how to get a research article published

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Find My Rep

Getting started

Choosing the right journal for you.

Submitting your article to a Sage journal

Promoting your article

Related resources you may find useful.

How to Get Your Journal Article Published guide

Our handy guide is a quick overview covering the publishing process from preparing your article and choosing a journal, to publication (5 minute read).

View the How to Get Your Journal Article Published guide

How to Get Published webinars

Free 1 hour monthly How to Get Published webinars cover topics including writing an article, navigating the peer review process, and what exactly it means when you hear “open access.” Join fellow researchers and expert speakers live, or watch our library of recordings on a variety of topics.

Browse our webinars

Sage Perspectives blog

Looking for tips on how to make sure your article goes smoothly through the peer review process, or how to write the right title for your article?

Read our blog

Sage Campus courses

Want something a bit more in-depth? Sage Campus courses are short and interactive (around 2 hours each) and cover a range of skills, including how to get published. Your library may already subscribe to the modules, or you may want to recommend that they do. Meanwhile, you can utilize the free modules.

Explore Sage Campus

Each journal has its own Aims & Scope, so the acceptance of articles is not just about quality, but also about being a good fit. Does your work reflect the scope of the Journal? Is Open Access important to you, and does the Journal have an Open Access model available? What is the readership of the Journal, and is that readership the right audience for your work? Researching the best match for your manuscript will significantly improve your chances of being accepted.

Watch our 2 minute video

If you already know in which Sage journal you’d like to publish your work, search for it and check the manuscript submission guidelines to make sure it is a good match. Or use the Sage Journal Recommender to tell us your article title and subjects and see which journals are a potential home for your manuscript. Be prepared to adjust your manuscript to match the scope and style of the desired journal.

Find journals with the Sage Journal Recommender or browse all Sage journals

Professional presentation of your work includes a precise and clear writing style, avoiding accidental plagiarism, and formatting your article to meet the criteria of your chosen journal. All of these take time and may not be skills inherent to your field of research. Sage Author Services can help you to prepare your manuscript to comply with these and other related standards, which could significantly improve your chance of acceptance.

Visit Sage Author Services

Submitting your article to a Sage journal

You’ve identified the right journal; now you need to make sure your manuscript is the perfect fit. Following the author guidelines can be the difference between possible acceptance and rejection, so it’s definitely worth following the required guidelines. We’ve a selection of resources and guides to help:

Watch How to Get Published: Submitting Your Paper (2 minute video)

Read our Article Submission infographic , a quick reminder of essentials

Here you’ll find chapter and verse on all aspects of our Manuscript Submission Guidelines

Ready to submit? Our online Submission Checklist will help you do a final check before sending your article to us.

Each journal retains editorial independence, which means their Guidelines will vary, so do go to the home page of your chosen journal to check anything you should be aware of. You can submit your article there too.

The academic world is crowded, how can you make your article stand out? If you are active on social media platforms, telling your followers about your article is one of the simplest and most effective things you can do.

Between us, we can improve the chances of your article being found, read, downloaded and cited – of your article and you making an impact. Our tips and guidance will show you how to promote your article alongside building your academic profile.

Read our tips on how to maximize your impact

How to Get Published for Librarians
How to Do Research and Get Published Webinar Series
Manuscript Submission Guidelines
Sage Author Services
Your Paper and Peer Review
Plain Language Summaries
Advance: a Sage preprints community
The Sage Production Process
Help Readers Find Your Article
Promote Your Article
Research Data Sharing Policies
Career and Networking Resources
Open Access Publishing Options
Top Reasons to Publish with Sage
Open Access Introduction for Authors
Journal Editor Gateway
Journal Reviewer Gateway
Ethics & Responsibility
Sage Editorial Policies
Publication Ethics Policies
Sage Chinese Author Gateway 中国作者资源
Open Resources & Current Initiatives
Discipline Hubs

International edition
Australia edition
Europe edition

How to get published in an academic journal: top tips from editors

Journal editors share their advice on how to structure a paper, write a cover letter - and deal with awkward feedback from reviewers

Overcoming writer’s block: three tips
How to write for an academic journal

Writing for academic journals is highly competitive. Even if you overcome the first hurdle and generate a valuable idea or piece of research - how do you then sum it up in a way that will capture the interest of reviewers?

There’s no simple formula for getting published - editors’ expectations can vary both between and within subject areas. But there are some challenges that will confront all academic writers regardless of their discipline. How should you respond to reviewer feedback? Is there a correct way to structure a paper? And should you always bother revising and resubmitting? We asked journal editors from a range of backgrounds for their tips on getting published.

The writing stage

1) Focus on a story that progresses logically, rather than chronologically

Take some time before even writing your paper to think about the logic of the presentation. When writing, focus on a story that progresses logically, rather than the chronological order of the experiments that you did. Deborah Sweet, editor of Cell Stem Cell and publishing director at Cell Press

2) Don’t try to write and edit at the same time

Open a file on the PC and put in all your headings and sub-headings and then fill in under any of the headings where you have the ideas to do so. If you reach your daily target (mine is 500 words) put any other ideas down as bullet points and stop writing; then use those bullet points to make a start the next day.

If you are writing and can’t think of the right word (eg for elephant) don’t worry - write (big animal long nose) and move on - come back later and get the correct term. Write don’t edit; otherwise you lose flow. Roger Watson, editor-in-chief, Journal of Advanced Nursing

3) Don’t bury your argument like a needle in a haystack

If someone asked you on the bus to quickly explain your paper, could you do so in clear, everyday language? This clear argument should appear in your abstract and in the very first paragraph (even the first line) of your paper. Don’t make us hunt for your argument as for a needle in a haystack. If it is hidden on page seven that will just make us annoyed. Oh, and make sure your argument runs all the way through the different sections of the paper and ties together the theory and empirical material. Fiona Macaulay, editorial board, Journal of Latin American Studies

4) Ask a colleague to check your work

One of the problems that journal editors face is badly written papers. It might be that the writer’s first language isn’t English and they haven’t gone the extra mile to get it proofread. It can be very hard to work out what is going on in an article if the language and syntax are poor. Brian Lucey, editor, International Review of Financial Analysis

5) Get published by writing a review or a response

Writing reviews is a good way to get published - especially for people who are in the early stages of their career. It’s a chance to practice at writing a piece for publication, and get a free copy of a book that you want. We publish more reviews than papers so we’re constantly looking for reviewers.

Some journals, including ours, publish replies to papers that have been published in the same journal. Editors quite like to publish replies to previous papers because it stimulates discussion. Yujin Nagasawa, c o-editor and review editor of the European Journal for Philosophy of Religion , philosophy of religion editor of Philosophy Compass

6) Don’t forget about international readers

We get people who write from America who assume everyone knows the American system - and the same happens with UK writers. Because we’re an international journal, we need writers to include that international context. Hugh McLaughlin, editor in chief, Social Work Education - the International Journal

7) Don’t try to cram your PhD into a 6,000 word paper

Sometimes people want to throw everything in at once and hit too many objectives. We get people who try to tell us their whole PhD in 6,000 words and it just doesn’t work. More experienced writers will write two or three papers from one project, using a specific aspect of their research as a hook. Hugh McLaughlin, editor in chief, Social Work Education - the International Journal

Submitting your work

8) Pick the right journal: it’s a bad sign if you don’t recognise any of the editorial board

Check that your article is within the scope of the journal that you are submitting to. This seems so obvious but it’s surprising how many articles are submitted to journals that are completely inappropriate. It is a bad sign if you do not recognise the names of any members of the editorial board. Ideally look through a number of recent issues to ensure that it is publishing articles on the same topic and that are of similar quality and impact. Ian Russell, editorial director for science at Oxford University Press

9) Always follow the correct submissions procedures

Often authors don’t spend the 10 minutes it takes to read the instructions to authors which wastes enormous quantities of time for both the author and the editor and stretches the process when it does not need to Tangali Sudarshan, editor, Surface Engineering

10) Don’t repeat your abstract in the cover letter We look to the cover letter for an indication from you about what you think is most interesting and significant about the paper, and why you think it is a good fit for the journal. There is no need to repeat the abstract or go through the content of the paper in detail – we will read the paper itself to find out what it says. The cover letter is a place for a bigger picture outline, plus any other information that you would like us to have. Deborah Sweet, editor of Cell Stem Cell and publishing director at Cell Press

11) A common reason for rejections is lack of context

Make sure that it is clear where your research sits within the wider scholarly landscape, and which gaps in knowledge it’s addressing. A common reason for articles being rejected after peer review is this lack of context or lack of clarity about why the research is important. Jane Winters, executive editor of the Institute of Historical Research’s journal, Historical Research and associate editor of Frontiers in Digital Humanities: Digital History

12) Don’t over-state your methodology

Ethnography seems to be the trendy method of the moment, so lots of articles submitted claim to be based on it. However, closer inspection reveals quite limited and standard interview data. A couple of interviews in a café do not constitute ethnography. Be clear - early on - about the nature and scope of your data collection. The same goes for the use of theory. If a theoretical insight is useful to your analysis, use it consistently throughout your argument and text. Fiona Macaulay, editorial board, Journal of Latin American Studies

Dealing with feedback

13) Respond directly (and calmly) to reviewer comments

When resubmitting a paper following revisions, include a detailed document summarising all the changes suggested by the reviewers, and how you have changed your manuscript in light of them. Stick to the facts, and don’t rant. Don’t respond to reviewer feedback as soon as you get it. Read it, think about it for several days, discuss it with others, and then draft a response. Helen Ball, editorial board, Journal of Human Lactation

14) Revise and resubmit: don’t give up after getting through all the major hurdles

You’d be surprised how many authors who receive the standard “revise and resubmit” letter never actually do so. But it is worth doing - some authors who get asked to do major revisions persevere and end up getting their work published, yet others, who had far less to do, never resubmit. It seems silly to get through the major hurdles of writing the article, getting it past the editors and back from peer review only to then give up. Fiona Macaulay, editorial board, Journal of Latin American Studies

15) It is acceptable to challenge reviewers, with good justification

It is acceptable to decline a reviewer’s suggestion to change a component of your article if you have a good justification, or can (politely) argue why the reviewer is wrong. A rational explanation will be accepted by editors, especially if it is clear you have considered all the feedback received and accepted some of it. Helen Ball, editorial board of Journal of Human Lactation

16) Think about how quickly you want to see your paper published

Some journals rank more highly than others and so your risk of rejection is going to be greater. People need to think about whether or not they need to see their work published quickly - because certain journals will take longer. Some journals, like ours, also do advance access so once the article is accepted it appears on the journal website. This is important if you’re preparing for a job interview and need to show that you are publishable. Hugh McLaughlin, editor in chief, Social Work Education - the International Journal

17) Remember: when you read published papers you only see the finished article

Publishing in top journals is a challenge for everyone, but it may seem easier for other people. When you read published papers you see the finished article, not the first draft, nor the first revise and resubmit, nor any of the intermediate versions – and you never see the failures. Philip Powell, managing editor of the Information Systems Journal

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Understanding the Publishing Process

What’s happening with my paper? The publication process explained

The path to publication can be unsettling when you’re unsure what’s happening with your paper. Learn about staple journal workflows to see the detailed steps required for ensuring a rigorous and ethical publication.

Your team has prepared the paper, written a cover letter and completed the submission form. From here, it can sometimes feel like a waiting game while the journal has your paper. It can be unclear exactly who is currently handling your paper as most individuals are only involved in a few steps of the overall process. Journals are responsible for overseeing the peer review, publication and archival process: editors, reviewers, technical editors, production staff and other internal staff all have their roles in ensuring submissions meet rigorous scientific and ethical reporting standards.

Read on for an inside look at how a conventional peer-reviewed journal helps authors transform their initial submission to a certified publication.

Note that the description below is based on the process at PLOS journals. It is likely that at other journals, various roles (e.g. technical editor) may in fact also be played by the editor, and some journals may not have journal staff at all, with all roles played by volunteer academics. As such, please consider the processes and waypoints, rather than who performs them, as the key information.

Internal Checks on New Submissions

Estimated time: 10 days.

When a journal first receives your submission, there are typically two separate checks to confirm that the paper is appropriate and ready for peer review:

Technical check. Performed by a technical editor to ensure that the submission has been properly completed and is ready for further assessment. Blurry figures, missing ethical statements, and incomplete author affiliations are common issues that are addressed at this initial stage. Typically, there are three technical checks: upon initial submission, alongside the first decision letter, and upon acceptance.
Editorial screening . Once a paper passes the first check, an editor with subject expertise assesses the paper and determines whether it is within the journal’s scope and if it could potentially meet the required publication criteria. While there may be requests for further information and minor edits from the author as needed, the paper will either be desk rejected by the editor or allowed to proceed to peer review.

Both editors at this point will additionally make notes for items to be followed-up on at later stages. The publication process involves finding a careful balance for when each check occurs. Early checks need to be thorough so that editors with relevant expertise can focus on the scientific content and more advanced reporting standards, but no one wants to be asked to reformat references only to have their paper desk rejected a few days later.

Peer Review

Estimated time: 1 month.

Depending on the journal’s editorial structure, the editor who performed the initial assessment may also oversee peer review or another editor with more specific expertise may be assigned. Regardless of the journal’s specific process, the various roles and responsibilities during peer review include:

When you have questions or are unsure who your manuscripts is currently with, reach out to the journal staff for help (eg. [email protected]). They will be your lifeline, connecting you to all the other contributors working to assess the manuscript.

Whether an editor needs a reminder that all reviews are complete or a reviewer has asked for an extension, the journal acts as a central hub of communication for those involved with the publication process. As editors and reviewers are used to hearing from journal staff about their duties, any messages you send to the journal can be forwarded to them with proper context and instructions on how to proceed appropriately. Additionally, journal staff will be able to inform you of any delays, such as reviewer availability during summer and holiday periods.

Revision Decision

Estimated time: 1 day.

Editors evaluate peer reviewer feedback and their own expert assessment of the manuscript to reach a decision. After your editor submits a decision on your manuscript, the journal may review it before formally processing the decision and sending it on to you.

A technical editor may scan the manuscript and the review comments to ensure that journal standards have been followed. At this stage, the technical editor will also add requests to ensure the paper, if published, will adhere to journal requirements for data sharing, copyright, ethical reporting and the like.

Performing the second technical check at this stage and adding the journal requirements to the decision letter ultimately saves time by allowing authors to resolve the journal’s queries while making revisions based on comments from the reviewers.

Revised Submission Received

Estimated time: 3 days.

Upon receiving your revised submission, a technical editor will assess the revisions to confirm that the requests from the journal have been properly addressed. Before the paper is returned to the editor for their consideration, the journal needs to be confident that the paper won’t have any issues related to the metadata and reporting standards that could prevent publication. The editor may contact you to resolve any serious issues, though minor items can wait until the paper is accepted.

Subsequent Peer Review

Estimated time: 2 weeks, highly variable.

When your resubmitted paper has passed the required checks, it’ll be assigned back to the same editor who handled it during the first round of peer review. At this point, your paper has gone through two sets of journal checks and one round of peer review. If all has gone well so far, the paper should feel quite solid both in terms of scientific content and proper reporting standards.

When the editor receives your revised paper, they are asked to check if all reviewer comments have been adequately addressed and if the paper now adheres to the journal’s publication criteria. Depending on the situation, some editors may feel confident making this decision based on their own expertise while others may re-invite the previous reviewers for their opinions.

Individual responsibilities are the same as the initial round of peer review, but it is generally expected that later stages of peer review proceed quicker unless new concerns have been introduced as part of the revision.

Preliminary Acceptance

Estimated time: 1 week.

Your editor is satisfied with the scientific quality of your work and has chosen to accept it in principle. Before it can proceed to production and typesetting, the journal office will perform it’s third and final technical check, requesting any formatting changes or additional details that may be required.

When fulfilling these final journal requests, double check the final files to confirm all information is correct. If you need to make changes beyond those specifically required in the decision letter, inform the journal and explain why you made the unrequested changes. Any change that could affect the scientific meaning of the work will need to be approved by the handling editor. While including your rationale for the changes will help avoid delays, if there are extensive changes made at this point the paper may need to go through another round of formal review.

Formal Acceptance and Publication

Estimated time: 2 weeks.

After a technical editor has confirmed that all requests from the provisional acceptance letter have been addressed, you will receive your formal acceptance letter. This letter indicates that your paper is being passed from the Editorial department to the production department—that all information has been editorially approved. The scientific content has been approved through peer review, and the journal’s publication requirements have been met.

Congratulations to you and your co-authors! Your article will be available as soon as the journal transforms the submission into a typeset, consistently structured scientific manuscript, ready to be read and cited by your peers.

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

Find My Rep

How to Get Published

Getting started

Choosing the right journal for you.

Submitting your article to a Sage journal

Promoting your article

Related resources you may find useful.

How to Get Your Journal Article Published guide

Our handy guide is a quick overview covering the publishing process from preparing your article and choosing a journal, to publication (5 minute read).

View the How to Get Your Journal Article Published guide

How to Get Published webinars

Browse our webinars

Sage Perspectives blog

Looking for tips on how to make sure your article goes smoothly through the peer review process, or how to write the right title for your article?

Read our blog

Sage Campus courses

Explore Sage Campus

Watch our 2 minute video

Find journals with the Sage Journal Recommender or browse all Sage journals

Visit Sage Author Services

Submitting your article to a Sage journal

Watch How to Get Published: Submitting Your Paper (2 minute video)

Read our Article Submission infographic , a quick reminder of essentials

Here you’ll find chapter and verse on all aspects of our Manuscript Submission Guidelines

Ready to submit? Our online Submission Checklist will help you do a final check before sending your article to us.

Read our tips on how to maximize your impact

How to Get Published for Librarians
How to Do Research and Get Published Webinar Series
Manuscript Submission Guidelines
Sage Author Services
Your Paper and Peer Review
Plain Language Summaries
Advance: a Sage preprints community
The Sage Production Process
Help Readers Find Your Article
Promote Your Article
Research Data Sharing Policies
Career and Networking Resources
Open Access Publishing Options
Top Reasons to Publish with Sage
Open Access Introduction for Authors
Journal Editor Gateway
Journal Reviewer Gateway
Ethics & Responsibility
Sage Editorial Policies
Publication Ethics Policies
Sage Chinese Author Gateway 中国作者资源
Open Resources & Current Initiatives
Discipline Hubs

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Research Papers

How to Write and Publish Your Research in a Journal

Last Updated: February 26, 2024 Fact Checked

Choosing a Journal

Writing the research paper, editing & revising your paper, submitting your paper, navigating the peer review process, research paper help.

This article was co-authored by Matthew Snipp, PhD and by wikiHow staff writer, Cheyenne Main . C. Matthew Snipp is the Burnet C. and Mildred Finley Wohlford Professor of Humanities and Sciences in the Department of Sociology at Stanford University. He is also the Director for the Institute for Research in the Social Science’s Secure Data Center. He has been a Research Fellow at the U.S. Bureau of the Census and a Fellow at the Center for Advanced Study in the Behavioral Sciences. He has published 3 books and over 70 articles and book chapters on demography, economic development, poverty and unemployment. He is also currently serving on the National Institute of Child Health and Development’s Population Science Subcommittee. He holds a Ph.D. in Sociology from the University of Wisconsin—Madison. There are 13 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 696,796 times.

Publishing a research paper in a peer-reviewed journal allows you to network with other scholars, get your name and work into circulation, and further refine your ideas and research. Before submitting your paper, make sure it reflects all the work you’ve done and have several people read over it and make comments. Keep reading to learn how you can choose a journal, prepare your work for publication, submit it, and revise it after you get a response back.

Things You Should Know

Create a list of journals you’d like to publish your work in and choose one that best aligns with your topic and your desired audience.
Prepare your manuscript using the journal’s requirements and ask at least 2 professors or supervisors to review your paper.
Write a cover letter that “sells” your manuscript, says how your research adds to your field and explains why you chose the specific journal you’re submitting to.

Step 1 Create a list of journals you’d like to publish your work in.

Ask your professors or supervisors for well-respected journals that they’ve had good experiences publishing with and that they read regularly.
Many journals also only accept specific formats, so by choosing a journal before you start, you can write your article to their specifications and increase your chances of being accepted.
If you’ve already written a paper you’d like to publish, consider whether your research directly relates to a hot topic or area of research in the journals you’re looking into.

Step 2 Look at each journal’s audience, exposure, policies, and procedures.

Review the journal’s peer review policies and submission process to see if you’re comfortable creating or adjusting your work according to their standards.
Open-access journals can increase your readership because anyone can access them.

Step 1 Craft an effective introduction with a thesis statement.

Scientific research papers: Instead of a “thesis,” you might write a “research objective” instead. This is where you state the purpose of your research.
“This paper explores how George Washington’s experiences as a young officer may have shaped his views during difficult circumstances as a commanding officer.”
“This paper contends that George Washington’s experiences as a young officer on the 1750s Pennsylvania frontier directly impacted his relationship with his Continental Army troops during the harsh winter at Valley Forge.”

Step 2 Write the literature review and the body of your paper.

Scientific research papers: Include a “materials and methods” section with the step-by-step process you followed and the materials you used. [5] X Research source
Read other research papers in your field to see how they’re written. Their format, writing style, subject matter, and vocabulary can help guide your own paper. [6] X Research source

Step 3 Write your conclusion that ties back to your thesis or research objective.

If you’re writing about George Washington’s experiences as a young officer, you might emphasize how this research changes our perspective of the first president of the U.S.
Link this section to your thesis or research objective.
If you’re writing a paper about ADHD, you might discuss other applications for your research.

Step 4 Write an abstract that describes what your paper is about.

Scientific research papers: You might include your research and/or analytical methods, your main findings or results, and the significance or implications of your research.
Try to get as many people as you can to read over your abstract and provide feedback before you submit your paper to a journal.

Step 1 Prepare your manuscript according to the journal’s requirements.

They might also provide templates to help you structure your manuscript according to their specific guidelines. [11] X Research source

Step 2 Ask 2 colleagues to review your paper and revise it with their notes.

Not all journal reviewers will be experts on your specific topic, so a non-expert “outsider’s perspective” can be valuable.

Step 1 Check your sources for plagiarism and identify 5 to 6 keywords.

If you have a paper on the purification of wastewater with fungi, you might use both the words “fungi” and “mushrooms.”
Use software like iThenticate, Turnitin, or PlagScan to check for similarities between the submitted article and published material available online. [15] X Research source

Step 2 Write a cover letter explaining why you chose their journal.

Header: Address the editor who will be reviewing your manuscript by their name, include the date of submission, and the journal you are submitting to.
First paragraph: Include the title of your manuscript, the type of paper it is (like review, research, or case study), and the research question you wanted to answer and why.
Second paragraph: Explain what was done in your research, your main findings, and why they are significant to your field.
Third paragraph: Explain why the journal’s readers would be interested in your work and why your results are important to your field.
Conclusion: State the author(s) and any journal requirements that your work complies with (like ethical standards”).
“We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal.”
“All authors have approved the manuscript and agree with its submission to [insert the name of the target journal].”

Step 3 Submit your article according to the journal’s submission guidelines.

Submit your article to only one journal at a time.
When submitting online, use your university email account. This connects you with a scholarly institution, which can add credibility to your work.

Step 1 Try not to panic when you get the journal’s initial response.

Accept: Only minor adjustments are needed, based on the provided feedback by the reviewers. A first submission will rarely be accepted without any changes needed.
Revise and Resubmit: Changes are needed before publication can be considered, but the journal is still very interested in your work.
Reject and Resubmit: Extensive revisions are needed. Your work may not be acceptable for this journal, but they might also accept it if significant changes are made.
Reject: The paper isn’t and won’t be suitable for this publication, but that doesn’t mean it might not work for another journal.

Step 2 Revise your paper based on the reviewers’ feedback.

Try organizing the reviewer comments by how easy it is to address them. That way, you can break your revisions down into more manageable parts.
If you disagree with a comment made by a reviewer, try to provide an evidence-based explanation when you resubmit your paper.

Step 3 Resubmit to the same journal or choose another from your list.

If you’re resubmitting your paper to the same journal, include a point-by-point response paper that talks about how you addressed all of the reviewers’ comments in your revision. [22] X Research source
If you’re not sure which journal to submit to next, you might be able to ask the journal editor which publications they recommend.

Expert Q&A

About This Article

To publish a research paper, ask a colleague or professor to review your paper and give you feedback. Once you've revised your work, familiarize yourself with different academic journals so that you can choose the publication that best suits your paper. Make sure to look at the "Author's Guide" so you can format your paper according to the guidelines for that publication. Then, submit your paper and don't get discouraged if it is not accepted right away. You may need to revise your paper and try again. To learn about the different responses you might get from journals, see our reviewer's explanation below. Did this summary help you? Yes No

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Historical Transactions

Royal historical society, blog and online resources.

Ten Tips for Getting Published in Academic Journals

by Administrator | Aug 17, 2021 | General , RHS Work | 0 comments

On Wednesday 21 July 2021 the Royal Historical Society hosted the first in a new series of training workshops for Post-Graduate and Early Career Historians: ‘ Getting Published: a guide to first articles and journal publishing’ .

The event brought together journal editors and publishers, recent first-time authors, and early career historians. Its aim was to demystify the process of journal publishing and provide practical advice and tips on how best to succeed. We recently released the event video which contains numerous tips and insights.

Here we pull out a ‘Top 10’ from the panel and discussion: 10 key things for historians to bear in mind when writing and submitting a first journal article.

Thanks again to our panellists and audience on 21 July and, also, to Dr Sarah Johanesen for choosing and summarising these 10 Points to Remember in this post.

1. Choose a self-contained section of your research (watch the discussion at 1:07:57 in the video)

ECR panellist Sasha Rasmussen took her first article from a section of her thesis ‘which didn’t need a lot of theoretical scaffolding, it was pretty self-contained. It made a concrete point’. While in your thesis you can lead a reader in, an article needs ‘to hit them with the point, and 8000 (ish) words is a lot shorter than you think it is.’

2. Use Journal websites (discussion at: 14:23)

Professor Sandra den Otter, co-editor of the Journal of British Studies , discussed the importance of researching which journals would be the best fit for your research – and using their websites to do so.

‘Sites provide information about the overarching mandate or aims of the journal, and they might signal openness to new methodological approaches that particularly suits your research, or preference for certain kinds of research. If you dip into the table of contents of recent issues of the journal, you’ll develop a sense of the tone of the journal too, you can sample articles to become more familiar’.

Other important information can also be found online such as length limits, style guides, submission instructions, and contact details for the editors.

3. Consider your goals and publishing beyond academia (discussion at: 1:27:00)

ECR panellist Dr Diya Gupta advised that if you don’t want an academic career, or feel as if one isn’t working out, ‘then it might not be worth the time investing in publishing in a top-rated journal’. You might want to consider other ways of getting your research out into the world, ‘turn it perhaps into a trade book and have more of a popular readership’.

‘Publishing in journals is one type of approach, and it’s about weighing up that type of type of approach with many other types of approaches you could also take’.

4. Position your work as making an intervention in current debates (discussion at: 08:17)

Professor Emma Griffin, President of the Royal Historical Society, has only recently stepped down from her position as co-editor of the Historical Journal . As an editor, she explained they are looking for ‘how relevant’ your work ‘is to other people who are working in broadly cognate areas’, and ‘how you’re going to intervene’ in scholarly conversation. She urged junior scholars to, ‘think in terms of an intervention, as well as in terms of the conversation.’

Panel 1 brought together journal editors and publishing specialists. Clockwise from top left: Professor Emma Griffin, Editor of ‘Historical Journal’ and RHS President; Professor Sandra den Otter, Editor of the ‘Journal of British Studies’; Dr Rebekah Lee, Editor of the ‘Journal of Southern African Studies’; and Professor Jane Winters of the School of Advanced Study, University of London

5. Your abstract is ‘your calling card’ (discussion at: 30.09)

Dr Rebekah Lee, co-editor of the Journal of Southern African Studies , explained that abstracts are often the only thing reviewers see before volunteering to review your paper, ‘it’s your calling card’.

‘so often I find that Junior scholars don’t spend enough time with actually getting their abstract quite right, and so it’s important, particularly in our case it’s an interdisciplinary journal’.

Make sure the abstract clearly indicates the debates your article intervenes in, your disciplinary, regional, and chronological focus, your source base, and your key arguments. If it ‘misrepresents or doesn’t fully represent what it is your papers about, then you’re not getting your paper sent to the best possible’ reviewer.

6. Rejection is not reflection (discussion at: 23:54)

Journal publishing is about fitting the right article with the right audience. So, as Professor den Otter reminded us:

‘it’s important not to leap to conclusions about the quality of your life’s work if your first approach to a journal is discouraging or does not run smoothly. Everyone has received discouraging news from journals, but persevere, and your work, with persistence and with an eye to its quality, will reach the audiences and join the conversations with which you’d like to engage.’

7. Review before the Reviewers (discussion at: 1:14:15)

Our third ECR panellist, Dr Jonah Miller, explained that even before sending his article to a Journal he received extensive advice from his supervisor, and got ‘feedback from a group of peers,’ through a reading group where they shared their work. He strongly suggests doing something like this as you ‘get lots of different perspectives, and it’s a kind of preliminary to the reviewers’ reports. And I think if I hadn’t done that then the reviewers reports would have been a lot harsher’.

8. Don’t panic about Open Access fees (discussion at: 41:40)

Our final editorial expert and Vice-President of the RHS, Professor Jane Winters, gave some valuable insight into Open Access publishing – particularly for those who must publish Open Access due to their funding – explaining the difference between Green Open Access and Gold Open Access.

‘A key point to remember is that you don’t ever have to pay a fee, the Green route to Open Access is absolutely fine. And it’s allowed by the vast majority of journals.’

Even where Gold access is absolutely required, ‘Your host institution will have a dedicated open access fund that you may be able to call on depending on demand.’ Although this is not an option for those without institutional affiliation, this is one of the ‘clear inequalities in the system, which the Royal Historical Society has been doing work to try and raise awareness’ of, so that it can be changed.

Though it came just too late for our event, UKRI has just announced its new policy on Open Access publications based on research it funds – e.g. via the Arts and Humanities Research Council. A separate RHS blog (10 August) explains what the UKRI decisions mean for historians: don’t worry, the AHRC funding does not refer to doctorates and publications arising from doctoral research.

Panel 2 brought together three recently published authors in conversation with Emma Griffin. Clockwise from top right: Dr Jonah Miller, Dr Diya Gupta and Sasha Rasmussen.

9. Check the Journal’s Open Access compliance (discussion at: 43:42)

Professor Winters added that when researching which journal you wish to send your article to, there is a useful online tool which allows you to check their Open Access policies.

‘It’s called, slightly oddly, Sherpa Romeo … and you can just enter a journal or publisher name, to find out more information about what they offer, and it’s very up to date and it’s tied to particular funders and their funding mandate.’

10. Just email and ask!

Throughout the panel on the Editors’ perspective and the Q&A session at the end this came through repeatedly. Journals are incredibly diverse, and what’s true for one may not be true for another.

Perhaps you have questions about support for authors who are neuro-divergent, non-native English speakers, or have other access requirements? You might want to know whether the journal would accept a translated piece, or allow re-publication elsewhere in a foreign language? Do you have queries about getting the rights to images, or want to know what level of support the journal can offer in getting funding for that?

If you cannot find the information you are looking for, simply email the editors. You may just be the first to ask, and you may be surprised by the support they can offer in fully realising your research

Where next?

You can check out further resources at the bottom of this page, and the articles of our ECR panel at the following journals:

Dr Diya Gupta , ‘Bodies in Hunger: Literary Representations of the Indian Home-Front During World War II’, Journal of War & Culture Studies , 13: 2 (2020), pp. 196-214. [URL: https://doi.org/10.1080/17526272.2019.1644274 ]
Dr Jonah Miller , ‘The Touch of the State: Stop and Search in England, c.1660–1750’, History Workshop Journal , 87 (Spring, 2019), pp. 52-71. [URL: https://doi.org/10.1093/hwj/dby034 ]
Sasha Rasmussen , ‘Musicians, Students, Listeners: Women and the Conservatoire in pre-war Paris and St Petersburg’, Cultural and Social History , 18: 2 (2021), pp.221-42. [URL: https://doi.org/10.1080/14780038.2021.1902608 ]

Our compiler, Dr Sarah Johanesen ‘s own first article, ‘‘That silken Priest ’: Catholic disguise and anti-popery on the English Mission (1569–1640)’ was published in Historical Research , 93 (Feb 2020), pp. 38-51 [URL: https://doi.org/10.1093/hisres/htz010 ]

Further Resources

(selected guides from publishers, blogs and learned societies).

Society of Historical Archaeology: How to publish your first journal article
Jobs.ac.uk: Getting Published
Taylor and Francis: Publishing your research
Sage: How to get published
Past & Present Society: Getting published in journals
NACBS: Publish, don’t perish!
American Historical Association: Publishing in journals , plus other articles on the AHA’s Perspectives on History
Get a Life, PhD: How to publish an article in an academic journal
Get a Life, PhD: How to respond to revise and resubmit
Further publishing guides and resources from the RHS (from 2014-15: covering journals, edited collections and monographs)

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The 4-Step Guide That Will Get Your Research Published

You’ve spent months and years working on your research project, sometimes sacrificing a good night’s sleep and, often, backing out of events you really wanted to go to. Finally it’s time…to get your research published!

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The scholarly publishing industry is huge and there are thousands of journals for researchers to choose from. However, given the scary high rejection rates of submission in peer-reviewed journals and the 6–12 months time taken to get published, how do you know which journal is your best bet?

Here are a few steps that you can take to significantly improve your chances of getting published:

1. Browse legit journals

As of 2015, the academic publishing market had an annual revenue of $20.5 Billion . This revenue has grown tremendously over the last two years. Consequently, this growth has given rise to a large number of predatory publishers who try to scam early-career researchers in return for getting their research published. Unfamiliar with the process of research publishing and attracted by the prospect of getting published sooner than thought, early- career researchers often fall prey to these publishers.

You can take a few measures to avoid getting scammed by these predatory journals:

Stay wary of unsolicited calls /emails — Reputable publishers don’t make cold calls or send unsolicited emails seeking submissions. It is mainly scammers who get access researchers’ details via Google Scholar, Academia.edu etc. and then do cold reach-outs.
Use Jeffrey Beall’s list : Jeffrey Beall built this list of predatory journals and publishers . If you find a publisher suspicious, check if their name appears on this list. If it does, be sure that you’re being mugged. Hence, stay away.
Non-indexed journals : PubMed, JSTOR, SCOPUS, SHERPA, and DOJA ( Directory of Open Journal Access ) are some of the popular databases of authentic journals. If you are unsure about a publisher’s authenticity, check if their journal is listed on these databases.
Non-clarity on APC ( Article Processing Charge)– Most Open Access journals charge APC. This is a definite fee about which you can find information on the journal’s website. However, predatory journals often falter while quoting APC or their websites or do not have a proper APC break-down.

Read more about identifying legitimate journals .

2. Choose the best-fit journal

Allaying your fears of being scammed by a predatory publisher is just step one towards getting your research published. The real test of your efforts starts at submission, when your paper is reviewed. This is the stage where most papers are rejected for not complying to a journals’ formatting guidelines. Each journal has its own formatting, styling and referencing guidelines. Failing to comply with these leads to rejection.

One common mistake that early-career researchers make is that they write a paper first and then decide the journal to get published in. Another mistake they make is to aim for the highest-ranked journal in their field for publication. This naturally increases the chances of rejection for first-timers.

Quality and reputation of journals matter. However, credibility of journals and getting accepted faster is of prime importance.

So a much better approach is to:

Write a list of journals in the area of your research. You can use your university’s library search or the internet to find the journals.
Once the list is ready, re-organize it according to the journals’ relevance and quality.
Check if the journals on your list have published on your specific topic in the past.
Look through your references and bibliography to see if your sources come from one or more of the journals on your list.

Together, points 3 and 4, should give you a good idea of the journals you should approach to maximize your chances of getting published.

3. Understand the submission process

As mentioned earlier, not complying with the guidelines of a journal is one of the most common reasons why research papers get rejected . Once you have decided the journal you want to publish in, visit the journal’s website and read through their guidelines. Almost all journals have a different submission process.

The guidelines of each journal tend to vary across the following details:

Minimum and maximum length of the article
Referencing
Formatting (includes space, font, margin, headings etc)
British (or Australian)/American English
Choice of medium –electronic, hard copy, or both

Use SciSpace (Formerly Typeset) to ensure that your paper is 100% compliant to journal guidelines.

Some submission advice

While submitting your article for publication make sure that you are submitting it to only one journal at a time, as most journals would refuse to consider an article for publication if it’s considered for publication in other journals. Most publications require researchers to declare that their work is not being considered for publishing in other journals.

Some journals only accept hard copy submissions through the post, while some only accept electronic submissions (in .doc, .docx), while others may require you to submit in both formats. It is, therefore, critical to read the submission guidelines carefully on the journal’s website.

4. Write a convincing journal cover letter

The role of a cover letter is to convince an editor that your research work is worth publishing in their journal. Hence, it is highly important that you write the letter with as much sincerity as you would write your manuscript text.

Here are some tips that you can use while writing the cover letter for your journal submission:

If possible, find out the name of the editor and address her by name. You can find out the name of the editor through the journal’s online submission system. This information is generally public.
In the first and the second paragraph of the cover letter state the name of your manuscript, include the names of the author/s, describe the reason behind your interest in the research work you have done, and the major findings from your research. Additionally, you can refer to prior work or the previous articles that you have published.
In the next paragraph, address the aim and scope of the journal. Write how your work contributes to the aim of the journal and falls within the scope of their scientific coverage. Also mention why your work would be valuable for their readers.
Finally, conclude the letter with statements that tell the editor that your manuscript is original and that no part of it is under consideration for publication elsewhere. A few journals also seek researchers to submit a list of the reviewers to whom your article can be sent for review. If the journal requests so, you should include the list in the concluding part the letter.

Once submitted, peer-review can take as long as six months. This primarily depends on how a publication has set up its peer-review process. A few publications have a two-stage review process wherein an editor first reviews articles to decide if they are worthy of peer-review. If your article passes this test, it’s then sent to a reviewer or a group of reviewers (these are academics from the field that you have written your article in). This process can take several months and you would, finally, get an email or a letter from the journal stating their decision.

If the journal decides to not publish your article, you would get the reviewer’s report and comments on your work. If you don’t, you can request to get them. This will help you improve your article before you send it to another journal for consideration.

It’s rare that a researcher’s work is accepted in the first attempt. However, most of the time it is not their research work, but the neglect researchers show while approaching publishers and presenting their research that fails them. If you perfect the approach you use to reach editors, you may get your research published in your first attempt!

In light of the fact that you are on the lookout for platforms that simplify research workflows, we recommend you check out SciSpace discover . Our suite of products can make your research workflows easier so that you can focus more on advancing science.

The best-in-class solution takes care of everything from literature search and discovery, profile management, research writing and formatting, and so much more.

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v.8(9); 2014 Sep

How to Write Articles that Get Published

Kirti nath jha.

1 Professor, Department of Ophthalmology, Mahatma Gandhi Medical College & Research Institute, Pondy-Cuddalore Main Road, Pillaiyarkuppam, Pondicherry, India.

Publications are essential for sharing knowledge, and career advancement. Writing a research paper is a challenge. Most graduate programmes in medicine do not offer hands-on training in writing and publishing in scientific journals. Beginners find the art and science of scientific writing a daunting task. ‘How to write a scientific paper?, Is there a sure way to successful publication ?’ are the frequently asked questions. This paper aims to answer these questions and guide a beginner through the process of planning, writing, and correction of manuscripts that attract the readers and satisfies the peer reviewers. A well-structured paper in lucid and correct language that is easy to read and edit, and strictly follows the instruction to the authors from the editors finds favour from the readers and avoids outright rejection. Making right choice of journal is a decision critical to acceptance. Perseverance through the peer review process is the road to successful publication.

Introduction

Writing and publishing scientific papers is the core business of every researcher [ 1 ]. The scientific output medical researchers generate is not only important for society to improve health through advancement of knowledge but also for the individual researcher’s career [ 2 ]. Effective scientific writing, however, is not easy [ 1 ] .

Scientific paper has a required structure and style. However, a research article is not only a technically rigid document, but also a subjective intellectual product. Therefore, it requires good skills in both structuring and phrasing. These skills are acquired through experience, and can also be taught [ 3 ]. ‘Uniform Requirements for Manuscripts Submitted to Biomedical Journals: Writing and Editing for Biomedical Publication’ gives the required technical and structural details of scientific papers [ 4 ]. Also, there is no dearth of literature on scientific writing and publishing. Ironically, most graduate programmes in medicine do not offer hands-on training in writing and publishing in scientific journals. Therefore, most authors learn the art and science of scientific writing the hard way; though there are papers that provide step-by-step guide to writing [ 5 ].

What constitute a good paper- worthy of publication? There are no straight answers. Some define a good paper as a clear, coherent, focussed, well-argued document that uses unambiguous language[ 3 ]. Editors and reviewers appreciate manuscripts that are easy to read and edit [ 4 ]. However, no foolproof rules exist for success in publishing a manuscript. Good scientific content of a paper alone does not guarantee its publication in a good journal [ 5 ].

This article presents a review of the selected articles on writing and publishing in biomedical journals and aims to provide beginners the basics of effective scientific writing, and tips on successful publishing.

Writing a Scientific Paper: Getting started.

When planning a scientific paper, Berk’s memo to the authors in the American Journal of Roentgenology is worth following [ 6 ]. He felt that getting the things right the first time improved the chances of acceptance and avoided revisions. He set out 5 guiding principles for the inexperienced authors : They are : 1. Determine the specific focus of your article, 2. Select the right journal, 3. Decide the type of article, 4. Follow the guidelines for authors published in the selected journal, 5. Revise, revise, and revise. Remember, ‘the most of the important work of composing a manuscript occurs during the study design that is critical for determining the resultant manuscript’s publication [ 7 ]. Therefore, study design and methodology requires careful planning; they form the touchstone on which results and conclusions are tested.

Preparing a Manuscript

The scope of work determines the type of article. The choice of journal depends upon the field a journal covers, area of research, time frame for publication, and the journal’s impact factor- a proxy for relative importance of the journal within its field. ‘Uniform requirements for manuscripts submitted to biomedical journals: writing and editing for biomedical publication’ provides the guidelines for preparing manuscripts for any journal [ 4 ]. The text of observational and experimental articles is usually divided into sections with the headings, introduction, methods, results, and discussion, the so-called “IMRAD” structure. Other types of articles have different structure. Therefore, it is necessary to familiarize with and strictly follow the instructions to the authors of the target journal.

To begin with read a paper written in the format you plan to write. Prepare a skeleton of your paper [ 8 ]. Note down the key points in each section. It is neither desirable nor practical to actually write the article sections in sequential order. Introduction and the discussion may wait till at the end. Abstract may be written the last.

Keep the language simple, concise and easy to understand. Follow UK or US English as desired by the journal. Remove all unnecessary words. Use active voice rather than passive. The sentences should begin with the operative word and end with the message. Expand the abbreviations when used for the first time. Check the grammar and spelling. A word processing tool may be helpful. However, many biomedical words do not exist in the vocabulary of the word processing tools. Here, the textbooks or a medical dictionary may be helpful.

Following text sequentially discusses the elements of the individual sections of a scientific paper. Peer-review and reasons for rejection are discussed subsequently.

Title: A good title should attract and inform the readers and be accurate [ 9 ]. It should make it stand out from other literature in the field [ 10 ]. Titles may be phrased in a variety ways. Some examples of descriptive and informative titles are given below:

- Correlation of Tear Fluorescein Clearance and Schirmer test scores with Ocular irritation symptoms (a descriptive title)

- What Are the Biomarkers for Glaucoma?

- Dry eyes: are new ideas drying up?

- Angiopoetin-2 levels are elevated in exudative pleural effusions (informative title)

As a rule, the title should contain all the words that the readers use for searching relevant literature. The authors may, to begin with, consider a number of titles and finally choose the most appropriate. Co-authors and peers may provide useful suggestions. Some journals also ask for short running titles in limited characters to be used at the top or bottom of the journal page. Provide a short running title whenever asked.

Abstract: The abstract reflects the main story of the scientific paper. While reading articles most readers go no beyond the abstracts. Therefore, the abstract should attract the readers to go further. Abstract may be structured or unstructured. Most journals ask for a structured abstracts within given word limit. Structured abstract is divided into:

1. Background: What is known and why is this study needed?

2. Methods: What did you do ?

3. Results: What did you find ?

4. Conclusion: What does it mean ?

Write the abstract in past perfect tense, active voice, and with no citations.

Provide word count, if asked, and key words for indexing, preferably confirming to medical subject heading (MeSH) vocabulary. MeSH vocabulary is available on www.PubMed.com .

Introduction: A crisp introduction is an essential ingredient of a good paper. A good introduction will ‘‘sell’’ the study to editors, reviewers, readers, and sometimes even the media [ 11 ]. It should tell what is known, what is unknown , and also the rationale behind the study. The introduction should start with the background of previous research, and state the aim, the research question, and the study design. Give in the introduction only the strictly pertinent references and do not include the data or conclusions from the work being reported.

Methods: The methods tell how the study was conducted and how the conclusions were arrived at. Methods of an original study have four basic elements; study design, setting and subjects, data collection, statistical methods, and ethical approval. Describe the type of study (prospective/ retrospective/ experimental/observational), the subjects or the study population (human/animal), the sample size and sample size calculation, recruitment of study population, methods of randomization, blinding, inclusion and exclusion criteria, measurement tools, outcome measures, and statistical analysis. The methods provides the readers insight into correctness or otherwise of design. Also, details of methods allow the readers to replicate the intervention or experiment so that they can try and test for themselves the efficacy of an intervention and the validity of conclusions. While describing new surgery or experiment provide sufficient details. When you followed a standard procedures described elsewhere, provide the relevant references.

Results: Results answers the research question without interpretation. Structure the results like the material and methods [ 12 ]. Be objective and use past tense. Remove all the superfluous details that does not form the part of study question, outcome measure or a factor affecting it. Start the results with recruitment process, and a description of demographic characteristic of the population. For controlled trials first describe the experimental group followed by the control group. Give both the percentage and the actual numerical values with decimals e.g. 90%(54/60). Wherever applicable present the values with mean, standard deviation (SD) and 95 % confidence interval. Describe the primary and secondary outcome, and also the unexpected findings. Give p-values with 95 % confidence interval to state the beneficial / adverse effects established by a test of statistical significance. Also provide effect sizes e.g. odds ratio or relative risk with 95 % confidence interval.

Do not over interpret the results. Over interpretetation of result may weaken the impact of conclusion and result in rejection of your paper.

Tables, charts, graphs and figures reduce the text and makes visual impact for easy reading. Number the figures, tables, charts, graphs and the photographs serially. Mention them in the text at appropriate places. Prepare the clinical photographs and diagrams on separate pages in desired format (e.g. JPEG, TIFF, or PNG of desired file size and resolution). Provide as a separate file for the legends for figures, charts, and the clinical photographs. Place the legends after the references. Additional media, like video, in desired file format of given file size, may be submitted for online journals.

Discussion: Discussion interprets the results. Keep it concise. Begin the discussion with brief recapitulation of the main findings (the answer to the research question) without repeating the results. Repeating results in the discussion is a common mistake. Refrain from bringing in new findings. Compare your results with the findings of similar studies by other authors and explain the reasons of variation. Emphasise the new findings. Interpret the unexpected. Underline the implications of your findings. Also, describe the strengths and weaknesses of your study. Finally provide a conclusion - the take home message.

References: References authenticate the scientific facts and statements. Include only the essential references. Cite most accessible reference, and the primary source rather than reviews. Eliminate archaic and irrelevant references, and references for established facts. Check the references for accuracy. Follow the referencing style suggested by the target journal. Most biomedical journals today follow Vancouver style or APA (American Psychological Association) style. There may be a limit for the number of references for a given type of article. Some journal offer online software for checking accuracy of the listed references. Limited number of references can be arranged manually. Especially designed referencing software is useful for maintaining and managing large volume of references. Annotation of references - sentence case or superscript- also varies. Follow the individual journal’s guidelines.

Submission: Revise your paper thoroughly before submission. Read it critically as you would another author’s paper. Ensure you have strictly followed the instruction to authors. Failure to adhere to the instructions may result in summary rejection of your paper. Check and recheck the language and grammar for errors. Create separate files for the cover letter, the abstract, the blinded article file (without author details); figures, charts, tables, and images, legends, and permission from copyright holder for use of published materials, etc. Provide in the cover letter the title, main findings, and their relevance. Ensure correctness of author details (name, surname, degree, etc.), authorship (first author, co-author, guarantor, corresponding author), and their mailing address and the institutional affiliation. Provide all the information desired by the editor including contribution of individual authors. Some journal require details of contributions of each author e.g., conception and design, data collection, statistical analysis, manuscript preparation and revision. Declare the conflict of interest, if any. Online submission shall remain incomplete unless you sequentially upload all the required sections, and the copyright transfer form signed by each author. The copyright transfer form should mention the corresponding author. Preserve the raw data and the final submission for future reference.

Reasons for Rejection: Rejection is an unpleasant situation, but common in scientific publishing. Initial rejection occurs at the editorial level. During the peer review, reviewers assess the quality of paper according to 2 main criteria: contribution to the field and the adequacy of research design [ 13 ]. Deficiency in the study design was the most commonly cited reason for outright manuscript rejection according to a study that queried the editors and reviewers [ 14 ]. A study that studied peer review evaluations of a large number of papers concluded that ‘the main determinant of the recommendation for acceptance or rejection of a given manuscript was the relationship between the experimental design, the results, and the conclusion. Inappropriate experimental design was again strongly associated with rejection [ 15 ].

Failure to adhere to the ‘instruction to the authors’ is another important reason for rejection. Plagiarism in any form is another reason for summary rejection. Available software readily check the submission for plagiarism. Also articles found unsuitable for the journal on account of their content, language, grammar, and format are summarily rejected. There are excellent works that have identified the’ principles to improve the likelihood of publication of a scientific manuscript’ and the reasons why manuscripts are not accepted for publication’ [ 6 , 13 , 16 ]. Lack of what improves the likelihood of acceptance, is the cause of rejection. Common reasons for rejection other than those mentioned above include: poor study design, insufficient problem statement, incomplete, inaccurate, or outdated review of literature, suboptimal reporting of results, getting carried away in the discussion, and poor writing [ 17 ]. Language poses a problem for researches from non-English-speaking countries. Some publishers provide paid language-services for manuscripts.

Peer review, Responding to Reviewers and Resubmission: Peer review is considered the virtue of science communication [ 18 ]. Peer review is an essential tool the journals apply to maintain high quality and standard of the articles published in their journals. The process starts after your paper is past the editorial scrutiny. It supplements the authors work in making it more acceptable to the wider readership. Some journals ask suggestions for potential reviewers, and also those reviewers you will wish not to review your paper. Reviewers may accept, reject or suggest minor/ major revisions. Provide point-wise response to the reviewer’s comments and in time resubmit the revised manuscript incorporating the suggestions for change. Highlight the changes in the revised manuscript. Remember, revision gives no guarantee for acceptance. But failure to respond and resubmit closes the door.

Writing and publishing is integral to research. Scientific manuscript has a required structure and style; the available literature provides adequate guidelines. Online abstracts and full text references, language services, and referencing software have made preparation of manuscript easier. Read the instructions carefully and adhere to them strictly. A beginner has to travel the learning curve of the writing, peer review, and publishing. Originality of content, valid study design, good manuscript- conforming to language, style, and format- are prerequisite for successful publication. Attention to details at every stage and perseverance through the arduous process of research, manuscript preparation, peer review and publication is essential for success.

Literature Search

A PubMed search of the database (1990 to 2004) was conducted. Following key words were used: writing, publishing, biomedical journals, and peer review. Additional sources included publications cited in other articles. Relevant articles were reviewed and included.

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GETTING YOUR RESEARCH ARTICLE PUBLISHED: 7 TOP TIPS FROM AN ACADEMIC EDITOR

How to publish your research article.

The intimidating challenge of getting the first academic article published is unavoidable for young researchers in the “publish or perish” environment. Writing your first academic article can be a little overwhelming if you do not know ways to publish your research article; so before publishing your research article, here are seven top tips and stages you need to follow:

1. Target an Appropriate Journal

Before getting your research article published, it is very important to choose one of the well-known journals in your field. When considering the journals for submitting manuscripts, you might tend to select one of the top journals in your field. You often see them in the reference lists of the research articles you consume.

However, you should note that the top journals are highly competitive; where only the best articles with high validity and significance are published. Such studies are most of the time financially supported by related institutions, which ensures the proper organization of the research. Several years of research experience of the author also contributes to the superiority of their research articles, which top journals are publishing. Therefore, there are lower chances for young researchers to publish their research article in high profile journals. Instead, newbies need to start their ways to success with long-tail journals.

2. Write now, Edit later

See all 5 Unusual Academic Tips

Composing and editing at the same time is not the preferred way of writing, especially when collecting ideas or deriving conclusions. Thus, you need to focus first on writing and then for editing. If you are not the type of person who can keep the messy draft, then consider editing your work periodically, after each paragraph or idea you finish. You can also follow these quick and handy tips for improving your writing skills.

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3. reference strategically.

References reflect the credibility of your academic article. All publishers, no matter if they are top or not, pay great attention to your reference list while reviewing manuscripts. If you want to publish your article, you should use only reliable academic sources rather than websites as Wikipedia or opinion-based blog articles. In turn, using credible academic sources will not only help you get your paper published, but further will make your article more reliable in the eyes of the readers. The most reliable sources of databases and article journals you can find here .

4. Ask Friends to Check Your Work before publishing the article

One of the most common situations the editors face is the unclear language of papers. Chances are, English is a non-native language for the author, chances are also that the author did not go an extra mile to proof-check his manuscript. Thus, one should note that when the language errors in the article distract and confuse the editor, it is hard to get the full meaning of the manuscript. For proofreading and checking the vocabulary and grammar, please, see the best online language checking tools .

5. Do not Cram the whole PhD into a 6,000-Word Paper

More often, some authors set too many objectives for their research trying to cover a broader topic. Some tricky advice from the experienced researchers is to write two or more articles based on one project, focusing on a specific aspect of the research. You can learn more on how to narrow down your chosen topic and write clear introduction and thesis statement here .

6. Explain why Your Work is Important

While writing, do not forget to set your argument throughout the sections clearly. Additionally, it is important to explain the significance of your research to your area of study. If your research is just a first minor step to a new area of research, mention it so the editor would consider this. Dr. Boué-Grabot said: “A clear description of the context is important to evaluate the novelty and importance of your work.”

7. Look through the Author’s Guidelines for the targeted Academic Journal before getting your research article published

The Author’s Guidelines for each journal is different; thus you need to look through the guidelines of the targeted journal on its website. These guidelines will let you know the journal’s requirements regarding the length, style, and format of your academic article and the topics that the journal focuses on. Following the guidelines is essential. Since as editors, we come across many manuscripts that are submitted to the wrong journal. Thai is why it is suggested not to reflect the topic of the journal. Thus, they end with immediate rejection.

Remember: when You Read Published Academic Articles, You only See the Final Version

Publishing research articles in top journals is, in fact, a daunting challenge for everyone even though you might think that it is easier for some people. The case is that some authors have already beaten this challenge. Other than this, when you read a published article, you see the improved version but not the draft, nor the first revise and resubmit, and you never see how much effort and resubmissions the author did in order to get his article published in a top journal.

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Take a look at our toolkit of resources which will help during the whole process of your research to conduct it easily and painlessly!

Predicting and improving complex beer flavor through machine learning

Michiel Schreurs ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3 na1 ,
Supinya Piampongsant 1 , 2 , 3 na1 ,
Miguel Roncoroni ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3 na1 ,
Lloyd Cool ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
Beatriz Herrera-Malaver ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
Christophe Vanderaa ORCID: orcid.org/0000-0001-7443-5427 4 ,
Florian A. Theßeling 1 , 2 , 3 ,
Łukasz Kreft ORCID: orcid.org/0000-0001-7620-4657 5 ,
Alexander Botzki ORCID: orcid.org/0000-0001-6691-4233 5 ,
Philippe Malcorps 6 ,
Luk Daenen 6 ,
Tom Wenseleers ORCID: orcid.org/0000-0002-1434-861X 4 &
Kevin J. Verstrepen ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3

Nature Communications volume 15 , Article number: 2368 ( 2024 ) Cite this article

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Chemical engineering
Gas chromatography
Machine learning
Metabolomics
Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules

Rudraksh Tuwani, Somin Wadhwa & Ganesh Bagler

Predicting odor from molecular structure: a multi-label classification approach

Kushagra Saini & Venkatnarayan Ramanathan

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

Lorenzo Pallante, Aigli Korfiati, … Marco A. Deriu

Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig. S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig. 1 , upper panel, Supplementary Data 1 and 2 , and Supplementary Fig. S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig. 1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data 1 , correlations between all chemical compounds are depicted in Supplementary Fig. S2 and correlation values can be found in Supplementary Data 2 . See Supplementary Data 4 for sensory panel assessments and Supplementary Data 5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig. S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data 3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p > 0.05), indicating good panel consistency (Supplementary Table S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig. 1 , bottom left panel and Supplementary Data 4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig. S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig. S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig. 2 , Supplementary Fig. S5 , Supplementary Data 6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data 6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig. S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig. 3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig. 3 and below).

RateBeer text mining results can be found in Supplementary Data 7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data 7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig. 3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table 1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2 = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table S3 and Supplementary Table S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig. 4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig. 4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig. 4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig. S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig. 4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig. S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig. S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig. S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig. S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2 = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig. S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig. S9 , Supplementary Table S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data 1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig. 5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig. 5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data 1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig. S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table S7 and Supplementary Table S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data 3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p < 0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

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

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA). Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

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VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

Corresponding author

Correspondence to Kevin J. Verstrepen .

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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The April 8 solar eclipse will bring weird sights, sounds and feelings

The darkening of the sky on April 8 will bring with it a plethora of strange phenomena.

a solar eclipse, showing the darkened solar disk with two bright points of light shining on the edge.

Bailey's beads appear around the moon

Diamond ring
Atmospheric manifestation
Revealing hidden stars and planets

A horned 'devil' may appear!

Shadow bands
Weird colors
Bending light
Temperature shift
Animal behavior

a map of the united states showing the path of the total solar eclipse on april 8, 2024

You can watch the total solar eclipse live on Space.com . And keep up with all the actions with our total solar eclipse 2024 live updates blog.

On Monday (April 8), the sky over a swathe of North America will briefly darken as the moon covers the sun completely during a total solar eclipse.

For a maximum of 4 minutes and 28 seconds along a 115-mile-wide (185 kilometers) and 10,000-mile-long (16,000 km) path passing through Mexico, the United States and Canada, the moon will completely cover the sun, and the sky will fall dark. And, outside this path of totality , observers will be treated to a partial solar eclipse, which is still a sight to behold.

It's not surprising that our ancient ancestors viewed total solar eclipses with trepidation and fear. They were often thought to be dire portents from the gods, indicating the deities' displeasure — or observations of the sun being consumed by fearsome creatures like dragons, which needed to be scared away with loud noises.

In our current (mostly) scientifically enlightened times, we have abandoned such supernatural explanations. But rejecting the concept of star-eating dragons doesn't mean that the total solar eclipse isn't capable of throwing up some unusual and phenomena that may leave witnesses scratching their heads.

Related: Total solar eclipse 2024: Everything you need to know

Space.com takes you through a brief list of some of the strange phenomena that may distract you as you attempt to take in the most exciting astronomical event of 2024!

Astronomical occurrences

view of a solar eclipse against a dark sky.

As the moon makes its final approach to the sun in the sky, light will begin streaming through the valleys, mountains, craters , and rough terrain at the edge of the lunar disk. This creates droplets of light at the very edge of the moon's disk called " Bailey's beads ."

Get the Space.com Newsletter

Breaking space news, the latest updates on rocket launches, skywatching events and more!

Bailey's beads are named after English astronomer Francis Bailey, who described the phenomenon in 1836. Ironically, Bailey may not actually be the discoverer of Bailey's beads. Edmond Halley made the first recorded observations of Bailey's beads during a solar eclipse on May 3, 1715, also correctly describing their cause.

On April 8, watch out for Bailey's Beads before the moon completely covers the sun , and again as the lunar disk begins to move away from the solar disk.

The sun gives the moon a diamond ring

view of a solar eclipse, showing a bright ring of white light around the edge of the blocked-out sun.

The appearance of Bailey's beads at the edge of the moon may be accompanied by another striking phenomenon.

A "diamond ring" happens during a total solar eclipse just moments before the point at which the moon completely covers the sun, known as totality. This is caused by the last sunlight streaming through the rugged terrain at the edge of the lunar disk.

During the 2024 total solar eclipse, skywatchers can attempt to observe a rare "double diamond ring" when two prominent points of sunlight appear at the edge of the moon.

Related: The moon: Everything you need to know about Earth's companion

The sun's atmosphere manifests

photo of a total solar eclipse, showing the sun's wispy outer atmosphere extending large distances away from the blacked-out solar disk

Normally, when observing the sun, the atmosphere of our star is "washed out" by light flowing from what can roughly be described as the solar surface, the photosphere.

During a total solar eclipse, however, photons from the photosphere are blocked, and the two elements of the solar atmosphere, the outer layer or corona and the inner layer or chromosphere become more visible.

The corona will appear as waving white streamers surrounding the blacked-out solar disk. The chromosphere will appear as a more subtle, thin halo of color, mostly pink and red, closer to the lunar disk. The eclipse may also reveal solar filaments made of plasma and molded by solar magnetic fields extending out from behind the moon.

Hidden stars and planets are revealed

the eclipsed sun and labelled planets and stars surrounding it as white dots on a black background.

Some skywatchers may be focused on other solar system bodies or stars farther afield than the sun during the April 8 total solar eclipse.

According to Space.com skywatching columnist Joe Rao , some of the planets that may make an appearance during totality, which would usually be hidden in daylight, will be Venus , around 15 degrees to the west of the sun, and Jupiter , 30 degrees east of the star. Other planets that may be visible are Saturn and Mars , which will be 35 degrees and 36 degrees to the west of the sun, respectively. (Your fist held at arm's length covers about 10 degrees of sky.)

For eclipse watchers on the path of totality outside Mexico and Texas, the brightest star over Earth, Sirius, will be over the horizon and should be visible toward the east-southeast. Among the other fainter stars that should become apparent are the Goat Star Capella , high in the east-northeast, and Rigel , lower in the southeast.

A comet with a swirl of light around its head

Don't panic! We haven't slipped back into medieval fears and superstition; the "horned devil" we are speaking about in this case is Comet 12P/Pons-Brooks . The comet, which can currently be seen in the night skies of the Northern Hemisphere, has a strange "horned aura" resulting from the outflow of its icy material that has resulted in it being nicknamed the " devil comet ."

This faux-satanic space rock visits the inner solar system around every 71 years and will be at its closest to the sun on April 21, 2024. That means Comet 12P/Pons-Brooks will be fairly close to the sun when the moon will obscure it on April 8, according to EarthSky.org .

If it is visible, the comet should appear between the sun and Jupiter, but closer to the gas giant. Whether it can be seen or not on the day of the eclipse will depend on the ferocity of the explosions of material that burst from it, boosting the size of its halo and the brightness of its glow.

Related: A 'horned' comet may be visible during the 2024 total solar eclipse

Strange light effects

Shadow bands sweeep across earth.

wavy lines of shadow on a sheet on the ground, with people and small telescopes in the background

Shadow bands are one of the strangest effects associated with solar eclipses. These alternating light and dark bands can sometimes be seen during these events on the ground and buildings in the path of the eclipse.

First occurring around the time of totality, shadow bands initially seem disordered and chaotic but become increasingly well-ordered and more prominent as totality approaches. These bands are caused by atmospheric turbulence over Earth, which causes refraction of and collimation of sunlight when the solar disk is 99% covered.

Shadow bands are tough to image because they are only briefly visible and don't occur everywhere in the path of totality. The 1-inch to 2-inch-wide (2.5 to 5 centimeters) bands slide across the ground and buildings at a pace of around 10 feet (3 m) per second.

Colors look different

During the solar eclipse, many skywatchers will see colors shifting, giving familiar objects unusual hues. This natural shift in color perception is caused by fluctuating light levels resulting from the darkening of the sun.

Colors the human eye sees as rich and vibrant in bright light, like red and orange, become muted compared with blues and greens, which brighten in dim light. The rapid dimming of the sun during a total solar eclipse heightens this phenomenon, called the " Purkinje shift ," making the eclipse a potentially surreal event in which the most familiar objects look almost alien.

Light gets bent!

Some scientists will see the total solar eclipse as an opportunity to test one of Albert Einstein 's most revolutionary ideas, general relativity .

This 1915 theory of gravity suggests that massive objects warp the very fabric of space-time . When light passes this warping from a background source, it too is curved, an effect called gravitational lensing . This results in those background sources apparently shifting position in the sky.

When the sun darkens on April 8, scientists have the chance to observe the shift in the position of background stars caused by the mass of our star and its warping effect on space-time.

Eclipses have a long history of being used to verify general relativity. The first experimental verification of the theory, which was not initially widely accepted by the scientific community, came about when two teams of scientists led by Frank Watson Dyson and Arthur Stanley Eddington observed gravitational lensing during the May 1919 total solar eclipse from the African island of Príncipe and the Brazilian town of Sobral.

The natural world shifts

Eclipses are cool.

The sun isn't just a source of light over Earth , it is also our planet's major source of heat. That means that, as the moon covers the sun on April 8, eclipse watchers may suddenly find themselves needing a jacket.

The amount by which the temperature fluctuates during the total solar eclipse will depend strongly on the location from which it is viewed. Temperatures could drop by as little as 5 degrees Fahrenheit (2.8 degrees Celsius) or as much 10 degrees F (5.6 degrees C).

As the moon covers the sun, onlookers may also experience a sudden drop in wind speed. As the moon moves away, winds will pick up again, but they could be blowing in an entirely different direction.

— A 'horned' comet may be visible during the 2024 total solar eclipse

— How photos of the April 8 solar eclipse will help us understand of the sun's atmosphere

— How fast will April's total solar eclipse travel?

Animals act differently

An illustration shows a grasshopper on a branch with the total eclipse in the background

Animal behavior is controlled by many factors, one of which is an internal biological clock that keeps track of a " circadian rhythm ." This rhythm is optimized by light, with diurnal animals prompted to sleep when the sun sets and nocturnal animals prompted to rise at the same time. That means the dimming light of the eclipse and the "false dusk" could prompt the release of hormones related to the sleeping and waking cycle in animals.

Additionally, the drop in temperature could cause a drop in atmospheric pressure that feels like an approaching storm to animals, making them anxious or fearful. Eclipse watchers may also expect to hear a dawn's chorus from birds as the eclipse ends, fooling our feathered friends into believing it is a new day.

During the total solar eclipse, one project that will be attempting to better understand the effect of the eclipse on wildlife is the NASA Soundscapes project. This multisensory approach will see citizen scientists making recordings of the sounds heard as the sky falls dark, and also make written accounts of what is seen, heard, or felt during the event.

Related: Solar eclipse glasses: Where to buy the best, high-quality eyewear

People get emotional

Saluki stadium at Southern Illinois University in Carbondale, Illinois, hosted a massive crowd of eclipse watchers.

It isn't just animals that will potentially get spooked by the darkening skies of the total solar eclipse eclipse. Humans can also be emotionally affected by these events.

NASA advises that, if skywatchers aren't too busy looking for Bailey's Beads, diamond rings, or shadow bands, they should take a look at the people around them and observe if anyone has a deep emotional response when the sun goes into totality.

Of course, these shifts in emotions are temporary. If you intend to view any eclipse stages, the most important thing to consider is how to safely view it. Looking at the sun without adequate protection at any time is harmful to the eyes, so eclipse watchers should take precautions on April 8.

Sunglasses, regardless of how dark they are, can't protect the eyes from the effects of the sun, so specialized eclipse glasses made from safe solar filter materials will be needed. If skywatchers intend to watch the event with a telescope, special filters will be needed to make this a safe viewing experience.

Our how to observe the sun safely guide tells you everything you need to know about safe solar observations.

Submit your photos! If you capture a photo of the April 8 total solar eclipse or any of these strange effects and would like to share it with Space.com's readers, send photos, videos, comments, and your name, location and content usage permission release to [email protected].

Join our Space Forums to keep talking space on the latest missions, night sky and more! And if you have a news tip, correction or comment, let us know at: [email protected].

Robert Lea is a science journalist in the U.K. whose articles have been published in Physics World, New Scientist, Astronomy Magazine, All About Space, Newsweek and ZME Science. He also writes about science communication for Elsevier and the European Journal of Physics. Rob holds a bachelor of science degree in physics and astronomy from the U.K.’s Open University. Follow him on Twitter @sciencef1rst.

No, you didn't see a solar flare during the total eclipse — but you may have seen something just as special

My formal 2024 solar eclipse apology

Watch an exclusive clip from the CNN' 'Space Shuttle Columbia: The Final Flight' finale (video)

Duke Health researchers publish study on hormone modification to enable lactation in transgender women

Duke researchers published a study on using hormone-modification technology to enable transgender women to lactate.

The report , titled “Novel Lactation Induction Protocol for a Transgender Woman Wishing to Breastfeed: A Case Report,” was published on March 27 in the National Library of Medicine. It is the fifth report ever published on lactation for a transgender woman, but is the only one that used metoclopramide for lactation induction.

Research was conducted by second-year medical student Esme Trahair, clinical pharmacist Sarah Kokosa, researcher Andy Weinhold, researcher Heather Parnell, Associate Professor of Medicine Carly Kelley and Andrea Dotson, assistant professor in family medicine and community health.

The subject of the study, a 50-year-old anonymous transgender woman, began to receive gender-affirming hormone therapy in 2018 and sought to breastfeed her grandchild in 2022.

According to the report, the woman’s “primary motivation for inducing lactation was to experience the bond from breastfeeding that she had not been able to experience with her own five children.”

Within four weeks of beginning a modified hormone regimen, which included 10 mg of metoclopramide three times daily, the patient was able to lactate.

The woman reported producing breast milk over a period of two weeks, during which she breastfed her four-month-old granddaughter multiple times.

Since breastfeeding the infant, the patient reported an emotional bond with her grandchild and female gender affirmation, as noted by her reports that breastfeeding “felt very different from formula feeding her other children; she stated she has a special bond with this baby for which she is grateful.”

The patient did not experience any side effects from the therapy. She said she wished she had known about this option earlier on, and hopes other transgender women can similarly realize their breastfeeding goals.

The report concluded by emphasizing the need for further research on lactation protocols for transgender individuals and a greater understanding of the priorities of transgender patients who wish to lactate.

The Chronicle requested an interview with an author of the study, who forwarded the request to the Duke Health News Office. The office did not respond for comment.

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Regions & Countries

9 facts about americans and marijuana.

People smell a cannabis plant on April 20, 2023, at Washington Square Park in New York City. (Leonardo Munoz/VIEWpress)

The use and possession of marijuana is illegal under U.S. federal law, but about three-quarters of states have legalized the drug for medical or recreational purposes. The changing legal landscape has coincided with a decades-long rise in public support for legalization, which a majority of Americans now favor.

Here are nine facts about Americans’ views of and experiences with marijuana, based on Pew Research Center surveys and other sources.

As more states legalize marijuana, Pew Research Center looked at Americans’ opinions on legalization and how these views have changed over time.

Data comes from surveys by the Center, Gallup , and the 2022 National Survey on Drug Use and Health from the U.S. Substance Abuse and Mental Health Services Administration. Information about the jurisdictions where marijuana is legal at the state level comes from the National Organization for the Reform of Marijuana Laws .

More information about the Center surveys cited in the analysis, including the questions asked and their methodologies, can be found at the links in the text.

Around nine-in-ten Americans say marijuana should be legal for medical or recreational use, according to a January 2024 Pew Research Center survey . An overwhelming majority of U.S. adults (88%) say either that marijuana should be legal for medical use only (32%) or that it should be legal for medical and recreational use (57%). Just 11% say the drug should not be legal in any form. These views have held relatively steady over the past five years.

A pie chart showing that only about 1 in 10 U.S. adults say marijuana should not be legal at all.

Views on marijuana legalization differ widely by age, political party, and race and ethnicity, the January survey shows.

A horizontal stacked bar chart showing that views about legalizing marijuana differ by race and ethnicity, age and partisanship.

While small shares across demographic groups say marijuana should not be legal at all, those least likely to favor it for both medical and recreational use include:

Older adults: 31% of adults ages 75 and older support marijuana legalization for medical and recreational purposes, compared with half of those ages 65 to 74, the next youngest age category. By contrast, 71% of adults under 30 support legalization for both uses.
Republicans and GOP-leaning independents: 42% of Republicans favor legalizing marijuana for both uses, compared with 72% of Democrats and Democratic leaners. Ideological differences exist as well: Within both parties, those who are more conservative are less likely to support legalization.
Hispanic and Asian Americans: 45% in each group support legalizing the drug for medical and recreational use. Larger shares of Black (65%) and White (59%) adults hold this view.

Support for marijuana legalization has increased dramatically over the last two decades. In addition to asking specifically about medical and recreational use of the drug, both the Center and Gallup have asked Americans about legalizing marijuana use in a general way. Gallup asked this question most recently, in 2023. That year, 70% of adults expressed support for legalization, more than double the share who said they favored it in 2000.

A line chart showing that U.S. public opinion on legalizing marijuana, 1969-2023.

Half of U.S. adults (50.3%) say they have ever used marijuana, according to the 2022 National Survey on Drug Use and Health . That is a smaller share than the 84.1% who say they have ever consumed alcohol and the 64.8% who have ever used tobacco products or vaped nicotine.

While many Americans say they have used marijuana in their lifetime, far fewer are current users, according to the same survey. In 2022, 23.0% of adults said they had used the drug in the past year, while 15.9% said they had used it in the past month.

While many Americans say legalizing recreational marijuana has economic and criminal justice benefits, views on these and other impacts vary, the Center’s January survey shows.

Economic benefits: About half of adults (52%) say that legalizing recreational marijuana is good for local economies, while 17% say it is bad. Another 29% say it has no impact.

A horizontal stacked bar chart showing how Americans view the effects of legalizing recreational marijuana.

Criminal justice system fairness: 42% of Americans say legalizing marijuana for recreational use makes the criminal justice system fairer, compared with 18% who say it makes the system less fair. About four-in-ten (38%) say it has no impact.
Use of other drugs: 27% say this policy decreases the use of other drugs like heroin, fentanyl and cocaine, and 29% say it increases it. But the largest share (42%) say it has no effect on other drug use.
Community safety: 21% say recreational legalization makes communities safer and 34% say it makes them less safe. Another 44% say it doesn’t impact safety.

Democrats and adults under 50 are more likely than Republicans and those in older age groups to say legalizing marijuana has positive impacts in each of these areas.

Most Americans support easing penalties for people with marijuana convictions, an October 2021 Center survey found . Two-thirds of adults say they favor releasing people from prison who are being held for marijuana-related offenses only, including 41% who strongly favor this. And 61% support removing or expunging marijuana-related offenses from people’s criminal records.

Younger adults, Democrats and Black Americans are especially likely to support these changes. For instance, 74% of Black adults favor releasing people from prison who are being held only for marijuana-related offenses, and just as many favor removing or expunging marijuana-related offenses from criminal records.

Twenty-four states and the District of Columbia have legalized small amounts of marijuana for both medical and recreational use as of March 2024, according to the National Organization for the Reform of Marijuana Laws (NORML), an advocacy group that tracks state-level legislation on the issue. Another 14 states have legalized the drug for medical use only.

A map of the U.S. showing that nearly half of states have legalized the recreational use of marijuana.

Of the remaining 12 states, all allow limited access to products such as CBD oil that contain little to no THC – the main psychoactive substance in cannabis. And 26 states overall have at least partially decriminalized recreational marijuana use , as has the District of Columbia.

In addition to 24 states and D.C., the U.S. Virgin Islands , Guam and the Northern Mariana Islands have legalized marijuana for medical and recreational use.

More than half of Americans (54%) live in a state where both recreational and medical marijuana are legal, and 74% live in a state where it’s legal either for both purposes or medical use only, according to a February Center analysis of data from the Census Bureau and other outside sources. This analysis looked at state-level legislation in all 50 states and the District of Columbia.

In 2012, Colorado and Washington became the first states to pass legislation legalizing recreational marijuana.

About eight-in-ten Americans (79%) live in a county with at least one cannabis dispensary, according to the February analysis. There are nearly 15,000 marijuana dispensaries nationwide, and 76% are in states (including D.C.) where recreational use is legal. Another 23% are in medical marijuana-only states, and 1% are in states that have made legal allowances for low-percentage THC or CBD-only products.

The states with the largest number of dispensaries include California, Oklahoma, Florida, Colorado and Michigan.

A map of the U.S. showing that cannabis dispensaries are common along the coasts and in a few specific states.

Note: This is an update of a post originally published April 26, 2021, and updated April 13, 2023.

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Americans overwhelmingly say marijuana should be legal for medical or recreational use

Religious americans are less likely to endorse legal marijuana for recreational use, four-in-ten u.s. drug arrests in 2018 were for marijuana offenses – mostly possession, two-thirds of americans support marijuana legalization, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

Watch CBS News

Space station crew captures image of moon's shadow during solar eclipse

By Stephen Smith

Updated on: April 9, 2024 / 3:33 PM EDT / CBS News

While millions of people looked skyward at Monday's total solar eclipse on Monday, a handful of earthlings took in a much different view of the rare phenomenon — from the International Space Station.

As the station orbited above southeastern Canada, flight engineers Matthews Dominick and Jeanette Epps managed to photograph and videotape the moon's shadow on Earth about 260 miles below them, NASA said on Monday.

The Expedition 71 crew had the chance to view the moon's shadow on Earth, or umbra, after spending the day completing cargo transfers, spacesuit maintenance and microgravity research, NASA said. The windows on the outpost's cupola — known as its "window to the world" — were open, allowing the astronauts to capture the cool images.

The International Space Station experienced about 90% totality during its flyover, and NASA posted a video of the event on social media:

The Exp 71 crew soared into the Moon’s shadow during the solar eclipse on Monday afternoon while working on cargo transfers, spacesuits, and science. More... https://t.co/8LXGHC95XO pic.twitter.com/kEWnOuu4zP — International Space Station (@Space_Station) April 8, 2024

More than 31 million people live in the path of totality , the area where the moon fully blocked out the sun , according to NASA. The path ranged between 108 and 122 miles wide. An additional 150 million people live within 200 miles of the path of totality.

Before soaring into the moon's shadow during the eclipse, the space station crew performed a variety of other tasks on Monday -- including orbital plumbing, fixing a pair of science freezers and ventilation maintenance.

The stunning image of the moon's shadow came just days after NASA released images that its Lunar Reconnaissance Orbiter captured of Korea Aerospace Research Institute's Danuri lunar orbiter during a flyby in March.

The two spacecraft, traveling in nearly parallel orbits, zipped past each other in opposite directions, and the LRO operations team "needed exquisite timing in pointing LROC to the right place at the right time to catch a glimpse of Danuri."

NASA's Lunar Reconnaissance Orbiter, which has been circling the moon for 15 years, captured several images -- which resemble a cosmic surfboard zooming through space -- during three orbits while it was close enough to Danuri to grab snapshots.

International Space Station

Stephen Smith is a senior editor for CBSNews.com.

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How to publish your research
The first step in publishing a research paper should always be selecting the journal you want to publish in. Choosing your target journal before you start writing means you can tailor your work to build on research that's already been published in that journal. This can help editors to see how a paper adds to the 'conversation' in their ...
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Check each target journal's mission statement, ask colleagues who have published there if your work is appropriate for it and read a current issue to see the kinds of articles it contains. "If your work isn't in line with what they publish, they will reject it out of hand and you will have wasted valuable time," says Dunn.
Publish with Elsevier: Step by step
Now that your article is published, you can promote it to achieve a bigger impact for your research. Sharing research, accomplishments and ambitions with a wider audience makes you more visible in your field. This helps you get cited more, enabling you to cultivate a stronger reputation, promote your research and move forward in your career.
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Free 1 hour monthly How to Get Published webinars cover topics including writing an article, navigating the peer review process, and what exactly it means when you hear "open access.". Join fellow researchers and expert speakers live, or watch our library of recordings on a variety of topics. Browse our webinars.
7 steps to publishing in a scientific journal
Authors enter the article title, a brief abstract and the field of research to get a list of the most appropriate journals for their article. For a full discussion of how to select an appropriate journal see Knight and Steinbach (2008). Less experienced scholars sometimes choose to submit their research work to two or more journals at the same ...
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Find resources about publishing in Sage Journals. Get started with the How to Get Published page. Browse Sage journals to find ones that fit your methodology, discipline, and/or scholarly focus. Use the Journal Recommender to find relevant journals. Find the Journal Author Gateway with a step-by-step guide to the process.
Understanding the Publishing Process
The publication process explained. The path to publication can be unsettling when you're unsure what's happening with your paper. Learn about staple journal workflows to see the detailed steps required for ensuring a rigorous and ethical publication. Your team has prepared the paper, written a cover letter and completed the submission form.
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The next step is production. Copy editing begins, and we'll contact you with your proofs. You'll also sign a publishing agreement. If you submitted to an Open Select journal, now is the time to choose whether to publish your article open access. Find out what happens when your article is in production.
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Research elements enable you to publish research output, such as data, software, methods, videos and much more, in brief, citable articles . If you're unsure which type of article to write, discuss your options with your supervisor or colleagues . For the purposes of this booklet, we offer guidance for writing and publishing a full article .
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3. Submit your article according to the journal's submission guidelines. Go to the "author's guide" (or similar) on the journal's website to review its submission requirements. Once you are satisfied that your paper meets all of the guidelines, submit the paper through the appropriate channels.
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The introduction and discussion sections of a research paper often take the most time and require a separate set of skills to "translate" findings to a broader context. At the beginning of developing a paper, Lasky-Su and her team put together an overarching outline and then spent months synthesizing the results.
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Here are a few steps that you can take to significantly improve your chances of getting published: 1. Browse legit journals. As of 2015, the academic publishing market had an annual revenue of $20.5 Billion. This revenue has grown tremendously over the last two years.
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Mention them in the text at appropriate places. Prepare the clinical photographs and diagrams on separate pages in desired format (e.g. JPEG, TIFF, or PNG of desired file size and resolution). Provide as a separate file for the legends for figures, charts, and the clinical photographs.
Getting Your Research Article Published: 7 Top Tips From an Academic
1. Target an Appropriate Journal. Before getting your research article published, it is very important to choose one of the well-known journals in your field. When considering the journals for submitting manuscripts, you might tend to select one of the top journals in your field. You often see them in the reference lists of the research ...
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Make an impact and build your research profile in the open with ScienceOpen. Search and discover relevant research in over 93 million Open Access articles and article records; Share your expertise and get credit by publicly reviewing any article; Publish your poster or preprint and track usage and impact with article- and author-level metrics; Create a topical Collection to advance your ...
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How to publish an article? – Step by step

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Ten Tips for Getting Published in Academic Journals

1. Choose a self-contained section of your research (watch the discussion at 1:07:57 in the video)

2. Use Journal websites (discussion at: 14:23)

3. Consider your goals and publishing beyond academia (discussion at: 1:27:00)

4. Position your work as making an intervention in current debates (discussion at: 08:17)

5. Your abstract is ‘your calling card’ (discussion at: 30.09)

6. Rejection is not reflection (discussion at: 23:54)

7. Review before the Reviewers (discussion at: 1:14:15)

8. Don’t panic about Open Access fees (discussion at: 41:40)

9. Check the Journal’s Open Access compliance (discussion at: 43:42)

10. Just email and ask!

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Navigating “Chinglish” Errors in Academic English Writing

Descriptive Research Design and Its Myriad Uses

Is The Use of AI in Manuscript Editing Feasible? Here’s Three Tips to Steer Clear of Potential Issues

How to Write Clear and Crisp Civil Engineering Papers? Here are 5 Key Tips to Consider

The Clear Path to An Impactful Paper: ②

The 4-Step Guide That Will Get Your Research Published

1. Browse legit journals

2. Choose the best-fit journal

3. Understand the submission process

Some submission advice

4. Write a convincing journal cover letter

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