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The Fastest Way to Read Research Papers self.__wrap_b=(t,n,e)=>{e=e||document.querySelector(`[data-br="${t}"]`);let s=e.parentElement,r=B=>e.style.maxWidth=B+"px";e.style.maxWidth="";let o=s.clientWidth,u=s.clientHeight,a=o/2-.25,c=o+.5,p;if(o){for(r(a),a=Math.max(e.scrollWidth,a);a+1 {self.__wrap_b(0,+e.dataset.brr,e)})).observe(s):process.env.NODE_ENV==="development"&&console.warn("The browser you are using does not support the ResizeObserver API. Please consider add polyfill for this API to avoid potential layout shifts or upgrade your browser. Read more: https://github.com/shuding/react-wrap-balancer#browser-support-information"))};self.__wrap_b(":Riim:",1)

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The Semantic Reader Open Research Platform

Semantic Reader Project is a collaborative effort of NLP + HCI researchers from non-profit, industry, and academic institutions to create interactive, intelligent reading interfaces for scholarly papers. Our research led to the creation of Semantic Reader, an application used by tens of thousands of scholars each week.

The Semantic Reader Open Research Platform provides resources that enable the broader research community to explore exciting challenges around novel research support tools: PaperMage , a library for processing and analyzing scholarly PDFs, and PaperCraft , a React UI component for building augmented and interactive reading interfaces. Join us in designing the future of scholarly reading interfaces with our open source libraries!

Open Source Libraries

We provide PaperMage + PaperCraft for building intelligent and interactive paper readers. Below we showcase how to extract text from a PDF to prompt a LLM for term definitions and then visually augment the paper with highlights and popups.

Process and Analyze Scholarly PDF Documents

Create Visually Augmented Interactive Readers

Research Prototype Showcase

Here we present several interactive demos to showcase systems you can build with PaperMage and PaperCraft.

Augmenting Research Papers with Author Talk Videos

Demo Paper Presentation

Synergi & Threddy

Clipping Research Threads from Papers for Synthesis and Exploration

Paper Presentation

Paper Plain

Making Medical Research Papers Approachable to Healthcare Consumers

Demo Code Tutorial Paper

LLM Paper Q&A

A GPT-powered PDF QA system with attribution support.

Demo Code Tutorial

Augmenting Citations in Papers with Persistent and Personalized Context

In-Production Paper Presentation

Localizing Incoming Citations from Follow on Papers in the Margins

Automatic highlights for skimming support of scientific papers

In-Production Paper

Augmenting Papers with Just-in-Time Definitions of Terms and Symbols

Founding Project Demo Paper

Publications

Semantic reader project overview.

The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces Kyle Lo, Joseph Chee Chang, Andrew Head, Jonathan Bragg, Amy X. Zhang, Cassidy Trier, Chloe Anastasiades, Tal August, Russell Authur, Danielle Bragg, Erin Bransom, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Yen-Sung Chen, Evie (Yu-Yen) Cheng, Yvonne Chou, Doug Downey, Rob Evans, Raymond Fok, F.Q. Hu, Regan Huff, Dongyeop Kang, Tae Soo Kim, Rodney Michael Kinney, A. Kittur, Hyeonsu B Kang, Egor Klevak, Bailey Kuehl, Michael Langan, Matt Latzke, Jaron Lochner, Kelsey MacMillan, Eric Stuart Marsh, Tyler Murray, Aakanksha Naik, Ngoc-Uyen Nguyen, Srishti Palani, Soya Park, Caroline Paulic, Napol Rachatasumrit, Smita R Rao, P. Sayre, Zejiang Shen, Pao Siangliulue, Luca Soldaini, Huy Tran, Madeleine van Zuylen, Lucy Lu Wang, Christopher Wilhelm, Caroline M Wu, Jiangjiang Yang, Angele Zamarron, Marti A. Hearst, Daniel S. Weld . ArXiv. 2023 .

Interactive and Intelligent Reading Interfaces

Qlarify: Bridging Scholarly Abstracts and Papers with Recursively Expandable Summaries Raymond Fok, Joseph Chee Chang, Tal August, Amy X. Zhang, Daniel S. Weld . ArXiv. 2023 .

Papeos: Augmenting Research Papers with Talk Videos Tae Soo Kim, Matt Latzke, Jonathan Bragg, Amy X. Zhang, Joseph Chee Chang . The ACM Symposium on User Interface Software and Technology. 2023 .

Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking Hyeonsu B Kang, Sherry Wu, Joseph Chee Chang, A. Kittur . The ACM Symposium on User Interface Software and Technology. 2023 .

🏆 Best Paper Award CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context Joseph Chee Chang, Amy X. Zhang, Jonathan Bragg, Andrew Head, Kyle Lo, Doug Downey, Daniel S. Weld . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2023 .

Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections Srishti Palani, Aakanksha Naik, Doug Downey, Amy X. Zhang, Jonathan Bragg, Joseph Chee Chang . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2023 .

CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading Napol Rachatasumrit, Jonathan Bragg, Amy X. Zhang, Daniel S. Weld . 27th International Conference on Intelligent User Interfaces. 2022 .

🏆 Best Paper Award Math Augmentation: How Authors Enhance the Readability of Formulas using Novel Visual Design Practices Andrew Head, Amber Xie, Marti A. Hearst . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2022 .

Scim: Intelligent Skimming Support for Scientific Papers Raymond Fok, Hita Kambhamettu, Luca Soldaini, Jonathan Bragg, Kyle Lo, Andrew Head, Marti A. Hearst, Daniel S. Weld . Proceedings of the 28th International Conference on Intelligent User Interfaces. 2022 .

Exploring Team-Sourced Hyperlinks to Address Navigation Challenges for Low-Vision Readers of Scientific Papers Soya Park, Jonathan Bragg, Michael Chang, K. Larson, Danielle Bragg . Proceedings of the ACM on Human-Computer Interaction. 2022 .

Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, Kyle Lo . ACM Transactions on Computer-Human Interaction. 2022 . Presentation at CHI 2024.

Threddy: An Interactive System for Personalized Thread-based Exploration and Organization of Scientific Literature Hyeonsu B Kang, Joseph Chee Chang, Yongsung Kim, A. Kittur . Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 2022 .

🏆 Best Paper Award SciA11y: Converting Scientific Papers to Accessible HTML Lucy Lu Wang, Isabel Cachola, Jonathan Bragg, Evie (Yu-Yen) Cheng, Chelsea Hess Haupt, Matt Latzke, Bailey Kuehl, Madeleine van Zuylen, Linda M. Wagner, Daniel S. Weld . Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility. 2021 .

Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols Andrew Head, Kyle Lo, Dongyeop Kang, Raymond Fok, Sam Skjonsberg, Daniel S. Weld, Marti A. Hearst . Proceedings of the CHI Conference on Human Factors in Computing Systems. 2020 .

Open Research Resources: Libraries, Models, Datasets

🏆 Best Paper Award PaperMage: A Unified Toolkit for Processing, Representing, and Manipulating Visually-Rich Scientific Documents Kyle Lo, Zejiang Shen, Benjamin Newman, Joseph Chee Chang, Russell Authur, Erin Bransom, Stefan Candra, Yoganand Chandrasekhar, Regan Huff, Bailey Kuehl, Amanpreet Singh, Chris Wilhelm, Angele Zamarron, Marti A. Hearst, Daniel S. Weld, Doug Downey, Luca Soldaini. Conference on Empirical Methods in Natural Language Processing: Demos. 2023.

A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents Benjamin Newman, Luca Soldaini, Raymond Fok, Arman Cohan, Kyle Lo . undefined. 2023 .

🏆 Best Paper Award LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization Kalpesh Krishna, Erin Bransom, Bailey Kuehl, Mohit Iyyer, Pradeep Dasigi, Arman Cohan, Kyle Lo . ArXiv. 2023 .

Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents Catherine Chen, Zejiang Shen, D. Klein, G. Stanovsky, Doug Downey, Kyle Lo . ArXiv. 2023 .

The Semantic Scholar Open Data Platform Rodney Michael Kinney, Chloe Anastasiades, Russell Authur, Iz Beltagy, Jonathan Bragg, Alexandra Buraczynski, Isabel Cachola, Stefan Candra, Yoganand Chandrasekhar, Arman Cohan, Miles Crawford, Doug Downey, Jason Dunkelberger, Oren Etzioni, Rob Evans, Sergey Feldman, Joseph Gorney, D. Graham, F.Q. Hu, Regan Huff, Daniel King, Sebastian Kohlmeier, Bailey Kuehl, Michael Langan, Daniel Lin, Haokun Liu, Kyle Lo, Jaron Lochner, Kelsey MacMillan, Tyler Murray, Christopher Newell, Smita R Rao, Shaurya Rohatgi, P. Sayre, Zejiang Shen, Amanpreet Singh, Luca Soldaini, Shivashankar Subramanian, A. Tanaka, Alex D Wade, Linda M. Wagner, Lucy Lu Wang, Christopher Wilhelm, Caroline Wu, Jiangjiang Yang, Angele Zamarron, Madeleine van Zuylen, Daniel S. Weld . ArXiv. 2023 .

VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups Zejiang Shen, Kyle Lo, Lucy Lu Wang, Bailey Kuehl, Daniel S. Weld, Doug Downey . Transactions of the Association for Computational Linguistics. 2021 .

Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions Dongyeop Kang, Andrew Head, Risham Sidhu, Kyle Lo, Daniel S. Weld, Marti A. Hearst . Proceedings of the First Workshop on Scholarly Document Processing @ ACL. 2020 .

See the  Project Overview Paper  to see a full list of contributors. † For questions and inquiries, please contact Joseph Chee Chang (PaperCraft & Intelligent reading interfaces), or Kyle Lo and Luca Soldaini (PaperMage & Scientific document processing).

Research Advisory Board

Intelligent reading interfaces research, scientific document processing research, research libraries and tooling.

A free, AI-powered research tool for scientific literature

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Stay Connected With Semantic Scholar Sign Up What Is Semantic Scholar? Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.

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Help you stay focused to read, by preparing all things you need, wysiwyg citation what you see is what you get, check the citation at where you see it, glance over references in sidebar, able to be filterd by citation selecting, mark citations for later reading, metadatas (doi, abstract, keyword, etc.) are provided by one click, take notes, which can be reviewed, give you the power to take full control of your understandings, add tags to any note, to orgnize them neatly, paper linking, link papers to any note, to build meshed understanding, review notes by timeline, which can be turned back to their context, scholar mind map, a graphic way to check your papers & notes & ideas, paper & keyword, your personal keywords based on what you read, note & tag, visualize notes and tags, better way to mangage your minds, sync with the best cloud storage service, it's super easy to sync it through google drive, dropbox, one drive or any other cloud service., ready paperly now is free, paperly is specially designed for researchers, and a first real sense of academic reader. try it.

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How to Read Research Papers— Unveiling AI Tool for Reading

Table of Contents

Reading research papers is an essential skill for students, academics, and professionals in various fields. It allows you to stay updated with the latest findings, develop critical thinking skills, and contribute to scholarly discussions. However, understanding these papers can be challenging due to their complex language and structure. That’s why we have written this article, which will provide you with comprehensive strategies on how to read a research paper effectively.

Let’s get started with how to identify the structure of a research paper!

Identify the structure of a research paper

Understanding the structure of a research paper is the first step toward how to read research paper effectively. Most research papers follow a standard structure, which includes an abstract , introduction , methodology , results, discussion and conclusion . Familiarizing yourself with the research paper structure can help you navigate the paper and understand its content.

Each section of a research paper serves a specific purpose. The abstract provides a summary of the entire research paper, the introduction presents the research question, the methodology explains how the research was conducted, the results section presents the findings, the discussion interprets these findings, and the conclusion summarizes the paper and suggests areas for future research.

Source: University of Wisconsin

Abstract: The abstract serves as a concise summary of the entire research paper. To efficiently grasp its content, focus on key elements such as the research question, methodology, and significant findings. This will provide a quick overview and help you decide whether the paper aligns with your interests.

Introduction: The research paper introduction sets the stage for the research, presenting the problem statement and the purpose of the study. Take note of the research gap, hypotheses, and objectives discussed here to understand the context of the paper.

Methodology: Understanding the methods employed in a study is crucial for evaluating the research's validity. Take note of the research design, data collection, and analysis methods to comprehend how the study was conducted.

Results: The results section presents the outcomes of the research. Approach this section with a critical mindset, assessing whether the results align with the research question and the methods used. Consider the implications of the findings within the broader context of the field.

Conclusion: The conclusion summarizes the key findings and their significance. It's a crucial part of the paper that brings together the entire study. Take the time to reflect on how the research contributes to the existing body of knowledge.

Citations: Follow the trail of references provided in the paper. This not only enhances your understanding but also leads you to related works that can deepen your knowledge of the subject.

More tips on how to read research papers effectively

Developing effective reading strategies can help you understand research papers more efficiently. These strategies include active reading, note-taking, and using AI tools for summarizing and understanding research papers.

Active reading involves engaging with the text, asking questions, and making connections. Note-taking helps you remember important information and organize your thoughts. Summarizing using AI tools allows you to condense the information and understand the main points of the paper easily.

Active Reading:

Active reading is a strategy that involves interacting with the text. This can include highlighting important information, making notes in the margins, and asking questions. Active reading can help you understand the content of the paper and remember it more effectively.

When reading a research paper, try to identify the main points, arguments, and evidence. Ask yourself questions like:

• What is the research question?
• What methods were used to answer it?
• What were the results? What conclusions were drawn?

This will help you engage with the paper and understand its content.

Source: https://idaho.pressbooks.pub/write/chapter/reading-for-writing/

Note-Taking:

Note-taking is another effective reading strategy. It involves writing down important information, ideas, and questions. Note-taking can help you remember the content of the paper, organize your thoughts, and prepare for discussions or writing assignments.

When taking notes, try to be concise and use your own words. This will help you understand the information and remember it more effectively. You can also use symbols or diagrams to represent complex ideas.

Source: University of Toronto

Using AI Tools to Summarize Research Paper:

When research papers are flooded with complex language, jargon, and acronyms, it’s important to use AI summarizer that helps you breakdown the sentences and makes it easier to read the information. In that case, you can make use of SciSpace Copilot which not only explains the highlighted section or paragraph, but also explains you the equations, tables, figures, and images present in the research paper. You can also rely on other AI tools to comprehend research papers in a short span of time.

Watch this video to learn how to use the AI summarizer:

Dealing with Technical Jargon:

Research papers often contain a lot of technical jargon. Don't be intimidated; instead, create a glossary for yourself. Look up unfamiliar terms and gradually build your understanding of the terminology used in your field of interest. As mentioned above, you can use AI summarizer to decode the jargon and get the essence of the research paper.

Joining Academic Communities:

Engage in discussions and forums related to your area of interest. Academic communities provide valuable insights, differing perspectives, and opportunities for networking with experts in the field.

Staying Updated on Research Trends:

To read research papers effectively, it's crucial to stay informed about the latest developments in your field. Subscribe to academic journals, follow reputable researchers on social media, and attend conferences or webinars to stay updated.

Using Academic Search Engines:

Make use of online tools and databases such as Google Scholar, PubMed, SciSpace , and academic journals to access a vast repository of research papers. These platforms often provide additional features like citation tracking and related articles, enriching your reading experience.

Also Read: Beast Academic Search Engines(2024)

Reading research papers is a complex task that requires a good understanding of the structure of a research paper, effective reading strategies, and the ability to interpret results. However, with practice and patience, you can develop these skills and become proficient at reading research papers.

Remember, the goal is not just to read the paper, but to understand it, evaluate it, and use it to contribute to your own research or professional development.

Frequently Asked Questions

Active reading helps understand research papers better. It involves activities like highlighting, taking notes, asking questions, and summarizing. This makes it easier to understand and evaluate the research material.

Taking notes during research helps you remember important information, stay organized, avoid plagiarism, think critically, and serve as a reference for future use, allowing you to revisit key points and findings as needed.

SciSpace notebook is the go-to tool for taking notes effortlessly

The best AI tool for reading research papers varies based on individual needs. A popular AI tools include SciSpace Copilot.

Using AI tools to read research papers is easy. First, choose a tool, example — SciSpace Copilot. Then, upload your paper. It analyzes it and explains it in a language of your choice. You can then use this summary to help with your research or understanding of the topic.

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Ten simple rules for reading a scientific paper

Maureen a. carey.

Division of Infectious Diseases and International Health, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia, United States of America

Kevin L. Steiner

William a. petri, jr, introduction.

“There is no problem that a library card can't solve” according to author Eleanor Brown [ 1 ]. This advice is sound, probably for both life and science, but even the best tool (like the library) is most effective when accompanied by instructions and a basic understanding of how and when to use it.

For many budding scientists, the first day in a new lab setting often involves a stack of papers, an email full of links to pertinent articles, or some promise of a richer understanding so long as one reads enough of the scientific literature. However, the purpose and approach to reading a scientific article is unlike that of reading a news story, novel, or even a textbook and can initially seem unapproachable. Having good habits for reading scientific literature is key to setting oneself up for success, identifying new research questions, and filling in the gaps in one’s current understanding; developing these good habits is the first crucial step.

Advice typically centers around two main tips: read actively and read often. However, active reading, or reading with an intent to understand, is both a learned skill and a level of effort. Although there is no one best way to do this, we present 10 simple rules, relevant to novices and seasoned scientists alike, to teach our strategy for active reading based on our experience as readers and as mentors of undergraduate and graduate researchers, medical students, fellows, and early career faculty. Rules 1–5 are big picture recommendations. Rules 6–8 relate to philosophy of reading. Rules 9–10 guide the “now what?” questions one should ask after reading and how to integrate what was learned into one’s own science.

Rule 1: Pick your reading goal

What you want to get out of an article should influence your approach to reading it. Table 1 includes a handful of example intentions and how you might prioritize different parts of the same article differently based on your goals as a reader.

1 Yay! Welcome!

2 A journal club is when a group of scientists get together to discuss a paper. Usually one person leads the discussion and presents all of the data. The group discusses their own interpretations and the authors’ interpretation.

Rule 2: Understand the author’s goal

In written communication, the reader and the writer are equally important. Both influence the final outcome: in this case, your scientific understanding! After identifying your goal, think about the author’s goal for sharing this project. This will help you interpret the data and understand the author’s interpretation of the data. However, this requires some understanding of who the author(s) are (e.g., what are their scientific interests?), the scientific field in which they work (e.g., what techniques are available in this field?), and how this paper fits into the author’s research (e.g., is this work building on an author’s longstanding project or controversial idea?). This information may be hard to glean without experience and a history of reading. But don’t let this be a discouragement to starting the process; it is by the act of reading that this experience is gained!

A good step toward understanding the goal of the author(s) is to ask yourself: What kind of article is this? Journals publish different types of articles, including methods, review, commentary, resources, and research articles as well as other types that are specific to a particular journal or groups of journals. These article types have different formatting requirements and expectations for content. Knowing the article type will help guide your evaluation of the information presented. Is the article a methods paper, presenting a new technique? Is the article a review article, intended to summarize a field or problem? Is it a commentary, intended to take a stand on a controversy or give a big picture perspective on a problem? Is it a resource article, presenting a new tool or data set for others to use? Is it a research article, written to present new data and the authors’ interpretation of those data? The type of paper, and its intended purpose, will get you on your way to understanding the author’s goal.

Rule 3: Ask six questions

When reading, ask yourself: (1) What do the author(s) want to know (motivation)? (2) What did they do (approach/methods)? (3) Why was it done that way (context within the field)? (4) What do the results show (figures and data tables)? (5) How did the author(s) interpret the results (interpretation/discussion)? (6) What should be done next? (Regarding this last question, the author(s) may provide some suggestions in the discussion, but the key is to ask yourself what you think should come next.)

Each of these questions can and should be asked about the complete work as well as each table, figure, or experiment within the paper. Early on, it can take a long time to read one article front to back, and this can be intimidating. Break down your understanding of each section of the work with these questions to make the effort more manageable.

Rule 4: Unpack each figure and table

Scientists write original research papers primarily to present new data that may change or reinforce the collective knowledge of a field. Therefore, the most important parts of this type of scientific paper are the data. Some people like to scrutinize the figures and tables (including legends) before reading any of the “main text”: because all of the important information should be obtained through the data. Others prefer to read through the results section while sequentially examining the figures and tables as they are addressed in the text. There is no correct or incorrect approach: Try both to see what works best for you. The key is making sure that one understands the presented data and how it was obtained.

For each figure, work to understand each x- and y-axes, color scheme, statistical approach (if one was used), and why the particular plotting approach was used. For each table, identify what experimental groups and variables are presented. Identify what is shown and how the data were collected. This is typically summarized in the legend or caption but often requires digging deeper into the methods: Do not be afraid to refer back to the methods section frequently to ensure a full understanding of how the presented data were obtained. Again, ask the questions in Rule 3 for each figure or panel and conclude with articulating the “take home” message.

Rule 5: Understand the formatting intentions

Just like the overall intent of the article (discussed in Rule 2), the intent of each section within a research article can guide your interpretation. Some sections are intended to be written as objective descriptions of the data (i.e., the Results section), whereas other sections are intended to present the author’s interpretation of the data. Remember though that even “objective” sections are written by and, therefore, influenced by the authors interpretations. Check out Table 2 to understand the intent of each section of a research article. When reading a specific paper, you can also refer to the journal’s website to understand the formatting intentions. The “For Authors” section of a website will have some nitty gritty information that is less relevant for the reader (like word counts) but will also summarize what the journal editors expect in each section. This will help to familiarize you with the goal of each article section.

Research articles typically contain each of these sections, although sometimes the “results” and “discussion” sections (or “discussion” and “conclusion” sections) are merged into one section. Additional sections may be included, based on request of the journal or the author(s). Keep in mind: If it was included, someone thought it was important for you to read.

Rule 6: Be critical

Published papers are not truths etched in stone. Published papers in high impact journals are not truths etched in stone. Published papers by bigwigs in the field are not truths etched in stone. Published papers that seem to agree with your own hypothesis or data are not etched in stone. Published papers that seem to refute your hypothesis or data are not etched in stone.

Science is a never-ending work in progress, and it is essential that the reader pushes back against the author’s interpretation to test the strength of their conclusions. Everyone has their own perspective and may interpret the same data in different ways. Mistakes are sometimes published, but more often these apparent errors are due to other factors such as limitations of a methodology and other limits to generalizability (selection bias, unaddressed, or unappreciated confounders). When reading a paper, it is important to consider if these factors are pertinent.

Critical thinking is a tough skill to learn but ultimately boils down to evaluating data while minimizing biases. Ask yourself: Are there other, equally likely, explanations for what is observed? In addition to paying close attention to potential biases of the study or author(s), a reader should also be alert to one’s own preceding perspective (and biases). Take time to ask oneself: Do I find this paper compelling because it affirms something I already think (or wish) is true? Or am I discounting their findings because it differs from what I expect or from my own work?

The phenomenon of a self-fulfilling prophecy, or expectancy, is well studied in the psychology literature [ 2 ] and is why many studies are conducted in a “blinded” manner [ 3 ]. It refers to the idea that a person may assume something to be true and their resultant behavior aligns to make it true. In other words, as humans and scientists, we often find exactly what we are looking for. A scientist may only test their hypotheses and fail to evaluate alternative hypotheses; perhaps, a scientist may not be aware of alternative, less biased ways to test her or his hypothesis that are typically used in different fields. Individuals with different life, academic, and work experiences may think of several alternative hypotheses, all equally supported by the data.

Rule 7: Be kind

The author(s) are human too. So, whenever possible, give them the benefit of the doubt. An author may write a phrase differently than you would, forcing you to reread the sentence to understand it. Someone in your field may neglect to cite your paper because of a reference count limit. A figure panel may be misreferenced as Supplemental Fig 3E when it is obviously Supplemental Fig 4E. While these things may be frustrating, none are an indication that the quality of work is poor. Try to avoid letting these minor things influence your evaluation and interpretation of the work.

Similarly, if you intend to share your critique with others, be extra kind. An author (especially the lead author) may invest years of their time into a single paper. Hearing a kindly phrased critique can be difficult but constructive. Hearing a rude, brusque, or mean-spirited critique can be heartbreaking, especially for young scientists or those seeking to establish their place within a field and who may worry that they do not belong.

Rule 8: Be ready to go the extra mile

To truly understand a scientific work, you often will need to look up a term, dig into the supplemental materials, or read one or more of the cited references. This process takes time. Some advisors recommend reading an article three times: The first time, simply read without the pressure of understanding or critiquing the work. For the second time, aim to understand the paper. For the third read through, take notes.

Some people engage with a paper by printing it out and writing all over it. The reader might write question marks in the margins to mark parts (s)he wants to return to, circle unfamiliar terms (and then actually look them up!), highlight or underline important statements, and draw arrows linking figures and the corresponding interpretation in the discussion. Not everyone needs a paper copy to engage in the reading process but, whatever your version of “printing it out” is, do it.

Rule 9: Talk about it

Talking about an article in a journal club or more informal environment forces active reading and participation with the material. Studies show that teaching is one of the best ways to learn and that teachers learn the material even better as the teaching task becomes more complex [ 4 – 5 ]; anecdotally, such observations inspired the phrase “to teach is to learn twice.”

Beyond formal settings such as journal clubs, lab meetings, and academic classes, discuss papers with your peers, mentors, and colleagues in person or electronically. Twitter and other social media platforms have become excellent resources for discussing papers with other scientists, the public or your nonscientist friends, or even the paper’s author(s). Describing a paper can be done at multiple levels and your description can contain all of the scientific details, only the big picture summary, or perhaps the implications for the average person in your community. All of these descriptions will solidify your understanding, while highlighting gaps in your knowledge and informing those around you.

Rule 10: Build on it

One approach we like to use for communicating how we build on the scientific literature is by starting research presentations with an image depicting a wall of Lego bricks. Each brick is labeled with the reference for a paper, and the wall highlights the body of literature on which the work is built. We describe the work and conclusions of each paper represented by a labeled brick and discuss each brick and the wall as a whole. The top brick on the wall is left blank: We aspire to build on this work and label this brick with our own work. We then delve into our own research, discoveries, and the conclusions it inspires. We finish our presentations with the image of the Legos and summarize our presentation on that empty brick.

Whether you are reading an article to understand a new topic area or to move a research project forward, effective learning requires that you integrate knowledge from multiple sources (“click” those Lego bricks together) and build upwards. Leveraging published work will enable you to build a stronger and taller structure. The first row of bricks is more stable once a second row is assembled on top of it and so on and so forth. Moreover, the Lego construction will become taller and larger if you build upon the work of others, rather than using only your own bricks.

Build on the article you read by thinking about how it connects to ideas described in other papers and within own work, implementing a technique in your own research, or attempting to challenge or support the hypothesis of the author(s) with a more extensive literature review. Integrate the techniques and scientific conclusions learned from an article into your own research or perspective in the classroom or research lab. You may find that this process strengthens your understanding, leads you toward new and unexpected interests or research questions, or returns you back to the original article with new questions and critiques of the work. All of these experiences are part of the “active reading”: process and are signs of a successful reading experience.

In summary, practice these rules to learn how to read a scientific article, keeping in mind that this process will get easier (and faster) with experience. We are firm believers that an hour in the library will save a week at the bench; this diligent practice will ultimately make you both a more knowledgeable and productive scientist. As you develop the skills to read an article, try to also foster good reading and learning habits for yourself (recommendations here: [ 6 ] and [ 7 ], respectively) and in others. Good luck and happy reading!

Acknowledgments

Thank you to the mentors, teachers, and students who have shaped our thoughts on reading, learning, and what science is all about.

Funding Statement

MAC was supported by the PhRMA Foundation's Postdoctoral Fellowship in Translational Medicine and Therapeutics and the University of Virginia's Engineering-in-Medicine seed grant, and KLS was supported by the NIH T32 Global Biothreats Training Program at the University of Virginia (AI055432). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Reading Research Effectively
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Reading a Scholarly Article or Research Paper

Identifying a research problem to investigate usually requires a preliminary search for and critical review of the literature in order to gain an understanding about how scholars have examined a topic. Scholars rarely structure research studies in a way that can be followed like a story; they are complex and detail-intensive and often written in a descriptive and conclusive narrative form. However, in the social and behavioral sciences, journal articles and stand-alone research reports are generally organized in a consistent format that makes it easier to compare and contrast studies and to interpret their contents.

General Reading Strategies

W hen you first read an article or research paper, focus on asking specific questions about each section. This strategy can help with overall comprehension and with understanding how the content relates [or does not relate] to the problem you want to investigate. As you review more and more studies, the process of understanding and critically evaluating the research will become easier because the content of what you review will begin to coalescence around common themes and patterns of analysis. Below are recommendations on how to read each section of a research paper effectively. Note that the sections to read are out of order from how you will find them organized in a journal article or research paper.

1.  Abstract

The abstract summarizes the background, methods, results, discussion, and conclusions of a scholarly article or research paper. Use the abstract to filter out sources that may have appeared useful when you began searching for information but, in reality, are not relevant. Questions to consider when reading the abstract are:

• Is this study related to my question or area of research?
• What is this study about and why is it being done ?
• What is the working hypothesis or underlying thesis?
• What is the primary finding of the study?
• Are there words or terminology that I can use to either narrow or broaden the parameters of my search for more information?

2.  Introduction

If, after reading the abstract, you believe the paper may be useful, focus on examining the research problem and identifying the questions the author is trying to address. This information is usually located within the first few paragraphs of the introduction or in the concluding paragraph. Look for information about how and in what way this relates to what you are investigating. In addition to the research problem, the introduction should provide the main argument and theoretical framework of the study and, in the last paragraphs of the introduction, describe what the author(s) intend to accomplish. Questions to consider when reading the introduction include:

• What is this study trying to prove or disprove?
• What is the author(s) trying to test or demonstrate?
• What do we already know about this topic and what gaps does this study try to fill or contribute a new understanding to the research problem?
• Why should I care about what is being investigated?
• Will this study tell me anything new related to the research problem I am investigating?

3.  Literature Review

The literature review describes and critically evaluates what is already known about a topic. Read the literature review to obtain a big picture perspective about how the topic has been studied and to begin the process of seeing where your potential study fits within the domain of prior research. Questions to consider when reading the literature review include:

• W hat other research has been conducted about this topic and what are the main themes that have emerged?
• What does prior research reveal about what is already known about the topic and what remains to be discovered?
• What have been the most important past findings about the research problem?
• How has prior research led the author(s) to conduct this particular study?
• Is there any prior research that is unique or groundbreaking?
• Are there any studies I could use as a model for designing and organizing my own study?

4.  Discussion/Conclusion

The discussion and conclusion are usually the last two sections of text in a scholarly article or research report. They reveal how the author(s) interpreted the findings of their research and presented recommendations or courses of action based on those findings. Often in the conclusion, the author(s) highlight recommendations for further research that can be used to develop your own study. Questions to consider when reading the discussion and conclusion sections include:

• What is the overall meaning of the study and why is this important? [i.e., how have the author(s) addressed the " So What? " question].
• What do you find to be the most important ways that the findings have been interpreted?
• What are the weaknesses in their argument?
• Do you believe conclusions about the significance of the study and its findings are valid?
• What limitations of the study do the author(s) describe and how might this help formulate my own research?
• Does the conclusion contain any recommendations for future research?

5.  Methods/Methodology

The methods section describes the materials, techniques, and procedures for gathering information used to examine the research problem. If what you have read so far closely supports your understanding of the topic, then move on to examining how the author(s) gathered information during the research process. Questions to consider when reading the methods section include:

• Did the study use qualitative [based on interviews, observations, content analysis], quantitative [based on statistical analysis], or a mixed-methods approach to examining the research problem?
• What was the type of information or data used?
• Could this method of analysis be repeated and can I adopt the same approach?
• Is enough information available to repeat the study or should new data be found to expand or improve understanding of the research problem?

6.  Results

After reading the above sections, you should have a clear understanding of the general findings of the study. Therefore, read the results section to identify how key findings were discussed in relation to the research problem. If any non-textual elements [e.g., graphs, charts, tables, etc.] are confusing, focus on the explanations about them in the text. Questions to consider when reading the results section include:

• W hat did the author(s) find and how did they find it?
• Does the author(s) highlight any findings as most significant?
• Are the results presented in a factual and unbiased way?
• Does the analysis of results in the discussion section agree with how the results are presented?
• Is all the data present and did the author(s) adequately address gaps?
• What conclusions do you formulate from this data and does it match with the author's conclusions?

7.  References

The references list the sources used by the author(s) to document what prior research and information was used when conducting the study. After reviewing the article or research paper, use the references to identify additional sources of information on the topic and to examine critically how these sources supported the overall research agenda. Questions to consider when reading the references include:

• Do the sources cited by the author(s) reflect a diversity of disciplinary viewpoints, i.e., are the sources all from a particular field of study or do the sources reflect multiple areas of study?
• Are there any unique or interesting sources that could be incorporated into my study?
• What other authors are respected in this field, i.e., who has multiple works cited or is cited most often by others?
• What other research should I review to clarify any remaining issues or that I need more information about?

NOTE :  A final strategy in reviewing research is to copy and paste the title of the source [journal article, book, research report] into Google Scholar . If it appears, look for a "cited by" followed by a hyperlinked number [e.g., Cited by 45]. This number indicates how many times the study has been subsequently cited in other, more recently published works. This strategy, known as citation tracking, can be an effective means of expanding your review of pertinent literature based on a study you have found useful and how scholars have cited it. The same strategies described above can be applied to reading articles you find in the list of cited by references.

Reading Tip

Specific Reading Strategies

Effectively reading scholarly research is an acquired skill that involves attention to detail and an ability to comprehend complex ideas, data, and theoretical concepts in a way that applies logically to the research problem you are investigating. Here are some specific reading strategies to consider.

As You are Reading

• Focus on information that is most relevant to the research problem; skim over the other parts.
• As noted above, read content out of order! This isn't a novel; you want to start with the spoiler to quickly assess the relevance of the study.
• Think critically about what you read and seek to build your own arguments; not everything may be entirely valid, examined effectively, or thoroughly investigated.
• Look up the definitions of unfamiliar words, concepts, or terminology. A good scholarly source is Credo Reference .

Taking notes as you read will save time when you go back to examine your sources. Here are some suggestions:

• Mark or highlight important text as you read [e.g., you can use the highlight text  feature in a PDF document]
• Take notes in the margins [e.g., Adobe Reader offers pop-up sticky notes].
• Highlight important quotations; consider using different colors to differentiate between quotes and other types of important text.
• Summarize key points about the study at the end of the paper. To save time, these can be in the form of a concise bulleted list of statements [e.g., intro has provides historical background; lit review has important sources; good conclusions].

Write down thoughts that come to mind that may help clarify your understanding of the research problem. Here are some examples of questions to ask yourself:

• Do I understand all of the terminology and key concepts?
• Do I understand the parts of this study most relevant to my topic?
• What specific problem does the research address and why is it important?
• Are there any issues or perspectives the author(s) did not consider?
• Do I have any reason to question the validity or reliability of this research?
• How do the findings relate to my research interests and to other works which I have read?

Adapted from text originally created by Holly Burt, Behavioral Sciences Librarian, USC Libraries, April 2018.

Another Reading Tip

When is it Important to Read the Entire Article or Research Paper

Laubepin argues, "Very few articles in a field are so important that every word needs to be read carefully." However, this implies that some studies are worth reading carefully. As painful and time-consuming as it may seem, there are valid reasons for reading a study in its entirety from beginning to end. Here are some examples:

• Studies Published Very Recently .  The author(s) of a recent, well written study will provide a survey of the most important or impactful prior research in the literature review section. This can establish an understanding of how scholars in the past addressed the research problem. In addition, the most recently published sources will highlight what is currently known and what gaps in understanding currently exist about a topic, usually in the form of the need for further research in the conclusion .
• Surveys of the Research Problem .  Some papers provide a comprehensive analytical overview of the research problem. Reading this type of study can help you understand underlying issues and discover why scholars have chosen to investigate the topic. This is particularly important if the study was published very recently because the author(s) should cite all or most of the key prior research on the topic. Note that, if it is a long-standing problem, there may be studies that specifically review the literature to identify gaps that remain. These studies often include the word review in their title [e.g., Hügel, Stephan, and Anna R. Davies. "Public Participation, Engagement, and Climate Change Adaptation: A Review of the Research Literature." Wiley Interdisciplinary Reviews: Climate Change 11 (July-August 2020): https://doi.org/10.1002/ wcc.645].
• Highly Cited .  If you keep coming across the same citation to a study while you are reviewing the literature, this implies it was foundational in establishing an understanding of the research problem or the study had a significant impact within the literature [positive or negative]. Carefully reading a highly cited source can help you understand how the topic emerged and motivated scholars to further investigate the problem. It also could be a study you need to cite as foundational in your own paper to demonstrate to the reader that you understand the roots of the problem.
• Historical Overview .  Knowing the historical background of a research problem may not be the focus of your analysis. Nevertheless, carefully reading a study that provides a thorough description and analysis of the history behind an event, issue, or phenomenon can add important context to understanding the topic and what aspect of the problem you may want to examine further.
• Innovative Methodological Design .  Some studies are significant and worth reading in their entirety because the author(s) designed a unique or innovative approach to researching the problem. This may justify reading the entire study because it can motivate you to think creatively about pursuing an alternative or non-traditional approach to examining your topic of interest. These types of studies are generally easy to identify because they are often cited in others works because of their unique approach to studying the research problem.
• Cross-disciplinary Approach .  R eviewing studies produced outside of your discipline is an essential component of investigating research problems in the social and behavioral sciences. Consider reading a study that was conducted by author(s) based in a different discipline [e.g., an anthropologist studying political cultures; a study of hiring practices in companies published in a sociology journal]. This approach can generate a new understanding or a unique perspective about the topic . If you are not sure how to search for studies published in a discipline outside of your major or of the course you are taking, contact a librarian for assistance.

Laubepin, Frederique. How to Read (and Understand) a Social Science Journal Article . Inter-University Consortium for Political and Social Research (ISPSR), 2013; Shon, Phillip Chong Ho. How to Read Journal Articles in the Social Sciences: A Very Practical Guide for Students . 2nd edition. Thousand Oaks, CA: Sage, 2015; Lockhart, Tara, and Mary Soliday. "The Critical Place of Reading in Writing Transfer (and Beyond): A Report of Student Experiences." Pedagogy 16 (2016): 23-37; Maguire, Moira, Ann Everitt Reynolds, and Brid Delahunt. "Reading to Be: The Role of Academic Reading in Emergent Academic and Professional Student Identities." Journal of University Teaching and Learning Practice 17 (2020): 5-12.

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How to Read a Research Paper – A Guide to Setting Research Goals, Finding Papers to Read, and More

If you work in a scientific field, you should try to build a deep and unbiased understanding of that field. This not only educates you in the best possible way but also helps you envision the opportunities in your space.

A research paper is often the culmination of a wide range of deep and authentic practices surrounding a topic. When writing a research paper, the author thinks critically about the problem, performs rigorous research, evaluates their processes and sources, organizes their thoughts, and then writes. These genuinely-executed practices make for a good research paper.

If you’re struggling to build a habit of reading papers (like I am) on a regular basis, I’ve tried to break down the whole process. I've talked to researchers in the field, read a bunch of papers and blogs from distinguished researchers, and jotted down some techniques that you can follow.

Let’s start off by understanding what a research paper is and what it is NOT!

What is a Research Paper?

A research paper is a dense and detailed manuscript that compiles a thorough understanding of a problem or topic. It offers a proposed solution and further research along with the conditions under which it was deduced and carried out, the efficacy of the solution and the research performed, and potential loopholes in the study.

A research paper is written not only to provide an exceptional learning opportunity but also to pave the way for further advancements in the field. These papers help other scholars germinate the thought seed that can either lead to a new world of ideas or an innovative method of solving a longstanding problem.

What Research Papers are NOT

There is a common notion that a research paper is a well-informed summary of a problem or topic written by means of other sources.

But you shouldn't mistake it for a book or an opinionated account of an individual’s interpretation of a particular topic.

Why Should You Read Research Papers?

What I find fascinating about reading a good research paper is that you can draw on a profound study of a topic and engage with the community on a new perspective to understand what can be achieved in and around that topic.

I work at the intersection of instructional design and data science. Learning is part of my day-to-day responsibilities. If the source of my education is flawed or inefficient, I’d fail at my job in the long term. This applies to many other jobs in Science with a special focus on research.

There are three important reasons to read a research paper:

• Knowledge —  Understanding the problem from the eyes of someone who has probably spent years solving it and has taken care of all the edge cases that you might not think of at the beginning.
• Exploration —  Whether you have a pinpointed agenda or not, there is a very high chance that you will stumble upon an edge case or a shortcoming that is worth following up. With persistent efforts over a considerable amount of time, you can learn to use that knowledge to make a living.
• Research and review —  One of the main reasons for writing a research paper is to further the development in the field. Researchers read papers to review them for conferences or to do a literature survey of a new field. For example, Yann LeCun’ s paper on integrating domain constraints into backpropagation set the foundation of modern computer vision back in 1989. After decades of research and development work, we have come so far that we're now perfecting problems like object detection and optimizing autonomous vehicles.

Not only that, with the help of the internet, you can extrapolate all of these reasons or benefits onto multiple business models. It can be an innovative state-of-the-art product, an efficient service model, a content creator, or a dream job where you are solving problems that matter to you.

Goals for Reading a Research Paper — What Should You Read About?

The first thing to do is to figure out your motivation for reading the paper. There are two main scenarios that might lead you to read a paper:

• Scenario 1 —  You have a well-defined agenda/goal and you are deeply invested in a particular field. For example, you’re an NLP practitioner and you want to learn how GPT-4 has given us a breakthrough in NLP. This is always a nice scenario to be in as it offers clarity.
• Scenario 2 —  You want to keep abreast of the developments in a host of areas, say how a new deep learning architecture has helped us solve a 50-year old biological problem of understanding protein structures. This is often the case for beginners or for people who consume their daily dose of news from research papers (yes, they exist!).

If you’re an inquisitive beginner with no starting point in mind, start with scenario 2. Shortlist a few topics you want to read about until you find an area that you find intriguing. This will eventually lead you to scenario 1.

ML Reproducibility Challenge

In addition to these generic goals, if you need an end goal for your habit-building exercise of reading research papers, you should check out the ML reproducibility challenge.

You’ll find top-class papers from world-class conferences that are worth diving deep into and reproducing the results.

They conduct this challenge twice a year and they have one coming up in Spring 2021. You should study the past three versions of the challenge, and I’ll write a detailed post on what to expect, how to prepare, and so on.

Now you must be wondering – how can you find the right paper to read?

How to Find the Right Paper to Read

In order to get some ideas around this, I reached out to my friend, Anurag Ghosh who is a researcher at Microsoft. Anurag has been working at the crossover of computer vision, machine learning, and systems engineering.

Here are a few of his tips for getting started:

• Always pick an area you're interested in.
• Read a few good books or detailed blog posts on that topic and start diving deep by reading the papers referenced in those resources.
• Look for seminal papers around that topic. These are papers that report a major breakthrough in the field and offer a new method perspective with a huge potential for subsequent research in that field. Check out papers from the morning paper or C VF - test of time award/Helmholtz prize (if you're interested in computer vision).
• Check out books like Computer Vision: Algorithms and Applications by Richard Szeliski and look for the papers referenced there.
• Have and build a sense of community. Find people who share similar interests, and join groups/subreddits/discord channels where such activities are promoted.

In addition to these invaluable tips, there are a number of web applications that I’ve shortlisted that help me narrow my search for the right papers to read:

• r/MachineLearning  — there are many researchers, practitioners, and engineers who share their work along with the papers they've found useful in achieving those results.

• Arxiv Sanity Preserver  — built by Andrej Karpathy to accelerate research. It is a repository of 142,846 papers from computer science, machine learning, systems, AI, Stats, CV, and so on. It also offers a bunch of filters, powerful search functionality, and a discussion forum to make for a super useful research platform.

• Google Research  — the research teams at Google are working on problems that have an impact on our everyday lives. They share their publications for individuals and teams to learn from, contribute to, and expedite research. They also have a Google AI blog that you can check out.

How to Read a Research Paper

After you have stocked your to-read list, then comes the process of reading these papers. Remember that NOT every paper is useful to read and we need a mechanism that can help us quickly screen papers that are worth reading.

To tackle this challenge, you can use this Three-Pass Approach by S. Keshav . This approach proposes that you read the paper in three passes instead of starting from the beginning and diving in deep until the end.

The three pass approach

• The first pass —  is a quick scan to capture a high-level view of the paper. Read the title, abstract, and introduction carefully followed by the headings of the sections and subsections and lastly the conclusion. It should take you no more than 5–10 mins to figure out if you want to move to the second pass.
• The second pass —  is a more focused read without checking for the technical proofs. You take down all the crucial notes, underline the key points in the margins. Carefully study the figures, diagrams, and illustrations. Review the graphs, mark relevant unread references for further reading. This helps you understand the background of the paper.
• The third pass —  reaching this pass denotes that you’ve found a paper that you want to deeply understand or review. The key to the third pass is to reproduce the results of the paper. Check it for all the assumptions and jot down all the variations in your re-implementation and the original results. Make a note of all the ideas for future analysis. It should take 5–6 hours for beginners and 1–2 hours for experienced readers.

Tools and Software to Keep Track of Your Pipeline of Papers

If you’re sincere about reading research papers, your list of papers will soon grow into an overwhelming stack that is hard to keep track of. Fortunately, we have software that can help us set up a mechanism to manage our research.

Here are a bunch of them that you can use:

• Mendeley [not free]  — you can add papers directly to your library from your browser, import documents, generate references and citations, collaborate with fellow researchers, and access your library from anywhere. This is mostly used by experienced researchers.

• Zotero [free & open source] —  Along the same lines as Mendeley but free of cost. You can make use of all the features but with limited storage space.

• Notion —  this is great if you are just starting out and want to use something lightweight with the option to organize your papers, jot down notes, and manage everything in one workspace. It might not stand anywhere in comparison with the above tools but I personally feel comfortable using Notion and I have created this board to keep track of my progress for now that you can duplicate:

⚠️ Symptoms of Reading a Research Paper

Reading a research paper can turn out to be frustrating, challenging, and time-consuming especially when you’re a beginner. You might face the following common symptoms:

• You might start feeling dumb for not understanding a thing a paper says.
• Finding yourself pushing too hard to understand the math behind those proofs.
• Beating your head against the wall to wrap it around the number of acronyms used in the paper. Just kidding, you’ll have to look up those acronyms every now and then.
• Being stuck on one paragraph for more than an hour.

Here’s a complete list of emotions that you might undergo as explained by Adam Ruben in this article .

Key Takeaways

We should be all set to dive right in. Here’s a quick summary of what we have covered here:

• A research paper is an in-depth study that offers an detailed explanation of a topic or problem along with the research process, proofs, explained results, and ideas for future work.
• Read research papers to develop a deep understanding of a topic/problem. Then you can either review papers as part of being a researcher, explore the domain and the kind of problems to build a solution or startup around it, or you can simply read them to keep abreast of the developments in your domain of interest.
• If you’re a beginner, start with exploration to soon find your path to goal-oriented research.
• In order to find good papers to read, you can use websites like arxiv-sanity, google research, and subreddits like r/MachineLearning.
• Reading approach — Use the 3-pass method to find a paper.
• Keep track of your research, notes, developments by using tools like Zotero/Notion.
• This can get overwhelming in no time. Make sure you start off easy and increment your load progressively.

Remember: Art is not a single method or step done over a weekend but a process of accomplishing remarkable results over time.

You can also watch the video on this topic on my YouTube channel :

Feel free to respond to this blog or comment on the video if you have some tips, questions, or thoughts!

If this tutorial was helpful, you should check out my data science and machine learning courses on Wiplane Academy . They are comprehensive yet compact and helps you build a solid foundation of work to showcase.

Web and Data Science Consultant | Instructional Design

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How to Read Research Papers: A Cheat Sheet for Graduate Students

• August 4, 2022
• PRODUCTIVITY

It is crucial to stay on top of the scientific literature in your field of interest. This will help you shape and guide your experimental plans and keep you informed about what your competitors are working on.

To get the most out of your literature reading time, you need to learn how to read scientific papers efficiently. The problem is that we simply don’t have enough time to read new scientific papers in our results-driven world. 

It takes a great deal of time for researchers to learn how to read research papers. Unfortunately, this skill is rarely taught.

I wasted a lot of time reading unnecessary papers in the past since I didn’t have an appropriate workflow to follow. In particular, I needed a way to determine if a paper would interest me before I read it from start to finish.

So, what’s the solution?

This is where I came across the Three-pass method for reading research papers. 

Here’s what I’ve learned from using the three pass methods and what tweaks I’ve made to my workflow to make it more personalized.

Build time into your schedule 

Before you read anything, you should set aside a set amount of time to read research papers. It will be very hard to read research papers if you do not have a schedule because you will only try to read them for a week or two, and then you will feel frustrated. An organized schedule reduces procrastination significantly.

 For example, I take 30-40 minutes each weekday morning to read a research paper I come across.

After you have determined a time “only” to read research papers, you have to have a proper workflow.

Develop a workflow

For example, I follow a customized version of the popular workflow, the “Three-pass method”. 

When you are beginning, you may follow the method exactly as described, but as you get more experienced, you can make some changes down the road.

Why you shouldn’t read the entire paper at once?

Oftentimes, the papers you think are so important and that you should read every single word are actually worth only 10 minutes of your time.

Unlike reading an article about science in a blog or newspaper, reading research papers is an entirely different experience. In addition to reading the sections in a different order, you must take notes, read them several times, and probably look up other papers for details. 

It may take you a long time to read one paper at first. But that’s okay because you are investing yourself in the process.

However, you’re wasting your time if you don’t have a proper workflow. 

Oftentimes, reading a whole paper might not be necessary to get the specific information you need.

The Three-pass concept

The key idea is to read the paper in up to three passes rather than starting at the beginning and plowing through it. With each pass, you accomplish specific goals and build upon the previous one.

The first pass gives you a general idea of the paper. A second pass will allow you to understand the content of the paper, but not its details. A third pass helps you understand the paper more deeply.

The first pass (Maximum: 10 minutes)

The paper is scanned quickly in the first pass to get an overview. Also, you can decide if any more passes are needed. It should take about five to ten minutes to complete this pass.

Carefully read the title, abstract, and introduction

You should be able to tell from the title what the paper is about. In addition, it is a good idea to look at the authors and their affiliations, which may be valuable for various reasons, such as future reference, employment, guidance, and determining the reliability of the research.

The abstract should provide a high-level overview of the paper. You may ask, What are the main goals of the author(s) and what are the high-level results? There are usually some clues in the abstract about the paper’s purpose. You can think of the abstract as a marketing piece.

As you read the introduction, make sure you only focus on the topic sentences, and you can loosely focus on the other content.

What is a topic sentence?

Topic sentences introduce a paragraph by introducing the one topic that will be the focus of that paragraph. 

The structure of a paragraph should match the organization of a paper. At the paragraph level, the topic sentence gives the paper’s main idea, just as the thesis statement does at the essay level. After that, the rest of the paragraph supports the topic.

In the beginning, I read the whole paragraph, and it took me more than 30 minutes to complete the first pass. By identifying topic sentences, I have revolutionized my reading game, as I am now only reading the summary of the paragraph, saving me a lot of time during the second and third passes.

Read the section and sub-section headings, but ignore everything else 

Regarding methods and discussions, do not attempt to read even topic sentences because you are trying to decide whether this article is useful to you.

Reading the headings and subheadings is the best practice. It allows you to get a feel for the paper without taking up a lot of time.

Read the conclusions

It is standard for good writers to present the foundations of their experiment at the beginning and summarize their findings at the end of their paper.

Therefore, you are well prepared to read and understand the conclusion after reading the abstract and introduction.

Many people overlook the importance of the first pass. In adopting the three-pass method into my workflow, I realized that many papers that I thought had high relevance did not require me to spend more time reading. 

Therefore, after the first pass, I can decide not to read it further, saving me a lot of time.

Glance over the references

You can mentally check off the ones you’ve already read.

As you read through the references, you will better understand what has been studied previously in the field of research.

First pass objectives

At the end of the first pass, you should be able to answer these questions: 

• What is the  category  of this paper? Is it an analytical paper? Is it only an “introductory” paper? (if this is the case, probably, you might not want to read further, but it depends on the information you are after)or is it an argumentative research paper?
• Does the  context  of the paper serve the purpose for what you are looking for? If not, this paper might not be worth passing on to the second stage of this method.
• Does the basic logic of the paper seem to be valid? How do you comment on the  correctness  of the paper?
• What is the main  output  of the paper, or is there output at all?
• Is the paper well written? How do you comment on the  clarity  of the paper?

After the first pass, you should have a good idea whether you want to continue reading the research paper.

Maybe the paper doesn’t interest you, you don’t understand the area enough, or the authors make an incorrect assumption. 

In the first pass, you should be able to identify papers that are not related to your area of research but may be useful someday. 

You can store your paper with relevant tags in your reference manager, as discussed in the previous blog post in the  Bulletproof Literature Management System  series.

This is the third post of the four-part blog series:  The Bulletproof Literature Management System . Follow the links below to read the other posts in the series:

• How to How to find Research Papers
• How to Manage Research Papers
• How to Read Research Papers (You are here)
• How to Organize Research Papers

The second pass (Maximum: 60 minutes)

You are now ready to make a second pass through the paper if you decide it is worth reading more.

You should now begin taking some high-level notes because there will be words and ideas that are unfamiliar to you. 

Most reference managers come with an in-built PDF reader. In this case, taking notes and highlighting notes in the built-in pdf reader is the best practice. This method will prevent you from losing your notes and allow you to revise them easily.

Don’t be discouraged by everything that does not make sense. You can just mark it and move on. It is recommended that you only spend about an hour working on the paper in the second pass. 

In the second pass:

• Start with the abstract, skim through the introduction, and give the methods section a thorough look. 
• Make sure you pay close attention to the figures, diagrams, and other illustrations on the paper. By just looking at the captions of the figures and tables in a well-written paper, you can grasp 90 percent of the information. 
• It is important to pay attention to the overall methodology . There is a lot of detail in the methods section. At this point, you do not need to examine every part. 
• Read the results and discussion sections to better understand the key findings.
• Make sure you mark the relevant references in the paper so you can find them later.

Objectives of the second pass

You should be able to understand the paper’s content. Sometimes, it may be okay if you cannot comprehend some details. However, you should now be able to see the main idea of the paper. Otherwise, it might be better to rest and go through the second pass without entering the third. 

This is a good time to summarize the paper. During your reading, make sure to make notes.

After the second pass, you can: 

• Return to the paper later(If you did not understand the basic idea of the paper)
• Move onto the thirst pass.

The third pass (Maximum: four hours)

You should go to the third stage (the third pass) for a complete understanding of the paper. It may take you a few hours this time to read the paper. However, you may want to avoid reading a single paper for longer than four hours, even at the third pass.

A great deal of attention to detail is required for this pass. Every statement should be challenged, and every assumption should be identified.

By the third pass, you will be able to summarize the paper so that not only do you understand the content, but you can also comment on limitations and potential future developments.

Color coding when reading research papers

Highlighting is one way I help myself learn the material when I read research papers. It is especially helpful to highlight an article when you return to it later. 

Therefore, I use different colors for different segments. To manage my references, I use Zotero. There is an inbuilt PDF reader in Zotero. I use the highlighting colors offered by this software. The most important thing is the concept or phrase I want to color code, not the color itself.

Here is my color coding system.

• Problem statement: Violet
• Questions to ask: Red (I highlight in red where I want additional questions to be asked or if I am unfamiliar with the concept)
• Conclusions: Green (in the discussion section, authors draw conclusions based on their data. I prefer to highlight these in the discussion section rather than in the conclusion section since I can easily locate the evidence there)
• Keywords: Blue
• General highlights and notes: Yellow

Minimize distractions

Even though I’m not a morning person, I forced myself to read papers in the morning just to get rid of distractions. In order to follow through with this process (at least when you are starting out), you must have minimum to no distractions because research papers contain a great deal of highly packed information.

It doesn’t mean you can’t have fun doing it, though. Make a cup of coffee and enjoy reading!

Images courtesy : Online working vector created by storyset – www.freepik.com

Aruna Kumarasiri

Founder at Proactive Grad, Materials Engineer, Researcher, and turned author. In 2019, he started his professional carrier as a materials engineer with the continuation of his research studies. His exposure to both academic and industrial worlds has provided many opportunities for him to give back to young professionals.

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How to read a research paper

Affiliation.

• 1 Department of Neurology, Mount Sinai School of Medicine, New York, New York 10029.
• PMID: 3189491
• DOI: 10.5014/ajot.42.9.596

A research paper includes several sections, each section having a particular purpose and containing a particular kind of information. This paper is a guide to reading a research paper. It describes the prototypical research paper and explains the purpose for each section. Issues for the astute reader to note are indicated and illustrated with examples from a research paper published in this issue of the journal.

• EXPLORE Random Article

How to Read Research Papers

Last Updated: October 11, 2022 References

This article was co-authored by Matthew Snipp, PhD . 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. This article has been viewed 9,788 times.

Research papers can be a great resource for academic information and scholarly references. Reading research papers can also help you understand how to write a good one. Start by skimming the paper, identifying key details that stand out to you. Then, do a critical read of the paper, reading it carefully a second or third time so you can look at it in depth. Once you have done a critical read, analyze the key arguments and ideas in the paper so you can fully understand it.

Skimming the Paper

• For example, if you read a title like “What Global Poverty Means in the 21st Century,” you can assume the paper will address the issue of global poverty in modern times.

• Ph.D stands for a doctor of philosophy and is the highest degree awarded by a university. M.D. stands for a doctor of medicine and is given to an individual who earns their medical degree.

• If you have a difficult time understanding the abstract of the paper, this may be a sign the paper is poorly written or does not have a clear focus.
• Abstracts that contain a lot of jargon or complex wording may indicate the paper will be hard to understand, especially if you do not have an academic background.

• For example, you may read headings like “Analysis of Poverty Statistics” or “Exploration of Poverty Solutions.” The author may also use questions in their headings, such as “Why is Poverty a Problem?” or “How Can We Address Poverty?”

• Check the citation style of the references to ensure they are correct, based on whether the paper was written in APA style, MLA style or Chicago style.
• Look at the title of the reference to check that the subject matter relates to the topic of the paper.
• If you are reading a paper about a topic you know well, you may check the reference list to see if you recognize any of the sources. You can then lean on your familiarity with them to better understand the paper as a whole.

• If you have a hard copy of the paper, mark it up with a pen, pencil, or highlighter as part of your note taking while you read. As you skim, look at the key details in the paper, rather than do a close read.
• Skimming the paper can take 1-2 hours, depending on how long the paper is.

Doing a Critical Read

• Some research papers have a research question instead of a hypothesis, where they pose a question to the reader and explore it in detail. A good research question will be specific, focusing on a particular idea or topic within a larger idea.
• For example, you may come across a hypothesis like, “Global poverty levels continue to rise due to the exploitation of workers in third world countries.” Or you may find a research question like, “How does the United States contribute to rising poverty levels in third world countries?”

• The body sections are often the most complex and detailed in the research paper. Take your time when reading these sections so you can look at them critically. Depending on the length of the paper, it can take 1-3 hours to fully unpack the body sections.

• Check for any graphs or charts that are poor quality or improperly labeled, as this may be a sign they are bad visuals.
• Read the labels on the graphs, charts, and figures to ensure they relate to the topic of the paper and are not misleading or incorrect.

• In some conclusion sections, the author may offer possible solutions for a topic or propose next steps that a governing body or an institution can take to address the topic.

Analyzing the Paper’s Ideas and Arguments

• Ask yourself questions like, “Is the author’s solution clear and easy to follow? What are the gaps or missing pieces of the author’s argument? Can I disprove or dispute the author’s argument?”

• For example, the author may discuss solutions to global poverty rates that feel new or different to you. Or they may present a new approach to measuring global poverty rates that you might find engaging and exciting.

• Note if the ideas in the research paper feels new compared to other works in the field. Check for any ideas or solutions in the paper that contradict the ideas in other scholarship on the topic.

• You can then refer to your list of questions if you have to discuss the paper in a class or for an assignment.
• The list of questions and concerns can also come in handy if you have to compose a summary or review of the research paper for a class.

Expert Q&A

You Might Also Like

• ↑ http://ccr.sigcomm.org/online/files/p83-keshavA.pdf
• ↑ https://www.eecs.harvard.edu/~michaelm/postscripts/ReadPaper.pdf
• ↑ Matthew Snipp, PhD. Research Fellow, U.S. Bureau of the Census. Expert Interview. 26 March 2020.
• ↑ https://cseweb.ucsd.edu/~wgg/CSE210/howtoread.html
• ↑ http://foreignpolicy.com/2010/07/09/how-to-read-research-papers/
• ↑ https://www.cc.gatech.edu/fac/Spencer.Rugaber/txt/research_paper.txt

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Clinical Reasoning of a Generative Artificial Intelligence Model Compared With Physicians

• 1 Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
• 2 Department of Medicine, Massachusetts General Hospital, Boston
• 3 Department of Pulmonary and Critical Care, Brigham and Women’s Hospital, Boston, Massachusetts

Large language models (LLMs) have shown promise in clinical reasoning, but their ability to synthesize clinical encounter data into problem representations remains unexplored. 1 - 3 We compared an LLM’s reasoning abilities against human performance using standards developed for physicians.

Read More About

Cabral S , Restrepo D , Kanjee Z, et al. Clinical Reasoning of a Generative Artificial Intelligence Model Compared With Physicians. JAMA Intern Med. Published online April 01, 2024. doi:10.1001/jamainternmed.2024.0295

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AI Index: State of AI in 13 Charts

In the new report, foundation models dominate, benchmarks fall, prices skyrocket, and on the global stage, the U.S. overshadows.

This year’s AI Index — a 500-page report tracking 2023’s worldwide trends in AI — is out.

The index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. This year’s report covers the rise of multimodal foundation models, major cash investments into generative AI, new performance benchmarks, shifting global opinions, and new major regulations.

Don’t have an afternoon to pore through the findings? Check out the high level here.

A Move Toward Open-Sourced

This past year, organizations released 149 foundation models, more than double the number released in 2022. Of these newly released models, 65.7% were open-source (meaning they can be freely used and modified by anyone), compared with only 44.4% in 2022 and 33.3% in 2021.

But At a Cost of Performance?

Closed-source models still outperform their open-sourced counterparts. On 10 selected benchmarks, closed models achieved a median performance advantage of 24.2%, with differences ranging from as little as 4.0% on mathematical tasks like GSM8K to as much as 317.7% on agentic tasks like AgentBench.

Biggest Players

Industry dominates AI, especially in building and releasing foundation models. This past year Google edged out other industry players in releasing the most models, including Gemini and RT-2. In fact, since 2019, Google has led in releasing the most foundation models, with a total of 40, followed by OpenAI with 20. Academia trails industry: This past year, UC Berkeley released three models and Stanford two.

Industry Dwarfs All

If you needed more striking evidence that corporate AI is the only player in the room right now, this should do it. In 2023, industry accounted for 72% of all new foundation models.

Prices Skyrocket

One of the reasons academia and government have been edged out of the AI race: the exponential increase in cost of training these giant models. Google’s Gemini Ultra cost an estimated $191 million worth of compute to train, while OpenAI’s GPT-4 cost an estimated $78 million. In comparison, in 2017, the original Transformer model, which introduced the architecture that underpins virtually every modern LLM, cost around $900.

What AI Race?

At least in terms of notable machine learning models, the United States vastly outpaced other countries in 2023, developing a total of 61 models in 2023. Since 2019, the U.S. has consistently led in originating the majority of notable models, followed by China and the UK.

Move Over, Human

As of 2023, AI has hit human-level performance on many significant AI benchmarks, from those testing reading comprehension to visual reasoning. Still, it falls just short on some benchmarks like competition-level math. Because AI has been blasting past so many standard benchmarks, AI scholars have had to create new and more difficult challenges. This year’s index also tracked several of these new benchmarks, including those for tasks in coding, advanced reasoning, and agentic behavior.

Private Investment Drops (But We See You, GenAI)

While AI private investment has steadily dropped since 2021, generative AI is gaining steam. In 2023, the sector attracted $25.2 billion, nearly ninefold the investment of 2022 and about 30 times the amount from 2019 (call it the ChatGPT effect). Generative AI accounted for over a quarter of all AI-related private investments in 2023.

U.S. Wins $$ Race

And again, in 2023 the United States dominates in AI private investment. In 2023, the $67.2 billion invested in the U.S. was roughly 8.7 times greater than the amount invested in the next highest country, China, and 17.8 times the amount invested in the United Kingdom. That lineup looks the same when zooming out: Cumulatively since 2013, the United States leads investments at $335.2 billion, followed by China with $103.7 billion, and the United Kingdom at $22.3 billion.

Where is Corporate Adoption?

More companies are implementing AI in some part of their business: In surveys, 55% of organizations said they were using AI in 2023, up from 50% in 2022 and 20% in 2017. Businesses report using AI to automate contact centers, personalize content, and acquire new customers. 

Younger and Wealthier People Worry About Jobs

Globally, most people expect AI to change their jobs, and more than a third expect AI to replace them. Younger generations — Gen Z and millennials — anticipate more substantial effects from AI compared with older generations like Gen X and baby boomers. Specifically, 66% of Gen Z compared with 46% of boomer respondents believe AI will significantly affect their current jobs. Meanwhile, individuals with higher incomes, more education, and decision-making roles foresee AI having a great impact on their employment.

While the Commonwealth Worries About AI Products

When asked in a survey about whether AI products and services make you nervous, 69% of Aussies and 65% of Brits said yes. Japan is the least worried about their AI products at 23%.  

Regulation Rallies

More American regulatory agencies are passing regulations to protect citizens and govern the use of AI tools and data. For example, the Copyright Office and the Library of Congress passed copyright registration guidance concerning works that contained material generated by AI, while the Securities and Exchange Commission developed a cybersecurity risk management strategy, governance, and incident disclosure plan. The agencies to pass the most regulation were the Executive Office of the President and the Commerce Department. 

The AI Index was first created to track AI development. The index collaborates with such organizations as LinkedIn, Quid, McKinsey, Studyportals, the Schwartz Reisman Institute, and the International Federation of Robotics to gather the most current research and feature important insights on the AI ecosystem. 

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Amanda Hoover

Students Are Likely Writing Millions of Papers With AI

Students have submitted more than 22 million papers that may have used generative AI in the past year, new data released by plagiarism detection company Turnitin shows.

A year ago, Turnitin rolled out an AI writing detection tool that was trained on its trove of papers written by students as well as other AI-generated texts. Since then, more than 200 million papers have been reviewed by the detector, predominantly written by high school and college students. Turnitin found that 11 percent may contain AI-written language in 20 percent of its content, with 3 percent of the total papers reviewed getting flagged for having 80 percent or more AI writing. (Turnitin is owned by Advance, which also owns Condé Nast, publisher of WIRED.) Turnitin says its detector has a false positive rate of less than 1 percent when analyzing full documents.

ChatGPT’s launch was met with knee-jerk fears that the English class essay would die . The chatbot can synthesize information and distill it near-instantly—but that doesn’t mean it always gets it right. Generative AI has been known to hallucinate , creating its own facts and citing academic references that don’t actually exist. Generative AI chatbots have also been caught spitting out biased text on gender and race . Despite those flaws, students have used chatbots for research, organizing ideas, and as a ghostwriter . Traces of chatbots have even been found in peer-reviewed, published academic writing .

Teachers understandably want to hold students accountable for using generative AI without permission or disclosure. But that requires a reliable way to prove AI was used in a given assignment. Instructors have tried at times to find their own solutions to detecting AI in writing, using messy, untested methods to enforce rules , and distressing students. Further complicating the issue, some teachers are even using generative AI in their grading processes.

Detecting the use of gen AI is tricky. It’s not as easy as flagging plagiarism, because generated text is still original text. Plus, there’s nuance to how students use gen AI; some may ask chatbots to write their papers for them in large chunks or in full, while others may use the tools as an aid or a brainstorm partner.

Students also aren't tempted by only ChatGPT and similar large language models. So-called word spinners are another type of AI software that rewrites text, and may make it less obvious to a teacher that work was plagiarized or generated by AI. Turnitin’s AI detector has also been updated to detect word spinners, says Annie Chechitelli, the company’s chief product officer. It can also flag work that was rewritten by services like spell checker Grammarly, which now has its own generative AI tool . As familiar software increasingly adds generative AI components, what students can and can’t use becomes more muddled.

Detection tools themselves have a risk of bias. English language learners may be more likely to set them off; a 2023 study found a 61.3 percent false positive rate when evaluating Test of English as a Foreign Language (TOEFL) exams with seven different AI detectors. The study did not examine Turnitin’s version. The company says it has trained its detector on writing from English language learners as well as native English speakers. A study published in October found that Turnitin was among the most accurate of 16 AI language detectors in a test that had the tool examine undergraduate papers and AI-generated papers.

Emily Mullin

Joel Khalili

C. Brandon Ogbunu

Schools that use Turnitin had access to the AI detection software for a free pilot period, which ended at the start of this year. Chechitelli says a majority of the service’s clients have opted to purchase the AI detection. But the risks of false positives and bias against English learners have led some universities to ditch the tools for now. Montclair State University in New Jersey announced in November that it would pause use of Turnitin’s AI detector. Vanderbilt University and Northwestern University did the same last summer.

“This is hard. I understand why people want a tool,” says Emily Isaacs, executive director of the Office of Faculty Excellence at Montclair State. But Isaacs says the university is concerned about potentially biased results from AI detectors, as well as the fact that the tools can’t provide confirmation the way they can with plagiarism. Plus, Montclair State doesn’t want to put a blanket ban on AI, which will have some place in academia. With time and more trust in the tools, the policies could change. “It’s not a forever decision, it’s a now decision,” Isaacs says.

Chechitelli says the Turnitin tool shouldn’t be the only consideration in passing or failing a student. Instead, it’s a chance for teachers to start conversations with students that touch on all of the nuance in using generative AI. “People don’t really know where that line should be,” she says.

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How Pew Research Center will report on generations moving forward

Journalists, researchers and the public often look at society through the lens of generation, using terms like Millennial or Gen Z to describe groups of similarly aged people. This approach can help readers see themselves in the data and assess where we are and where we’re headed as a country.

Pew Research Center has been at the forefront of generational research over the years, telling the story of Millennials as they came of age politically and as they moved more firmly into adult life . In recent years, we’ve also been eager to learn about Gen Z as the leading edge of this generation moves into adulthood.

But generational research has become a crowded arena. The field has been flooded with content that’s often sold as research but is more like clickbait or marketing mythology. There’s also been a growing chorus of criticism about generational research and generational labels in particular.

Recently, as we were preparing to embark on a major research project related to Gen Z, we decided to take a step back and consider how we can study generations in a way that aligns with our values of accuracy, rigor and providing a foundation of facts that enriches the public dialogue.

A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations.

We set out on a yearlong process of assessing the landscape of generational research. We spoke with experts from outside Pew Research Center, including those who have been publicly critical of our generational analysis, to get their take on the pros and cons of this type of work. We invested in methodological testing to determine whether we could compare findings from our earlier telephone surveys to the online ones we’re conducting now. And we experimented with higher-level statistical analyses that would allow us to isolate the effect of generation.

What emerged from this process was a set of clear guidelines that will help frame our approach going forward. Many of these are principles we’ve always adhered to , but others will require us to change the way we’ve been doing things in recent years.

Here’s a short overview of how we’ll approach generational research in the future:

We’ll only do generational analysis when we have historical data that allows us to compare generations at similar stages of life. When comparing generations, it’s crucial to control for age. In other words, researchers need to look at each generation or age cohort at a similar point in the life cycle. (“Age cohort” is a fancy way of referring to a group of people who were born around the same time.)

When doing this kind of research, the question isn’t whether young adults today are different from middle-aged or older adults today. The question is whether young adults today are different from young adults at some specific point in the past.

To answer this question, it’s necessary to have data that’s been collected over a considerable amount of time – think decades. Standard surveys don’t allow for this type of analysis. We can look at differences across age groups, but we can’t compare age groups over time.

Another complication is that the surveys we conducted 20 or 30 years ago aren’t usually comparable enough to the surveys we’re doing today. Our earlier surveys were done over the phone, and we’ve since transitioned to our nationally representative online survey panel , the American Trends Panel . Our internal testing showed that on many topics, respondents answer questions differently depending on the way they’re being interviewed. So we can’t use most of our surveys from the late 1980s and early 2000s to compare Gen Z with Millennials and Gen Xers at a similar stage of life.

This means that most generational analysis we do will use datasets that have employed similar methodologies over a long period of time, such as surveys from the U.S. Census Bureau. A good example is our 2020 report on Millennial families , which used census data going back to the late 1960s. The report showed that Millennials are marrying and forming families at a much different pace than the generations that came before them.

Even when we have historical data, we will attempt to control for other factors beyond age in making generational comparisons. If we accept that there are real differences across generations, we’re basically saying that people who were born around the same time share certain attitudes or beliefs – and that their views have been influenced by external forces that uniquely shaped them during their formative years. Those forces may have been social changes, economic circumstances, technological advances or political movements.

When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

The tricky part is isolating those forces from events or circumstances that have affected all age groups, not just one generation. These are often called “period effects.” An example of a period effect is the Watergate scandal, which drove down trust in government among all age groups. Differences in trust across age groups in the wake of Watergate shouldn’t be attributed to the outsize impact that event had on one age group or another, because the change occurred across the board.

Changing demographics also may play a role in patterns that might at first seem like generational differences. We know that the United States has become more racially and ethnically diverse in recent decades, and that race and ethnicity are linked with certain key social and political views. When we see that younger adults have different views than their older counterparts, it may be driven by their demographic traits rather than the fact that they belong to a particular generation.

Controlling for these factors can involve complicated statistical analysis that helps determine whether the differences we see across age groups are indeed due to generation or not. This additional step adds rigor to the process. Unfortunately, it’s often absent from current discussions about Gen Z, Millennials and other generations.

When we can’t do generational analysis, we still see value in looking at differences by age and will do so where it makes sense. Age is one of the most common predictors of differences in attitudes and behaviors. And even if age gaps aren’t rooted in generational differences, they can still be illuminating. They help us understand how people across the age spectrum are responding to key trends, technological breakthroughs and historical events.

Each stage of life comes with a unique set of experiences. Young adults are often at the leading edge of changing attitudes on emerging social trends. Take views on same-sex marriage , for example, or attitudes about gender identity .

Many middle-aged adults, in turn, face the challenge of raising children while also providing care and support to their aging parents. And older adults have their own obstacles and opportunities. All of these stories – rooted in the life cycle, not in generations – are important and compelling, and we can tell them by analyzing our surveys at any given point in time.

When we do have the data to study groups of similarly aged people over time, we won’t always default to using the standard generational definitions and labels. While generational labels are simple and catchy, there are other ways to analyze age cohorts. For example, some observers have suggested grouping people by the decade in which they were born. This would create narrower cohorts in which the members may share more in common. People could also be grouped relative to their age during key historical events (such as the Great Recession or the COVID-19 pandemic) or technological innovations (like the invention of the iPhone).

By choosing not to use the standard generational labels when they’re not appropriate, we can avoid reinforcing harmful stereotypes or oversimplifying people’s complex lived experiences.

Existing generational definitions also may be too broad and arbitrary to capture differences that exist among narrower cohorts. A typical generation spans 15 to 18 years. As many critics of generational research point out, there is great diversity of thought, experience and behavior within generations. The key is to pick a lens that’s most appropriate for the research question that’s being studied. If we’re looking at political views and how they’ve shifted over time, for example, we might group people together according to the first presidential election in which they were eligible to vote.

With these considerations in mind, our audiences should not expect to see a lot of new research coming out of Pew Research Center that uses the generational lens. We’ll only talk about generations when it adds value, advances important national debates and highlights meaningful societal trends.

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How Teens and Parents Approach Screen Time

Who are you the art and science of measuring identity, u.s. centenarian population is projected to quadruple over the next 30 years, older workers are growing in number and earning higher wages, teens, social media and technology 2023, most popular.

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The Research-Backed Benefits of Daily Rituals

• Michael I. Norton

A survey of more than 130 HBR readers asked how they use rituals to start their days, psych themselves up for stressful challenges, and transition when the workday is done.

While some may cringe at forced corporate rituals, research shows that personal and team rituals can actually benefit the way we work. The authors’ expertise on the topic over the past decade, plus a survey of nearly 140 HBR readers, explores the ways rituals can set us up for success before work, get us psyched up for important presentations, foster a strong team culture, and help us wind down at the end of the day.

“Give me a W ! Give me an A ! Give me an L ! Give me a squiggly! Give me an M ! Give me an A ! Give me an R ! Give me a T !”

• Michael I. Norton is the Harold M. Brierley Professor of Business Administration at the Harvard Business School. He is the author of The Ritual Effect and co-author of Happy Money: The Science of Happier Spending . His research focuses on happiness, well-being, rituals, and inequality. See his faculty page here .

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Computer Science > Machine Learning

Title: megalodon: efficient llm pretraining and inference with unlimited context length.

Abstract: The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy. We introduce Megalodon, a neural architecture for efficient sequence modeling with unlimited context length. Megalodon inherits the architecture of Mega (exponential moving average with gated attention), and further introduces multiple technical components to improve its capability and stability, including complex exponential moving average (CEMA), timestep normalization layer, normalized attention mechanism and pre-norm with two-hop residual configuration. In a controlled head-to-head comparison with Llama2, Megalodon achieves better efficiency than Transformer in the scale of 7 billion parameters and 2 trillion training tokens. Megalodon reaches a training loss of 1.70, landing mid-way between Llama2-7B (1.75) and 13B (1.67). Code: this https URL

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