what are research articles

Guide to Scholarly Articles

• What is a Scholarly Article?
• Scholarly vs. Popular vs. Trade Articles

Types of Scholarly Articles

Qualitative, quantitative, and mixed-methods articles, why does this matter.

• Anatomy of Scholarly Articles
• Tips for Reading Scholarly Articles

Scholarly articles come in many different formats each with their own function in the scholarly conversation. The following are a few of the major types of scholarly articles you are likely to encounter as you become a part of the conversation. Identifying the different types of scholarly articles and knowing their function will help you become a better researcher.

Original/Empirical Studies

• Note: Empirical studies can be subdivided into qualitative studies, quantitative studies, or mixed methods studies. See below for more information  
• Usefulness for research:  Empirical studies are useful because they provide current original research on a topic which may contain a hypothesis or interpretation to advance or to disprove. 

Literature Reviews

• Distinguishing characteristic:  Literature reviews survey and analyze a clearly delaminated body of scholarly literature.  
• Usefulness for research: Literature reviews are useful as a way to quickly get up to date on a particular topic of research.

Theoretical Articles

• Distinguishing characteristic:  Theoretical articles draw on existing scholarship to improve upon or offer a new theoretical perspective on a given topic.
• Usefulness for research:  Theoretical articles are useful because they provide a theoretical framework you can apply to your own research.

Methodological Articles

• Distinguishing characteristic:  Methodological articles draw on existing scholarship to improve or offer new methodologies for exploring a given topic.
• Usefulness for research:  Methodological articles are useful because they provide a methodologies you can apply to your own research.

Case Studies

• Distinguishing characteristic:  Case studies focus on individual examples or instances of a phenomenon to illustrate a research problem or a a solution to a research problem.
• Usefulness for research:  Case studies are useful because they provide information about a research problem or data for analysis.

Book Reviews

• Distinguishing characteristic:  Book reviews provide summaries and evaluations of individual books.
• Usefulness for research:  Book reviews are useful because they provide summaries and evaluations of individual books relevant to your research.

Adapted from the Publication manual of the American Psychological Association : the official guide to APA style. (Sixth edition.). (2013). American Psychological Association.

Qualitative articles  ask "why" questions where as  quantitative  articles  ask "how many/how much?" questions. These approaches are are not mutually exclusive. In fact, many articles combine the two in a  mixed-methods  approach. 

We can think of these different kinds of scholarly articles as different tools designed for different tasks. What research task do you need to accomplish? Do you need to get up to date on a give topic? Find a literature review. Do you need to find a hypothesis to test or to extend? Find an empirical study. Do you need to explore methodologies? Find a methodological article.

• << Previous: Scholarly vs. Popular vs. Trade Articles
• Next: Anatomy of Scholarly Articles >>
• Last Updated: Aug 23, 2023 8:53 AM
• URL: https://researchguides.library.tufts.edu/scholarly-articles

• Research Guides

Reading for Research: Social Sciences

Structure of a research article.

• Structural Read

Guide Acknowledgements

How to Read a Scholarly Article from the Howard Tilton Memorial Library at Tulane University

Strategic Reading for Research   from the Howard Tilton Memorial Library at Tulane University

Bridging the Gap between Faculty Expectation and the Student Experience: Teaching Students toAnnotate and Synthesize Sources

Librarian for Sociology, Environmental Sociology, MHS and Public Policy Studies

Academic writing has features that vary only slightly across the different disciplines. Knowing these elements and the purpose of each serves help you to read and understand academic texts efficiently and effectively, and then apply what you read to your paper or project.

Social Science (and Science) original research articles generally follow IMRD: Introduction- Methods-Results-Discussion

Introduction

• Introduces topic of article
• Presents the "Research Gap"/Statement of Problem article will address
• How research presented in the article will solve the problem presented in research gap.
• Literature Review. presenting and evaluating previous scholarship on a topic.  Sometimes, this is separate section of the article. 

​Method & Results

• How research was done, including analysis and measurements.  
• Sometimes labeled as "Research Design"
• What answers were found
• Interpretation of Results (What Does It Mean? Why is it important?)
• Implications for the Field, how the study contributes to the existing field of knowledge
• Suggestions for further research
• Sometimes called Conclusion

You might also see IBC: Introduction - Body - Conclusion

• Identify the subject
• State the thesis 
• Describe why thesis is important to the field (this may be in the form of a literature review or general prose)

Body  

• Presents Evidence/Counter Evidence
• Integrate other writings (i.e. evidence) to support argument 
• Discuss why others may disagree (counter-evidence) and why argument is still valid
• Summary of argument
• Evaluation of argument by pointing out its implications and/or limitations 
• Anticipate and address possible counter-claims
• Suggest future directions of research
• Next: Structural Read >>
• Last Updated: Jan 19, 2024 10:44 AM
• URL: https://researchguides.library.vanderbilt.edu/readingforresearch

Finding Scholarly Articles: Home

What's a Scholarly Article?

Your professor has specified that you are to use scholarly (or primary research or peer-reviewed or refereed or academic) articles only in your paper. What does that mean?

Scholarly or primary research articles are peer-reviewed , which means that they have gone through the process of being read by reviewers or referees  before being accepted for publication. When a scholar submits an article to a scholarly journal, the manuscript is sent to experts in that field to read and decide if the research is valid and the article should be published. Typically the reviewers indicate to the journal editors whether they think the article should be accepted, sent back for revisions, or rejected.

To decide whether an article is a primary research article, look for the following:

• The author’s (or authors') credentials and academic affiliation(s) should be given;
• There should be an abstract summarizing the research;
• The methods and materials used should be given, often in a separate section;
• There are citations within the text or footnotes referencing sources used;
• Results of the research are given;
• There should be discussion   and  conclusion ;
• With a bibliography or list of references at the end.

Caution: even though a journal may be peer-reviewed, not all the items in it will be. For instance, there might be editorials, book reviews, news reports, etc. Check for the parts of the article to be sure.   

You can limit your search results to primary research, peer-reviewed or refereed articles in many databases. To search for scholarly articles in  HOLLIS , type your keywords in the box at the top, and select  Catalog&Articles  from the choices that appear next.   On the search results screen, look for the  Show Only section on the right and click on  Peer-reviewed articles . (Make sure to  login in with your HarvardKey to get full-text of the articles that Harvard has purchased.)

Many of the databases that Harvard offers have similar features to limit to peer-reviewed or scholarly articles.  For example in Academic Search Premier , click on the box for Scholarly (Peer Reviewed) Journals  on the search screen.

Review articles are another great way to find scholarly primary research articles.   Review articles are not considered "primary research", but they pull together primary research articles on a topic, summarize and analyze them.  In Google Scholar , click on Review Articles  at the left of the search results screen. Ask your professor whether review articles can be cited for an assignment.

A note about Google searching.  A regular Google search turns up a broad variety of results, which can include scholarly articles but Google results also contain commercial and popular sources which may be misleading, outdated, etc.  Use Google Scholar  through the Harvard Library instead.

About Wikipedia .  W ikipedia is not considered scholarly, and should not be cited, but it frequently includes references to scholarly articles. Before using those references for an assignment, double check by finding them in Hollis or a more specific subject  database .

Still not sure about a source? Consult the course syllabus for guidance, contact your professor or teaching fellow, or use the Ask A Librarian service.

• Last Updated: Oct 3, 2023 3:37 PM
• URL: https://guides.library.harvard.edu/FindingScholarlyArticles

Harvard University Digital Accessibility Policy

What is a Scholarly Article: What is a scholarly article

Determineif a source is scholarly, determine if a source is scholarly, what is a scholarly source.

Scholarly sources (also referred to as academic, peer-reviewed, or refereed sources) are written by experts in a particular field and serve to keep others interested in that field up to date on the most recent research, findings, and news. These resources will provide the most substantial information for your research and papers.

What is peer-review?

When a source has been peer-reviewed, it has undergone the review and scrutiny of a review board of colleagues in the author’s field. They evaluate this source as part of the body of research for a particular discipline and make recommendations regarding its publication in a journal, revisions prior to publication, or, in some cases, reject its publication.

Why use scholarly sources?

Scholarly sources’ authority and credibility improve the quality of your own paper or research project.

How can I tell if a source is scholarly?

The following characteristics can help you differentiate scholarly sources from those that are not. Be sure to look at the criteria in each category when making your determination, rather than basing your decision on only one piece of information.

• Are author names provided?
• Are the authors’ credentials provided?
• Are the credentials relevant to the information provided?
• Who is the publisher of the information?
• Is the publisher an academic institution, scholarly, or professional organization?
• Is their purpose for publishing this information evident?
• Who is the intended audience of this source?
• Is the language geared toward those with knowledge of a specific discipline rather than the general public?
• Why is the information being provided?
• Are sources cited?
• Are there charts, graphs, tables, and bibliographies included?
• Are research claims documented?
• Are conclusions based on evidence provided?
• How long is the source?

Currency/Timeliness

• Is the date of publication evident?

Additional Tips for Specific Scholarly Source Types

Each resource type below will also have unique criteria that can be applied to it to determine if it is scholarly.

• Books published by a University Press are likely to be scholarly.
• Professional organizations and the U.S. Government Printing Office can also be indicators that a book is scholarly.
• Book reviews can provide clues as to if a source is scholarly and highlight the intended audience. See our  Find Reviews  guide to locate reviews on titles of interest.
• Are the author’s professional affiliations provided?
• Who is the publisher?
• How frequently is the periodical published?
• How many and what kinds of advertisements are present? For example, is the advertising clearly geared towards readers in a specific discipline or occupation?
• For more information about different periodical types, see our  Selecting Sources  guide.
• What is the domain of the page (for example: .gov, .edu, etc.)?
• Who is publishing or sponsoring the page?
• Is contact information for the author/publisher provided?
• How recently was the page updated?
• Is the information biased? Scholarly materials published online should not have any evidence of bias.

Is My Source Scholarly? (Accessible View)

Step 1: Source

The article is most likely scholarly if:

• You found the article in a library database or Google Scholar
• The journal the article appears in is peer-reviewed

Move to Step 2: Authors

Step 2: Authors

The source is most likely scholarly if:

• The authors’ credentials are provided
• The authors are affiliated with a university or other research institute

Move to Step 3: Content

Step 3: Content

• The source is longer than 10 pages
• Has a works cited or bibliography
• It does not attempt to persuade or bias the reader
• It attempts to persuade or bias the reader, but treats the topic objectively, the information is well-supported, and it includes a works cited or bibliography

If the article meets the criteria in Steps 1-3 it is most likely scholarly.

Common Characteristics of a Scholarly Article

Common characteristics of scholarly (research) articles.

Articles in scholarly journals may also be called research journals, peer reviewed journals, or refereed journals. These types of articles share many common features, including:

• articles always provide the name of the author or multiple authors
• author(s) always have academic credentials (e.g. biologist, chemist, anthropologist, lawyer)
• articles often have a sober, serious look
• articles may contain many graphs and charts; few glossy pages or color pictures
• author(s) write in the language of the discipline (e.g. biology, chemistry, anthropology, law, etc.)
• authors write for other scholars, and emerging scholars
• authors always cite their sources in footnotes, bibliographies, notes, etc.
• often (but not always) associated with universities or professional organizations

Types of Scholarly Articles

Peer Review in 3 Minutes

North Carolina State University (NCSU) Libraries (3:15)

• What do peer reviewers do?  How are they similar to or different from editors?
• Who are the primary customers of scholarly journals?
• Do databases only include peer-reviewed articles?  How do you know?

Is my source scholarly

Is My Source Scholarly?: INFOGRAPHIC

This infographic is part of the Illinois Library's Determine if a source is scholarly.

"Is my source scholarly" by Illinois Library  https://www.library.illinois.edu/ugl/howdoi/scholarly/

Anatomy of a Scholarly Article: Interactive Tutorial

Typical Sections of a Peer-Reviewed Research Article

Typical sections of peer-reviewed research articles.

Research articles in many disciplines are organized into standard sections. Although these sections may vary by discipline, common sections include:

• Introduction
• Materials and Methods

It's not hard to spot these sections; just look for bold headings in the article, as shown in these illustrations:

• Last Updated: Oct 22, 2020 11:31 AM
• URL: https://libguides.mccd.edu/WhatisaScholarlyArticle

Welcome to the new OASIS website! We have academic skills, library skills, math and statistics support, and writing resources all together in one new home.

• Walden University
• Faculty Portal

Evaluating Resources: Research Articles

Research articles.

A research article is a journal article in which the authors report on the research they did. Research articles are always primary sources. Whether or not a research article is peer reviewed depends on the journal that publishes it.

Published research articles follow a predictable pattern and will contain most, if not all, of the sections listed below. However, the names for these sections may vary.

• Title & Author(s)
• Introduction
• Methodology

To learn about the different parts of a research article, please view this tutorial:

Short video: How to Read Scholarly Articles

Learn some tips on how to efficiently read scholarly articles.

Video: How to Read a Scholarly Article

(4 min 16 sec) Recorded August 2019 Transcript 

More information

The Academic Skills Center and the Writing Center both have helpful resources on critical and academic reading that can further help you understand and evaluate research articles.

• Academic Skills Center Guide: Developing Your Reading Skills
• Academic Skills Center Webinar Archive: Savvy Strategies for Academic Reading

If you'd like to learn how to find research articles in the Library, you can view this Quick Answer.

• Quick Answer: How do I find research articles?
• Previous Page: Primary & Secondary Sources
• Office of Student Disability Services

Walden Resources

Departments.

• Academic Residencies
• Academic Skills
• Career Planning and Development
• Customer Care Team
• Field Experience
• Military Services
• Student Success Advising
• Writing Skills

Centers and Offices

• Center for Social Change
• Office of Academic Support and Instructional Services
• Office of Degree Acceleration
• Office of Research and Doctoral Services
• Office of Student Affairs

Student Resources

• Doctoral Writing Assessment
• Form & Style Review
• Quick Answers
• ScholarWorks
• SKIL Courses and Workshops
• Walden Bookstore
• Walden Catalog & Student Handbook
• Student Safety/Title IX
• Legal & Consumer Information
• Website Terms and Conditions
• Cookie Policy
• Accessibility
• Accreditation
• State Authorization
• Net Price Calculator
• Cost of Attendance
• Contact Walden

Walden University is a member of Adtalem Global Education, Inc. www.adtalem.com Walden University is certified to operate by SCHEV © 2024 Walden University LLC. All rights reserved.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Trending Articles

• Targeting Ras-, Rho-, and Rab-family GTPases via a conserved cryptic pocket. Morstein J, et al. Cell. 2024. PMID: 39255801
• A brain-to-gut signal controls intestinal fat absorption. Lyu Q, et al. Nature. 2024. PMID: 39261733
• Gasdermin D-mediated metabolic crosstalk promotes tissue repair. Chi Z, et al. Nature. 2024. PMID: 39260418
• m 6 A mRNA methylation in brown fat regulates systemic insulin sensitivity via an inter-organ prostaglandin signaling axis independent of UCP1. Xiao L, et al. Cell Metab. 2024. PMID: 39255799
• SGLT2 inhibitor promotes ketogenesis to improve MASH by suppressing CD8 + T cell activation. Liu W, et al. Cell Metab. 2024. PMID: 39243758

Latest Literature

• J Biol Chem (2)
• J Clin Endocrinol Metab (1)
• J Immunol (2)
• J Neurosci (7)
• PLoS One (37)
• Proc Natl Acad Sci U S A (5)

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Maxwell Library | Bridgewater State University

Today's Hours: 

• Maxwell Library
• Scholarly Journals and Popular Magazines
• Differences in Research, Review, and Opinion Articles

Scholarly Journals and Popular Magazines: Differences in Research, Review, and Opinion Articles

• Where Do I Start?
• How Do I Find Peer-Reviewed Articles?
• How Do I Compare Periodical Types?
• Where Can I find More Information?

Research Articles, Reviews, and Opinion Pieces

Scholarly or research articles are written for experts in their fields. They are often peer-reviewed or reviewed by other experts in the field prior to publication. They often have terminology or jargon that is field specific. They are generally lengthy articles. Social science and science scholarly articles have similar structures as do arts and humanities scholarly articles. Not all items in a scholarly journal are peer reviewed. For example, an editorial opinion items can be published in a scholarly journal but the article itself is not scholarly. Scholarly journals may include book reviews or other content that have not been peer reviewed.

Empirical Study: (Original or Primary) based on observation, experimentation, or study. Clinical trials, clinical case studies, and most meta-analyses are empirical studies.

Review Article: (Secondary Sources) Article that summarizes the research in a particular subject, area, or topic. They often include a summary, an literature reviews, systematic reviews, and meta-analyses.

Clinical case study (Primary or Original sources): These articles provide real cases from medical or clinical practice. They often include symptoms and diagnosis.

Clinical trials ( Health Research): Th ese articles are often based on large groups of people. They often include methods and control studies. They tend to be lengthy articles.

Opinion Piece:  An opinion piece often includes personal thoughts, beliefs, or feelings or a judgement or conclusion based on facts. The goal may be to persuade or influence the reader that their position on this topic is the best.

Book review: Recent review of books in the field. They may be several pages but tend to be fairly short. 

Social Science and Science Research Articles

The majority of social science and physical science articles include

• Journal Title and Author
• Abstract 
• Introduction with a hypothesis or thesis
• Literature Review
• Methods/Methodology
• Results/Findings

Arts and Humanities Research Articles

In the Arts and Humanities, scholarly articles tend to be less formatted than in the social sciences and sciences. In the humanities, scholars are not conducting the same kinds of research experiments, but they are still using evidence to draw logical conclusions.  Common sections of these articles include:

• an Introduction
• Discussion/Conclusion
• works cited/References/Bibliography

Research versus Review Articles

• 6 Article types that journals publish: A guide for early career researchers
• INFOGRAPHIC: 5 Differences between a research paper and a review paper
• Michigan State University. Empirical vs Review Articles
• UC Merced Library. Empirical & Review Articles
• << Previous: Where Do I Start?
• Next: How Do I Find Peer-Reviewed Articles? >>
• Last Updated: Jan 24, 2024 10:48 AM
• URL: https://library.bridgew.edu/scholarly

Phone: 508.531.1392 Text: 508.425.4096 Email: [email protected]

Feedback/Comments

Privacy Policy

Website Accessibility

Follow us on Facebook Follow us on Twitter

Explore millions of high-quality primary sources and images from around the world, including artworks, maps, photographs, and more.

Explore migration issues through a variety of media types

• Part of Street Art Graphics
• Part of The Journal of Economic Perspectives, Vol. 34, No. 1 (Winter 2020)
• Part of Cato Institute (Aug. 3, 2021)
• Part of University of California Press
• Part of Open: Smithsonian National Museum of African American History & Culture
• Part of Indiana Journal of Global Legal Studies, Vol. 19, No. 1 (Winter 2012)
• Part of R Street Institute (Nov. 1, 2020)
• Part of Leuven University Press
• Part of UN Secretary-General Papers: Ban Ki-moon (2007-2016)
• Part of Perspectives on Terrorism, Vol. 12, No. 4 (August 2018)
• Part of Leveraging Lives: Serbia and Illegal Tunisian Migration to Europe, Carnegie Endowment for International Peace (Mar. 1, 2023)
• Part of UCL Press

Harness the power of visual materials—explore more than 3 million images now on JSTOR.

Enhance your scholarly research with underground newspapers, magazines, and journals.

Explore collections in the arts, sciences, and literature from the world’s leading museums, archives, and scholars.

What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

23k Accesses

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

Agnes, M., & Guralnik, D. B. (Eds.). (2008). Hypothesis. In Webster’s new world college dictionary (4th ed.). Wiley.

Google Scholar  

Britannica. (n.d.). Scientific method. In Encyclopaedia Britannica . Retrieved July 15, 2022 from https://www.britannica.com/science/scientific-method

Brownell, W. A., & Moser, H. E. (1949). Meaningful vs. mechanical learning: A study in grade III subtraction . Duke University Press..

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L., & Hiebert, J. (2019b). Posing significant research questions. Journal for Research in Mathematics Education, 50 (2), 114–120. https://doi.org/10.5951/jresematheduc.50.2.0114

Article   Google Scholar  

Cambridge University Press. (n.d.). Hypothesis. In Cambridge dictionary . Retrieved July 15, 2022 from https://dictionary.cambridge.org/us/dictionary/english/hypothesis

Cronbach, J. L. (1957). The two disciplines of scientific psychology. American Psychologist, 12 , 671–684.

Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology. American Psychologist, 30 , 116–127.

Cronbach, L. J. (1986). Social inquiry by and for earthlings. In D. W. Fiske & R. A. Shweder (Eds.), Metatheory in social science: Pluralisms and subjectivities (pp. 83–107). University of Chicago Press.

Hay, C. M. (Ed.). (2016). Methods that matter: Integrating mixed methods for more effective social science research . University of Chicago Press.

Merriam-Webster. (n.d.). Explain. In Merriam-Webster.com dictionary . Retrieved July 15, 2022, from https://www.merriam-webster.com/dictionary/explain

National Research Council. (2002). Scientific research in education . National Academy Press.

Weis, L., Eisenhart, M., Duncan, G. J., Albro, E., Bueschel, A. C., Cobb, P., Eccles, J., Mendenhall, R., Moss, P., Penuel, W., Ream, R. K., Rumbaut, R. G., Sloane, F., Weisner, T. S., & Wilson, J. (2019a). Mixed methods for studies that address broad and enduring issues in education research. Teachers College Record, 121 , 100307.

Weisner, T. S. (Ed.). (2005). Discovering successful pathways in children’s development: Mixed methods in the study of childhood and family life . University of Chicago Press.

Download references

Author information

Authors and affiliations.

School of Education, University of Delaware, Newark, DE, USA

James Hiebert, Anne K Morris & Charles Hohensee

Department of Mathematical Sciences, University of Delaware, Newark, DE, USA

Jinfa Cai & Stephen Hwang

You can also search for this author in PubMed   Google Scholar

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and permissions

Copyright information

© 2023 The Author(s)

About this chapter

Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

Download citation

DOI : https://doi.org/10.1007/978-3-031-19078-0_1

Published : 03 December 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-19077-3

Online ISBN : 978-3-031-19078-0

eBook Packages : Education Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Find and Read Articles

PLOS publishes a suite of peer-reviewed Open Access journals that feature quality research, expert commentary, and critical analysis across all scientific disciplines.

New Articles by Email

Journal alerts.

PLOS articles publish daily and roll up into monthly issues. To receive a regular email alert with a list of articles published in each issue, sign up at the bottom of any journal homepage.

Choose how often to receive alerts and manage subscriptions from your PLOS account.

• Sign in to your account, then click the Profile button at the top of the page. 
• Navigate to the Alerts tab.
• Under Journal Alerts , check the boxes to choose weekly or monthly delivery.

The weekly email alert from PLOS ONE delivers all new articles by default.

Select Send me a custom email alert to receive articles from specific subject areas. Select one or more subject areas to add them to the email.

Search alerts

Set up a search alert to be notified when new PLOS articles are published relevant to a personalized search. To start, create or sign in to your PLOS account. Read more about PLOS accounts .

Click the link underneath the search box to navigate to the advanced search page.

Customize the search criteria by subject, article type, author, or a variety of other fields. Choose any combination of PLOS journals. 

Once you have a satisfactory set of results, select a delivery method at the top of the page. PLOS offers two delivery methods for these notices: email and RSS feed. Read more about PLOS RSS feeds .

To receive new articles by email, click the  search alert  button. Name the search, choose the email frequency, and click Save .

Unsubscribe

Unsubscribe from email alerts in your PLOS account . Sign in to your account from the top of any journal page, and click the  Profile  button.

Journal alerts Navigate to Alerts > Journal Alerts. ​Uncheck the box to unsubscribe from a specific mailing list.

Saved search alerts Navigate to  Alerts > Search Alerts . Click X  to stop receiving the alert.

New Articles by RSS

PLOS RSS feeds are regular updates with article titles and abstracts, collected in your browser or a feed reader. Use the feeds if you do not want alerts delivered by email but do want to know when new articles are published or added to a saved search. Find out how to create a search alert feed .

RSS feeds are available by clicking the RSS icon on each journal home page. Use any feed reader to collect and read the article list.

Browse by Subject Area

Each PLOS article offers a list of subject area tags based on its subject matter. Click on any term to view other PLOS articles that use the same tag.

Sign up for subject-specific alerts while browsing subject areas on the PLOS ONE site.

• Sign in or create a PLOS account .
• Click the Browse button at the top of any PLOS ONE page.

• Select a subject area from the menu to view the associated articles from PLOS ONE .

• Click the email alert button in the top right corner of the page. Then, click Save  to subscribe.

Sign Up for a PLOS Account

Sign up for a free PLOS account to manage email alerts and post comments on articles.

Create an account

Click create account at the top of any PLOS journal page to sign up.

After you fill in the form, you’ll receive an email to verify your account and complete registration.

Customize and edit your account

Sign in by clicking the button in the upper right corner from any PLOS journal page.

Click the Profile button at the top of the page to edit your account details, including personal information, email address, password, and email subscriptions.

The first time you sign in to your account, create a profile with a username and basic identifying information. Your username is attached to all comments that you post on PLOS web sites.

• Election 2024
• Entertainment
• Newsletters
• Photography
• AP Investigations
• AP Buyline Personal Finance
• AP Buyline Shopping
• Press Releases
• Israel-Hamas War
• Russia-Ukraine War
• Global elections
• Asia Pacific
• Latin America
• Middle East
• Election results
• Google trends
• AP & Elections
• College football
• Auto Racing
• Movie reviews
• Book reviews
• Financial Markets
• Business Highlights
• Financial wellness
• Artificial Intelligence
• Social Media

Studies on pigeon-guided missiles, swimming abilities of dead fish among Ig Nobels winners

A study that explores the use of pigeons to guide missiles and one that looks at separating drunk worms from sober worms were among the winners of this year’s Ig Nobels, the prize for comical scientific achievement. (AP Video shot by Rodrique Ngowi)

People in the audience throw paper airplanes toward the stage during a performance at the Ig Nobel Prize ceremony at Massachusetts Institute of Technology in Cambridge, Mass., Thursday, Sept. 12, 2024. (AP Photo/Steven Senne)

A team of researchers perform a demonstration during a performance showing that many mammals are capable of breathing through their anus while accepting the 2024 Ig Nobel prize in physiology at the Ig Nobel Prize ceremony at Massachusetts Institute of Technology, in Cambridge, Mass., Thursday, Sept. 12, 2024. (AP Photo/Steven Senne)

Professor James Liao displays a stuffed fish while accepting a prize for physics for demonstrating and explaining the swimming abilities of a dead trout during a performance at the Ig Nobel Prize ceremony at Massachusetts Institute of Technology in Cambridge, Mass., Thursday, Sept. 12, 2024. (AP Photo/Steven Senne)

FILE - Students walk past the “Great Dome” atop Building 10 on the Massachusetts Institute of Technology campus in Cambridge, Mass, April 3, 2017. (AP Photo/Charles Krupa, File)

Professor Sander Woutersen, right, displays an oversized stuffed worm while accepting a shared Ig Nobel Prize in chemistry for working with a team of researchers using chromatography to separate drunk and sober worms, during a performance, Thursday, Sept. 12, 2024, at the Ig Nobel Prize ceremony at Massachusetts Institute of Technology, in Cambridge, Mass. (AP Photo/Steven Senne)

A performer places a stuffed toy cat on an inflatable cow, Thursday, Sept. 12, 2024, to demonstrate exploding a paper bag next to a cat that’s standing on the back of a cow, to explore how and when cows spew their milk, at the Ig Nobel Prize ceremony on the campus of Massachusetts Institute of Technology, in Cambridge, Mass. (AP Photo/Steven Senne)

Eric Maskin, 2007 Nobel Laureate in Economics, right, presents an Ig Nobel award to a members of a team of researchers who who used chromatography to separate drunk and sober worms, during a performance at the Ig Nobel Prize ceremony at Massachusetts Institute of Technology, in Cambridge, Mass., Thursday, Sept. 12, 2024. (AP Photo/Steven Senne)

• Copy Link copied

BOSTON (AP) — A study that explores the feasibility of using pigeons to guide missiles and one that looks at the swimming abilities of dead fish were among the winners Thursday of this year’s Ig Nobels, the prize for comical scientific achievement.

Held less than a month before the actual Nobel Prizes are announced, the 34th annual Ig Nobel prize ceremony at the Massachusetts Institute of Technology was organized by the Annals of Improbable Research magazine’s website to make people laugh and think. Winners received a transparent box containing historic items related to Murphy’s Law — the theme of the night — and a nearly worthless Zimbabwean $10 trillion bill. Actual Nobel laureates handed the winners their prizes.

“While some politicians were trying to make sensible things sound crazy, scientists discovered some crazy-sounding things that make a lot of sense,” Marc Abrahams, master of ceremonies and editor of the magazine, said in an e-mail interview.

The ceremony started with Kees Moliker, winner of 2003 Ig Nobel for biology, giving out safety instructions. His prize was for a study that documented the existence of homosexual necrophilia in mallard ducks.

“This is the duck,” he said, holding up a duck. “This is the dead one.”

After that, someone came on stage wearing a yellow target on their chest and a plastic face mask. Soon, they were inundated with people in the audience throwing paper airplanes at them.

Then, the awards began — several dry presentations which were interrupted by a girl coming on stage and repeatedly yelling “Please stop. I’m bored.” The awards ceremony was also was broken up by an international song competition inspired by Murphy’s Law, including one about coleslaw and another about the legal system.

The winners were honored in 10 categories, including for peace and anatomy. Among them were scientists who showed a vine from Chile imitates the shapes of artificial plants nearby and another study that examined whether the hair on people’s heads in the Northern Hemisphere swirled in the same direction as someone’s hair in the Southern Hemisphere.

Other winners include a group of scientists who showed that fake medicine that causes side effects can be more effective than fake medicine that doesn’t cause side effects and one showing that some mammals are capable of breathing through their anus — winners who came on stage wearing a fish-inspired hats.

Julie Skinner Vargas accepted the peace prize on behalf of her late father B.F. Skinner, who wrote the pigeon-missile study. Skinner Vargas is also the head of the B.F. Skinner Foundation.

“I want to thank you for finally acknowledging his most important contribution,” she said. “Thank you for putting the record straight.”

James Liao, a biology professor at the University of Florida, accepted the physics prize for his study demonstrating and explaining the swimming abilities of a dead trout.

“I discovered that a live fish moved more than a dead fish but not by much,” Liao said, holding up a fake fish. “A dead trout towed behind a stick also flaps its tail to the beat of the current like a live fish surfing on swirling eddies, recapturing the energy in its environment. A dead fish does live fish things.”

Cautious Optimism on OSTP Research Cybersecurity Requirements

The Office of Science and Technology Policy has released its final requirements for research security programs, which federal research funding agencies will have to apply to colleges and universities that average $50 million or more per year in federal research grants. The requirements include potentially positive guidelines for research cybersecurity at covered institutions.

In early 2023, the White House Office of Science and Technology Policy (OSTP) released its initial proposal for a "research security program standard requirement." All federal research funding agencies would have to apply the requirement to colleges and universities that receive more than $50 million per year in federal research funding. Footnote 1 The development of these comprehensive research security mandates stems from National Security Presidential Memorandum – 33 (NSPM-33), "Supported Research and Development National Security Policy." When finalized, the "standard requirement" would establish the basic parameters for the research security programs that covered institutions must have in place to continue competing for federal research grants.

Most of the proposed framework addresses research security issues such as faculty conflicts of interest and commitment and research talent recruitment programs of foreign governments. However, it also includes a research cybersecurity section that essentially would make the cybersecurity guidelines for Federal contract information (FCI) the standards for higher education research cybersecurity. As the Policy team discussed in our review of this issue last summer, EDUCAUSE member feedback indicated that the FCI basic safeguards do not fit well with higher education research environments because they are primarily intended for administrative contexts and data. Footnote 2 EDUCAUSE urged OSTP to revamp its proposed research security program guidance and focus on allowing institutions to pursue a risk management approach to research cybersecurity. Rather than the one-size-fits-all checklist model that the FCI guidelines would impose, a risk management approach would enable institutions to prioritize cybersecurity measures and resources based on national security risks associated with research areas and projects.

EDUCAUSE was not alone in asking OSTP to alter its course and base its research security program guidance on risk management. The Association of American Universities (AAU), the Association of Public and Land-grant Universities (APLU), and the Council on Governmental Relations (COGR) also stressed the need for a risk management emphasis in other areas of higher education research security. Fortunately, OSTP heard the combined input of our respective associations. Rather than rushing forward with research security program requirements that largely reflected those in its original proposal, OSTP took roughly one year to rethink its guidance before releasing the final version on July 9, 2024. The final research security program guidelines do not base research cybersecurity program requirements on the FCI safeguards. Instead, OSTP points to a pending report on higher education research cybersecurity from the National Institute of Standards and Technology (NIST).

As the first element of the standardized requirement, federal research agencies shall require institutions of higher education to certify that the institution will implement a cybersecurity program consistent with the cybersecurity resource for research institutions described in the CHIPS and Science Act, [18] within one year after the National Institute of Standards and Technology (NIST) of the Department of Commerce publishes that resource. Footnote 3

Footnote 18 in the memorandum (in brackets above) identifies the relevant NIST report as NIST Interagency Report (IR) 8481: Cybersecurity for Research: Findings and Possible Paths Forward , which is currently available in "Initial Public Draft" (IPD) form. The CHIPS and Science Act provision from which the report stems required NIST to explore the resources it could develop to better support research cybersecurity at higher education institutions. Footnote 4 NIST conducted substantial outreach to EDUCAUSE and its members in pursuing the project, leading to a draft that largely incorporates the recommendations of our research cybersecurity community. It is a welcome development to see OSTP cite the report as the governing reference for research cybersecurity under its research security program guidelines.

Although OSTP's reliance on a report that reflects substantial EDUCAUSE member input provides a basis for cautious optimism regarding how federal research agencies will implement research cybersecurity requirements, there is still room for agency compliance efforts to jump the rails. The OSTP memorandum does not explain or provide parameters for what constitutes "a cybersecurity program consistent with" the NIST report (emphasis added). Footnote 5 Given the overall tenor of the guidelines, which stress the importance of federal research agencies providing substantial flexibility and discretion to higher education institutions in establishing and maintaining research security programs, research agencies might reasonably develop policies and procedures that allow institutions to draw from the range of resources identified in the NIST report—as well as models and frameworks similar to them—in determining the basis of their programs. However, the lack of guidance on what "consistent with" means may leave space for agencies to mandate that their grantees implement specific frameworks or measures presented in the NIST report. Such a development could produce substantial risks for institutions and agencies alike, given that not all resources identified in the draft NIST report will necessarily lead to optimal—or even appropriate—outcomes in all higher education research contexts.

Our concern about the potential for agencies to mandate inappropriate requirements is exacerbated by the fact that the NIST report was not written for the purposes for which OSTP is applying it. As previously mentioned, the CHIPS and Science Act charged NIST with identifying ways the agency could better support higher education research cybersecurity. Given that task, the current draft of the report—not surprisingly—focuses on highlighting a variety of options that institutions might explore to advance their research cybersecurity posture. This focus does not exactly match how OSTP wants to use the report in its research security program guidelines. The advisory nature of the NIST report may lend itself to the institutional flexibility and discretion that the OSTP memo implies should be the basis of federal agency approaches to research (cyber)security. However, the report does not provide clear direction about what cybersecurity should look like for research security programs that comply with NSPM-33. Without a definitive framework, both research agencies and higher education institutions may struggle to determine what constitutes compliance.

Fortunately, EDUCAUSE members should not have to wait long to get a sense of whether federal agencies that fund research will either try to be highly prescriptive or allow covered institutions to choose what elements of the NIST report—or options similar to them—will form the basis of their research cybersecurity programs. The memo from OSTP states that agencies will have six months from the date the memo was published to provide OSTP and the Office of Management and Budget (OMB) with their proposed implementation plans for the research security program guidelines. Once those agency plans are submitted, colleges and universities should be able to better understand what agencies' compliance regimes might look like. Agencies will then have another six months to implement their policies and processes, with institutions getting up to eighteen months from that point to ensure that they have compliant research security programs. Footnote 6 Based on these time frames, we should see research agency implementation plans by early January 2025, with the final execution of those plans due by mid-2025. Institutions would then have to achieve compliance with the relevant agency policies and processes by around December 2026.

Remember, though, that OSTP provides a unique timeline for its research cybersecurity requirements. As stated above, institutions will have one year from the publication of the NIST final report to ensure that they have research cybersecurity programs that are "consistent with" the report. With that in mind, NIST could try to align the release of its final report with the timeline for institutional compliance with OSTP's research security program guidelines. In this case, the overall measures mandated by the OSTP guidelines would have to be in place by the end of 2026. However, nothing in the OSTP memo precludes NIST from starting the research cybersecurity clock much sooner by releasing its final report at some point later this year or in early 2025. At this juncture, we will have to wait for NIST to provide more information about its plans, which will most likely include making some adjustments between the draft and final versions to account for how research agencies and higher education institutions will have to make use of the final report for compliance purposes.

EDUCAUSE will continue to monitor developments in this space and look for opportunities to inform OSTP, NIST, and agency implementation efforts. In the interim, EDUCAUSE members should review the draft NIST report for reference points that align with their current institutional research cybersecurity program and for resources they might find useful in strengthening their research cybersecurity posture given NSPM-33 and the OSTP research security guidelines that derive from it.

• Arati Prabhakar, Memorandum for the Heads of Federal Research Agencies, "Guidelines for Research Security Programs at Covered Institutions," (Office of Science and Technology Policy, Executive Office of the President, July 9, 2024), 3. Jump back to footnote 1 in the text. ↩
• EDUCAUSE letter to Stacy Murphy, Deputy Chief Operations Officer/Security Officer, Office of Science and Technology Policy,  "Regarding Comment on Research Security Programs,"  June 5, 2023. Jump back to footnote 2 in the text. ↩
• Prabhakar, "Guidelines for Research Security Programs," 4. Jump back to footnote 3 in the text. ↩
• Jarret Cummings, "NIST Explores Developing Research Cybersecurity Resources for Higher Ed,"   EDUCAUSE Review , August 1, 2023. Jump back to footnote 4 in the text. ↩
• Prabhakar, "Guidelines for Research Security Programs," 4–5. Jump back to footnote 5 in the text. ↩
• Ibid., 9. Jump back to footnote 6 in the text. ↩

Jarret Cummings is Senior Advisor, Policy and Government Relations, at EDUCAUSE.

© 2024 EDUCAUSE. The content of this work is licensed under a Creative Commons BY-NC-ND 4.0 International License.

Research Forum Brief | June 2024

MatterGen: A Generative Model for Materials Design

Share this page

Presented by  Tian Xie at  Microsoft Research Forum, June 2024

“Materials design is the cornerstone of modern technology. Many of the challenges our society is facing today are bottlenecked by finding a good material. … If we can find a novel material that conducts lithium very well, it will be a key component for our next-generation battery technology. The same applies to many other domains.” – Tian Xie, Principal Research Manager, Microsoft Research AI for Science

Transcript: Lightning Talk

Tian Xie , Principal Research Manager, Microsoft Research AI for Science

Tian Xie introduces MatterGen , a generative model that creates new inorganic materials based on a broad range of property conditions required by the application, aiming to shift the traditional paradigm of materials design with generative AI.

Microsoft Research Forum, June 4, 2024

TIAN XIE: Hello, everyone. My name is Tian, and I’m from Microsoft Research AI for Science. I’m excited to be here to share with you MatterGen, our latest model that brings generative AI to materials design.

Materials design is the cornerstone of modern technology. Many of the challenges our society is facing today are bottlenecked by finding a good material. For example, if we can find a novel material that conducts lithium very well, it will be a key component for our next-generation battery technology. The same applies to many other domains, like finding a novel material for solar cells, carbon capture, and quantum computers. Traditionally, materials design is conducted by search-based methods. We search through a list of candidates and gradually filter them using a list of design criteria for the application. Like for batteries, we need the materials to contain lithium, to be stable, to have a high lithium-ion conductivity, and each filtering step can be conducted using simulation-based methods or AI emulators. At the end, we get five to 10 candidates that we’re sending to the lab for experimental synthesis.

In MatterGen, we hope to rethink this process with generative AI. We’re aiming to directly generate materials given the design requirements for the target application, bypassing the process of searching through candidates. You can think of it as using text-to-image generative models like DALL-E to generate the images given a prompt rather than needing to search through the entire internet for images via a search engine. The core of MatterGen is a diffusion model specifically designed for materials. A material can be represented by its unit cell, the smallest repeating unit of the infinite periodic structure. It has three components: atom types, atom positions, and periodic lattice. We designed the forward process to corrupt all three components towards a random structure and then have a model to reverse this process to generate a novel material. Conceptually, it is similar to using a diffusion model for images, but we build a lot of inductive bias like equivariance and periodicity into the model because we’re operating on a sparse data region as in most scientific domains.

Given this diffusion architecture, we train the base model of MatterGen using the structure of all known stable materials. Once trained, we can generate novel, stable materials by sampling from the base model unconditionally. To generate the material given desired conditions, we further fine-tune this base model by adding conditions to each layer of the network using a ControlNet-style parameter-efficient fine-tuning approach. The condition can be anything like a specific chemistry, symmetry, or any target property. Once fine-tuned, the model can directly generate the materials given desired conditions. Since we use fine-tuning, we only need a small labeled dataset to generate the materials given the corresponding condition, which is actually very useful for the users because it’s usually computationally expensive to generate a property-labeled dataset for materials.

Here’s an example of how MatterGen generates novel materials in the strontium-vanadium- oxygen chemical system. It generates candidates with lower energy than two other competing methods: random structure search and substitution. The resulting structure looks very reasonable and is proven to be stable using computational methods. MatterGen also generates materials given desired magnetic, electronic, and mechanical properties. The most impressive result here is that we can shift the distribution of generated material towards extreme values compared with training property. This is very significant because most of the materials design problem involves finding materials with extreme properties, like finding superhard materials, magnets with high magnetism, which is difficult to do with traditional search-based methods and is the key advantage of generative models.

Our major next step is to bring this generative AI–designed materials into the real life, making real-world impact in a variety of domains like battery design, solar cell design, and carbon capture. One limitation is that we only have validated this AI-generated materials using computation. We’re working with experimental partners to synthesize them in the wet lab. It is a nontrivial process, but we keep improving our model, getting feedbacks from the experimentalist, and we are looking forward to a future where generative AI–designed materials can make real-world impact in a broad range of domains. Here’s a link to our paper in case you want to learn more about the details. We look forward to any comments and feedbacks that you might have. Thank you very much.

MatterGen: Designing materials with generative AI 

By Tian Xie

MatterGen, a model developed by Microsoft Research AI for Science, applies generative AI to materials design.

Why is this important?

Materials design is the cornerstone of modern technology. Many of the challenges our society is facing today are bottlenecked by scientists’ inability to find good materials that can unlock solutions. If we can find a novel material that conducts lithium ion extremely well, for example, it will be a key component of next-generation battery technology. The same applies to many other domains, like finding novel materials for solar cells, carbon capture, and quantum computers. 

Traditionally, materials design is conducted by search-based methods. We search through a list of candidates and gradually filter them down with a list of design requirements for the application. With batteries, for example, we need the material to contain lithium, to be stable, to have high lithium-ion conductivity, and so on. Each filtering step can be conducted using quantum mechanical simulations or AI emulators. Finally, we end up with 5-10 candidates that can be sent to the lab for experimental synthesis.

In MatterGen , we hope to rethink this process using generative AI. We aim to directly generate materials, given the design requirements for the target application, bypassing the tedious process of searching through a large number of candidates. You can think of it as using text-image generative models like DALLE to generate images given a detailed prompt, rather than using a search engine to scour the entire Internet for specific images.

The core of MatterGen is a diffusion model specifically designed for materials. A material can be represented by its unit cell, the smallest repeating unit of the infinite periodic structure. It has three components: atom types; atom positions; and the periodic lattice. We design the forward process to corrupt all three components toward a random material, and then train a model to reverse the corruption process to generate novel materials. Conceptually, it is similar to a diffusion model for images, but we build a lot of inductive bias, like equivariance and periodicity, into the model because we operate on the sparse data region as in most scientific domains.

Given this diffusion architecture, we train the base model of MatterGen using the structure of all known stable materials. Once the model is trained, we can generate novel, stable materials by sampling from the base model unconditionally. 

To generate materials given the desired conditions, we further fine-tune this base model by adding conditions to each layer of the network, using a ControlNet-style parameter efficient fine-tuning approach. The conditions can be anything, like a specific chemistry, symmetry, or any target property. Once fine-tuned, the model can directly generate materials given desired conditions. Since we use fine-tuning, we only need a small labeled material dataset to generate materials with the corresponding condition, which is very useful for users, because it is often computationally expensive to generate property labels for materials.

Here is an example of how MatterGen generates novel materials in the Sr-V-O (Strontium-Vanadium-Oxygen) chemical system. It generates candidates with lower energy than two other competing methods: random structure search and substitution. The resulting structures look quite reasonable and are proven to be stable using computational methods. 

MatterGen can also generate materials given desired magnetic, electronic, and mechanical properties. The most impressive result here is that we can shift the distribution of generated materials toward extreme values compared with training property distribution. This is very significant, because most materials design problems involve finding materials with extreme properties, such as finding super hard materials or magnets with high magnesium, which is difficult with traditional search-based methods. 

Our next major step is to use these generative AI designed materials to make real-world impacts in a variety of domains, such as battery design, solar-cell design, and carbon capture. One limitation is that we have only validated these AI-generated materials with computation. We are working with experimental partners to synthesize them in the lab. This is not a trivial process, but we will keep improving our models with feedback from the experimentalists. We look forward to a future where generative AI can disrupt the current materials design process and find revolutionary materials that can positively change everyone’s life.

Related resources

• Research Lab Microsoft Research AI for Science 
• Publication MatterGen: a generative model for inorganic materials design 
• Blog MatterGen: Property-guided materials design 
• Follow on X
• Like on Facebook
• Follow on LinkedIn
• Subscribe on Youtube
• Follow on Instagram
• Subscribe to our RSS feed

Share this page:

• Share on Facebook
• Share on LinkedIn
• Share on Reddit

We've detected unusual activity from your computer network

To continue, please click the box below to let us know you're not a robot.

Why did this happen?

Please make sure your browser supports JavaScript and cookies and that you are not blocking them from loading. For more information you can review our Terms of Service and Cookie Policy .

For inquiries related to this message please contact our support team and provide the reference ID below.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts

Latest science news, discoveries and analysis

Weird signal that baffled seismologists traced to mega-landslide in Greenland

The brain aged more slowly in monkeys given a cheap diabetes drug

AI chatbot gets conspiracy theorists to question their convictions

First private spacewalk a success! What the SpaceX mission means for science

Why do we crumble under pressure science has the answer, famed pacific island’s population 'crash' debunked by ancient dna, us election debate: what harris and trump said about science, why some people enter menopause early — and how that could affect their cancer risk, europe sidelines alzheimer’s drug: lessons must be learnt henrik zetterberg.

How we slashed our lab’s carbon footprint

Red light, green light: flickering fluorophores reveal biochemistry in cells

The human costs of the research-assessment culture

The baseless stat that could be harming Indigenous conservation efforts

Academics say flying to meetings harms the climate — but they carry on, ancient dna debunks rapa nui ‘ecological suicide’ theory, human embryo models are getting more realistic — raising ethical questions, why does heart disease affect so many young south asians.

How to support Indigenous Peoples on biodiversity: be rigorous with data

Mpox: apply COVID lessons to control outbreak in Africa

Data on SDGs are riddled with gaps. Citizens can help

Wildfires are spreading fast in canada — we must strengthen forests for the future, why the next pandemic could come from the arctic — and what to do about it christian sonne, current issue.

Bioprospecting marine microbial genomes to improve biotechnology

Spectroscopic confirmation of two luminous galaxies at a redshift of 14, one month convection timescale on the surface of a giant evolved star, research analysis.

Rapa Nui’s population history rewritten using ancient DNA

Menopause age shaped by genes that influence mutation risk

Swirling star bubbles offer a glimpse of the Sun’s future

Future optoelectronics unlocked by ‘doping’ strategy

Long-lasting heart-failure treatment could be a game-changer, lipid recycling by macrophage cells drives the growth of brain cancer, thread, read, rewind, repeat: towards using nanopores for protein sequencing, cell-to-cell tunnels rescue neurons from degeneration.

The grassroots organizations continuing the fight for Ukrainian science

How a struggling biotech company became a university ‘spin-in’

I make fake eyes for those who need them, massive attack’s science-led drive to lower music’s carbon footprint, books & culture.

How influencers and algorithms mobilize propaganda — and distort reality

Consider the finches: Books in brief

Why repairing forests is not just about planting trees

Candidate 1143172 cover letter: junior pot scrubber, nature podcast.

Latest videos

Nature briefing.

An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies