PubMed Central® (PMC) is a free full-text archive of biomedical and life sciences journal article at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). The PubMed Central (PMC) Article Datasets include full-text articles archived in PMC and made available under license terms that allow for text mining and other types of secondary analysis and reuse. The articles are organized on AWS based on general license type:

The PMC Open Access (OA) Subset, which includes all articles in PMC with a machine-readable Creative Commons license

The Author Manuscript Dataset, which includes all articles collected under a funder policy in PMC and made available in machine-readable formats for text mining

These datasets collectively span more than half of PMC’s total collection of full-text articles. PMC enables access to these datasets to expand the impact of open access and publicly-funded research; enable greater machine learning across the spectrum of scientific research; reach new audiences; and open new doors for discovery. The bucket in this registry contains individual articles in NISO Z39.96-2015 JATS XML format as well as in plain text as extracted from the XML. The bucket is updated daily with new and updated articles. Also included are file lists that include metadata for articles in each dataset.

This dataset contains NLM's database of citations and abstracts in the fields of medicine, nursing, dentistry, veterinary medicine, health care systems, and preclinical sciences.

Unlock valuable biomedical knowledge with our comprehensive PubMed Dataset, designed for researchers, analysts, and healthcare professionals to track medical advancements, explore drug discoveries, and analyze scientific literature.

Dataset Features

Scientific Articles & Abstracts: Access structured data from PubMed, including article titles, abstracts, authors, publication dates, and journal sources. Medical Research & Clinical Studies: Retrieve data on clinical trials, drug research, disease studies, and healthcare innovations. Keywords & MeSH Terms: Extract key medical subject headings (MeSH) and keywords to categorize and analyze research topics. Publication & Citation Data: Track citation counts, journal impact factors, and author affiliations for academic and industry research.

Customizable Subsets for Specific Needs Our PubMed Dataset is fully customizable, allowing you to filter data based on publication date, research category, keywords, or specific journals. Whether you need broad coverage for medical research or focused data for pharmaceutical analysis, we tailor the dataset to your needs.

Popular Use Cases

Pharmaceutical Research & Drug Development: Analyze clinical trial data, drug efficacy studies, and emerging treatments. Medical & Healthcare Intelligence: Track disease outbreaks, healthcare trends, and advancements in medical technology. AI & Machine Learning Applications: Use structured biomedical data to train AI models for predictive analytics, medical diagnosis, and literature summarization. Academic & Scientific Research: Access a vast collection of peer-reviewed studies for literature reviews, meta-analyses, and academic publishing. Regulatory & Compliance Monitoring: Stay updated on medical regulations, FDA approvals, and healthcare policy changes.

Whether you're conducting medical research, analyzing healthcare trends, or developing AI-driven solutions, our PubMed Dataset provides the structured data you need. Get started today and customize your dataset to fit your research objectives.

PubMed is a free resource supporting the search and retrieval of biomedical and life sciences literature with the aim of improving health–both globally and personally.

The PubMed database contains citations and abstracts of biomedical literature. It does not include full text journal articles; however, links to the full text are often present when available from other sources, such as the publisher's website or PubMed Central (PMC).

See the PubMed User Guide for more information. https://pubmed.ncbi.nlm.nih.gov/help/

Public Health Reports Impact Factor 2025-2026 - ResearchHelpDesk - Public Health Reports is the official journal of the Office of the U.S. Surgeon General and the U.S. Public Health Service and has been published since 1878. It is published bimonthly, plus supplement issues, through an official agreement with the Association of Schools and Programs of Public Health. The journal is peer-reviewed and publishes original research, reviews, and commentaries in the areas of public health practice and methodology, public health law, and teaching at schools and programs of public health. Issues contain regular commentaries by the U.S. Surgeon General and executives of the U.S. Department of Health and Human Services and the Office of the Assistant Secretary of Health. The journal focuses upon such topics as tobacco control, teenage violence, occupational disease and injury, immunization, drug policy, lead screening, health disparities, and many other key and emerging public health issues. In addition to the six regular issues, PHR produces supplemental issues approximately 2-5 times per year which focus on specific topics that are of particular interest to our readership. The journal's contributors are on the front line of public health and they present their work in a readable and accessible format. Abstract & indexing Clarivate Analytics: Current Contents - Clinical Medicine Clarivate Analytics: Science Citation Index (SCI) Clarivate Analytics: Social Sciences Citation Index (SSCI) Clarivate Analytics: Science Citation Index Expanded (SCIE) CABI: Global Health Clarivate Analytics: Current Contents - Social & Behavioral Sciences EBSCO EMBASE/Excerpta Medica Ovid JSTOR PubMed Central (PMC) PAIS International - ProQuest ProQuest Statistical Reference Index PubMed: MEDLINE Scopus

BMC public health Impact Factor 2025-2026 - ResearchHelpDesk - BMC Public Health is an open access, peer-reviewed journal that considers articles on the epidemiology of disease and the understanding of all aspects of public health. The journal has a special focus on the social determinants of health, the environmental, behavioral, and occupational correlates of health and disease, and the impact of health policies, practices and interventions on the community. Indexing Details CABI CAS Current contents Citebase DOAJ EmCare Medscape SOCOLAR Embase Food Science and Technology Abstracts Global Health OAIster MEDLINE PubMed Central PubMed Scopus Science Citation Index Expanded SCImago ​Zetoc

Journal of Ophthalmology Impact Factor 2024-2025 - ResearchHelpDesk - Journal of Ophthalmology is a peer-reviewed, Open Access journal that publishes original research, review, and clinical studies related to the anatomy, physiology and diseases of the eye. Submissions should focus on focusing on new diagnostic and surgical techniques, instrument and therapy updates, as well as clinical trials and research findings. Journal of Ophthalmology is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative. It operates a fully open access publishing model which allows open global access to its published content. This model is supported through Article Processing Charges. The most recent Impact Factor for Journal of Ophthalmology is 1.580 according to the 2018 Journal Citation Reports released by Clarivate Analytics in 2019. The journal’s most recent CiteScore is 1.78 according to the CiteScore 2018 metrics released by Scopus. Journal of Ophthalmology is included in many leading abstracting and indexing databases. Academic OneFile Academic Search Alumni Edition Academic Search Complete Airiti Library Biological Sciences Chemical Abstracts Service (CAS) CINAHL Plus with Full Text CNKI Scholar Directory of Open Access Journals (DOAJ) EBSCO Discovery Service EBSCOhost Connection EBSCOhost Research Databases Expanded Academic ASAP Expanded Academic Index Google Scholar Health and Wellness Resource Center Health Reference Center Academic HINARI Access to Research in Health Programme InfoTrac Custom journals J-Gate Portal Journal Citation Reports - Science Edition Open Access Journals Integrated Service System Project (GoOA) Primo Central Index PubMed PubMed Central SafetyLit Science Citation Index Expanded Scopus The Summon Service WorldCat Discovery Services

This dataset was collected from the PubMed portal to MEDLINE and other repositories of biomedical research (https://www.ncbi.nlm.nih.gov/pubmed/). Analysis of the dataset led to the paper "Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine", published in PLOS One (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173444). The raw data were pre-processed using the script "clean.r" in the project directory on GitHub (https://github.com/corybrunson/coauthor) to obtain the file presented here.

The dataset is formatted as a data table (https://cran.r-project.org/web/packages/data.table/index.html), a class of data frame in R, and saved as a .RData file, which can be loaded into an R session via `load("path/to/dataset/pmDat.RData")`. The fields are as follows:

• `pmid` - the unique publication identifier (PMID) used by PubMed
• `jid` - the unique journal identifier used by PubMed
• `issn` - the (print) ISSN of the journal
• `ym` - the month and year of publication
• `nau` - the number of authors credited by the publication (up to any limits imposed by PubMed, and counting each author collective as a single author)
• `cau` - whether any corporate author was credited
• `rev` - whether the publication was tagged as a review
• `trial` - whether the publication was tagged as a clinical trial
• `npmt` - the number of MeSH terms assigned to the publication that were flagged as "major" topics
• `nmh` - the number of top-level MeSH headings assigned to the publication
• `supp` - whether the publication was tagged as having received financial support
• `ng` - the number of grants acknowledged by the publication
• `co` - the country in which the journal was published

Note that the field values for any publication can be validated by searching for the PMID in PubMed.

Canadian Journal of Infectious Diseases and Medical Microbiology Impact Factor 2025-2026 - ResearchHelpDesk - Canadian Journal of Infectious Diseases and Medical Microbiology is a peer-reviewed, Open Access journal that publishes original research articles, review articles, and clinical studies related to infectious diseases of bacterial, viral and parasitic origin. The journal welcomes articles describing research on pathogenesis, epidemiology of infection, diagnosis and treatment, antibiotics and resistance, and immunology. Canadian Journal of Infectious Diseases and Medical Microbiology is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative. It operates a fully open access publishing model which allows open global access to its published content. This model is supported through Article Processing Charges. Canadian Journal of Infectious Diseases and Medical Microbiology is included in many leading abstracting and indexing databases. Abstracting and Indexing The following is a list of the Abstracting and Indexing databases that cover Canadian Journal of Infectious Diseases and Medical Microbiology published by Hindawi. Abstracts on Hygiene and Communicable Diseases Agricultural Economics Database Agroforestry Abstracts Botanical Pesticides CAB Abstracts Directory of Open Access Journals (DOAJ) EMBASE Global Health Google Scholar Journal Citation Reports - Science Edition Open Access Journals Integrated Service System Project (GoOA) Primo Central Index PubMed PubMed Central Science Citation Index Expanded Scopus The Summon Service WorldCat Discovery Services All of Hindawi’s content is archived in Portico, which provides permanent archiving for electronic scholarly journals, as well as via the LOCKSS initiative.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

PubMed Central (PMC) is a free, digital archive of full text biomedical and life sciences journal literature.

The achievement of a robust, effective and responsible form of data sharing is currently regarded as a priority for biological and bio-medical research. Empirical evaluations of data sharing may be regarded as an indispensable first step in the identification of critical aspects and the development of strategies aimed at increasing availability of research data for the scientific community as a whole. Research concerning human genetic variation represents a potential forerunner in the establishment of widespread sharing of primary datasets. However, no specific analysis has been conducted to date in order to ascertain whether the sharing of primary datasets is common-practice in this research field. To this aim, we analyzed a total of 543 mitochondrial and Y chromosomal datasets reported in 508 papers indexed in the Pubmed database from 2008 to 2011. A substantial portion of datasets (21.9%) was found to have been withheld, while neither strong editorial policies nor high impact factor proved to be effective in increasing the sharing rate beyond the current figure of 80.5%. Disaggregating datasets for research fields, we could observe a substantially lower sharing in medical than evolutionary and forensic genetics, more evident for whole mtDNA sequences (15.0% vs 99.6%). The low rate of positive responses to e-mail requests sent to corresponding authors of withheld datasets (28.6%) suggests that sharing should be regarded as a prerequisite for final paper acceptance, while making authors deposit their results in open online databases which provide data quality control seems to provide the best-practice standard. Finally, we estimated that 29.8% to 32.9% of total resources are used to generate withheld datasets, implying that an important portion of research funding does not produce shared knowledge. By making the scientific community and the public aware of this important aspect, we may help popularize a more effective culture of data sharing.

American Journal of Public Health Impact Factor 2025-2026 - ResearchHelpDesk - The American Journal of Public Health is a monthly peer-reviewed public health journal published by the American Public Health Association covering health policy and public health. The journal was established in 1911 and its stated mission is to advance public health research, policy, practice, and education. The journal occasionally publishes themed supplements. The editor-in-chief is Alfredo Morabia. The journal has been criticized for extending its open access embargo from 2 to 10 years as of June 1, 2013. Abstracting and indexing Biological Abstracts BIOSIS Previews Chemical Abstracts Service CINAHL Current Contents/Clinical Medicine Current Contents/Life Sciences Current Contents/Social & Behavioral Sciences Embase/Excerpta Medica Food Science and Technology Abstracts Index Medicus/MEDLINE/PubMed Psychological Abstracts/PsycINFO Science Citation Index Scopus Social Sciences Citation Index

Hype - PubMed dataset Prepared by Apratim Mishra This dataset captures ‘Hype’ within biomedical abstracts sourced from PubMed. The selection chosen is ‘journal articles’ written in English, published between 1975 and 2019, totaling ~5.2 million. The classification relies on the presence of specific candidate ‘hype words’ and their abstract location. Therefore, each article (PMID) might have multiple instances in the dataset due to the presence of multiple hype words in different abstract sentences. The candidate hype words are 35 in count: 'major', 'novel', 'central', 'critical', 'essential', 'strongly', 'unique', 'promising', 'markedly', 'excellent', 'crucial', 'robust', 'importantly', 'prominent', 'dramatically', 'favorable', 'vital', 'surprisingly', 'remarkably', 'remarkable', 'definitive', 'pivotal', 'innovative', 'supportive', 'encouraging', 'unprecedented', 'enormous', 'exceptional', 'outstanding', 'noteworthy', 'creative', 'assuring', 'reassuring', 'spectacular', and 'hopeful’. This is version 3 of the dataset. Added new file - WSD_hype.tsv File 1: hype_dataset_final.tsv Primary dataset. It has the following columns: 1. PMID: represents unique article ID in PubMed 2. Year: Year of publication 3. Hype_word: Candidate hype word, such as ‘novel.’ 4. Sentence: Sentence in abstract containing the hype word. 5. Hype_percentile: Abstract relative position of hype word. 6. Hype_value: Propensity of hype based on the hype word, the sentence, and the abstract location. 7. Introduction: The ‘I’ component of the hype word based on IMRaD 8. Methods: The ‘M’ component of the hype word based on IMRaD 9. Results: The ‘R’ component of the hype word based on IMRaD 10. Discussion: The ‘D’ component of the hype word based on IMRaD File 2: hype_removed_phrases_final.tsv Secondary dataset with same columns as File 1. Hype in the primary dataset is based on excluding certain phrases that are rarely hype. The phrases that were removed are included in File 2 and modeled separately. Removed phrases: 1. Major: histocompatibility, component, protein, metabolite, complex, surgery 2. Novel: assay, mutation, antagonist, inhibitor, algorithm, technique, series, method, hybrid 3. Central: catheters, system, design, composite, catheter, pressure, thickness, compartment 4. Critical: compartment, micelle, temperature, incident, solution, ischemia, concentration, thinking, nurses, skills, analysis, review, appraisal, evaluation, values 5. Essential: medium, features, properties, opportunities, oil 6. Unique: model, amino 7. Robust: regression 8. Vital: capacity, signs, organs, status, structures, staining, rates, cells, information 9. Outstanding: questions, issues, question, questions, challenge, problems, problem, remains 10. Remarkable: properties 11. Definite: radiotherapy, surgery File 3: WSD_hype.tsv Includes hype-based disambiguation for candidate words targeted for WSD (Word sense disambiguation)

After changing the language of its articles from Korean or English to English only in 2012 [1], Archives of Plastic Surgery (APS) became an international journal, as evidenced by various metrics [2] and its inclusion in the Web of Science Core Collection in 2012 and Scopus in 2013. From 2018 to July 2020, authors from 45 countries published in APS (Suppl. 1). The 289 most recent articles from 2018 to present have been cited by researchers from 44 countries in articles in the Web of Science (Suppl. 2). These results originated from the APS editors’ laborious work on editing and publishing; furthermore, the content itself is top-tier in the field of plastic surgery. In this Editorial, I would like to explain the influence of two international public platforms that helped APS reach the international level through exposure to worldwide researchers and surgeons: PubMed Central (PMC) and Crossref. Of course, the influence of Google or Google Scholar on the exposure of APS to global researchers may have been greater than that of PubMed Central or Crossref. However, PubMed Central and Crossref are also powerful platforms for physicians and researchers.

Complete Search Strategy for Medline/PubMed.

Inspied by this post on biostar: http://www.biostars.org/post/show/54473/calculating-time-from-submission-to-publication-degree-of-burden-in-submitting-a-paper/ initialy asked by Ryan Delahanty

the script 'pubmed.sh" downloads the the journals from http://www.ncbi.nlm.nih.gov/books/NBK3827/table/pubmedhelp.pubmedhelptable45/ , the 'eignefactors' from http://www.eigenfactor.org for each journal , It scans pubmed (starting from year=2000) and get the difference between the date(submitted) and the date(accepted). The code for the java program used to download pubmed is available here: https://github.com/lindenb/jsandbox/blob/master/src/sandbox/PubmedDump.java

Note: pubmed contains some errors: e.g. submitted > accepted (http://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=20591334&retmode=xml) or some dates in the future: ( http://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=12921703&retmode=xml )

Indian Journal of Community Medicine Impact Factor 2025-2026 - ResearchHelpDesk - The Indian Journal of Community Medicine (IJCM), is the official organ & the only official journal of the Indian Association of Preventive and Social Medicine (IAPSM). It is a peer-reviewed journal which is published Quarterly. The journal publishes research articles, focusing on biostatistics, epidemiology, family health care, public health administration, national health problems, medical anthropology, health care delivery and social medicine, invited annotations and comments, invited papers on recent advances, clinical and epidemiological diagnosis and management; editorial correspondence and book reviews. Abstracting and Indexing Information The journal is registered with the following abstracting partners: CNKI (China National Knowledge Infrastructure), Baidu Scholar, EBSCO Publishing's Electronic Databases, Ex Libris – Primo Central, Google Scholar, Infotrieve, Hinari, ProQuest, National Science Library, TdNet, Wanfang Data The journal is indexed with, or included in, the following: Emerging Sources Citation Index, DOAJ, Indian Science Abstracts,MedInd, PubMed Central, IndMed, Scimago Journal Ranking, Web of Science, SCOPUS.

Stigmatizing language or non-person-centered language (PCL) has been shown to impact patients negatively, especially in the case of obesity. This has led many associations, such as the American Medical Association (AMA) and the International Committee of Medical Journal Editors (ICMJE) to enact guidelines prohibiting the use of stigmatizing language in medical research. In 2018, the AMA adopted PCL guidelines, including a specific obesity amendment that all researchers should adhere to. Our primary objective was to determine if PCL guidelines specific to obesity have been properly obeyed in the most interacted with sports medicine journals. We searched within PubMed for obesity-related articles between 2019 and 2022 published in the top ten most interacted sports medicine journals based on Google Metrics data. A predetermined list of stigmatizing and non-PCL terms/language was searched within each article.

This dataset captures ‘Hype’ and 'Diversity', including article-level (pmid) and author-level (auid) data within biomedical abstracts sourced from PubMed. The selection chosen is ‘journal articles’ written in English, published between 1991 and 2014, totaling 421,580 (merged_df). The classification of hype relies on the presence of specific candidate ‘hype words’ and their abstract location. Therefore, each article (PMID) might have multiple instances in the dataset due to the presence of multiple hype words in different abstract sentences. Diversity is classified for ethnicity, gender, academic age, and topical expertise for authors based on the Rao-Sterling Diversity index. File1: merged_auids.csv (Important columns defined) • AUID: a unique ID for each author • Genni: gender prediction • Ethnea: ethnicity prediction ################################################# File2: merged_df.csv (Important columns defined) - pmid: unique paper - auid: all unique auids (author-name unique identification) - year: Year of paper publication - no_authors: Author count - journal: Journal name - years: first year of publication for every author - Country-temporal: Country of affiliation for every author - h_index: Journal h-index - TimeNovelty: Paper Time novelty - nih_funded: Binary variable indicating funding for any author - prior_cites_mean: Mean of all authors’ prior citation rate - insti_impact: All unique institutions’ citation rate - mesh_vals: Top MeSH values for every author of that paper - hype_word: Candidate hype word, such as ‘novel' - hype_value: Propensity of hype based on the hype word, the sentence, and the abstract location - hype_percentile: Abstract relative position of hype word - relative_citation_ratio: RCR