic members of the consortium are: Universidade Católica Portuguesa (UCP); Vrije Universiteit Brussel (VUB); Aalborg Universitet (AAU); Université de Lille (ULILLE); Université de Liège (ULIEGE); Hellenic Foundation for European and Foreign Policy (ELIAMEP); Tartu Ülikool (UTARTU); Central European University Gmbh (CEU).

https://www.siae.it .

https://www.verifi.media .

The European Commission’s recent legislative initiatives in the areas of standard essential patents, artificial intelligence, platform-to-business trading practices, as well as competition law all rely on fairness as one of their objectives, namely: Proposal for a Regulation of the European Parliament and of the Council on standard essential patents and amending Regulation (EU) 2017/1001 [2023] COM(2023) 232 final; Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence and amending certain Union legislative acts [2021] COM/2021/206 final (“Draft Artificial Intelligence Act”); Regulation (EU) 2019/1150 of the European Parliament and of the Council of 20 June 2019 on promoting fairness and transparency for business users of online intermediation services [2019] OJ L186/57 (“Platform-to-Business Regulation”). Regulation (EU) 2022/1925 ( supra note 2).

Statistics evidenced a dramatic fall of the music business between 1999 and 2014, when global revenues from physical and digital music sales declined by 42%, from $25.2 to 14.6 billion. See IFPI, “Global Music Report 2018: Annual State of the Industry” https://www.ifpi.org/ifpi-global-music-report-2018/ , accessed 2 November 2023.

“Value gap” is an expression used for the first time by representatives of the music industry in Brussels to describe the impoverishment of their sector as a consequence of widely uncompensated uses of copyright works across online platforms and a sharp difference between the licensing fees paid by social media and the fees paid by music streaming services: see , for instance, Smith, Desbrosses and Moore ( 2016 ).

Cunningham and Craig ( 2019 ), pp. 11–14.

Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC [2019] OJ L130/92 (“2019 Copyright Directive”).

2019 Copyright Directive, Art. 17.

2019 Copyright Directive, Art. 18.

2019 Copyright Directive, Art. 19.

Wikström ( 2020 ), p. 367.

Laing ( 1999 ), p. 31; Sarikakis ( 2007 ); Littoz-Monnet ( 2007 ); Iosifidis ( 2011 ); Donders et al. ( 2014 ).

See European Commission, “Music Moves Europe”: https://culture.ec.europa.eu/cultural-and-creative-sectors/music/music-moves-europe , accessed 2 November 2023.

The complex infrastructure of the DSA is designed not to interfere, but rather to be complementary with the copyright-specific mechanism of Art. 17: see on this topic, Quintais and Schwemer ( 2022 ), p. 191; Rosati ( 2021 ).

Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the Internal Market [2000] OJ L178/1 (“e-Commerce Directive”).

A non-exhaustive list of these initiatives includes the following ones: Sophie Stalla-Bourdillon et al (40 academics), Open Letter to the European Commission – On the Importance of Preserving the Consistency and Integrity of the EU Acquis Relating to Content Monitoring within the Information Society, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2850483 , accessed on 2 November 2023; European Copyright Society, “General Opinion on the EU Copyright Reform Package”, 2017, available at https://europeancopyrightsocietydotorg.files.wordpress.com/2015/12/ecs-opinion-on-eu-copyright-reform-def.pdf , accessed on 2 November 2023; Max Planck Institute for Innovation and Competition (2017), Position Statement on the Proposed Modernization of European Copyright Rules: Art. 13, available at: https://www.ip.mpg.de/fileadmin/ipmpg/content/stellungnahmen/MPI_Position_Statement_PART_G_incl_Annex-2017_03_01.pdf , accessed on 2 November 2023. See also Cory Doctorow, “Four million Europeans’ signatures opposing Article 13 have been delivered to the European Parliament” (EFF, 10 December 2018) https://www.eff.org/deeplinks/2018/12/four-million-europeans-signatures-opposing-article-13-have-been-delivered-european , accessed on 2 November 2023. Among the academic contributions following the adoption of the directive, see Dusollier ( 2020 ), p. 979, who describes Art. 17 as a “monster provision” considering its size and “hazardousness”. At an earlier stage, very critical scholars included Frosio ( 2017 ), p. 565; and Senftleben et al. ( 2018 ).

Rosati ( 2022 ), p. 397.

Although this initiative was consistent with Poland’s dissenting vote at the time the EU Council adopted Directive 2019/790, this case suddenly transformed the Polish government, a notorious antagonist (at least until very recently) of EU institutions vis-à-vis the affirmation of human rights and the rule of law, into a noble and tireless paladin of freedom of expression: see C-401/19 Poland v. Parliament and Council , ECLI:EU:C:2022:297. It is worth recalling that the Polish rule-of-law crisis culminated in infringement proceedings launched by the European Commission against Poland, alleging a failure to fulfil its obligations under Art. 19(1)(2) of the Treaty of the European Union (TEU) and Art. 47 of the Charter of the Fundamental Rights of the European Union. In the subsequent appeal, the ECJ ruled that Poland indeed infringed the principle of judicial independence under Art. 19(1)(2) TEU when lowering the retirement age of Supreme Court judges: see case C-619/18 European Commission v. Republic of Poland , ECLI:EU:C:2019:531.

A good example of scholars’ focus on the importance of safeguarding users’ freedom of expression and information in the online environment when implementing Art. 17 of the 2019 Copyright Directive is provided by Quintais et al. ( 2019 ), pp. 277–282. In a similar way, Geiger and Jütte claim that Art. 17 fails to properly address the need to strike a fair balance between competing interests, emphasising the negative effect of filtering mechanisms on users’ fundamental rights: see Geiger and Jütte ( 2021 ), pp. 532–534. Other contributions emphasise how Art. 17 can negatively impact on the platforms’ freedom to conduct business: see , for instance, Reda et al. ( 2020 ), at pp. 42–49, claiming that the provisions of Art. 17 are not capable of achieving a fair balance between the fundamental right to conduct a business and other rights, as they place a significant economic burden on online content-sharing service providers. See also Geiger and Jütte, mentioned above, p. 542, maintaining that Art. 17 imposes immense obligations on social media platforms, restricting their freedom to conduct a business.

Among the most recent judgments, see , for instance, ECtHR Fredrik Neij and Peter Sunde Kolmisoppi (The Pirate Bay) v. Sweden , 40397/12, where the Court stressed that intellectual property – more specifically the “rights of the copyright-holders” – is a form of “property” that benefits from the protection afforded by Art. 1 of Protocol No. 1 to the ECHR against unauthorised dissemination of protected works through file-sharing technologies. At an earlier stage, ECtHR Case Ashby Donald et autres v. France , 36769/08 founded the protection of the copyright of fashion houses in their own creations (against unauthorised photographers invoking their right to freedom of expression) again on the grounds of the constitutional protection of “property” under Art. 1 of Protocol No. 1 of the ECHR. For a detailed review of the ECtHR case law on intellectual property rights, see Geiger and Izyumenko ( 2018 ), p. 9.

In C-401/19 Poland v. Parliament and Council , the ECJ provides an analysis of the principle of proportionality under paras. 63–69 and explicitly states, in para. 82, that “in the context of the review of proportionality referred to in Article 52(1) of the Charter, it must be noted, first of all, that the limitation on the exercise of the right to freedom of expression and information of users of online content-sharing services, referred to in paragraph 69 above, meets the need to protect the rights and freedoms of others within the meaning of Article 52(1) of the Charter, that is, in this case, the need to protect intellectual property guaranteed in Article 17(2) of the Charter.”

See C-401/19 Poland v. Parliament and Council , paras. 92–99.

See Strowel ( 2020 ), pp. 40–46, who emphasises how the constant, explicit reference to intellectual property as a fundamental right in the case law of the ECJ has played a central role in strengthening the protection and enforcement of copyright, especially in digital settings. As argued by this author, this explicit recognition under EU law provides an even stronger foundation for the qualification of authors’ rights as human rights. This is consistent with Art. 27(2) of the 1948 Universal Declaration of Human Rights at the international level, which protects the moral and material interests of authors resulting from their scientific, literary, or artistic productions. It is worth recalling that while the concept of authors’ rights as moral rights is eminently European, it is gaining traction because of technological challenges even in systems – like the United States – that have historically neglected this concept: see , for instance, Sundara Rajan ( 2019 ), pp. 257–258.

As stressed by Strowel ( 2020 ), pp. 40–52, the recent case law of the ECJ reveals a careful approach in the examination of copyright disputes in the digital environment. The author stresses how, in several cases, the principle of fair balance made copyright claims prevail over defences based on freedom of expression and other fundamental rights (such as the right to privacy) because of the necessity to guarantee a high level of protection to intellectual property rights, as embodied in the EU legislation and as requested under Art. 17(2) of the EU Charter of Fundamental Rights. See , for instance: C-275/06 Promusicae v. Telefonica , ECLI:EU:C:2008:54; C-160/15 GS Media v. Sanoma et al. , ECLI:EU:C:2016:644; Case C-161/17 Land Nordrhein-Westfalen v. Dirk Renckoff , ECLI: EU:C:2018:634; C-476/17 Pelham GmbH and Others v. Ralf Hütter and Florian Schneider-Esleben , ECLI:EU:C: 2019:624.

For a more positive view on Art. 17’s impact on fundamental rights, see , for instance, Cabay ( 2020 ).

Mazziotti ( 2021 ).

The fact that prior attempts to improve rights information through standard tools such as the Global Repertoire Database (GRD) have largely failed can help solve a data-sharing dilemma that has only grown worse with the exponential increase in the availability of content on access-based platforms. On the failure of the GRD see , for instance, Milosic ( 2015 ).

Directive 2014/26/EU of the European Parliament and of the Council of 26 February 2014 on collective management of copyright and related rights and multi-territorial licensing of rights in musical works for online use in the internal market [2014] OJ L84/72 (“CRM Directive”).

Draft Artificial Intelligence Act ( supra note 7).

Proposal for a Regulation of the European Parliament and of the Council on harmonised rules on fair access to and use of data [2022] COM(2022) 68 final (“Draft Data Act”).

Rochet and Tirole ( 2002 ), p. 549; Poell et al. ( 2019 ), p. 1; Evans et al. ( 2005 ), p. 189.

Rochet and Tirole ( 2006 ), p. 645.

Luck ( 2016 ), p. 46.

Poell et al. ( 2019 ); Vlassis et al. ( 2020 ).

Van Audenhove et al. ( 2016 ).

Croll ( 2015 ).

Kastrenakes ( 2019 ).

Iqbal ( 2023 ).

Ferraro et al. ( 2021 ).

Vlassis et al. ( 2020 ).

Mazziotti ( 2020 ), p. 1027.

Flew and Gillett ( 2021 ), p. 231.

Castells et al. ( 2015 ).

Zarsky ( 2016 ), p. 118.

Bozdag and Van Den Hoven ( 2015 ), p. 249.

Helberger ( 2012 ), p. 65.

Bozdag ( 2013 ), p. 209.

Noble ( 2018 ).

Nechushtai and Lewis ( 2019 ), p. 298.

Chen et al. ( 2020 ).

Masnick and Ho ( 2014 ).

Gourville and Soman ( 2005 ), p. 382.

Kunaver and Požrl ( 2017 ), p. 154.

Haim et al. ( 2018 ), p. 330.

Snickars ( 2017 ), p. 184; Snickars and Mähler ( 2018 ).

Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC [2016] OJ L119/1 (“GDPR”).

Human-Num is a French infrastructure that aims at supporting research communities by providing services, assessments, and tools for digital research data. See https://www.huma-num.fr/ , accessed on 2 November 2023.

See https://github.com/dataiku , accessed on 2 November 2023.

Melchiorre et al. ( 2021 ) p. 1.

See various legislative initiatives of the European Commission, cited above ( supra note 7).

See the Treaty on the Functioning of the European Union (TFEU), Art. 167, para. 4.

Johansson et al. ( 2018 ), p. 165.

Mazziotti ( 2021 ), pp. 214–215.

Scott et al. ( 1990 ), pp. 474–494.

D’Agostino ( 1995 ), pp. 396–405.

König and Gorman ( 2017 ).

Friedman ( 2013 ).

De Rond and Miller ( 2005 ), p. 321.

Lach ( 2014 ), pp. 88–93.

Rogers et al. ( 2005 ).

Villeneuve et al. ( 2020 ), p. 197.

Sanz-Menéndez et al. ( 2001 ), pp. 47–58.

Relevant instruments and reports include: Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the Internal Market (2000) OJ L178/1 (“e-Commerce Directive”); Directive 2001/29/EC of the European Parliament and of the Council of 22 May 2001 on the harmonisation of certain aspects of copyright and related rights in the information society (2001) OJ L167/10; Regulatory framework for electronic communications and services (2003); Commission, “A Digital Agenda for Europe” (Communication) COM (2010) 245 final; Commission, “A Digital Single Market Strategy for Europe” (Communication) COM (2015) 0192 final; Commission, “The AB Music Working Group Report” (2016) Publications Office of the European Union https://op.europa.eu/en/publication-detail/-/publication/f5479d95-2fca-11e7-9412-01aa75ed71a1 , accessed on 2 November 2023; Commission, “New European Agenda for Culture” (Communication) COM (2018) 267 final; Commission, “Proposal for a Regulation establishing the New Creative Europe programme” COM (2018) 366 final; Council Conclusions on the Work Plan for Culture 2019-2022 [2018] OJ C460/12; Commission, “Music Moves Europe – First Dialogue Meeting-Report” (2019) https://culture.ec.europa.eu/sites/default/files/library/mme-conference-report-web.pdf , accessed on 2 November 2023; 2019 Copyright Directive; Proposal for a Regulation of the European Parliament and of the Council on contestable and fair markets in the digital sector (Digital Markets Act) COM (2020) 842 final; Proposal for a Regulation of the European Parliament and of the Council on a Single Market For Digital Services (Digital Services Act) and amending Directive 2000/31/EC COM (2020) 825 final; Commission, ‘Report from the Conference ‘Diversity and Competitiveness of the European Music Sector’ with EU Member States Experts” (2021) https://culture.ec.europa.eu/document/report-conference-diversity-and-competitiveness-european-music-sector-eu-member-states-experts , accessed on 2 November 2023.

Commission, “Music Moves Europe – First Dialogue Meeting-Report” (2019) https://culture.ec.europa.eu/sites/default/files/library/mme-conference-report-web.pdf , accessed on 2 November 2023.

See various legislative initiatives of the European Commission, cited above ( supra note7).

Directive 2001/29/EC of the European Parliament and of the Council of 22 May 2001 on the harmonisation of certain aspects of copyright and related rights in the information society [2001] OJ L167/10 (“Information Society Directive”).

Arts. 33–43 DSA.

On this front, our analysis will be comparative in nature. Namely, it will compare the copyright treatment of user-generated content platforms under EU and US law, in particular the case law based upon the US Digital Millennium Copyright Act (DMCA) 1998, which amended the US Copyright Act (17 US Code), Section 512(c).

For the full list of the Advisory Board’s members, see the Fair Muse’s website at https://fairmuse.eu/team/ , accessed on 2 November 2023.

Cunningham and Craig ( 2019 ), p. 15, where the authors emphasise that social media entertainment has, from the beginning, a global dimension because its content is not primarily based on intellectual property’s territorial control (as it is, instead, in the film and TV broadcasting sectors); Mazziotti ( 2021 ).

CRM Directive ( supra note 32).

Ranaivoson et al. ( 2013 ), p. 665.

The project also aims to consider the recent actions of the French, German, and Italian competition authorities, which have been particularly active in enforcing competition rules against large online platforms. See , for instance, as regards the French Competition Authority: Decision 21-D-11 of June 07, 2021, against Google regarding practices implemented in the online advertising sector; Decision 22-D-12 of June 16, 2022, against Meta regarding practices implemented in the online advertising sector. As regards the German Competition Authority, see Decision B6-22/16 of 6 February 2019 against Facebook for data handling practices; Decision V-43/20 of 21 December 2022 against Google for data handling practices in the case of Google News Showcases. In Italy, see the proceedings launched in April 2023 by the Italian Competition Authority against Meta for abuse of economic dependence towards SIAE, available at https://www.agcm.it/dotcmsdoc/allegati-news/A559%20avvio%20e%20caut.pdf , accessed on 2 November 2023.

See , for instance, Wu ( 2018 ), p. 132.

De Voldere et al. ( 2017 ).

Kostovska et al. ( 2021 ), pp. 6–26.

Negus ( 2019 ), p. 367.

DIGITALEUROPE is an organisation that represents the digital technology sector in Europe. See https://www.digitaleurope.org/ , accessed on 2 November 2023.

DOT Europe is an association of the main internet companies active in Europe, including leading social media and streaming platforms. See https://doteurope.eu/ , accessed on 2 November 2023.

European live music association is a non-profit organisation that supports the European live music industries. See https://www.elmnet.org/ , accessed on 2 November 2023.

European Music Council is a non-profit organisation whose mission is to develop and promote music of all genres and types. See https://www.emc-imc.org/ , accessed on 2 November 2023.

European Composer and Songwriter Alliance (ECSA) focuses on protecting and advancing the rights of composers and songwriters. See https://composeralliance.org/ , accessed on 2 November 2023.

IMPALA is the European organisation for independent music companies and national associations. See https://www.impalamusic.org/ , accessed on 2 November 2023.

Ballon ( 2007 ), p. 6.

Osterwalder and Pigneur ( 2010 ).

Loecherbach and Trilling ( 2020 ), p. 53.

Snickars and Mähler ( 2018 ).

Ferraro et al. ( 2019 ).

Puschmann ( 2019 ), p. 824.

Melchiorre et al. ( 2021 ).

Htun et al. ( 2021 ).

Mehrotra et al. ( 2018 ).

Based on the research conducted by the politics research hub of Fair MusE, as elaborated in Section 3.1 .

See the discussion in Section 3.1 above.

See an overview of the relevant issues in Section 4.2 above.

From both legal and economic perspectives, as elaborated in Sections 5.1.1 B, E, F and 5.1.2 A.

See Section 5.1.2 above.

As laid down in the Fair MusE’s Gender Action Plan.

See the discussion in Section 4.2 concerning the impact of algorithms on the discoverability of niche or marginal repertoires.

See Section 5.1.3 above.

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Mazziotti, G., Ranaivoson, H. Can Online Music Platforms Be Fair? An Interdisciplinary Research Manifesto. IIC 55 , 249–279 (2024). https://doi.org/10.1007/s40319-023-01420-w

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Title: hardware-aware parallel prompt decoding for memory-efficient acceleration of llm inference.

Abstract: The auto-regressive decoding of Large Language Models (LLMs) results in significant overheads in their hardware performance. While recent research has investigated various speculative decoding techniques for multi-token generation, these efforts have primarily focused on improving processing speed such as throughput. Crucially, they often neglect other metrics essential for real-life deployments, such as memory consumption and training cost. To overcome these limitations, we propose a novel parallel prompt decoding that requires only $0.0002$% trainable parameters, enabling efficient training on a single A100-40GB GPU in just 16 hours. Inspired by the human natural language generation process, $PPD$ approximates outputs generated at future timesteps in parallel by using multiple prompt tokens. This approach partially recovers the missing conditional dependency information necessary for multi-token generation, resulting in up to a 28% higher acceptance rate for long-range predictions. Furthermore, we present a hardware-aware dynamic sparse tree technique that adaptively optimizes this decoding scheme to fully leverage the computational capacities on different GPUs. Through extensive experiments across LLMs ranging from MobileLlama to Vicuna-13B on a wide range of benchmarks, our approach demonstrates up to 2.49$\times$ speedup and maintains a minimal runtime memory overhead of just $0.0004$%. More importantly, our parallel prompt decoding can serve as an orthogonal optimization for synergistic integration with existing speculative decoding, showing up to $1.22\times$ further speed improvement. Our code is available at this https URL .

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