how to read mathematical research papers

University of Illinois Chicago

University library, search uic library collections.

Find items in UIC Library collections, including books, articles, databases and more.

Advanced Search

Search UIC Library Website

Find items on the UIC Library website, including research guides, help articles, events and website pages.

• Search Collections
• Search Website
• UIC Library
• Subject and Course Guides
• Mathematics
• How to read a scientific paper

Mathematics: How to read a scientific paper

• Research Databases
• Books and More
• Miscellaneous Mathematics Resources
• Math Audio-Visual Resources
• Writing Help

Structure of a Technical Paper

How to Read a Scientific Paper

• How to Read a Paper A short work on how to read academic papers, organized as an academic paper. Some of the advice on doing a literature survey works better in the author's field (CS) but most the material works for everyone.
• How to Read a Research Paper Part of an assignment on how to read academic papers for a CS class, it describes some strategies and lays out some expectations in terms of time and effort that should be useful.
• How to Read Scientific Papers Without Reading Every Word A blog post that gives a similar but differently worded take on the same issue.
• How to read Mathematics An article discussing how to go about reading a math article or chapter.

  Health Promotion Practice. (2020). How to Read a Scholarly Article [Infographic].

 http://healthpromotionpracticenotes.com/2020/07/new-  tool-how-to-read-a-scholarly-article-infographic/  

Assistant Professor and Reference & Liaison Librarian (STEM)

• << Previous: Math Audio-Visual Resources
• Next: Writing Help >>
• Last Updated: Feb 18, 2024 1:49 PM
• URL: https://researchguides.uic.edu/math

Reference management. Clean and simple.

How to read a scientific paper: a step-by-step guide

Scientific paper format

How to read a scientific paper in 3 steps, step 1: identify your motivations for reading a scientific paper, step 2: use selective reading to gain a high-level understanding of the scientific paper, step 3: read straight through to achieve a deep understanding of a scientific paper, frequently asked questions about reading a scientific paper efficiently, related articles.

A scientific paper is a complex document. Scientific papers are divided into multiple sections and frequently contain jargon and long sentences that make reading difficult. The process of reading a scientific paper to obtain information can often feel overwhelming for an early career researcher.

But the good news is that you can acquire the skill of efficiently reading a scientific paper, and you can learn how to painlessly obtain the information you need.

In this guide, we show you how to read a scientific paper step-by-step. You will learn:

• The scientific paper format
• How to identify your reasons for reading a scientific paper
• How to skim a paper
• How to achieve a deep understanding of a paper.

Using these steps for reading a scientific paper will help you:

• Obtain information efficiently
• Retain knowledge more effectively
• Allocate sufficient time to your reading task.

The steps below are the result of research into how scientists read scientific papers and our own experiences as scientists.

Firstly, how is a scientific paper structured?

The main sections are Abstract, Introduction, Methods, Results, and Discussion. In the table below, we describe the purpose of each component of a scientific paper.

Because the structured format of a scientific paper makes it easy to find the information you need, a common technique for reading a scientific paper is to cherry-pick sections and jump around the paper.

In a YouTube video, Dr. Amina Yonis shows this nonlinear practice for reading a scientific paper. She justifies her technique by stating that “By reading research papers like this, you are enabling yourself to have a disciplined approach, and it prevents yourself from drowning in the details before you even get a bird’s-eye view”.

Selective reading is a skill that can help you read faster and engage with the material presented. In his article on active vs. passive reading of scientific papers, cell biologist Tung-Tien Sun defines active reading as "reading with questions in mind" , searching for the answers, and focusing on the parts of the paper that answer your questions.

Therefore, reading a scientific paper from start to finish isn't always necessary to understand it. How you read the paper depends on what you need to learn. For example, oceanographer Ken Hughes suggests that you may read a scientific paper to gain awareness of a theory or field, or you may read to actively solve a problem in your research.

To successfully read a scientific paper, we advise using three strategies:

• Identify your motivations for reading a scientific paper
• Use selective reading to gain a high-level understanding of the scientific paper
• Read straight through to achieve a deep understanding of a scientific paper .

All 3 steps require you to think critically and have questions in mind.

Before you sit down to read a scientific paper, ask yourself these three questions:

• Why do I need to read this paper?
• What information am I looking for?
• Where in the paper am I most likely to find the information I need?

Is it background reading or a literature review for a research project you are currently working on? Are you getting into a new field of research? Do you wish to compare your results with the ones presented in the paper? Are you following an author’s work, and need to keep up-to-date on their current research? Are you keeping tabs on emerging methods in your field?

All of these intentions require a different reading approach.

For example, if you're delving into a new field of research, you'll want to read the introduction to gather background information and seminal references. The discussion section will also be important to understand the broader context of the findings.

If you aim to extend the work presented in a paper, and this study will be the starting point for your work, it's crucial to read the paper deeply.

If your focus is on the study design and techniques used by the authors, you'll spend most of your time reading and understanding the methods section.

Sometimes you'll need to read a paper to discuss it in your own research. This may be to compare or contrast your work with the paper's content, or to stimulate a discussion on future applications of your work.

If you are following an author’s work, a quick skim might suffice to understand how the paper fits into their overall research program.

Tip: Knowing why you want to read the paper facilitates how you will read the paper. Depending on your needs, your approach may take the form of a surface-level reading or a deep and thorough reading.

Knowing your motivations will guide your navigation through the paper because you have already identified which sections are most likely to contain the information you need. Approaching reading a paper in this way saves you time and makes the task less daunting.

➡️ Learn more about how to write a literature review

Begin by gaining an overview of the paper by following these simple steps:

• Read the title. What type of paper is it? Is it a journal article, a review, a methods paper, or a commentary?
• Read the abstract . The abstract is a summary of the study. What is the study about? What question was addressed? What methods were used? What did the authors find, and what are the key findings? What do the authors think are the implications of the work? Reading the abstract immediately tells you whether you should invest the time to read the paper fully.
• Look at the headings and subheadings, which describe the sections and subsections of the paper. The headings and subheadings outline the story of the paper.
• Skim the introduction. An introduction has a clear structure. The first paragraph is background information on the topic. If you are new to the field, you will read this closely, whereas an expert in that field will skim this section. The second component defines the gap in knowledge that the paper aims to address. What is unknown, and what research is needed? What problem needs to be solved? Here, you should find the questions that will be addressed by the study, and the goal of the research. The final paragraph summarizes how the authors address their research question, for example, what hypothesis will be tested, and what predictions the authors make. As you read, make a note of key references. By the end of the introduction, you should understand the goal of the research.
• Go to the results section, and study the figures and tables. These are the data—the meat of the study. Try to comprehend the data before reading the captions. After studying the data, read the captions. Do not expect to understand everything immediately. Remember, this is the result of many years of work. Make a note of what you do not understand. In your second reading, you will read more deeply.
• Skim the discussion. There are three components. The first part of the discussion summarizes what the authors have found, and what they think the implications of the work are. The second part discusses some (usually not all!) limitations of the study, and the final part is a concluding statement.
• Glance at the methods. Get a brief overview of the techniques used in the study. Depending on your reading goals, you may spend a lot of time on this section in subsequent readings, or a cursory reading may be sufficient.
• Summarize what the paper is about—its key take-home message—in a sentence or two. Ask yourself if you have got the information you need.
• List any terminology you may need to look up before reading the paper again.
• Scan the reference list. Make a note of papers you may need to read for background information before delving further into the paper.

Congratulations, you have completed the first reading! You now have gained a high-level perspective of the study, which will be enough for many research purposes.

Now that you have an overview of the work and you have identified what information you want to obtain, you are ready to understand the paper on a deeper level. Deep understanding is achieved in the second and subsequent readings with note-taking and active reflection. Here is a step-by-step guide.

• Active engagement with the material
• Critical thinking
• Creative thinking
• Synthesis of information
• Consolidation of information into memory.

Highlighting sentences helps you quickly scan the paper and be reminded of the key points, which is helpful when you return to the paper later.

Notes may include ideas, connections to other work, questions, comments, and references to follow up on.

There are many ways for taking notes on a paper. You can:

• Print out the paper, and write your notes in the margins.
• Annotate the paper PDF from your desktop computer, or mobile device .
• Use personal knowledge management software, like Notion , Obsidian, or Evernote, for note-taking. Notes are easy to find in a structured database and can be linked to each other.
• Use reference management tools to take notes. Having your notes stored with the scientific papers you’ve read has the benefit of keeping all your ideas in one place. Some reference managers, like Paperpile, allow you to add notes to your papers, and highlight key sentences on PDFs .

Note-taking facilitates critical thinking and helps you evaluate the evidence that the authors present. Ask yourself questions like:

• What new contribution has the study made to the literature?
• How have the authors interpreted the results? (Remember, the authors have thought about their results more deeply than anybody else.)
• What do I think the results mean?
• Are the findings well-supported?
• What factors might have affected the results, and have the authors addressed them?
• Are there alternative explanations for the results?
• What are the strengths and weaknesses of the study?
• What are the broader implications of the study?
• What should be done next?

Note-taking also encourages creative thinking . Ask yourself questions like:

• What new ideas have arisen from reading the paper?
• How does it connect with your work?
• What connections to other papers can you make?
• Write a summary of the paper in your own words. This is your attempt to integrate the new knowledge you have gained with what you already know from other sources and to consolidate that information into memory. You may find that you have to go back and re-read some sections to confirm some of the details.
• Discuss the paper with others. You may find that even at this stage, there are still aspects of the paper that you are striving to understand. It is now a good time to reach out to others—peers in your program, your advisor, or even on social media. In their 10 simple rules for reading a scientific paper , Maureen Carey and coauthors suggest that participating in journal clubs, where you meet with peers to discuss interesting or important scientific papers, is a great way to clarify your understanding.
• A scientific paper can be read over many days. According to research presented in the book " Make it Stick: The Science of Successful Learning " by writer Peter Brown and psychology professors Henry Roediger and Mark McDaniel, "spaced practice" is more effective for retaining information than focusing on a single skill or subject until it is mastered. This involves breaking up learning into separate periods of training or studying. Applying this research to reading a scientific paper suggests that spacing out your reading by breaking the work into separate reading sessions can help you better commit the information in a paper to memory.

A dense journal article may need many readings to be understood fully. It is useful to remember that many scientific papers result from years of hard work, and the expectation of achieving a thorough understanding in one sitting must be modified accordingly. But, the process of reading a scientific paper will get easier and faster with experience.

The best way to read a scientific paper depends on your needs. Before reading the paper, identify your motivations for reading a scientific paper, and pinpoint the information you need. This will help you decide between skimming the paper and reading the paper more thoroughly.

Don’t read the paper from beginning to end. Instead, be aware of the scientific paper format. Take note of the information you need before starting to read the paper. Then skim the paper, jumping to the appropriate sections in the paper, to get the information you require.

It varies. Skimming a scientific paper may take anywhere between 15 minutes to one hour. Reading a scientific paper to obtain a deep understanding may take anywhere between 1 and 6 hours. It is not uncommon to have to read a dense paper in chunks over numerous days.

First, read the introduction to understand the main thesis and findings of the paper. Pay attention to the last paragraph of the introduction, where you can find a high-level summary of the methods and results. Next, skim the paper by jumping to the results and discussion. Then carefully read the paper from start to finish, taking notes as you read. You will need more than one reading to fully understand a dense research paper.

To read a scientific paper critically, be an active reader. Take notes, highlight important sentences, and write down questions as you read. Study the data. Take care to evaluate the evidence presented in the paper.

• University Libraries Databases
• WMU Research Guides

Mathematics

• Understanding math concepts
• Finding sources

Tackling difficult reading

• Writing (and citing)
• Math education resources
• Resources for grad students and faculty

Are you feeling overwhelmed by the thought of reading the articles you've found? Are you struggling to understand what you read? Having a strategy will help:

Learn "How to read and understand a paper"

Papers in mathematics tend to follow a general structure and nobody expects you to read them all from start to finish. This short article covers the organization of a paper, how to decide whether to read a paper, and how to understand it.

Try predatory reading

You don't need to read every word! This strategy will help you read to find the information you need and save time.

Taking notes is a crucial part of the research process.

Take notes in your own words. If quoting, always use quotation marks and write down the page number. This practice will help you avoid accidental plagiarism .

Background reading

The sources in the Understanding math concepts section are easier to understand and will help ease you into challenging topics.

Also look up any words you don't understand!

Ask your professor for help understanding concepts; ask a librarian for help with your reading and note-taking strategy, or for help finding more appropriate sources to read.

• make an appointment to meet virtually or in-person
• contact the library with email or live chat
• << Previous: Finding sources
• Next: Writing (and citing) >>

• Last Updated: Mar 25, 2024 2:14 PM
• URL: https://libguides.wmich.edu/math

Reading mathematics

Resources for students, resources for college/university teachers, resources for middle school and high school teachers.

The following resources are about reading mathematics to understand it.

• Terry Tao’s blog What’s New has a section On Writing with a subsection on reading writing. Additional blog posts suggest ways to further deepen understanding: Learn and relearn your field and Ask yourself dumb questions–and answer them!
• Stewart, I., “How to Learn Math,” Letters to a Young Mathematician , Basic Books, 2006, pp. 62-70. ( Book Review at MAA website ) This letter is from a wonderful collection of letters from a mathematician to “Meg,” as she progresses from a high school student wondering whether higher levels of math are anything more than “bigger numbers and harder calculations,” to a tenured professor. The letters are unerringly encouraging while explaining myriad aspects of what it’s like to be a mathematician. The letter, “How to Learn Math,” is to Meg when she is a college student and explains what to do to get past sticking points when reading. The author also advises Meg to “read around the subject” to gain a sense of the larger picture within which any subject fits.
• Gerver, R., “Reading and Keeping a Research Journal,” Writing Math Research Papers: A Guide for Students and Instructors Key Curriculum Press, 2004. This book chapter provides guidance for reading the literature relevant to a research project. Subsections include “Preparing to Read,” “Reading and Taking Notes,” and “Conquering Difficult Concepts.”
• “ How to Read Mathematics ” by Shai Simonson and Fernando Gouvea Provides advice such as “don’t miss the big picture” and “make the idea your own” and illustrates how this advice can be applied to an example of mathematical writing. From Rediscovering Mathematics by Shai Simonson.
• Section 0.1 “ Reading Mathematics ” of Vector Calculus, Linear Algebra, and Differential Forms: A Unified Approach by John H. Hubbard, Prentice Hall 2002 provides helpful, readable advice such as that it’s more important to understand a theorem statement than its proof, and it’s OK to skip ahead to examples.
• Mark Tomforde, “ Tips for reading your mathematics textbook ” A two-page handout for undergraduates.
• “How to Read a Research Paper” by Matt Baker. This AMS Early Career Notice provides students with tips for reading and understanding mathematical papers.
• See also the resources for teachers below, some of which include resources for students.
• Ashley Reiter, “ Helping Undergraduates Learn to Read Mathematics ” MAA 1998 This webpage about reading theorems and definitions provides guidance to students with commentary for teachers.
• Students in M.I.T.’s undergraduate S eminar in Information Theory  to analyze a well-written key paper in the field to identify strategies the author used to help readers understand, convince readers, and interest readers. Here is the pre-workshop reading assignment, with questions to focus the reading. By Susan Ruff and Peter Shor.
• In M.I.T.’s undergraduate  Seminar in Theoretical Computer Science , which is taken primarily by juniors and seniors, students write a term paper on a topic of their choice. To do so, they must find and read sources, including mathematics research articles. Attached are a suggested reading strategy (student resource) and an in-class activity designed to introduce students to the reading strategy and to familiarize them with some of the common features of mathematics papers that facilitate the finding of information within the paper. By Susan Ruff and Zachary Remscrim.
• Andersen, J., “ Teaching Students to Read Technical Material: The Use of Reading Outlines ” Math Forum – Orlando Presentation In this online essay, Janet Andersen describes how she structures her classes to encourage students to read the textbook. Given as a presentation at the 1996 Joint Mathematics Meetings in Orlando, Florida.

If you know of any good resources for elementary, middle, or high school, please let us know .

• Shepherd, Mary D., and Carla C. van de Sande. “Reading mathematics for understanding—from novice to expert.”  The Journal of Mathematical Behavior  35 (2014): 74-86.
• Lara Alcock, “ How do people read mathematics ” In this blog post, Lara Alcock briefly summarizes and provides links to the research she has done with her collaborators into how experts and novices read mathematics, and the effectiveness of a pedagogical intervention designed to help novices become more expert at reading proofs.
• Shepherd, Mary D., Annie Selden, and John Selden. “Difficulties First-Year University Mathematics Students Have in Reading Their Mathematics Textbook. Technical Report. No. 2009-1.”  Online Submission  (2009). The researchers find that first year students have a hard time successfully completing straightforward tasks based on a reading, despite being good at mathematics and good readers in general (per ACT scores and use of metacognitive reading strategies).

Encourage students to read critically: not all mathematics is written well. Students can learn much about how to write mathematics by noticing what does and doesn’t work well for them as readers.

If you know of other good resources related to reading mathematics, please let us know .

What is Math Comm

Latest updates.

• Teamwork workshop
• Giving a lecture or workshop on writing
• Written genres
• Reading Assignment-Info Thy Writing Workshop
• Number Theory–Scott Carnahan
• Types of proof & proof-writing strategies

Recent Blog Posts

• Best Writing on Mathematics 2015
• 2014 MAA Writing Award Winners: American Mathematical Monthly
• 2014 MAA Writing Award Winners: Mathematics Magazine
• 2014 MAA Writing Award Winners: College Mathematics Journal
• 2014 MAA Writing Award Winner: Math Horizons
• Math by the Minute on Capitol Hill
• MAA Writing Awards
• Course Communities
• MAA Reviews
• Classroom Capsules & Notes

Accessibility

all the numbers are changing, but what doesn't change is the relationship between x and y: y is always one more than twice x. That is, y=2x+1. Finding what doesn't change "tames" the situation. So, you have tamed this problem! Yay. And if you want a fancy mathematical name for things that don’t vary, we call these things "invariants." The number of messed-up recruits is invariant, even though they are all wiggling back and forth, trying to figure out which way is right!

3) Encourage generalizations

So, of course, the next question that comes to my mind is how to generalize what you’ve already discovered: there are 15 ways that 2 mistakes can be arranged in a line of 6 recruits. What about a different number of mistakes? Or a different number of recruits? Is there some way to predict? Or, alternatively, is there some way to predict how these 15 ways of making mistakes will play out as the recruits try to settle themselves down? Which direction interests you?

4) Inquire about reasoning and rigor

The students were looking at the number of ways the recruits could line up with 2 out of n faced the wrong way: Anyway, I had a question of my own. It looks like the number of possibilities increases pretty fast, as the number of recruits increases. For example, I counted 15 possibilities in your last set (the line of six). What I wonder is this: when the numbers get that large, how you can possibly know that you've found all the possibilities? (For example, I noticed that >>>><< is missing.) The question "How do I know I've counted 'em all?" is actually quite a big deal in mathematics, as mathematicians are often called upon to find ways of counting things that nobody has ever listed (exactly like the example you are working on).

The students responded by finding a pattern for generating the lineups in a meaningful order: The way that we can prove that we have all the possibilities is that we can just add the number of places that the second wrong person could be in. For example, if 2 are wrong in a line of 6, then the first one doesn’t move and you count the space in which the second one can move in. So for the line of six, it would be 5+4+3+2+1=15. That is the way to make sure that we have all the ways. Thanks so much for giving challenges. We enjoyed thinking!

5) Work towards proof

a) The group wrote the following: When we found out that 6 recruits had 15 different starting arrangements, we needed more information. We needed to figure out how many starting positions are there for a different number of recruits.

By drawing out the arrangements for 5 recruits and 7 recruits we found out that the number of starting arrangements for the recruit number before plus that recruit number before it would equal the number of starting arrangements for that number of recruits.

We also found out that if you divide the starting arrangements by the number of recruits there is a pattern.

To which the mentor replied: Wow! I don't think (in all the years I've been hanging around mathematics) I've ever seen anyone describe this particular pattern before! Really nice! If you already knew me, you'd be able to predict what I'm about to ask, but you don't, so I have to ask it: "But why?" That is, why is this pattern (the 6, 10, 15, 21, 28…) the pattern that you find for this circumstance (two recruits wrong in lines of lengths, 4, 5, 6, 7, 8…)? Answering that—explaining why you should get those numbers and why the pattern must continue for longer lines—is doing the kind of thing that mathematics is really about.

b) Responding to students studying a circular variation of raw recruits that never settled down: This is a really interesting conclusion! How can you show that it will always continue forever and that it doesn’t matter what the original arrangement was? Have you got a reason or did you try all the cases or…? I look forward to hearing more from you.

6) Distinguish between examples and reasons

a) You have very thoroughly dealt with finding the answer to the problem you posed—it really does seem, as you put it, "safe to say" how many there will be. Is there a way that you can show that that pattern must continue? I guess I’d look for some reason why adding the new recruit adds exactly the number of additional cases that you predict. If you could say how the addition of one new recruit depends on how long the line already is, you’d have a complete proof. Want to give that a try?

b) A student, working on Amida Kuji and having provided an example, wrote the following as part of a proof: In like manner, to be given each relationship of objects in an arrangement, you can generate the arrangement itself, for no two different arrangements can have the same object relationships. The mentor response points out the gap and offers ways to structure the process of extrapolating from the specific to the general: This statement is the same as your conjecture, but this is not a proof. You repeat your claim and suggest that the example serves as a model for a proof. If that is so, it is up to you to make the connections explicit. How might you prove that a set of ordered pairs, one per pair of objects forces a unique arrangement for the entire list? Try thinking about a given object (e.g., C) and what each of its ordered pairs tells us? Try to generalize from your example. What must be true for the set of ordered pairs? Are all sets of n C2 ordered pairs legal? How many sets of n C2 ordered pairs are there? Do they all lead to a particular arrangement? Your answers to these questions should help you work toward a proof of your conjecture.

9) Encourage extensions

What you’ve done—finding the pattern, but far more important, finding the explanation (and stating it so clearly)—is really great! (Perhaps I should say "finding and stating explanations like this is real mathematics"!) Yet it almost sounded as if you put it down at the very end, when you concluded "making our project mostly an interesting coincidence." This is a truly nice piece of work!

The question, now, is "What next?" You really have completely solved the problem you set out to solve: found the answer, and proved that you’re right!

I began looking back at the examples you gave, and noticed patterns in them that I had never seen before. At first, I started coloring parts red, because they just "stuck out" as noticeable and I wanted to see them better. Then, it occurred to me that I was coloring the recruits that were back-to-back, and that maybe I should be paying attention to the ones who were facing each other, as they were "where the action was," so I started coloring them pink. (In one case, I recopied your example to do the pinks.) To be honest, I’m not sure what I’m looking for, but there was such a clear pattern of the "action spot" moving around that I thought it might tell me something new. Anything come to your minds?

10) Build a Mathematical Community

I just went back to another paper and then came back to yours to look again. There's another pattern in the table. Add the recruits and the corresponding starting arrangements (for example, add 6 and 15) and you get the next number of starting arrangements. I don't know whether this, or your 1.5, 2, 2.5, 3, 3.5… pattern will help you find out why 6, 10, 15… make sense as answers, but they might. Maybe you can work with [your classmates] who made the other observation to try to develop a complete understanding of the problem.

11) Highlight Connections

Your rule—the (n-1)+(n-2)+(n-3)+… +3+2+1 part—is interesting all by itself, as it counts the number of dots in a triangle of dots. See how?

12) Wrap Up

This is really a very nice and complete piece of work: you've stated a problem, found a solution, and given a proof (complete explanation of why that solution must be correct). To wrap it up and give it the polish of a good piece of mathematical research, I'd suggest two things.

The first thing is to extend the idea to account for all but two mistakes and the (slightly trivial) one mistake and all but one mistake. (If you felt like looking at 3 and all but 3, that'd be nice, too, but it's more work—though not a ton—and the ones that I suggested are really not more work.)

The second thing I'd suggest is to write it all up in a way that would be understandable by someone who did not know the problem or your class: clear statement of the problem, the solution, what you did to get the solution, and the proof.

I look forward to seeing your masterpiece!

Advice for Keeping a Formal Mathematics Research Logbook

As part of your mathematics research experience, you will keep a mathematics research logbook. In this logbook, keep a record of everything you do and everything you read that relates to this work. Write down questions that you have as you are reading or working on the project. Experiment. Make conjectures. Try to prove your conjectures. Your journal will become a record of your entire mathematics research experience. Don’t worry if your writing is not always perfect. Often journal pages look rough, with notes to yourself, false starts, and partial solutions. However, be sure that you can read your own notes later and try to organize your writing in ways that will facilitate your thinking. Your logbook will serve as a record of where you are in your work at any moment and will be an invaluable tool when you write reports about your research.

Ideally, your mathematics research logbook should have pre-numbered pages. You can often find numbered graph paper science logs at office supply stores. If you can not find a notebook that has the pages already numbered, then the first thing you should do is go through the entire book putting numbers on each page using pen.

• Date each entry.

• Work in pen.

• Don’t erase or white out mistakes. Instead, draw a single line through what you would like ignored. There are many reasons for using this approach:

– Your notebook will look a lot nicer if it doesn’t have scribbled messes in it.

– You can still see what you wrote at a later date if you decide that it wasn’t a mistake after all.

– It is sometimes useful to be able to go back and see where you ran into difficulties.

– You’ll be able to go back and see if you already tried something so you won’t spend time trying that same approach again if it didn’t work.

• When you do research using existing sources, be sure to list the bibliographic information at the start of each section of notes you take. It is a lot easier to write down the citation while it is in front of you than it is to try to find it at a later date.

• Never tear a page out of your notebook. The idea is to keep a record of everything you have done. One reason for pre-numbering the pages is to show that nothing has been removed.

• If you find an interesting article or picture that you would like to include in your notebook, you can staple or tape it onto a page.

Advice for Keeping a Loose-Leaf Mathematics Research Logbook

Get yourself a good loose-leaf binder, some lined paper for notes, some graph paper for graphs and some blank paper for pictures and diagrams. Be sure to keep everything that is related to your project in your binder.

– Your notebook will look a lot nicer if it does not have scribbled messes in it.

• Be sure to keep everything related to your project. The idea is to keep a record of everything you have done.

• If you find an interesting article or picture that you would like to include in your notebook, punch holes in it and insert it in an appropriate section in your binder.

Making Mathematics Home | Mathematics Projects | Students | Teachers | Mentors | Parents | Hard Math Café |

• Math Careers

Search form

• MAA Centennial
• Spotlight: Archives of American Mathematics
• MAA Officers
• MAA to the Power of New
• Council and Committees
• MAA Code of Conduct
• Policy on Conflict of Interest
• Statement about Conflict of Interest
• Recording or Broadcasting of MAA Events
• Policy for Establishing Endowments and Funds
• Avoiding Implicit Bias
• Copyright Agreement
• Principal Investigator's Manual
• Planned Giving
• The Icosahedron Society
• Our Partners
• Advertise with MAA
• Employment Opportunities
• Staff Directory
• 2022 Impact Report
• In Memoriam
• Membership Categories
• Become a Member
• Membership Renewal
• MERCER Insurance
• MAA Member Directories
• New Member Benefits
• The American Mathematical Monthly
• Mathematics Magazine
• The College Mathematics Journal
• How to Cite
• Communications in Visual Mathematics
• About Convergence
• What's in Convergence?
• Convergence Articles
• Mathematical Treasures
• Portrait Gallery
• Paul R. Halmos Photograph Collection
• Other Images
• Critics Corner
• Problems from Another Time
• Conference Calendar
• Guidelines for Convergence Authors
• Math Horizons
• Submissions to MAA Periodicals
• Guide for Referees
• Scatterplot
• Math Values
• MAA Book Series
• MAA Press (an imprint of the AMS)
• MAA Library Recommendations
• Additional Sources for Math Book Reviews
• About MAA Reviews
• Mathematical Communication
• Information for Libraries
• Author Resources
• MAA MathFest
• Proposal and Abstract Deadlines
• MAA Policies
• Invited Paper Session Proposals
• Contributed Paper Session Proposals
• Panel, Poster, Town Hall, and Workshop Proposals
• Minicourse Proposals
• MAA Section Meetings
• Virtual Programming
• Joint Mathematics Meetings
• Calendar of Events
• MathFest Programs Archive
• MathFest Abstract Archive
• Historical Speakers
• Information for School Administrators
• Information for Students and Parents
• Registration
• Getting Started with the AMC
• AMC Policies
• AMC Administration Policies
• Important AMC Dates
• Competition Locations
• Invitational Competitions
• Putnam Competition Archive
• AMC International
• Curriculum Inspirations
• Sliffe Award
• MAA K-12 Benefits
• Mailing List Requests
• Statistics & Awards
• Submit an NSF Proposal with MAA
• MAA Distinguished Lecture Series
• Common Vision
• CUPM Curriculum Guide
• Instructional Practices Guide
• Möbius MAA Placement Test Suite
• META Math Webinar May 2020
• Progress through Calculus
• Survey and Reports
• "Camp" of Mathematical Queeries
• DMEG Awardees
• National Research Experience for Undergraduates Program (NREUP)
• Neff Outreach Fund Awardees
• Tensor SUMMA Grants
• Tensor Women & Mathematics Grants
• Grantee Highlight Stories
• "Best Practices" Statements
• CoMInDS Summer Workshop 2023
• MAA Travel Grants for Project ACCCESS
• 2024 Summer Workshops
• Minority Serving Institutions Leadership Summit
• Previous Workshops
• Frequently Asked Questions
• Course Resources
• Industrial Math Case Studies
• Participating Faculty
• 2020 PIC Math Student Showcase
• Previous PIC Math Workshops on Data Science
• Dates and Locations
• Past Programs
• Leadership Team
• Support Project NExT
• Section NExT
• Section Officers Meeting History
• Preparations for Section Meetings
• Bylaws Template
• Editor Lectures Program
• MAA Section Lecturer Series
• Officer Election Support
• Section Awards
• Section Liaison Programs
• Section Visitors Program
• Expense Reimbursement
• Guidelines for Bylaw Revisions
• Guidelines for Local Arrangement Chair and/or Committee
• Guidelines for Section Webmasters
• MAA Logo Guidelines
• MAA Section Email Policy
• Section Newsletter Guidelines
• Statement on Federal Tax ID and 501(c)3 Status
• Communication Support
• Guidelines for the Section Secretary and Treasurer
• Legal & Liability Support for Section Officers
• Section Marketing Services
• Section in a Box
• Subventions and Section Finances
• Web Services
• Joining a SIGMAA
• Forming a SIGMAA
• History of SIGMAA
• SIGMAA Officer Handbook
• MAA Connect
• Meetings and Conferences for Students
• Opportunities to Present
• Information and Resources
• MAA Undergraduate Student Poster Session
• Undergraduate Research Resources
• MathFest Student Paper Sessions
• Research Experiences for Undergraduates
• Student Poster Session FAQs
• High School
• A Graduate School Primer
• Reading List
• Student Chapters
• Awards Booklets
• Carl B. Allendoerfer Awards
• Regulations Governing the Association's Award of The Chauvenet Prize
• Trevor Evans Awards
• Paul R. Halmos - Lester R. Ford Awards
• Merten M. Hasse Prize
• George Pólya Awards
• David P. Robbins Prize
• Beckenbach Book Prize
• Euler Book Prize
• Daniel Solow Author’s Award
• Henry L. Alder Award
• Deborah and Franklin Tepper Haimo Award
• Certificate of Merit
• Gung and Hu Distinguished Service
• JPBM Communications Award
• Meritorious Service
• MAA Award for Inclusivity
• T. Christine Stevens Award
• Dolciani Award Guidelines
• Morgan Prize Information
• Selden Award Eligibility and Guidelines for Nomination
• Selden Award Nomination Form
• AMS-MAA-SIAM Gerald and Judith Porter Public Lecture
• Etta Zuber Falconer
• Hedrick Lectures
• James R. C. Leitzel Lecture
• Pólya Lecturer Information
• Putnam Competition Individual and Team Winners
• D. E. Shaw Group AMC 8 Awards & Certificates
• Maryam Mirzakhani AMC 10 A Awards & Certificates
• Two Sigma AMC 10 B Awards & Certificates
• Jane Street AMC 12 A Awards & Certificates
• Akamai AMC 12 B Awards & Certificates
• High School Teachers
• MAA Social Media

You are here

Writing math research papers: a guide for students and instructors.

• From Amazon
• From the MAA Bookstore

temp hardcoded node--book.tpl.php

Robert Gerver

• Table of Contents

Writing Math Research Papers  is primarily a guide for high school students that describes how to write aand present mathematics research papers. But it’s really much more than that: it’s a systematic presentation of a philosophy that writing about math helps many students to understand it, and a practical method to move students from the relatively passive role of someone doing what is assigned to them, to creative thinkers and published writers who contribute to the mathematical literature.

As experienced writers know, the actual writing is not the half of it. William Zinsser once taught a writing class at the New School for Social Research which involved no writing at all: students talked through their ideas in class and through that process discovered the real story which could be written from their tangle of experiences, hopes and dreams. The actual writing was secondary, once they understood how to find the story and organize it.

Gerver, an experienced high school mathematics teacher, takes a similar approach. The primary audience is high school students who want to prepare formal papers or presentations, for contests or for a “math day” at their high school. But the discovery, research and organizational processes involved in writing an original paper, as opposed to rehashing information from a reference book, can help any student learn and understand math, and the experience will be useful even if the paper is never written.

Gerver leads students through a discovery process beginning with examining their own knowledge of mathematics and reviewing the basics of problem solving. The “math annotation” project follows next, in which students organize their class notes for one topic for presentation to their peers, resulting in a product similar to a section of a textbook or handbook, complete with illustrations and the necessary background and review material. Practical advice about finding a topic, developing it by keeping a research journal, and creating a final product, either a research paper or oral presentation, follows.

Writing Math Research Papers  is directed primarily to students, and could be assigned as a supplementary textbook for high school mathematics classes. It will also be useful to teachers who incorporate writing into their classes or who serve as mentors to the math club, and for student teachers in similar situations. An appendix for teachers includes practical advice about helping students through the research and writing process, organizing consultations, and grading the student papers and presentations. Excerpts from student research papers are included as well, and more materials are available from the web site www.keypress.com/wmrp .

Robert Gerver, PhD, is a mathematics instructor at North Shore High School in New York. He received the Presidential Award for Excellence in Mathematical Teaching in 1988 and the Tandy Prize and Chevron Best Practices Award in Education in 1997. He has been publishing mathematics. Dr. Gerver has written eleven mathematics textbooks and numerous articles, and holds two U.S. patents for educational devices.

Sarah Boslaugh, ( [email protected] ) is a Performance Review Analyst for BJC HealthCare and an Adjunct Instructor in the Washington University School of Medicine, both in St. Louis, MO. Her books include An Intermediate Guide to SPSS Programming: Using Syntax for Data Management  (Sage, 2004), Secondary Data Sources for Public Health: A Practical Guide (Cambridge, 2007), and Statistics in a Nutshell (O'Reilly, forthcoming), and she is Editor-in-Chief of The Encyclopedia of Epidemiology (Sage, forthcoming).

• Log in to post comments

Dummy View - NOT TO BE DELETED

MAA Publications

• Periodicals
• MAA History
• Policies and Procedures
• Support MAA
• Member Discount Programs
• Propose a Session
• MathFest Archive
• Putnam Competition
• AMC Resources
• Curriculum Resources
• Outreach Initiatives
• Professional Development
• Communities

Connect with MAA

Mathematical Association of America P: (800) 331-1622 F: (240) 396-5647 Email: [email protected]

Copyright © 2024

• Terms of Use
• Privacy Policy
• Mobile Version

How to Effectively Write a Mathematics Research Paper

Mathematics research papers are different from standard academic research papers in important ways, but not so different that they require an entirely separate set of guidelines. Mathematical papers rely heavily on logic and a specific type of language, including symbols and regimented notation. There are two basic structures of mathematical research papers: formal and informal exposition .

Structure and Style

Formal Exposition

The author must start with an outline that develops the logical structure of the paper. Each hypothesis and deduction should flow in an orderly and linear fashion using formal definitions and notation. The author should not repeat a proof or substitute words or phrases that differ from the definitions already established within the paper. The theorem-proof format, definitions, and logic fall under this style.

Informal Exposition

Informal exposition complements the formal exposition by providing the reasoning behind the theorems and proofs. Figures, proofs, equations, and mathematical sentences do not necessarily speak for themselves within a mathematics research paper . Authors will need to demonstrate why their hypotheses and deductions are valid and how they came to prove this. Analogies and examples fall under this style.

Conventions of Mathematics

Clarity is essential for writing an effective mathematics research paper. This means adhering to strong rules of logic, clear definitions, theorems and equations that are physically set apart from the surrounding text, and using math symbols and notation following the conventions of mathematical language. Each area incorporates detailed guidelines to assist the authors.

Related: Do you have questions on language, grammar, or manuscript drafting? Get personalized answers on the FREE Q&A Forum!

Logic is the framework upon which every good mathematics research paper is built. Each theorem or equation must flow logically.

Definitions

In order for the reader to understand the author’s work, definitions for terms and notations used throughout the paper must be set at the beginning of the paper. It is more effective to include this within the Introduction section of the paper rather than having a stand-alone section of definitions.

Theorems and Equations

Theorems and equations should be physically separated from the surrounding text. They will be used as reference points throughout, so they should have a well-defined beginning and end.

Math Symbols and Notations

Math symbols and notations are standardized within the mathematics literature. Deviation from these standards will cause confusion amongst readers. Therefore, the author should adhere to the guidelines for equations, units, and mathematical notation, available from various resources .

Protocols for mathematics writing get very specific – fonts, punctuation, examples, footnotes, sentences, paragraphs, and the title, all have detailed constraints and conventions applied to their usage. The American Mathematical Society is a good resource for additional guidelines.

LaTeX and Wolfram

Mathematical sentences contain equations, figures, and notations that are difficult to typeset using a typical word-processing program. Both LaTeX and Wolfram have expert typesetting capabilities to assist authors in writing.

LaTeX is highly recommended for researchers whose papers constitute mathematical figures and notation. It produces professional-looking documents and authentically represents mathematical language.

Wolfram Language & System Documentation Center’s Mathematica has sophisticated and convenient mathematical typesetting technology that produces professional-looking documents.

The main differences between the two systems are due to cost and accessibility. LaTeX is freely available, whereas Wolfram is not. In addition, any updates in Mathematica will come with an additional charge. LaTeX is an open-source system, but Mathematica is closed-source.

Good Writing and Logical Constructions

Regardless of the document preparation system selected, publication of a mathematics paper is similar to the publication of any academic research in that it requires good writing. Authors must apply a strict, logical construct when writing a mathematics research paper.

There are resources that provide very specific guidelines related to following sections to write and publish a mathematics research paper.

• Concept of a math paper
• Title, acknowledgment, and list of authors
• Introduction
• Body of the work
• Conclusion, appendix, and references
• Publication of a math paper
• Preprint archive
• Choice of the journal, submission
• Publication

The critical elements of a mathematics research paper are good writing and a logical construct that allows the reader to follow a clear path to the author’s conclusions.

Good advice. For me, writing an essay on mathematics was very difficult. I did not have enough time and knowledge to write a quality essay. I worked a lot in the library and read many articles on the Internet. I studied information about essay writing. But I couldn’t finish the essay in full. I had to look for professional writers on the subject of mathematics. He helped me finish a few paragraphs. The work was delivered on time and on an excellent assessment.

Comments are closed.

Enago Academy's Most Popular Articles

• Reporting Research

Beyond Spellcheck: How copyediting guarantees error-free submission

Submitting a manuscript is a complex and often an emotional experience for researchers. Whether it’s…

• Old Webinars
• Webinar Mobile App

How to Find the Right Journal and Fix Your Manuscript Before Submission

Selection of right journal Meets journal standards Plagiarism free manuscripts Rated from reviewer's POV

• Manuscripts & Grants

Research Aims and Objectives: The dynamic duo for successful research

Picture yourself on a road trip without a destination in mind — driving aimlessly, not…

How Academic Editors Can Enhance the Quality of Your Manuscript

Avoiding desk rejection Detecting language errors Conveying your ideas clearly Following technical requirements

Effective Data Presentation for Submission in Top-tier Journals

Importance of presenting research data effectively How to create tables and figures How to avoid…

Top 4 Guidelines for Health and Clinical Research Report

Top 10 Questions for a Complete Literature Review

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

What should universities' stance be on AI tools in research and academic writing?

Advertisement

Mathematics

Mathematician wins turing award for harnessing randomness.

Avi Wigderson has won the 2023 Turing award for his work on understanding how randomness can shape and improve computer algorithms

By Alex Wilkins

10 April 2024

Avi Wigderson, winner of the 2023 Turing award

Peter Badge

The mathematician Avi Wigderson has won the 2023 Turing award, often referred to as the Nobel prize for computing, for his work on understanding how randomness can shape and improve computer algorithms.

Wigderson, who also won the prestigious Abel prize in 2021 for his mathematical contributions to computer science, was taken aback by the award. “The [Turing] committee fooled me into believing that we were going to have some conversation about collaborating,” he says. “When I zoomed in, the whole committee was there and they told me. I was excited, surprised and happy.”

Is everything predetermined? Why physicists are reviving a taboo idea

Computers work in a predictable way at the hardware level, but this can make it difficult for them to model real-world problems, which often have elements of randomness and unpredictability. Wigderson, at the Institute for Advanced Study in Princeton, New Jersey, has shown over a decades-long career that computers can also harness randomness in the algorithms that they run.

In the 1980s, Wigderson and his colleagues discovered that by inserting randomness into some algorithms, they could make them easier and faster to solve, but it was unclear how general this technique was. “We were wondering whether this randomness is essential, or maybe you can always get rid of it somehow if you’re clever enough,” he says.

One of Wigderson’s most important discoveries was making clear the relationship between types of problems, in terms of their difficulty to solve, and randomness. He also showed that certain algorithms that contained randomness and were hard to run could be made deterministic, or non-random, and easier to run.

Sign up to our The Daily newsletter

The latest science news delivered to your inbox, every day.

These findings helped computer scientists better understand one of the most famous unproven conjectures in computer science, called “ P ≠ NP ”, which proposes that easy and hard problems for a computer to solve are fundamentally different. Using randomness, Wigderson discovered special cases where the two classes of problem were the same.

Wigderson first started exploring the relationship between randomness and computers in the 1980s, before the internet existed, and was attracted to the ideas he worked on by intellectual curiosity, rather than how they might be used. “I’m a very impractical person,” he says. “I’m not really motivated by applications.”

However, his ideas have become important for a wide swath of modern computing applications, from cryptography to cloud computing. “Avi’s impact on the theory of computation in the last 40 years is second to none,” says Oded Goldreich at the Weizmann Institute of Science in Israel. “The diversity of the areas to which he has contributed is stunning.”

Quantum computers are revealing an unexpected new theory of reality

A powerful new idea about how the laws of physics work could bring breakthroughs on everything from quantum gravity to consciousness, says researcher Chiara Marletto

One of the unexpected ways in which Wigderson’s ideas are now widely used was his work, with Goldreich and others, on zero-knowledge proofs, which detail ways of verifying information without revealing the information itself. These methods are fundamental for cryptocurrencies and blockchains today as a way to establish trust between different users.

Although great strides in the theory of computation have been made over Wigderson’s career, he says that the field is still full of interesting and unsolved problems. “You can’t imagine how happy I am that I am where I am, in the field that I’m in,” he says. “It’s bursting with intellectual questions.”

Wigderson will receive a $1 million prize as part of the Turing award.

Article amended on 10 April 2024

• mathematics /

Sign up to our weekly newsletter

Receive a weekly dose of discovery in your inbox! We'll also keep you up to date with New Scientist events and special offers.

More from New Scientist

Explore the latest news, articles and features

Microsoft and Quantinuum’s quantum computer may be most reliable yet

Ai chatbots are improving at an even faster rate than computer chips.

Subscriber-only

Mathematicians plan computer proof of Fermat's last theorem

Quantum computer uses a time crystal as a control dial, popular articles.

Trending New Scientist articles

• Download PDF
• Share X Facebook Email LinkedIn
• Permissions

Prediction Models and Clinical Outcomes—A Call for Papers

• 1 Department of Medicine, University of Washington, Seattle
• 2 Deputy Editor, JAMA Network Open
• 3 Epidemiology, Rutgers The State University of New Jersey, New Brunswick
• 4 Statistical Editor, JAMA Network Open
• 5 Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
• 6 Editor, JAMA Network Open

The need to classify disease and predict outcomes is as old as medicine itself. Nearly 50 years ago, the advantage of applying multivariable statistics to these problems became evident. 1 Since then, the increasing availability of databases containing often-complex clinical information from tens or even hundreds of millions of patients, combined with powerful statistical techniques and computing environments, has spawned exponential growth in efforts to create more useful, focused, and accurate prediction models. JAMA Network Open receives dozens of manuscripts weekly that present new or purportedly improved instruments intended to predict a vast array of clinical outcomes. Although we are able to accept only a small fraction of those submitted, we have, nonetheless, published nearly 2000 articles dealing with predictive models over the past 6 years.

The profusion of predictive models has been accompanied by the growing recognition of the necessity for standards to help ensure accuracy of these models. An important milestone was the publication of the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis ( TRIPOD ) guidelines nearly a decade ago. 2 TRIPOD is a reporting guideline intended to enable readers to better understand the methods used in published studies but does not prescribe what actual methods should be applied. Since then, while the field has continued to advance and technology improve, many predictive models in widespread use, when critically evaluated, have been found to neither adhere to reporting standards nor perform as well as expected. 3 , 4

There are numerous reasons why performance of models falls short, even when efforts are made to adhere to methodologic standards. Despite the vast amounts of data that are often brought to bear, they may not be appropriate to the task, or they may have been collected and analyzed in ways that are biased. Additionally, that some models fall short may simply reflect the inherent difficulty of predicting relatively uncommon events that occur as a result of complex biological processes occurring within complex clinical environments. Moreover, clinical settings are highly variable, and predictive models typically perform worse outside of the environments in which they were developed. A comprehensive discussion of these issues is beyond the scope of this article, but as physicist Neils Bohr once remarked, “it is very difficult to predict—especially the future.” 5

Although problems with accuracy are well documented, hundreds of predictive models are in regular use in clinical practice and are frequently the basis for critically important decisions. Many such models have been widely adopted without subsequent efforts to confirm that they actually continue to perform as expected. That is not to say that such models are without utility, because even a suboptimal model may perform better than an unaided clinician. Nevertheless, we believe that a fresh examination of selected, well-established predictive models is warranted if not previously done. JAMA Network Open has published articles addressing prediction of relatively common clinical complications, such as recurrent gastrointestinal bleeding. 6 We think there remains considerable opportunity for research in this vein. In particular, we seek studies that examine current performance of commonly applied clinical prediction rules. We are particularly interested in studies using data from a variety of settings and databases as well as studies that simultaneously assess multiple models addressing the same or similar outcomes.

We also remain interested in the derivation of new models that address a clear clinical need. They should utilize data that are commonly collected as part of routine care, or in principle can be readily extracted from electronic health records. We generally require that prediction models be validated with at least 1 other dataset distinct from the development dataset. In practice, this means data from different health systems or different publicly available or commercial datasets. We note that internal validation techniques, such as split samples, hold-out, k -fold, and others, are not designed to overcome the intrinsic differences between data sources and, therefore, are not suited to quantifying performance externally. While the population to which the models apply should be described explicitly, ideally any such models should be applicable to patients from the wide range of races, ethnicities, and backgrounds commonly encountered in clinic practice. Most importantly, we are interested in examples of models that have been evaluated in clinical settings, assessing their feasibility and potential clinical benefit. This includes studies with negative as well as positive outcomes.

Please see the journal’s Instructions for Authors for information on manuscript preparation and submission. 7 This is not a time-limited call for studies on this topic.

Published: April 12, 2024. doi:10.1001/jamanetworkopen.2024.9640

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Fihn SD et al. JAMA Network Open .

Corresponding Author: Stephan D. Fihn, MD, MPH, Department of Medicine, University of Washington, 325 Ninth Ave, Box 359780, Seattle, WA 98104 ( [email protected] ).

Conflict of Interest Disclosures: Dr Berlin reported receiving consulting fees from Kenvue related to acetaminophen outside the submitted work. No other disclosures were reported.

See More About

Fihn SD , Berlin JA , Haneuse SJPA , Rivara FP. Prediction Models and Clinical Outcomes—A Call for Papers. JAMA Netw Open. 2024;7(4):e249640. doi:10.1001/jamanetworkopen.2024.9640

Manage citations:

© 2024

Select Your Interests

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Get the latest research based on your areas of interest.

Others also liked.

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

Help | Advanced Search

Computer Science > Computation and Language

Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

• Computer Vision
• Federated Learning
• Reinforcement Learning
• Natural Language Processing
• New Releases
• AI Dev Tools
• Advisory Board Members
• 🐝 Partnership and Promotion

REASONEVAL achieves state-of-the-art performance on human-labeled datasets and can accurately detect different errors generated by perturbation. It reveals that enhanced final-answer accuracy doesn’t consistently improve the quality of reasoning steps for complex mathematical problems. The method’s assessment also aids in data selection. Observations highlight significant decreases in validity scores for logical and calculation errors, while redundancy scores remain stable. REASONEVAL distinguishes between errors affecting validity and those introducing redundancy.

In conclusion, the research introduces REASONEVAL, an effective metric for assessing reasoning step quality based on correctness and efficiency. Experimentation confirms its ability to identify diverse errors and competitive performance compared to existing methods. REASONEVAL exposes inconsistencies between final-answer accuracy and reasoning step quality while also proving effective in data selection for training.

Check out the  Paper.   All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on  Twitter . Join our  Telegram Channel ,   Discord Channel , and  LinkedIn Gr oup .

If you like our work, you will love our  newsletter..

Don’t Forget to join our  40k+ ML SubReddit

Mohammad Asjad

Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

• Mohammad Asjad https://www.marktechpost.com/author/mohammad_asjad/ Meta AI Presents MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
• Mohammad Asjad https://www.marktechpost.com/author/mohammad_asjad/ This Machine Learning Paper Introduce PISSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
• Mohammad Asjad https://www.marktechpost.com/author/mohammad_asjad/ Microsoft Researchers Propose Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
• Mohammad Asjad https://www.marktechpost.com/author/mohammad_asjad/ This Machine Learning Paper Introduces JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

RELATED ARTICLES MORE FROM AUTHOR

Llm2vec: a simple ai approach to transform any decoder-only llm into a text encoder achieving sota performance on mteb in the unsupervised and supervised..., microsoft and cmu researchers propose a machine learning method to train an aac (automated audio captioning) system using only text, cohere ai unveils rerank 3: a cutting-edge foundation model designed to optimize enterprise search and rag (retrieval augmented generation) systems, top statistics books to read in 2024, deep learning architectures from cnn, rnn, gan, and transformers to encoder-decoder architectures, samba-coe v0.3: redefining ai efficiency with advanced routing capabilities, llm2vec: a simple ai approach to transform any decoder-only llm into a text encoder..., microsoft and cmu researchers propose a machine learning method to train an aac (automated..., cohere ai unveils rerank 3: a cutting-edge foundation model designed to optimize enterprise search....

• AI Magazine
• Privacy & TC
• Cookie Policy

🐝 FREE AI Courses on RAG + Deployment of an Healthcare AI App + LangChain Colab Notebook all included

Thank You 🙌

Privacy Overview

Prestigious cancer research institute has retracted 7 studies amid controversy over errors

Seven studies from researchers at the prestigious Dana-Farber Cancer Institute have been retracted over the last two months after a scientist blogger alleged that images used in them had been manipulated or duplicated.

The retractions are the latest development in a monthslong controversy around research at the Boston-based institute, which is a teaching affiliate of Harvard Medical School. 

The issue came to light after Sholto David, a microbiologist and volunteer science sleuth based in Wales, published a scathing post on his blog in January, alleging errors and manipulations of images across dozens of papers produced primarily by Dana-Farber researchers . The institute acknowledged errors and subsequently announced that it had requested six studies to be retracted and asked for corrections in 31 more papers. Dana-Farber also said, however, that a review process for errors had been underway before David’s post. 

Now, at least one more study has been retracted than Dana-Farber initially indicated, and David said he has discovered an additional 30 studies from authors affiliated with the institute that he believes contain errors or image manipulations and therefore deserve scrutiny.

The episode has imperiled the reputation of a major cancer research institute and raised questions about one high-profile researcher there, Kenneth Anderson, who is a senior author on six of the seven retracted studies. 

Anderson is a professor of medicine at Harvard Medical School and the director of the Jerome Lipper Multiple Myeloma Center at Dana-Farber. He did not respond to multiple emails or voicemails requesting comment. 

The retractions and new allegations add to a larger, ongoing debate in science about how to protect scientific integrity and reduce the incentives that could lead to misconduct or unintentional mistakes in research. 

The Dana-Farber Cancer Institute has moved relatively swiftly to seek retractions and corrections. 

“Dana-Farber is deeply committed to a culture of accountability and integrity, and as an academic research and clinical care organization we also prioritize transparency,” Dr. Barrett Rollins, the institute’s integrity research officer, said in a statement. “However, we are bound by federal regulations that apply to all academic medical centers funded by the National Institutes of Health among other federal agencies. Therefore, we cannot share details of internal review processes and will not comment on personnel issues.”

The retracted studies were originally published in two journals: One in the Journal of Immunology and six in Cancer Research. Six of the seven focused on multiple myeloma, a form of cancer that develops in plasma cells. Retraction notices indicate that Anderson agreed to the retractions of the papers he authored.

Elisabeth Bik, a microbiologist and longtime image sleuth, reviewed several of the papers’ retraction statements and scientific images for NBC News and said the errors were serious. 

“The ones I’m looking at all have duplicated elements in the photos, where the photo itself has been manipulated,” she said, adding that these elements were “signs of misconduct.” 

Dr.  John Chute, who directs the division of hematology and cellular therapy at Cedars-Sinai Medical Center and has contributed to studies about multiple myeloma, said the papers were produced by pioneers in the field, including Anderson. 

“These are people I admire and respect,” he said. “Those were all high-impact papers, meaning they’re highly read and highly cited. By definition, they have had a broad impact on the field.” 

Chute said he did not know the authors personally but had followed their work for a long time.

“Those investigators are some of the leading people in the field of myeloma research and they have paved the way in terms of understanding our biology of the disease,” he said. “The papers they publish lead to all kinds of additional work in that direction. People follow those leads and industry pays attention to that stuff and drug development follows.”

The retractions offer additional evidence for what some science sleuths have been saying for years: The more you look for errors or image manipulation, the more you might find, even at the top levels of science. 

Scientific images in papers are typically used to present evidence of an experiment’s results. Commonly, they show cells or mice; other types of images show key findings like western blots — a laboratory method that identifies proteins — or bands of separated DNA molecules in gels. 

Science sleuths sometimes examine these images for irregular patterns that could indicate errors, duplications or manipulations. Some artificial intelligence companies are training computers to spot these kinds of problems, as well. 

Duplicated images could be a sign of sloppy lab work or data practices. Manipulated images — in which a researcher has modified an image heavily with photo editing tools — could indicate that images have been exaggerated, enhanced or altered in an unethical way that could change how other scientists interpret a study’s findings or scientific meaning. 

Top scientists at big research institutions often run sprawling laboratories with lots of junior scientists. Critics of science research and publishing systems allege that a lack of opportunities for young scientists, limited oversight and pressure to publish splashy papers that can advance careers could incentivize misconduct. 

These critics, along with many science sleuths, allege that errors or sloppiness are too common , that research organizations and authors often ignore concerns when they’re identified, and that the path from complaint to correction is sluggish. 

“When you look at the amount of retractions and poor peer review in research today, the question is, what has happened to the quality standards we used to think existed in research?” said Nick Steneck, an emeritus professor at the University of Michigan and an expert on science integrity.

David told NBC News that he had shared some, but not all, of his concerns about additional image issues with Dana-Farber. He added that he had not identified any problems in four of the seven studies that have been retracted. 

“It’s good they’ve picked up stuff that wasn’t in the list,” he said. 

NBC News requested an updated tally of retractions and corrections, but Ellen Berlin, a spokeswoman for Dana-Farber, declined to provide a new list. She said that the numbers could shift and that the institute did not have control over the form, format or timing of corrections. 

“Any tally we give you today might be different tomorrow and will likely be different a week from now or a month from now,” Berlin said. “The point of sharing numbers with the public weeks ago was to make clear to the public that Dana-Farber had taken swift and decisive action with regard to the articles for which a Dana-Farber faculty member was primary author.” 

She added that Dana-Farber was encouraging journals to correct the scientific record as promptly as possible. 

Bik said it was unusual to see a highly regarded U.S. institution have multiple papers retracted. 

“I don’t think I’ve seen many of those,” she said. “In this case, there was a lot of public attention to it and it seems like they’re responding very quickly. It’s unusual, but how it should be.”

Evan Bush is a science reporter for NBC News. He can be reached at [email protected].

Adobe Community

• Global community
• 日本語コミュニティ Dedicated community for Japanese speakers
• 한국 커뮤니티 Dedicated community for Korean speakers
• Discussions
• Re: I just want to read my research papers

I just want to read my research papers

Copy link to clipboard

never-displayed

Read our research on: Gun Policy | International Conflict | Election 2024

Regions & Countries

About half of americans say public k-12 education is going in the wrong direction.

About half of U.S. adults (51%) say the country’s public K-12 education system is generally going in the wrong direction. A far smaller share (16%) say it’s going in the right direction, and about a third (32%) are not sure, according to a Pew Research Center survey conducted in November 2023.

Pew Research Center conducted this analysis to understand how Americans view the K-12 public education system. We surveyed 5,029 U.S. adults from Nov. 9 to Nov. 16, 2023.

The survey was conducted by Ipsos for Pew Research Center on the Ipsos KnowledgePanel Omnibus. The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey is weighted by gender, age, race, ethnicity, education, income and other categories.

Here are the questions used for this analysis , along with responses, and the survey methodology .

A majority of those who say it’s headed in the wrong direction say a major reason is that schools are not spending enough time on core academic subjects.

These findings come amid debates about what is taught in schools , as well as concerns about school budget cuts and students falling behind academically.

Related: Race and LGBTQ Issues in K-12 Schools

Republicans are more likely than Democrats to say the public K-12 education system is going in the wrong direction. About two-thirds of Republicans and Republican-leaning independents (65%) say this, compared with 40% of Democrats and Democratic leaners. In turn, 23% of Democrats and 10% of Republicans say it’s headed in the right direction.

Among Republicans, conservatives are the most likely to say public education is headed in the wrong direction: 75% say this, compared with 52% of moderate or liberal Republicans. There are no significant differences among Democrats by ideology.

Similar shares of K-12 parents and adults who don’t have a child in K-12 schools say the system is going in the wrong direction.

A separate Center survey of public K-12 teachers found that 82% think the overall state of public K-12 education has gotten worse in the past five years. And many teachers are pessimistic about the future.

Related: What’s It Like To Be A Teacher in America Today?

Why do Americans think public K-12 education is going in the wrong direction?

We asked adults who say the public education system is going in the wrong direction why that might be. About half or more say the following are major reasons:

• Schools not spending enough time on core academic subjects, like reading, math, science and social studies (69%)
• Teachers bringing their personal political and social views into the classroom (54%)
• Schools not having the funding and resources they need (52%)

About a quarter (26%) say a major reason is that parents have too much influence in decisions about what schools are teaching.

How views vary by party

Americans in each party point to different reasons why public education is headed in the wrong direction.

Republicans are more likely than Democrats to say major reasons are:

• A lack of focus on core academic subjects (79% vs. 55%)
• Teachers bringing their personal views into the classroom (76% vs. 23%)

In turn, Democrats are more likely than Republicans to point to:

• Insufficient school funding and resources (78% vs. 33%)
• Parents having too much say in what schools are teaching (46% vs. 13%)

Views also vary within each party by ideology.

Among Republicans, conservatives are particularly likely to cite a lack of focus on core academic subjects and teachers bringing their personal views into the classroom.

Among Democrats, liberals are especially likely to cite schools lacking resources and parents having too much say in the curriculum.

Note: Here are the questions used for this analysis , along with responses, and the survey methodology .

Sign up for our weekly newsletter

Fresh data delivered Saturday mornings

‘Back to school’ means anytime from late July to after Labor Day, depending on where in the U.S. you live

Among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, 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 .