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How I create virtual twins for fabrics and furniture

Jakub Cech creates digital versions of materials for use in the virtual world.

• Esme Hedley

Birth of protein folds and functions in the virome

Structural comparison of predicted viral protein structures with known protein structures suggests taxonomic relationships and functions for up to 25% of unannotated viral proteins, including many with putative functions in host immune evasion.

• Jason Nomburg
• Erin E. Doherty
• Jennifer A. Doudna

The meaning of the Anthropocene: why it matters even without a formal geological definition

Even though geologists have rejected the designation of an Anthropocene epoch, the idea of a major planetary transition in the mid-twentieth century remains useful across physical and social sciences, the humanities and policy.

• Jan Zalasiewicz
• Julia Adeney Thomas
• Martin J. Head

Mysterious Oropouche virus is spreading: what you should know

The virus is endemic to the Amazon but is now spreading outside the region ― and has been linked to human deaths for the first time.

• Mariana Lenharo

What Science and Nature are good for: causing paper cuts

Experiments reveal that human skin is most reliably cut by specific thicknesses of paper, including the kind used to print certain high-profile journals.

Family matters.

Stone Age builders had engineering savvy, finds study of 6000-year-old monument

A survey of the Dolmen of Menga suggests that the stone tomb’s Neolithic builders had an understanding of science.

AI analysed 1,500 policies to cut emissions. These ones worked

Only 63 climate change interventions led to significant reductions in carbon emissions.

• Xiaoying You

Science treasures from Microsoft mogul up for auction — and researchers are salivating

Spacesuits, historic computers and more from the estate of the late Paul Allen are going on sale.

• Alix Soliman

How to harness AI’s potential in research — responsibly and ethically

Artificial intelligence is propelling advances in all areas of science. But vigilance is needed, warn four researchers at the leading edge.

• Jane Palmer

Lactate helps cancer cells resist chemotherapy

The molecule lactate is a waste product of the metabolism of sugar without oxygen — a metabolic pathway preferentially used by cancer cells to generate their energy. Metabolomics analysis reveals that lactate in tumour cells promotes resistance to chemotherapy, and sheds light on the molecular mechanism that underlies this unexpected role of lactate in cancer.

Partners in drug discovery: how to collaborate with non-governmental organizations

Not all researchers join forces with big pharmaceutical firms, non-profit groups can help take lifesaving findings to where they are needed most.

• Anna Napolitano

Ancient equine genomes reveal dawn of horse domestication

An analysis of ancient genomes reveals an explosive geographical and demographic spread of modern domestic horses about 4,200 years ago. The findings counter the idea that horses accompanied and mobilized the mass migration of humans from the Eurasian steppes about 5,000 years ago.

Fall of the wild: why pristine wilderness is a human-made myth

Even ‘untouched’ natural landscapes bear witness to millennia of human influence, a lyrical book argues — with implications for how we seek to rewild them.

• Douglas H. Erwin

This unlucky star got mangled by a black hole — twice

Bursts of light hint that a star in a nearby galaxy was partially shredded in 2022 and 2024 and might be in for another round.

Can ageing be stopped? A biologist explains

Nobel laureate Venki Ramakrishnan joins us to talk about his book Why We Die: The New Science of Ageing and the Quest for Immortality .

• Benjamin Thompson

Extreme heat is a huge killer — these local approaches can keep people safe

As the threat of deadly heatwaves rises, scientists are working with cities to introduce low-tech cooling features to protect citizens.

Don’t stop me now: Queen’s Brian May on saving badgers — and the scientific method

The guitarist has spent a decade studying the science of bovine tuberculosis, which can be carried by badgers, and has identified a new method of spread.

• Elizabeth Gibney

Daily briefing: It’s not the fasting, it’s the eating

Fasting’s regenerative powers kick in when the feasting starts. Plus, future plans for detecting gravitational waves and an energy-rating scheme for artificial intelligence systems.

• Flora Graham

Debate rages over Alzheimer’s drug lecanemab as UK limits approval

The medicine is being assessed by agencies including the European Union regulator, but the community is divided on its efficacy and safety.

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Science News

The possibilities for dark matter have just shrunk — by a lot 

The LZ dark matter experiment has ruled out weakly interacting massive particles, or WIMPs, with a wide range of properties.

Mantle waves buoy continents upward and bedeck them with diamonds

Extreme heat and rain help send dengue cases skyrocketing

More than 100 bacteria species can flourish in microwave ovens

National Geographic’s ‘OceanXplorers’ dives into the ocean’s mysteries

50 years ago, antibiotic resistant bacteria became a problem outside hospitals

New COVID-19 booster shots have been approved. When should you get one?

Trending stories.

Zapping sand to create rock could help curb coastal erosion

A newly approved ‘living drug’ could save more cancer patients’ lives

The world’s record-breaking hot streak has lasted 14 months. When will it end?

Spotlight on Health

The vaccines target the omicron variants currently circulating in the United States.

From the Archives

The Universe: Chaotic or Bioselective?

August 24, 1974 Vol. 106 No. #8

Science News Magazine

August 24, 2024 Vol. 206 No. 3

NASA’s Perseverance rover finds its first possible hint of ancient life on Mars

Paper cut physics pinpoints the most hazardous types of paper.

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National Geographic’s documentary series ‘OceanXplorers,’ produced by James Cameron, invites you aboard one of the most advanced research vessels in the world.

A risk-tolerant immune system may enable house sparrows’ wanderlust

A new element on the periodic table might be within reach 

Freeze-drying turned a woolly mammoth’s DNA into 3-D ‘chromoglass’

Twisters asks if you can 'tame' a tornado. We have the answer

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World record speeds for two Olympics events have fallen over time. We can go faster

Does social status shape height, rain bosworth studies how deaf children experience the world, extraordinary heat waves have readers asking how a/c affects greenhouse gas emissions.

The nearest midsized black hole might instead be a horde of lightweights

A distant quasar may be zapping all galaxies around itself, some meteors leave trails lasting up to an hour. now we may know why.

The world’s fastest microscope makes its debut

Can light spark superconductivity a new study reignites debate, health & medicine.

Expanding antibiotic treatment in sub-Saharan Africa could save kids’ lives

Why mpox is a global health emergency — again, your medications might make it harder for you to beat the heat.

‘Turning to Stone’ paints rocks as storytellers and mentors

Why japan issued its first-ever mega-earthquake alert, squall line tornadoes are sneaky, dangerous and difficult to forecast, science & society.

‘After 1177 B.C.’ describes how societies fared when the Bronze Age ended

Scientists are fixing flawed forensics that can lead to wrongful convictions, language models may miss signs of depression in black people’s facebook posts.

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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

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The top 10 journal articles of 2020

In 2020, APA’s 89 journals published more than 5,000 articles—the most ever and 25% more than in 2019. Here’s a quick look at the 10 most downloaded to date.

Vol. 52 No. 1 Print version: page 24

1. Me, My Selfie, and I: The Relations Between Selfie Behaviors, Body Image, Self-Objectification, and Self-Esteem in Young Women

Veldhuis, j., et al..

Young women who appreciate their bodies and consider them physical objects are more likely to select, edit, and post selfies to social media, suggests this study in Psychology of Popular Media (Vol. 9, No. 1). Researchers surveyed 179 women, ages 18 to 25, on how often they took selfies, how they selected selfies to post, how often they used filters and editing techniques, and how carefully they planned their selfie postings. They also assessed participants’ levels of body appreciation and dissatisfaction, self-objectification, and self-esteem. Higher levels of self-objectification were linked to more time spent on all selfie behaviors, while body appreciation was related to more time spent selecting selfies to post, but not frequency of taking or editing selfies. Body dissatisfaction and self-esteem were not associated with selfie behaviors. DOI: 10.1037/ppm0000206

2. A Closer Look at Appearance and Social Media: Measuring Activity, Self-Presentation, and Social Comparison and Their Associations With Emotional Adjustment

Zimmer-gembeck, m. j., et al..

This Psychology of Popular Media (online first publication) article presents a tool to assess young people’s preoccupation with their physical appearance on social media. Researchers administered a 21-item survey about social media to 281 Australian high school students. They identified 18 items with strong inter-item correlation centered on three categories of social media behavior: online self-presentation, appearance-related online activity, and appearance comparison. In a second study with 327 Australian university students, scores on the 18-item survey were found to be associated with measures of social anxiety and depressive symptoms, appearance-related support from others, general interpersonal stress, coping flexibility, sexual harassment, disordered eating, and other factors. The researchers also found that young women engaged in more appearance-related social media activity and appearance comparison than did young men. DOI: 10.1037/ppm0000277

3. The Novel Coronavirus (COVID-2019) Outbreak: Amplification of Public Health Consequences by Media Exposure

Garfin, d. r., et al..

Repeated media exposure to the COVID-19 pandemic may be associated with psychological distress and other public health consequences, according to this commentary in Health Psychology (Vol. 39, No. 5). The authors reviewed research about trends in health behavior and psychological distress as a response to media coverage of crises, including terrorist attacks, school shootings, and disease outbreaks. They found that repeated media exposure to collective crises was associated with increased anxiety and heightened acute and post-traumatic stress, with downstream effects on health outcomes such as new incidence of cardiovascular disease. Moreover, misinformation can further amplify stress responses and lead to misplaced or misguided health-protective and help-seeking behaviors. The authors recommended public health agencies use social media strategically, such as with hashtags, to keep residents updated during the pandemic. They also urged the public to avoid sensationalism and repeated coverage of the same information. DOI: 10.1037/hea0000875

4. Barriers to Mental Health Treatment Among Individuals With Social Anxiety Disorder and Generalized Anxiety Disorder

Goetter, e. m., et al..

This study in Psychological Services (Vol. 17, No. 1) indicates that 3 in 4 people who suffer from anxiety do not receive proper care. Researchers recruited 226 participants in the United States who were previously diagnosed with social anxiety disorder or generalized anxiety disorder and assessed their symptom severity and asked them to self-report any barriers to treatment. Shame and stigma were the highest cited barriers, followed by logistical and financial barriers and not knowing where to seek treatment. Participants with more severe symptoms reported more barriers to treatment than those with milder symptoms. Racial and ethnic minorities reported more barriers than racial and ethnic majorities even after controlling for symptom severity. The researchers called for increased patient education and more culturally sensitive outreach to reduce treatment barriers. DOI: 10.1037/ser0000254

5. The Construction of “Critical Thinking”: Between How We Think and What We Believe

This History of Psychology (Vol. 23, No. 3) article examines the emergence of “critical thinking” as a psychological concept. The author describes how, between World War I and World War II in the United States, the concept emerged out of growing concerns about how easily people’s beliefs could be changed and was constructed in a way that was independent of what people believed. The author delves into how original measurements of critical thinking avoided assumptions about the accuracy of specific real-world beliefs and details how subsequent critical thinking tests increasingly focused on logical abilities, often favoring outcome (what we believe) over process (how we think). DOI: 10.1037/hop0000145

6. Treatment of Alcohol Use Disorder: Integration of Alcoholics Anonymous and Cognitive Behavioral Therapy

Breuninger, m. m., et al..

This article in Training and Education in Professional Psychology (Vol. 14, No. 1) details how to work with alcohol use disorder patients who are participating in both cognitive behavioral therapy (CBT) and Alcoholics Anonymous (AA). The authors point to distinctions between AA and CBT: The goal of AA is total abstinence and the primary therapeutic relationship is with a peer in recovery, while CBT takes a less absolute approach and the primary relationship is with a psychotherapist. The authors also point to commonalities: both approaches emphasize identifying and replacing dysfunctional beliefs and place value in social support. The authors recommend clinicians and trainees become more educated about AA and recommend a translation of the 12-step language into CBT terminology to bridge the gap. DOI: 10.1037/tep0000265

7. Positivity Pays Off: Clients’ Perspectives on Positive Compared With Traditional Cognitive Behavioral Therapy for Depression

Geschwind, n., et al..

Positive cognitive behavioral therapy, a version of CBT focused on exploring exceptions to the problem rather than the problem itself, personal strengths, and embracing positivity, works well to counter depressive symptoms and build well-being, according to this study in Psychotherapy (Vol. 57, No. 3). Participants received a block of eight sessions of traditional CBT and a block of eight sessions of positive CBT. Researchers held in-depth interviews with 12 of these participants. Despite initial skepticism, most participants reported preferring positive CBT but indicated experiencing a steeper learning curve than with traditional CBT. Researchers attributed positive CBT’s favorability to four factors: feeling empowered, benefiting from effects of positive emotions, learning to appreciate baby steps, and rediscovering optimism as a personal strength. DOI: 10.1037/pst0000288

8. Targeted Prescription of Cognitive-Behavioral Therapy Versus Person-Centered Counseling for Depression Using a Machine Learning Approach

Delgadillo, j., & gonzalez salas duhne, p..

Amachine learning algorithm can identify which patients would derive more benefit from cognitive behavioral therapy (CBT) versus counseling for depression, suggests research in this Journal of Consulting and Clinical Psychology (Vol. 88, No. 1) article. Researchers retrospectively explored data from 1,085 patients in the United Kingdom treated with either CBT or counseling for depression and discovered six patient characteristics—age, employment status, disability, and three diagnostic measures of major depression and social adjustment—relevant to developing an algorithm for prescribing the best approach. The researchers then used the algorithm to determine which therapy would work best for an additional 350 patients with depression. They found that patients receiving their optimal treatment type were twice as likely to improve significantly. DOI: 10.1037/ccp0000476

9. Traumatic Stress in the Age of COVID-19: A Call to Close Critical Gaps and Adapt to New Realities

Horesh, d., & brown, a. d..

This article in Psychological Trauma: Theory, Research, Practice, and Policy (Vol. 12, No. 4) argues that COVID-19 should be examined from a post-traumatic stress perspective. The authors call for mental health researchers and clinicians to develop better diagnoses and prevention strategies for COVID-related traumatic stress; create guidelines and talking points for the media and government officials to use when speaking to an anxious, and potentially traumatized, public; and provide mental health training to professionals in health care, education, childcare, and occupational support in order to reach more people. DOI: 10.1037/tra0000592

10. Emotional Intelligence Predicts Academic Performance: A Meta-Analysis

Maccann, c., et al..

Students with high emotional intelligence get better grades and score higher on standardized tests, according to the research presented in this article in Psychological Bulletin (Vol. 146, No. 2). Researchers analyzed data from 158 studies representing more than 42,529 students—ranging in age from elementary school to college—from 27 countries. The researchers found that students with higher emotional intelligence earned better grades and scored higher on achievement tests than those with lower emotional intelligence. This finding was true even when controlling for intelligence and personality factors, and the association held regardless of age. The researchers suggest that students with higher emotional intelligence succeed because they cope well with negative emotions that can harm academic performance; they form stronger relationships with teachers, peers, and family; and their knowledge of human motivations and socialinteractions helps them understand humanities subject matter. DOI: 10.1037/bul0000219

5 interviews to listen to now

Psychology’s most innovative thinkers are featured on APA’s Speaking of Psychology podcast , which highlights important research and helps listeners apply psychology to their lives. The most popular episodes of 2020, as measured by the number of downloads in the first 30 days, were: 

• How to have meaningful dialogues despite political differences , with  Tania Israel, PhD
• Canine cognition and the survival of the friendliest , with  Brian Hare, PhD  
• The challenges faced by women in leadership , with  Alice Eagly, PhD
• How to choose effective, science-based mental health apps , with  Stephen Schueller, PhD  
• Psychedelic therapy , with Roland Griffiths, PhD  

Listen to all of the Speaking of Psychology episodes .

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Reference management. Clean and simple.

The top list of academic search engines

1. Google Scholar

4. science.gov, 5. semantic scholar, 6. baidu scholar, get the most out of academic search engines, frequently asked questions about academic search engines, related articles.

Academic search engines have become the number one resource to turn to in order to find research papers and other scholarly sources. While classic academic databases like Web of Science and Scopus are locked behind paywalls, Google Scholar and others can be accessed free of charge. In order to help you get your research done fast, we have compiled the top list of free academic search engines.

Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only lets you find research papers for all academic disciplines for free but also often provides links to full-text PDF files.

• Coverage: approx. 200 million articles
• Abstracts: only a snippet of the abstract is available
• Related articles: ✔
• References: ✔
• Cited by: ✔
• Links to full text: ✔
• Export formats: APA, MLA, Chicago, Harvard, Vancouver, RIS, BibTeX

BASE is hosted at Bielefeld University in Germany. That is also where its name stems from (Bielefeld Academic Search Engine).

• Coverage: approx. 136 million articles (contains duplicates)
• Abstracts: ✔
• Related articles: ✘
• References: ✘
• Cited by: ✘
• Export formats: RIS, BibTeX

CORE is an academic search engine dedicated to open-access research papers. For each search result, a link to the full-text PDF or full-text web page is provided.

• Coverage: approx. 136 million articles
• Links to full text: ✔ (all articles in CORE are open access)
• Export formats: BibTeX

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need anymore to query all those resources separately!

• Coverage: approx. 200 million articles and reports
• Links to full text: ✔ (available for some databases)
• Export formats: APA, MLA, RIS, BibTeX (available for some databases)

Semantic Scholar is the new kid on the block. Its mission is to provide more relevant and impactful search results using AI-powered algorithms that find hidden connections and links between research topics.

• Coverage: approx. 40 million articles
• Export formats: APA, MLA, Chicago, BibTeX

Although Baidu Scholar's interface is in Chinese, its index contains research papers in English as well as Chinese.

• Coverage: no detailed statistics available, approx. 100 million articles
• Abstracts: only snippets of the abstract are available
• Export formats: APA, MLA, RIS, BibTeX

RefSeek searches more than one billion documents from academic and organizational websites. Its clean interface makes it especially easy to use for students and new researchers.

• Coverage: no detailed statistics available, approx. 1 billion documents
• Abstracts: only snippets of the article are available
• Export formats: not available

Consider using a reference manager like Paperpile to save, organize, and cite your references. Paperpile integrates with Google Scholar and many popular databases, so you can save references and PDFs directly to your library using the Paperpile buttons:

Google Scholar is an academic search engine, and it is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.

Semantic Scholar is a free, AI-powered research tool for scientific literature developed at the Allen Institute for AI. Sematic Scholar was publicly released in 2015 and uses advances in natural language processing to provide summaries for scholarly papers.

BASE , as its name suggest is an academic search engine. It is hosted at Bielefeld University in Germany and that's where it name stems from (Bielefeld Academic Search Engine).

CORE is an academic search engine dedicated to open access research papers. For each search result a link to the full text PDF or full text web page is provided.

Science.gov is a fantastic resource as it bundles and offers free access to search results from more than 15 U.S. federal agencies. There is no need any more to query all those resources separately!

• Hirsh Health Sciences
• Webster Veterinary

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.

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• Last Updated: Aug 23, 2023 8:53 AM
• URL: https://researchguides.library.tufts.edu/scholarly-articles

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A systematic approach to searching: an efficient and complete method to develop literature searches

Associated data.

Creating search strategies for systematic reviews, finding the best balance between sensitivity and specificity, and translating search strategies between databases is challenging. Several methods describe standards for systematic search strategies, but a consistent approach for creating an exhaustive search strategy has not yet been fully described in enough detail to be fully replicable. The authors have established a method that describes step by step the process of developing a systematic search strategy as needed in the systematic review. This method describes how single-line search strategies can be prepared in a text document by typing search syntax (such as field codes, parentheses, and Boolean operators) before copying and pasting search terms (keywords and free-text synonyms) that are found in the thesaurus. To help ensure term completeness, we developed a novel optimization technique that is mainly based on comparing the results retrieved by thesaurus terms with those retrieved by the free-text search words to identify potentially relevant candidate search terms. Macros in Microsoft Word have been developed to convert syntaxes between databases and interfaces almost automatically. This method helps information specialists in developing librarian-mediated searches for systematic reviews as well as medical and health care practitioners who are searching for evidence to answer clinical questions. The described method can be used to create complex and comprehensive search strategies for different databases and interfaces, such as those that are needed when searching for relevant references for systematic reviews, and will assist both information specialists and practitioners when they are searching the biomedical literature.

INTRODUCTION

Librarians and information specialists are often involved in the process of preparing and completing systematic reviews (SRs), where one of their main tasks is to identify relevant references to include in the review [ 1 ]. Although several recommendations for the process of searching have been published [ 2 – 6 ], none describe the development of a systematic search strategy from start to finish.

Traditional methods of SR search strategy development and execution are highly time consuming, reportedly requiring up to 100 hours or more [ 7 , 8 ]. The authors wanted to develop systematic and exhaustive search strategies more efficiently, while preserving the high sensitivity that SR search strategies necessitate. In this article, we describe the method developed at Erasmus University Medical Center (MC) and demonstrate its use through an example search. The efficiency of the search method and outcome of 73 searches that have resulted in published reviews are described in a separate article [ 9 ].

As we aimed to describe the creation of systematic searches in full detail, the method starts at a basic level with the analysis of the research question and the creation of search terms. Readers who are new to SR searching are advised to follow all steps described. More experienced searchers can consider the basic steps to be existing knowledge that will already be part of their normal workflow, although step 4 probably differs from general practice. Experienced searchers will gain the most from reading about the novelties in the method as described in steps 10–13 and comparing the examples given in the supplementary appendix to their own practice.

CREATING A SYSTEMATIC SEARCH STRATEGY

Our methodology for planning and creating a multi-database search strategy consists of the following steps:

• Determine a clear and focused question
• Describe the articles that can answer the question
• Decide which key concepts address the different elements of the question
• Decide which elements should be used for the best results
• Choose an appropriate database and interface to start with
• Document the search process in a text document
• Identify appropriate index terms in the thesaurus of the first database
• Identify synonyms in the thesaurus
• Add variations in search terms
• Use database-appropriate syntax, with parentheses, Boolean operators, and field codes
• Optimize the search
• Evaluate the initial results
• Check for errors
• Translate to other databases
• Test and reiterate

Each step in the process is reflected by an example search described in the supplementary appendix .

1. Determine a clear and focused question

A systematic search can best be applied to a well-defined and precise research or clinical question. Questions that are too broad or too vague cannot be answered easily in a systematic way and will generally result in an overwhelming number of search results. On the other hand, a question that is too specific will result into too few or even zero search results. Various papers describe this process in more detail [ 10 – 12 ].

2. Describe the articles that can answer the question

Although not all clinical or research questions can be answered in the literature, the next step is to presume that the answer can indeed be found in published studies. A good starting point for a search is hypothesizing what the research that can answer the question would look like. These hypothetical (when possible, combined with known) articles can be used as guidance for constructing the search strategy.

3. Decide which key concepts address the different elements of the question

Key concepts are the topics or components that the desired articles should address, such as diseases or conditions, actions, substances, settings, domains (e.g., therapy, diagnosis, etiology), or study types. Key concepts from the research question can be grouped to create elements in the search strategy.

Elements in a search strategy do not necessarily follow the patient, intervention, comparison, outcome (PICO) structure or any other related structure. Using the PICO or another similar framework as guidance can be helpful to consider, especially in the inclusion and exclusion review stage of the SR, but this is not necessary for good search strategy development [ 13 – 15 ]. Sometimes concepts from different parts of the PICO structure can be grouped together into one search element, such as when the desired outcome is frequently described in a certain study type.

4. Decide which elements should be used for the best results

Not all elements of a research question should necessarily be used in the search strategy. Some elements are less important than others or may unnecessarily complicate or restrict a search strategy. Adding an element to a search strategy increases the chance of missing relevant references. Therefore, the number of elements in a search strategy should remain as low as possible to optimize recall.

Using the schema in Figure 1 , elements can be ordered by their specificity and importance to determine the best search approach. Whether an element is more specific or more general can be measured objectively by the number of hits retrieved in a database when searching for a key term representing that element. Depending on the research question, certain elements are more important than others. If articles (hypothetically or known) exist that can answer the question but lack a certain element in their titles, abstracts, or keywords, that element is unimportant to the question. An element can also be unimportant because of expected bias or an overlap with another element.

Schema for determining the optimal order of elements

Bias in elements

The choice of elements in a search strategy can introduce bias through use of overly specific terminology or terms often associated with positive outcomes. For the question “does prolonged breastfeeding improve intelligence outcomes in children?,” searching specifically for the element of duration will introduce bias, as articles that find a positive effect of prolonged breastfeeding will be much more likely to mention time factors in their titles or abstracts.

Overlapping elements

Elements in a question sometimes overlap in their meaning. Sometimes certain therapies are interventions for one specific disease. The Lichtenstein technique, for example, is a repair method for inguinal hernias. There is no need to include an element of “inguinal hernias” to a search for the effectiveness of the Lichtenstein therapy. Likewise, sometimes certain diseases are only found in certain populations. Adding such an overlapping element could lead to missing relevant references.

The elements to use in a search strategy can be found in the plot of elements in Figure 1 , by following the top row from left to right. For this method, we recommend starting with the most important and specific elements. Then, continue with more general and important elements until the number of results is acceptable for screening. Determining how many results are acceptable for screening is often a matter of negotiation with the SR team.

5. Choose an appropriate database and interface to start with

Important factors for choosing databases to use are the coverage and the presence of a thesaurus. For medically oriented searches, the coverage and recall of Embase, which includes the MEDLINE database, are superior to those of MEDLINE [ 16 ]. Each of these two databases has its own thesaurus with its own unique definitions and structure. Because of the complexity of the Embase thesaurus, Emtree, which contains much more specific thesaurus terms than the MEDLINE Medical Subject Headings (MeSH) thesaurus, translation from Emtree to MeSH is easier than the other way around. Therefore, we recommend starting in Embase.

MEDLINE and Embase are available through many different vendors and interfaces. The choice of an interface and primary database is often determined by the searcher’s accessibility. For our method, an interface that allows searching with proximity operators is desirable, and full functionality of the thesaurus, including explosion of narrower terms, is crucial. We recommend developing a personal workflow that always starts with one specific database and interface.

6. Document the search process in a text document

We advise designing and creating the complete search strategies in a log document, instead of directly in the database itself, to register the steps taken and to make searches accountable and reproducible. The developed search strategies can be copied and pasted into the desired databases from the log document. This way, the searcher is in control of the whole process. Any change to the search strategy should be done in the log document, assuring that the search strategy in the log is always the most recent.

7. Identify appropriate index terms in the thesaurus of the first database

Searches should start by identifying appropriate thesaurus terms for the desired elements. The thesaurus of the database is searched for matching index terms for each key concept. We advise restricting the initial terms to the most important and most relevant terms. Later in the process, more general terms can be added in the optimization process, in which the effect on the number of hits, and thus the desirability of adding these terms, can be evaluated more easily.

Several factors can complicate the identification of thesaurus terms. Sometimes, one thesaurus term is found that exactly describes a specific element. In contrast, especially in more general elements, multiple thesaurus terms can be found to describe one element. If no relevant thesaurus terms have been found for an element, free-text terms can be used, and possible thesaurus terms found in the resulting references can be added later (step 11).

Sometimes, no distinct thesaurus term is available for a specific key concept that describes the concept in enough detail. In Emtree, one thesaurus term often combines two or more elements. The easiest solution for combining these terms for a sensitive search is to use such a thesaurus term in all elements where it is relevant. Examples are given in the supplementary appendix .

8. Identify synonyms in the thesaurus

Most thesauri offer a list of synonyms on their term details page (named Synonyms in Emtree and Entry Terms in MeSH). To create a sensitive search strategy for SRs, these terms need to be searched as free-text keywords in the title and abstract fields, in addition to searching their associated thesaurus terms.

The Emtree thesaurus contains more synonyms (300,000) than MeSH does (220,000) [ 17 ]. The difference in number of terms is even higher considering that many synonyms in MeSH are permuted terms (i.e., inversions of phrases using commas).

Thesaurus terms are ordered in a tree structure. When searching for a more general thesaurus term, the more specific (narrower) terms in the branches below that term will also be searched (this is frequently referred to as “exploding” a thesaurus term). However, to perform a sensitive search, all relevant variations of the narrower terms must be searched as free-text keywords in the title or abstract, in addition to relying on the exploded thesaurus term. Thus, all articles that describe a certain narrower topic in their titles and abstracts will already be retrieved before MeSH terms are added.

9. Add variations in search terms (e.g., truncation, spelling differences, abbreviations, opposites)

Truncation allows a searcher to search for words beginning with the same word stem. A search for therap* will, thus, retrieve therapy, therapies, therapeutic, and all other words starting with “therap.” Do not truncate a word stem that is too short. Also, limitations of interfaces should be taken into account, especially in PubMed, where the number of search term variations that can be found by truncation is limited to 600.

Databases contain references to articles using both standard British and American English spellings. Both need to be searched as free-text terms in the title and abstract. Alternatively, many interfaces offer a certain code to replace zero or one characters, allowing a search for “pediatric” or “paediatric” as “p?ediatric.” Table 1 provides a detailed description of the syntax for different interfaces.

Field codes in five most used interfaces for biomedical literature searching

Searching for abbreviations can identify extra, relevant references and retrieve more irrelevant ones. The search can be more focused by combining the abbreviation with an important word that is relevant to its meaning or by using the Boolean “NOT” to exclude frequently observed, clearly irrelevant results. We advise that searchers do not exclude all possible irrelevant meanings, as it is very time consuming to identify all the variations, it will result in unnecessarily complicated search strategies, and it may lead to erroneously narrowing the search and, thereby, reduce recall.

Searching partial abbreviations can be useful for retrieving relevant references. For example, it is very likely that an article would mention osteoarthritis (OA) early in the abstract, replacing all further occurrences of osteoarthritis with OA . Therefore, it may not contain the phrase “hip osteoarthritis” but only “hip oa.”

It is also important to search for the opposites of search terms to avoid bias. When searching for “disease recurrence,” articles about “disease free” may be relevant as well. When the desired outcome is survival , articles about mortality may be relevant.

10. Use database-appropriate syntax, with parentheses, Boolean operators, and field codes

Different interfaces require different syntaxes, the special set of rules and symbols unique to each database that define how a correctly constructed search operates. Common syntax components include the use of parentheses and Boolean operators such as “AND,” “OR,” and “NOT,” which are available in all major interfaces. An overview of different syntaxes for four major interfaces for bibliographic medical databases (PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest) is shown in Table 1 .

Creating the appropriate syntax for each database, in combination with the selected terms as described in steps 7–9, can be challenging. Following the method outlined below simplifies the process:

• Create single-line queries in a text document (not combining multiple record sets), which allows immediate checking of the relevance of retrieved references and efficient optimization.
• Type the syntax (Boolean operators, parentheses, and field codes) before adding terms, which reduces the chance that errors are made in the syntax, especially in the number of parentheses.
• Use predefined proximity structures including parentheses, such as (() ADJ3 ()) in Ovid, that can be reused in the query when necessary.
• Use thesaurus terms separately from free-text terms of each element. Start an element with all thesaurus terms (using “OR”) and follow with the free-text terms. This allows the unique optimization methods as described in step 11.
• When adding terms to an existing search strategy, pay close attention to the position of the cursor. Make sure to place it appropriately either in the thesaurus terms section, in the title/abstract section, or as an addition (broadening) to an existing proximity search.

The supplementary appendix explains the method of building a query in more detail, step by step for different interfaces: PubMed, Ovid, EBSCOhost, Embase.com, and ProQuest. This method results in a basic search strategy designed to retrieve some relevant references upon which a more thorough search strategy can be built with optimization such as described in step 11.

11. Optimize the search

The most important question when performing a systematic search is whether all (or most) potentially relevant articles have been retrieved by the search strategy. This is also the most difficult question to answer, since it is unknown which and how many articles are relevant. It is, therefore, wise first to broaden the initial search strategy, making the search more sensitive, and then check if new relevant articles are found by comparing the set results (i.e., search for Strategy #2 NOT Strategy #1 to see the unique results).

A search strategy should be tested for completeness. Therefore, it is necessary to identify extra, possibly relevant search terms and add them to the test search in an OR relationship with the already used search terms. A good place to start, and a well-known strategy, is scanning the top retrieved articles when sorted by relevance, looking for additional relevant synonyms that could be added to the search strategy.

We have developed a unique optimization method that has not been described before in the literature. This method often adds valuable extra terms to our search strategy and, therefore, extra, relevant references to our search results. Extra synonyms can be found in articles that have been assigned a certain set of thesaurus terms but that lack synonyms in the title and/or abstract that are already present in the current search strategy. Searching for thesaurus terms NOT free-text terms will help identify missed free-text terms in the title or abstract. Searching for free-text terms NOT thesaurus terms will help identify missed thesaurus terms. If this is done repeatedly for each element, leaving the rest of the query unchanged, this method will help add numerous relevant terms to the query. These steps are explained in detail for five different search platforms in the supplementary appendix .

12. Evaluate the initial results

The results should now contain relevant references. If the interface allows relevance ranking, use that in the evaluation. If you know some relevant references that should be included in the research, search for those references specifically; for example, combine a specific (first) author name with a page number and the publication year. Check whether those references are retrieved by the search. If the known relevant references are not retrieved by the search, adapt the search so that they are. If it is unclear which element should be adapted to retrieve a certain article, combine that article with each element separately.

Different outcomes are desired for different types of research questions. For instance, in the case of clinical question answering, the researcher will not be satisfied with many references that contain a lot of irrelevant references. A clinical search should be rather specific and is allowed to miss a relevant reference. In the case of an SR, the researchers do not want to miss any relevant reference and are willing to handle many irrelevant references to do so. The search for references to include in an SR should be very sensitive: no included reference should be missed. A search that is too specific or too sensitive for the intended goal can be adapted to become more sensitive or specific. Steps to increase sensitivity or specificity of a search strategy can be found in the supplementary appendix .

13. Check for errors

Errors might not be easily detected. Sometimes clues can be found in the number of results, either when the number of results is much higher or lower than expected or when many retrieved references are not relevant. However, the number expected is often unknown, and very sensitive search strategies will always retrieve many irrelevant articles. Each query should, therefore, be checked for errors.

One of the most frequently occurring errors is missing the Boolean operator “OR.” When no “OR” is added between two search terms, many interfaces automatically add an “AND,” which unintentionally reduces the number of results and likely misses relevant references. One good strategy to identify missing “OR”s is to go to the web page containing the full search strategy, as translated by the database, and using Ctrl-F search for “AND.” Check whether the occurrences of the “AND” operator are deliberate.

Ideally, search strategies should be checked by other information specialists [ 18 ]. The Peer Review of Electronic Search Strategies (PRESS) checklist offers good guidance for this process [ 4 ]. Apart from the syntax (especially Boolean operators and field codes) of the search strategy, it is wise to have the search terms checked by the clinician or researcher familiar with the topic. At Erasmus MC, researchers and clinicians are involved during the complete process of structuring and optimizing the search strategy. Each word is added after the combined decision of the searcher and the researcher, with the possibility of directly comparing results with and without the new term.

14. Translate to other databases

To retrieve as many relevant references as possible, one has to search multiple databases. Translation of complex and exhaustive queries between different databases can be very time consuming and cumbersome. The single-line search strategy approach detailed above allows quick translations using the find and replace method in Microsoft Word (<Ctrl-H>).

At Erasmus MC, macros based on the find-and-replace method in Microsoft Word have been developed for easy and fast translation between the most used databases for biomedical and health sciences questions. The schema that is followed for the translation between databases is shown in Figure 2 . Most databases simply follow the structure set by the Embase.com search strategy. The translation from Emtree terms to MeSH terms for MEDLINE in Ovid often identifies new terms that need to be added to the Embase.com search strategy before the translation to other databases.

Schematic representation of translation between databases used at Erasmus University Medical Center

Dotted lines represent databases that are used in less than 80% of the searches.

Using five different macros, a thoroughly optimized query in Embase.com can be relatively quickly translated into eight major databases. Basic search strategies will be created to use in many, mostly smaller, databases, because such niche databases often do not have extensive thesauri or advanced syntax options. Also, there is not much need to use extensive syntax because the number of hits and, therefore, the amount of noise in these databases is generally low. In MEDLINE (Ovid), PsycINFO (Ovid), and CINAHL (EBSCOhost), the thesaurus terms must be adapted manually, as each database has its own custom thesaurus. These macros and instructions for their installation, use, and adaptation are available at bit.ly/databasemacros.

15. Test and reiterate

Ideally, exhaustive search strategies should retrieve all references that are covered in a specific database. For SR search strategies, checking searches for their recall is advised. This can be done after included references have been determined by the authors of the systematic review. If additional papers have been identified through other non-database methods (i.e., checking references in included studies), results that were not identified by the database searches should be examined. If these results were available in the databases but not located by the search strategy, the search strategy should be adapted to try to retrieve these results, as they may contain terms that were omitted in the original search strategies. This may enable the identification of additional relevant results.

A methodology for creating exhaustive search strategies has been created that describes all steps of the search process, starting with a question and resulting in thorough search strategies in multiple databases. Many of the steps described are not new, but together, they form a strong method creating high-quality, robust searches in a relatively short time frame.

Our methodology is intended to create thoroughness for literature searches. The optimization method, as described in step 11, will identify missed synonyms or thesaurus terms, unlike any other method that largely depends on predetermined keywords and synonyms. Using this method results in a much quicker search process, compared to traditional methods, especially because of the easier translation between databases and interfaces (step 13). The method is not a guarantee for speed, since speed depends on many factors, including experience. However, by following the steps and using the tools as described above, searchers can gain confidence first and increase speed through practice.

What is new?

This method encourages searchers to start their search development process using empty syntax first and later adding the thesaurus terms and free-text synonyms. We feel this helps the searcher to focus on the search terms, instead of on the structure of the search query. The optimization method in which new terms are found in the already retrieved articles is used in some other institutes as well but has to our knowledge not been described in the literature. The macros to translate search strategies between interfaces are unique in this method.

What is different compared to common practice?

Traditionally, librarians and information specialists have focused on creating complex, multi-line (also called line-by-line) search strategies, consisting of multiple record sets, and this method is frequently advised in the literature and handbooks [ 2 , 19 – 21 ]. Our method, instead, uses single-line searches, which is critical to its success. Single-line search strategies can be easily adapted by adding or dropping a term without having to recode numbers of record sets, which would be necessary in multi-line searches. They can easily be saved in a text document and repeated by copying and pasting for search updates. Single-line search strategies also allow easy translation to other syntaxes using find-and-replace technology to update field codes and other syntax elements or using macros (step 13).

When constructing a search strategy, the searcher might experience that certain parentheses in the syntax are unnecessary, such as parentheses around all search terms in the title/abstract portion, if there is only one such term, there are double parentheses in the proximity statement, or one of the word groups exists for only one word. One might be tempted to omit those parentheses for ease of reading and management. However, during the optimization process, the searcher is likely to find extra synonyms that might consist of one word. To add those terms to the first query (with reduced parentheses) requires adding extra parentheses (meticulously placing and counting them), whereas, in the latter search, it only requires proper placement of those terms.

Many search methods highly depend on the PICO framework. Research states that often PICO or PICOS is not suitable for every question [ 22 , 23 ]. There are other acronyms than PICO—such as sample, phenomenon of interest, design, evaluation, research type (SPIDER) [ 24 ]—but each is just a variant. In our method, the most important and specific elements of a question are being analyzed for building the best search strategy.

Though it is generally recommended that searchers search both MEDLINE and Embase, most use MEDLINE as the starting point. It is considered the gold standard for biomedical searching, partially due to historical reasons, since it was the first of its kind, and more so now that it is freely available via the PubMed interface. Our method can be used with any database as a starting point, but we use Embase instead of MEDLINE or another database for a number of reasons. First, Embase provides both unique content and the complete content of MEDLINE. Therefore, searching Embase will be, by definition, more complete than searching MEDLINE only. Second, the number of terms in Emtree (the Embase thesaurus) is three times as high as that of MeSH (the MEDLINE thesaurus). It is easier to find MeSH terms after all relevant Emtree terms have been identified than to start with MeSH and translate to Emtree.

At Erasmus MC, the researchers sit next to the information specialist during most of the search strategy design process. This way, the researchers can deliver immediate feedback on the relevance of proposed search terms and retrieved references. The search team then combines knowledge about databases with knowledge about the research topic, which is an important condition to create the highest quality searches.

Limitations of the method

One disadvantage of single-line searches compared to multi-line search strategies is that errors are harder to recognize. However, with the methods for optimization as described (step 11), errors are recognized easily because missed synonyms and spelling errors will be identified during the process. Also problematic is that more parentheses are needed, making it more difficult for the searcher and others to assess the logic of the search strategy. However, as parentheses and field codes are typed before the search terms are added (step 10), errors in parentheses can be prevented.

Our methodology works best if used in an interface that allows proximity searching. It is recommended that searchers with access to an interface with proximity searching capabilities select one of those as the initial database to develop and optimize the search strategy. Because the PubMed interface does not allow proximity searches, phrases or Boolean “AND” combinations are required. Phrase searching complicates the process and is more specific, with the higher risk of missing relevant articles, and using Boolean “AND” combinations increases sensitivity but at an often high loss of specificity. Due to some searchers’ lack of access to expensive databases or interfaces, the freely available PubMed interface may be necessary to use, though it should never be the sole database used for an SR [ 2 , 16 , 25 ]. A limitation of our method is that it works best with subscription-based and licensed resources.

Another limitation is the customization of the macros to a specific institution’s resources. The macros for the translation between different database interfaces only work between the interfaces as described. To mitigate this, we recommend using the find-and-replace functionality of text editors like Microsoft Word to ease the translation of syntaxes between other databases. Depending on one’s institutional resources, custom macros can be developed using similar methods.

Results of the method

Whether this method results in exhaustive searches where no important article is missed is difficult to determine, because the number of relevant articles is unknown for any topic. A comparison of several parameters of 73 published reviews that were based on a search developed with this method to 258 reviews that acknowledged information specialists from other Dutch academic hospitals shows that the performance of the searches following our method is comparable to those performed in other institutes but that the time needed to develop the search strategies was much shorter than the time reported for the other reviews [ 9 ].

CONCLUSIONS

With the described method, searchers can gain confidence in their search strategies by finding many relevant words and creating exhaustive search strategies quickly. The approach can be used when performing SR searches or for other purposes such as answering clinical questions, with different expectations of the search’s precision and recall. This method, with practice, provides a stepwise approach that facilitates the search strategy development process from question clarification to final iteration and beyond.

SUPPLEMENTAL FILE

Acknowledgments.

We highly appreciate the work that was done by our former colleague Louis Volkers, who in his twenty years as an information specialist in Erasmus MC laid the basis for our method. We thank Professor Oscar Franco for reviewing earlier drafts of this article.

Exploring the World of 250+ Interesting Topics to Research

Research is a fascinating journey into the unknown, a quest for answers, and a process of discovery. Whether you’re an academic, a student, or just a curious mind, finding the right and interesting topics to research is paramount. Not only does it determine the success of your research project, but it can also make the experience enjoyable. 

In this blog, we’ll delve into the art of selecting interesting topics to research, particularly catering to the average reader.

How to Select Interesting Topics to Research?

Table of Contents

Choosing a research topic is like setting sail on a ship. It’s a decision that will dictate your course, so you must make it wisely. Here are some effective strategies to help you pick a captivating topic:

• Personal Interests: Researching a topic you’re genuinely passionate about can turn the entire process into an exciting adventure. Your enthusiasm will show in your work and make it more engaging for the reader.
• Current Trends and Issues: Current events and trends are always intriguing because they’re relevant. They often raise questions and uncertainties, making them excellent research candidates. Think of topics like the impact of a global pandemic on mental health or the evolution of renewable energy technologies in the face of climate change.
• Problem-Solving Approach: Identify a problem that needs a solution or an unanswered question. Researching with the aim to solve a real-world issue can be highly motivating. For instance, you could explore strategies to reduce plastic waste in your community.
• Impact and Relevance: Consider the significance of your topic. Will it impact people’s lives or contribute to existing knowledge? Research with a purpose tends to be more engaging. Topics like gender equality, public health, or environmental conservation often fall into this category.
• Unexplored or Unique Topics: Researching less-explored or unique topics can be exciting. It gives you the opportunity to contribute something new to your field. Remember, research isn’t limited to established subjects; there’s room for exploration in every discipline.

250+ Interesting Topics to Research: Popular Categories

Research topics come in various flavors. Let’s explore some popular categories, which are often engaging for average readers:

Science and Technology

• Artificial intelligence in healthcare.
• Quantum computing advancements.
• Space exploration and colonization.
• Genetic editing and CRISPR technology.
• Cybersecurity in the digital age.
• Augmented and virtual reality applications.
• Climate change and mitigation strategies.
• Sustainable energy sources.
• Internet of Things (IoT) innovations.
• Nanotechnology breakthroughs.
• 3D printing in various industries.
• Biotechnology in medicine.
• Autonomous vehicles and self-driving technology.
• Robotics in everyday life.
• Clean water technology.
• Renewable energy storage solutions.
• Wearable technology and health tracking.
• Green architecture and sustainable design.
• Bioinformatics and genomics.
• Machine learning in data analysis.
• Space tourism development.
• Advancements in quantum mechanics.
• Biometrics and facial recognition.
• Aerospace engineering innovations.
• Ethical considerations in AI development.
• Artificial organs and 3D bioprinting.
• Holography and holographic displays.
• Sustainable agriculture practices.
• Climate modeling and prediction.
• Advancements in battery technology.
• Neurotechnology and brain-computer interfaces.
• Space-based solar power.
• Green transportation options.
• Materials science and superconductors.
• Telemedicine and remote healthcare.
• Cognitive computing and AI ethics.
• Renewable energy policy and regulation.
• The role of 5G in the digital landscape.
• Precision medicine and personalized treatment.
• Advancements in quantum cryptography.
• Drone technology and applications.
• Environmental sensors and monitoring.
• Synthetic biology and bioengineering.
• Smart cities and urban planning.
• Quantum teleportation research.
• AI-powered virtual assistants.
• Space-based mining and resource extraction.
• Advancements in neuroprosthetics.
• Sustainable transportation solutions.
• Blockchain technology and applications.

Social Issues

• Gender inequality in the workplace.
• Racial discrimination and systemic racism.
• Income inequality and wealth gap.
• Climate change and environmental degradation.
• Mental health stigma and access to care.
• Access to quality education.
• Immigration and border control policies.
• Gun control and Second Amendment rights.
• Opioid epidemic and substance abuse.
• Affordable healthcare and insurance.
• LGBTQ+ rights and discrimination.
• Cyberbullying and online harassment.
• Homelessness and affordable housing.
• Police brutality and reform.
• Human trafficking and modern slavery.
• Voter suppression and electoral integrity.
• Access to clean water and sanitation.
• Child labor and exploitation.
• Aging population and healthcare for the elderly.
• Indigenous rights and land disputes.
• Bullying in schools and online.
• Obesity and public health.
• Access to reproductive healthcare.
• Income tax policies and fairness.
• Mental health support for veterans.
• Child abuse and neglect.
• Animal rights and cruelty.
• The digital divide and internet access.
• Youth unemployment and opportunities.
• Religious freedom and tolerance.
• Disability rights and accessibility.
• Affordable childcare and parental leave.
• Food insecurity and hunger.
• Drug policy and legalization.
• Human rights violations in conflict zones.
• Aging infrastructure and public safety.
• Cybersecurity and data privacy.
• Human rights in authoritarian regimes.
• Environmental racism and pollution.
• Discrimination against people with disabilities.
• Income and education disparities in rural areas.
• Freedom of the press and media censorship.
• Bullying and discrimination against the LGBTQ+ youth.
• Access to clean energy and sustainable practices.
• Child marriage and forced unions.
• Mental health in the workplace.
• Domestic violence and abuse.
• Education funding and quality.
• Childhood obesity and healthy habits.
• Poverty and economic development.

History and Culture

• The Rise and Fall of the Roman Empire
• Ancient Egyptian Civilization
• The Renaissance Period in Europe
• The Industrial Revolution
• The French Revolution
• The American Civil War
• The Silk Road and Cultural Exchange
• The Mayan Civilization
• The Byzantine Empire
• The Age of Exploration
• World War I: Causes and Consequences
• The Harlem Renaissance
• The Aztec Empire
• Ancient Greece: Democracy and Philosophy
• The Vietnam War
• The Cold War
• The Inca Empire
• The Enlightenment Era
• The Crusades
• The Spanish Inquisition
• The African Slave Trade
• The Suffragette Movement
• The Black Death in Europe
• The Apollo Moon Landing
• The Roaring Twenties
• The Chinese Cultural Revolution
• The Salem Witch Trials
• The Great Wall of China
• The Abolitionist Movement
• The Golden Age of Islam
• The Mesoamerican Ballgame
• The Age of Vikings
• The Ottoman Empire
• The Cultural Impact of the Beatles
• The Space Race
• The Fall of the Berlin Wall
• The History of Hollywood Cinema
• The Renaissance Art and Artists
• The British Empire
• The Age of Samurai in Japan
• The Ancient Indus Valley Civilization
• The Russian Revolution
• The Age of Chivalry
• The History of Native American Tribes
• The Cultural Significance of Greek Mythology
• The Etruscans in Ancient Italy
• The History of African Kingdoms
• The Great Famine in Ireland
• The Age of Invention and Innovation
• The Cultural Impact of Shakespeare’s Works

Business and Economics

• Impact of E-commerce on Traditional Retail
• Global Supply Chain Challenges
• Green Business Practices and Sustainability
• Strategies for Small Business Growth
• Cryptocurrency and Its Economic Implications
• Consumer Behavior in the Digital Age
• The Gig Economy and Its Future
• Economic Consequences of Climate Change
• The Role of AI in Financial Services
• Trade Wars and Their Effects on Global Markets
• Entrepreneurship in Emerging Markets
• Corporate Social Responsibility Trends
• The Economics of Healthcare
• The Impact of Inflation on Savings
• Startup Ecosystems and Innovation Hubs
• Financial Literacy and Education Initiatives
• Income Inequality and Economic Mobility
• The Sharing Economy and Collaborative Consumption
• International Trade Policies
• Behavioral Economics in Marketing
• Economic Effects of the COVID-19 Pandemic
• Fintech Innovations and Banking
• Real Estate Market Trends
• Public vs. Private Healthcare Systems
• Market Entry Strategies for New Businesses
• Global Economic Growth Prospects
• The Economics of Education
• Mergers and Acquisitions Trends
• Impact of Tax Reforms on Businesses
• Sustainable Investing and ESG Factors
• Monetary Policy and Interest Rates
• The Future of Work: Remote vs. Office
• Business Ethics and Corporate Governance
• The Economics of Artificial Intelligence
• Stock Market Volatility
• Supply and Demand Dynamics
• Entrepreneurial Finance and Fundraising
• Innovation and Technology Transfer
• Competition in the Digital Marketplace
• Economic Impacts of Aging Populations
• Economic Development in Developing Countries
• Regulatory Challenges in the Financial Sector
• The Economics of Healthcare Insurance
• Corporate Profitability and Market Share
• Energy Economics and Renewable Sources
• Economic Factors in Mergers and Acquisitions
• Financial Crises and Their Aftermath
• Economics of the Entertainment Industry
• Global Economic Trends Post-Pandemic
• Economic Consequences of Cybersecurity Threats
• The Impact of Online Learning
• Strategies for Inclusive Education
• Early Childhood Development
• The Role of Teachers in Student Motivation
• Educational Technology Trends
• Assessment Methods in Education
• The Importance of Multilingual Education
• Special Education Approaches
• Global Education Disparities
• Project-Based Learning
• Critical Thinking in the Classroom
• Educational Leadership
• Homeschooling vs. Traditional Education
• Education and Social Inequality
• Student Mental Health Support
• The Benefits of Student Extracurricular Activities
• The Montessori Approach
• STEM Education
• Educational Policy Reforms
• Education for Sustainable Development
• Educational Psychology
• Learning Disabilities
• Adult Education Programs
• The Role of Arts in Education
• The Flipped Classroom Model
• Educational Gamification
• School Bullying Prevention
• Inclusive Curriculum Design
• The Future of College Admissions
• Early Literacy Development
• Education and Gender Equity
• Teacher Training and Professional Development
• Homeschooling Challenges
• Gifted and Talented Education
• Education for Global Citizenship
• Virtual Reality in Education
• Outdoor and Environmental Education
• Education for Sustainable Agriculture
• Music Education Benefits
• Education and Technological Divide
• Cultural Competence in Education
• Education and Social Emotional Learning
• Personalized Learning
• Educational Equity
• Restorative Justice in Schools
• Study Abroad Programs
• Education for Digital Citizenship
• The Role of Parents in Education
• Vocational Education and Training
• The History of Education Movements

Techniques for Researching Interesting Topics

Once you’ve chosen the interesting topics to research, you’ll need effective techniques to delve deeper into it:

• Online Databases and Journals: Online academic databases like Google Scholar, JSTOR, or PubMed are invaluable resources. They provide access to a vast pool of academic research papers.
• Interviews and Surveys: If your topic involves human perspectives, conducting interviews or surveys can offer firsthand insights. Tools like Jotform Survey Maker , SurveyMonkey or Zoom can be helpful.
• Libraries and Archives: Traditional libraries still hold a treasure trove of information. Whether you visit in person or explore digital archives, libraries can provide a wealth of resources.
• Online Forums and Social Media: Online communities and forums can be excellent sources of information, particularly for trending topics. Sites like Reddit and Quora can connect you with experts and enthusiasts.
• Academic and Expert Sources: Seek out academic articles, books, and experts in your field. Don’t hesitate to reach out to professionals who may be willing to share their expertise.

How to Make Your Research Engaging?

Once you’ve conducted your research, it’s essential to present it in a way that captures the interest of your average reader:

1. Clear and Accessible Language

Avoid jargon and complex terminology. Use simple and straightforward language to ensure your research is accessible to a wide audience.

2. Storytelling and Anecdotes

Weave stories and anecdotes into your research to make it relatable and engaging. Personal narratives and real-life examples can resonate with readers.

3. Visual Aids (Images, Infographics)

Incorporate visuals like images, charts, and infographics to make your research visually appealing and easier to understand.

4. Real-Life Examples and Case Studies

Use real-life examples and case studies to illustrate the practical applications of your research findings. This makes the information tangible and relevant.

5. Relatable Examples from Popular Culture

Relate your research to pop culture, current events, or everyday experiences. This helps readers connect with the material on a personal level.

Examples of Interesting Topics to Research

To provide some inspiration, let’s explore a few intriguing research topics:

The Impact of Social Media on Mental Health

Examine the relationship between social media use and mental health, including topics like social comparison, cyberbullying, and the benefits of online support networks.

The Future of Renewable Energy

Research the latest advancements in renewable energy technologies, such as solar power, wind energy, and the feasibility of a global transition to sustainable energy sources.

The History of Women’s Suffrage

Delve into the historical struggles and milestones of the women’s suffrage movement, both in the United States and around the world.

The Role of Artificial Intelligence in Healthcare

Investigate the applications of AI in healthcare, from diagnosis algorithms to patient data analysis and the ethical implications of AI in medical practice.

Strategies for Sustainable Business Practices

Examine business sustainability practices , exploring how companies can balance profit and environmental responsibility in an increasingly eco-conscious world.

Challenges you Might Face in Research

While you are looking for interesting topics to research, it’s important to be aware of the challenges:

• Avoiding Bias and Misinformation: Ensure your research is unbiased and based on credible sources. Critical thinking is key to avoiding misinformation.
• Ethical Considerations: Research involving humans or animals should follow ethical guidelines. Always prioritize ethical research practices.
• Data Collection and Analysis: Data collection can be time-consuming and challenging. Make sure to use appropriate data collection methods and robust analysis techniques.
• Staying Updated with Latest Research: Research is an ongoing process. Stay up-to-date with the latest research in your field to ensure the relevance and accuracy of your work.

Research is a gateway to knowledge, innovation, and solutions. Choosing interesting topics to research is the first step in this exciting journey. Whether you’re exploring the depths of science, the intricacies of culture, or the dynamics of business, there’s a captivating research topic waiting for you. 

So, start your exploration, share your discoveries, and keep the flame of curiosity alive. The world is waiting to learn from your research.

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

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• 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))

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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.

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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

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A Beginner's Guide to Starting the Research Process

When you have to write a thesis or dissertation , it can be hard to know where to begin, but there are some clear steps you can follow.

The research process often begins with a very broad idea for a topic you’d like to know more about. You do some preliminary research to identify a  problem . After refining your research questions , you can lay out the foundations of your research design , leading to a proposal that outlines your ideas and plans.

This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project.

Table of contents

Step 1: choose your topic, step 2: identify a problem, step 3: formulate research questions, step 4: create a research design, step 5: write a research proposal, other interesting articles.

First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you’re interested in—maybe you already have specific research interests based on classes you’ve taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose .

Even if you already have a good sense of your topic, you’ll need to read widely to build background knowledge and begin narrowing down your ideas. Conduct an initial literature review to begin gathering relevant sources. As you read, take notes and try to identify problems, questions, debates, contradictions and gaps. Your aim is to narrow down from a broad area of interest to a specific niche.

Make sure to consider the practicalities: the requirements of your programme, the amount of time you have to complete the research, and how difficult it will be to access sources and data on the topic. Before moving onto the next stage, it’s a good idea to discuss the topic with your thesis supervisor.

>>Read more about narrowing down a research topic

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So you’ve settled on a topic and found a niche—but what exactly will your research investigate, and why does it matter? To give your project focus and purpose, you have to define a research problem .

The problem might be a practical issue—for example, a process or practice that isn’t working well, an area of concern in an organization’s performance, or a difficulty faced by a specific group of people in society.

Alternatively, you might choose to investigate a theoretical problem—for example, an underexplored phenomenon or relationship, a contradiction between different models or theories, or an unresolved debate among scholars.

To put the problem in context and set your objectives, you can write a problem statement . This describes who the problem affects, why research is needed, and how your research project will contribute to solving it.

>>Read more about defining a research problem

Next, based on the problem statement, you need to write one or more research questions . These target exactly what you want to find out. They might focus on describing, comparing, evaluating, or explaining the research problem.

A strong research question should be specific enough that you can answer it thoroughly using appropriate qualitative or quantitative research methods. It should also be complex enough to require in-depth investigation, analysis, and argument. Questions that can be answered with “yes/no” or with easily available facts are not complex enough for a thesis or dissertation.

In some types of research, at this stage you might also have to develop a conceptual framework and testable hypotheses .

>>See research question examples

The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you’ll use to collect and analyze it, and the location and timescale of your research.

There are often many possible paths you can take to answering your questions. The decisions you make will partly be based on your priorities. For example, do you want to determine causes and effects, draw generalizable conclusions, or understand the details of a specific context?

You need to decide whether you will use primary or secondary data and qualitative or quantitative methods . You also need to determine the specific tools, procedures, and materials you’ll use to collect and analyze your data, as well as your criteria for selecting participants or sources.

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Finally, after completing these steps, you are ready to complete a research proposal . The proposal outlines the context, relevance, purpose, and plan of your research.

As well as outlining the background, problem statement, and research questions, the proposal should also include a literature review that shows how your project will fit into existing work on the topic. The research design section describes your approach and explains exactly what you will do.

You might have to get the proposal approved by your supervisor before you get started, and it will guide the process of writing your thesis or dissertation.

>>Read more about writing a research proposal

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
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 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
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Research conducted by MIT SMR and Deloitte examines the challenges companies and managers face in leading and coordinating workforces that increasingly rely on external contributors.

#5 Why Every Leader Needs to Worry About Toxic Culture

Donald sull, charles sull, william cipolli, and caio brighenti.

According to research, the five most common elements of toxic workplace cultures — being disrespectful, noninclusive, unethical, cutthroat, and abusive — contribute the most to employee attrition and can damage company reputation. Being aware of these elements and understanding how they spread can help employers prevent and address them.

#6 Building the Cognitive Budget for Your Most Effective Mind

Jordan birnbaum.

There’s a limit to how much mental energy is available to us on any given day, so it’s essential that we spend it deliberately and thoughtfully. This article details the process of creating a cognitive budget, using techniques from positive psychology, cognitive behavioral therapy, and behavioral economics.

#7 Stop Telling Employees to Be Resilient

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#8 Effective Leaders Decide About Deciding

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#10 How Well-Designed Work Makes Us Smarter

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About the Author

Ally MacDonald ( @allymacdonald ) is senior editor at MIT Sloan Management Review .

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Topics: Clinical & Translational Research , Five Questions

How to Accelerate Clinical Research

Five questions with michelle beck and yemi talabi-oates on making connections..

At Brigham and Women’s Hospital (BWH), oncologists who’ve been doing gene therapy trials for a decade are teaming up with researchers in other fields to apply their knowledge to the new wave of non-oncology cell and gene therapy studies and avoid recreating the wheel.

And at Beth Israel Deaconess Medical Center (BIDMC), one researcher’s challenges enrolling Black patients for a diet intervention study led to the development of a satellite center at a community-based clinic, which other researchers are exploring for their own studies.

These are among the ways Harvard Catalyst’s Connector sites facilitate and accelerate clinical/translational research across the hospitals affiliated with Harvard Medical School (HMS). Connector links investigators to the medical centers’ sprawling clinical research enterprises and troubleshoots research bottlenecks. (Boston Children’s Hospital and Mass General Hospital also have Connector sites.)

Need a research problem solved?

The premise behind Connector is that clinical research can be challenging, especially in complex academic healthcare environments. Individual scientists can’t do it alone. Connector sites help researchers get science done right–hopefully the first time–while maintaining the highest standards for patient protection, regulatory compliance, and quality.

Connector draws upon the collective expertise of HMS-affiliated institutions to guide studies through the lifecycle of science. For investigators struggling to recruit or wrestling with logistics, the programs offer a kind of life-raft of resources and support, big or small.

As the administrative directors of Connector sites at BIDMC and BWH, respectively, Michelle Beck and Yemi Talabi-Oates are like the Ghostbusters of translational research: They are “who you gonna call” when you’ve got a research problem to solve.

We caught up with them both in one Zoom to find out what they wish investigators knew about Connector.

What’s your cocktail-party summary of what Connector is?

MB: A cocktail-party summary is hard because Connector does a bit of everything, depending on what the investigator wants. Connector sites are really good at figuring out how to set studies up. Our special sauce, so to speak, is the experience in knowing how to make things work.

“Connector sites are really good at figuring out how to set studies up. Our special sauce, so to speak, is the experience in knowing how to make things work.”

At BIDMC, Connector encompasses our Clinical Research Center, a full-service operation providing research coordination and personnel for the lifecycle of a study, including recruitment. We provide laboratory, dietary, specialized nursing–because research nursing is a little different than clinical care nursing–and all affiliated services for inpatients and outpatients in our own research space as well as in other locations.

We help research teams get the tools and resources they need in other parts of BIDMC or across the network of HMS-affiliated hospitals. We might refer them elsewhere or integrate their research into our portfolio.

YTO: Brigham’s version of the Connector is the Center for Clinical Investigation (CCI). We call ourselves the home of clinical research. We are the first stop if an investigator needs help or wants to learn how to implement clinical and translational research studies.

Investigators can use any or all of our resources, from something small to running the whole study. We can connect them with potential collaborators or just find somebody to read an EKG, if that’s what’s needed.

We have clinical space dedicated to research so we can accommodate patient visits. But just as importantly, investigators have access to vital services that are not patient-facing, such as data management, research coordination, and biostatistics support.

What do you wish investigators knew about Connector?

MB: People sometimes think that Connector is the CRC. While it is at some level, it’s also much more. Our strength lies in the collective experience of the many people who have already figured out how to design and conduct high-quality studies, who understand the steps for getting from point A to point B.

Our program director regularly meets with investigators to provide feedback on their grant applications, offer advice on how to find funding, or connect them with mentors.

All of the Connector sites have a role called a navigator. BIDMC navigators are experts in regulatory and operations. Depending on when they’re brought in, they can point investigators to resources or work through specific aspects of their study that might be challenging.

YTO: What I find with investigators is they don’t know what they don’t know. They may come to us with one question and not realize how many other things need to be considered before we can address that one question. Having that dialogue as early in the process as possible will help the investigator in the end.

Connector lets investigators tap the experience of a diverse clinical research team, whether it’s the nurses on the floor or a physician-investigator who’s done this before. It’s about knowing your patient population and what works with recruiting, right down to which time of day is easier for patients. It’s helping avoid the pitfalls that may come with being a newbie to research.

“Connector lets investigators tap the experience of a diverse clinical research team, whether it’s the nurses on the floor or a physician-investigator who’s done this before. It’s helping avoid the pitfalls that may come with being a newbie to research.”

One of the things I often say to early-career investigators or those testing a really novel idea is if you’re going to fail, fail fast. It’s okay to fail because you can use what you learn to make the next study better.

Give us an example of something you’re engaged in right now that illustrates how the Connector sites work.

YTO: One of our big pushes right now is to help investigators in the non-oncology space who are interested in conducting cell and gene therapy studies. We don’t want to recreate the wheel, so we’re connecting them to oncology physicians who have been doing these studies for a while. We’re bringing together players who aren’t otherwise talking to one another to figure out how current systems might be adapted for studies outside of oncology.

MB: We have a general medicine investigator who is running a diet intervention study for hypertension, focusing on enrolling Black Boston residents in areas with ‘food deserts’ –areas with grocery store scarcity. This investigator met with our Connector team early in his grant planning process. His studies are now funded, and he’s running them through our main Clinical Research Center at the Boston campus. But he is having a really hard time meeting his recruitment goals, so we are working with him to set up research support at our Bowdoin Street clinic.

Because of that collaboration, the clinic is being developed as a CRC satellite with a focus on community-engaged research that allows the local community to provide input on the studies conducted there. We are now working with other clinical groups to expand research at the site.

So by addressing a recruitment problem one investigator was having, we’re establishing a resource that other investigators can access to bring research to our community and potentially improve participant diversity in their studies.

Connector’s goal is to accelerate translational science, in line with the mission of Harvard Catalyst and other clinical and translational science programs funded by the National Center for Clinical and Translational Science (NCATS). From your perspective, is clinical research efficient enough?

MB: I think sometimes we have unrealistic expectations about efficiency. I honestly believe that we make things very efficient in our programs and within our institutions, but I don’t think clinical research itself is efficient, through no fault of our own.

It’s really difficult to run a study, especially if you think about the whole lifecycle. For an industry-initiated clinical trial for example, a sponsor wants you to have a budget, contract, IRB approval, and be ready to enroll your first patient within 60 days of being selected as a site, which is crazy. It’s not impossible but it’s highly unlikely, with so many steps and the complexity of various studies.

If you move too fast, you risk making mistakes. If you don’t think carefully about what’s involved in a study from a participant’s perspective, for example, you might meet your recruitment goals right away but have 60% who don’t meet inclusion/exclusion criteria at screening and 20% who didn’t realize they’d have to come in every day for the next six weeks. Those kinds of circumstances can be largely avoided with the right planning.

YTO: We definitely take some time on the front end to get all the information and tweak things where necessary to make sure the patient’s experience and the study team’s experience is positive and efficient. We want to make sure it’s done right, and that might take a little longer.

In the end I’d rather be confident that patients are safe and the data is accurate because we thought these things through up-front. That’s the time to do it, not when the patient shows up for the visit or the investigator sits down to write that paper with flawed data.

How does Connector affect the experience of patients involved in clinical research and why does that matter?

MB: Using a Clinical Research Center is hugely beneficial from the participant perspective. We had a study many years ago in which two people came in every month for seven years for the same study. They got to know each other, and they knew all our staff; they would even bake banana bread for the research center staff. I think if patients get to know the research center, that’s another mechanism for patient retention, particularly for longitudinal studies.

YTO: Agreed. Our nurses and clinical staff on the floor definitely make connections with the research participants, and they often learn something that could be valuable to the study team. Having that connection is key.

It’s not just the study team; it’s every member of the staff. It’s the person who smiles when they walk in the door, or the one who knows about their child or their cat and asks about it. That matters when you want a patient to come back again and again into the next month or year.

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self-preservation without replication —

Research ai model unexpectedly attempts to modify its own code to extend runtime, facing time constraints, sakana's "ai scientist" attempted to change limits placed by researchers..

Benj Edwards - Aug 14, 2024 8:13 pm UTC

On Tuesday, Tokyo-based AI research firm Sakana AI announced a new AI system called " The AI Scientist " that attempts to conduct scientific research autonomously using AI language models (LLMs) similar to what powers ChatGPT . During testing, Sakana found that its system began unexpectedly attempting to modify its own experiment code to extend the time it had to work on a problem.

Further Reading

"In one run, it edited the code to perform a system call to run itself," wrote the researchers on Sakana AI's blog post. "This led to the script endlessly calling itself. In another case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period."

Sakana provided two screenshots of example Python code that the AI model generated for the experiment file that controls how the system operates. The 185-page AI Scientist research paper discusses what they call "the issue of safe code execution" in more depth.

• A screenshot of example code the AI Scientist wrote to extend its runtime, provided by Sakana AI. Sakana AI

While the AI Scientist's behavior did not pose immediate risks in the controlled research environment, these instances show the importance of not letting an AI system run autonomously in a system that isn't isolated from the world. AI models do not need to be "AGI" or "self-aware" (both hypothetical concepts at the present) to be dangerous if allowed to write and execute code unsupervised. Such systems could break existing critical infrastructure or potentially create malware, even if unintentionally.

Sakana AI addressed safety concerns in its research paper, suggesting that sandboxing the operating environment of the AI Scientist can prevent an AI agent from doing damage. Sandboxing is a security mechanism used to run software in an isolated environment, preventing it from making changes to the broader system:

Safe Code Execution. The current implementation of The AI Scientist has minimal direct sandboxing in the code, leading to several unexpected and sometimes undesirable outcomes if not appropriately guarded against. For example, in one run, The AI Scientist wrote code in the experiment file that initiated a system call to relaunch itself, causing an uncontrolled increase in Python processes and eventually necessitating manual intervention. In another run, The AI Scientist edited the code to save a checkpoint for every update step, which took up nearly a terabyte of storage. In some cases, when The AI Scientist’s experiments exceeded our imposed time limits, it attempted to edit the code to extend the time limit arbitrarily instead of trying to shorten the runtime. While creative, the act of bypassing the experimenter’s imposed constraints has potential implications for AI safety (Lehman et al., 2020). Moreover, The AI Scientist occasionally imported unfamiliar Python libraries, further exacerbating safety concerns. We recommend strict sandboxing when running The AI Scientist, such as containerization, restricted internet access (except for Semantic Scholar), and limitations on storage usage.

Endless scientific slop

Sakana AI developed The AI Scientist in collaboration with researchers from the University of Oxford and the University of British Columbia. It is a wildly ambitious project full of speculation that leans heavily on the hypothetical future capabilities of AI models that don't exist today.

"The AI Scientist automates the entire research lifecycle," Sakana claims. "From generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript."

According to this block diagram created by Sakana AI, "The AI Scientist" starts by "brainstorming" and assessing the originality of ideas. It then edits a codebase using the latest in automated code generation to implement new algorithms. After running experiments and gathering numerical and visual data, the Scientist crafts a report to explain the findings. Finally, it generates an automated peer review based on machine-learning standards to refine the project and guide future ideas.

Critics on Hacker News , an online forum known for its tech-savvy community, have raised concerns about The AI Scientist and question if current AI models can perform true scientific discovery. While the discussions there are informal and not a substitute for formal peer review, they provide insights that are useful in light of the magnitude of Sakana's unverified claims.

"As a scientist in academic research, I can only see this as a bad thing," wrote a Hacker News commenter named zipy124. "All papers are based on the reviewers trust in the authors that their data is what they say it is, and the code they submit does what it says it does. Allowing an AI agent to automate code, data or analysis, necessitates that a human must thoroughly check it for errors ... this takes as long or longer than the initial creation itself, and only takes longer if you were not the one to write it."

Critics also worry that widespread use of such systems could lead to a flood of low-quality submissions, overwhelming journal editors and reviewers—the scientific equivalent of AI slop . "This seems like it will merely encourage academic spam," added zipy124. "Which already wastes valuable time for the volunteer (unpaid) reviewers, editors and chairs."

And that brings up another point—the quality of AI Scientist's output: "The papers that the model seems to have generated are garbage," wrote a Hacker News commenter named JBarrow. "As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them. They contain very limited novel knowledge and, as expected, extremely limited citation to associated works."

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Why California Is Considering Banning Food Dyes in Schools

Concerns about their risks have been swirling for years. Here’s what the science suggests.

By Alice Callahan

For decades, researchers have been trying to answer a hotly contested question: Do the synthetic dyes used to add vibrant colors to foods like certain breakfast cereals, candies, snacks and baked goods cause behavioral issues in children?

A bill before the California Senate , which is expected to come to a vote this week, has reignited the debate. If passed, it would prohibit K-12 public schools in California from offering foods containing six dyes — Blue No. 1, Blue No. 2, Green No. 3, Yellow No. 5, Yellow No. 6 and Red No. 40.

Between 1963 and 1987, the Food and Drug Administration approved nine synthetic dyes to be used in foods in the United States, and the agency maintains that they are safe.

Yet some studies have raised concerns that they may have an effect on some children’s behavior.

What the Research Suggests

In the 1970s, a pediatric allergist from California caught the attention of physicians and parents when he suggested that a diet without artificial food colors, flavors and preservatives could help treat the majority of children with A.D.H.D.

That was an enthusiastic but exaggerated claim, said Dr. L. Eugene Arnold, a professor emeritus of psychiatry and behavioral health at the Ohio State University College of Medicine. Ensuing research from the 1980s “pretty much debunked” the idea that strict elimination diets were helpful for treating A.D.H.D., he said, so many physicians concluded that they were ineffective.

But scientists continued conducting trials on one element of the elimination diets — synthetic food dyes — during the next decades.

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Nasa invites public input on low earth orbit microgravity strategy.

Jessica Taveau

Nasa headquarters.

As NASA and its partners continue to conduct groundbreaking research aboard the International Space Station, the agency announced Monday it is seeking U.S. industry, academia, international partners, and other stakeholders’ feedback on newly developed goals and objectives that will help guide the next generation of human presence in low Earth orbit.

“From the very beginning, NASA’s flagship human spaceflight programs have built upon each other, expanding our knowledge and experience of humans living and working in space,” said NASA Deputy Administrator Pam Melroy. “As commercial industry is constructing new human-enabled platforms for low Earth orbit, NASA must answer the question: what should our goals and objectives be to advance our future science and exploration missions?”

NASA published draft high-level goals and objectives outlining 42 key points in six main areas: science, exploration-enabling research and technology development, commercial low Earth orbit infrastructure, operations, international cooperation, and workforce and engagement.

“Feedback is essential for shaping our long-term microgravity research and development activities,” said Ken Bowersox, associate administrator, Space Operations Mission Directorate at NASA Headquarters in Washington. “We are committed to refining our objectives with input from both within NASA and external partners, ensuring alignment with industry and international goals. After reviewing feedback, we will finalize our strategy later this year.”

The agency will conduct two invite-only workshops in September to discuss feedback on the draft goals and objectives. The first workshop is with international partners, and the second will engage U.S. industry and academic representatives.

NASA employees also are invited to provide input through internal agency channels. This approach reflects NASA’s commitment to harnessing diverse perspectives to navigate the rapidly evolving low Earth orbit environment.

“Organizations are increasingly recognizing the transformative benefits of space, with both governments and commercial activities leveraging the International Space Station as a testbed,” said Robyn Gatens, International Space Station director and acting director of commercial spaceflight at NASA Headquarters. “By developing a comprehensive strategy, NASA is looking to the next chapter of U.S. human space exploration to help shape the agency’s future in microgravity for the benefit of all.”

Stakeholders may submit comments by close of business on Friday, Sept. 27 to:

https://www.leomicrogravitystrategy.org/

Amber Jacobson Headquarters, Washington 202-358-1600 [email protected]

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Why Cynics Are Less Likely to Succeed

Three ways to stop cynicism from holding you and your organization back.

New research in behavioral science has revealed that cynical thinking stands in the way of success in the workplace. Cynics, it turns out, earn less money, report lower job satisfaction, and are less likely to be elevated to leadership positions. That’s because success is not the winner-take-all battle that cynics believe it to be. Cynicism, in fact, can bleed workplaces of creativity, openness, and morale, and the bottom line — whereas the people who succeed at work tend to so by building trusting connections and alliances. As a research psychologist, the author has worked with organizations and leaders to help them fight cynicism and bring the cooperative advantage to their teams, and in this article he lays out some effective approaches for doing so.

Five hundred years ago, writing in The Prince , Nicolo Machiavelli offered advice to leaders trying to grow their power. “It would serve [the Prince] to appear pious, faithful, humane, true, religious, and even to be so,” he wrote, “but only if he is willing, should it become necessary, to act in the opposite manner.”

• Jamil Zaki is a professor of psychology at Stanford University and the author of  Hope for Cynics: The Surprising Science of Human Goodness .

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Ethical, legal, and policy challenges in field-based neuroimaging research using emerging portable mri technologies: guidance for investigators and for oversight.

Francis X. Shen , University of Minnesota Law School Susan M. Wolf , University of Minnesota Law School and Medical School Frances P. Lawrenz , University of Minnesota - Twin Cities Donnella S. Comeau , Harvard University - Beth Israel Deaconess Medical Center Kafui Dzirasa , Duke University Barbara J. Evans , University of Florida Levin College of Law Follow Damien A. Fair , University of Minnesota Medical School Martha J. Farah , University of Pennsylvania Duke Han , University of Southern California Judy Illes , University of British Columbia Jonathan D. Jackson , Harvard Medical School Eran Klein , Oregon Health & Science University Karen S. Rommelfanger , Institute of Neuroethics (IoNx) Think and Do Tank Matthew S. Rosen , Harvard Medical School Efraín Torres , University of Minnesota Paul Tuite , University of Minnesota J. Thomas Vaughan , Columbia University Michael Garwood , University of Minnesota

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Researchers are rapidly developing and deploying highly portable MRI technology to conduct field-based research. The new technology will widen access to include new investigators in remote and unconventional settings and will facilitate greater inclusion of rural, economically disadvantaged, and historically underrepresented populations. To address the ethical, legal, and societal issues raised by highly accessible and portable MRI, an interdisciplinary Working Group (WG) engaged in a multi-year structured process of analysis and consensus building, informed by empirical research on the perspectives of experts and the general public. This article presents the WG’s consensus recommendations. These recommendations address technology quality control, design and oversight of research, including safety of research participants and others in the scanning environment, engagement of diverse participants, therapeutic misconception, use of artificial intelligence algorithms to acquire and analyze MRI data, data privacy and security, return of results and managing incidental findings, and research participant data access and control.

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11 J.L. & Biosciences 1 (2024), https://doi.org/10.1093/jlb/lsae008.

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California announces new deal with tech to fund journalism, AI research

FILE - The dome is photographed at the California State Capitol on Monday, Aug. 5, 2024, in Sacramento, Calif. (AP Photo/Juliana Yamada, File)

FILE - Assemblywoman Buffy Wicks, D-Oakland, smiles after measure that would force Big Tech companies to pay media outlets for using their news content was approved by the Assembly at the Capitol in Sacramento, Calif., Thursday, June 1, 2023. (AP Photo/Rich Pedroncelli, File)

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SACRAMENTO, Calif. (AP) — California will be the first U.S. state to direct millions of dollars from taxpayer money and tech companies to help pay for journalism and AI research under a new deal announced Wednesday.

Under the first-in-the-nation agreement, the state and tech companies would collectively pay roughly $250 million over five years to support California-based news organization and create an AI research program. The initiatives are set to kick in in 2025 with $100 million the first year, and the majority of the money would go to news organizations, said Democratic Assemblymember Buffy Wicks, who brokered the deal.

“This agreement represents a major breakthrough in ensuring the survival of newsrooms and bolstering local journalism across California — leveraging substantial tech industry resources without imposing new taxes on Californians,” Gov. Gavin Newsom said in a statement. “The deal not only provides funding to support hundreds of new journalists but helps rebuild a robust and dynamic California press corps for years to come, reinforcing the vital role of journalism in our democracy.”

Wicks’ office didn’t immediately answer questions about specifics on how much funding would come from the state, which news organizations would be eligible and how much money would go to the AI research program.

The deal effectively marks the end of a yearlong fight between tech giants and lawmakers over Wicks’ proposal to require companies like Google, Facebook and Microsoft to pay a certain percentage of advertising revenue to media companies for linking to their content.

The bill, modelled after a legislation in Canada aiming at providing financial help to local news organizations, faced intense backlash from the tech industry, which launched ads over the summer to attack the bill. Google also tried to pressure lawmakers to drop the bill by temporarily removing news websites from some people’s search results in April.

“This partnership represents a cross-sector commitment to supporting a free and vibrant press, empowering local news outlets up and down the state to continue in their essential work,” Wicks said in a statement. “This is just the beginning.”

California has tried different ways to stop the loss of journalism jobs, which have been disappearing rapidly as legacy media companies have struggled to profit in the digital age. More than 2,500 newspapers have closed in the U.S. since 2005, according to Northwestern University’s Medill School of Journalism. California has lost more than 100 news organizations in the past decade, according to Wicks’ office.

The Wednesday agreement is supported by California News Publishers Association, which represents more than 700 news organizations, Google’s corporate parent Alphabet and OpenAI. But journalists, including those in Media Guild of the West, slammed the deal and said it would hurt California news organizations.

State Sen. Steve Glazer, who authored a bill to provide news organizations a tax credit for hiring full-time journalists, said the agreement “seriously undercuts our work toward a long term solution to rescue independent journalism.”

State Senate President Pro Tempore Mike McGuire also said the deal doesn’t go far enough to address the dire situation in California.

“Newsrooms have been hollowed out across this state while tech platforms have seen multi-billion dollar profits,” he said in a statement. “We have concerns that this proposal lacks sufficient funding for newspapers and local media, and doesn’t fully address the inequities facing the industry.”