SynthesisBriefings in bioinformatics2021
How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles.
Synthesis in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Pre-COVID-19 body mass index and post-acute cardiovascular, gastrointestinal, and neuropsychiatric outcomes among children and young adults with SARS-CoV-2 infection: An EHR-based cohort study from the RECOVER Initiative.The Journal of infection · 2026Article
- Incorporating preprints in systematic reviews: a preliminary study of a novel method for rapid evidence synthesis.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Risk of neuropsychiatric and related conditions associated with SARS-CoV-2 infection: a difference-in-differences analysis.Nature communications · 2025Article
- Negative control-calibrated difference-in-difference analyses: addressing unmeasured confounding in RWD with application to racial/ethnic differences.NPJ digital medicine · 2025Article
- COLA-GLM: collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data.NPJ digital medicine · 2025Article
- Efficient and scalable construction of clinical variable networks for complex diseases with RAMEN.Cell reports methods · 2025Article
- Consistency of Daily Number of Reported COVID-19 Cases in 191 Countries From 2020 to 2022: Comparative Analysis of 2 Major Data Sources.JMIR public health and surveillance · 2025Article
- Systematic Guidelines for Effective Utilization of COVID-19 Databases in Genomic, Epidemiologic, and Clinical Research.Viruses · 2023Review
- Why don't we share data and code? Perceived barriers and benefits to public archiving practices.Proceedings. Biological sciences · 2022Article
- How often do cancer researchers make their data and code available and what factors are associated with sharing?BMC medicine · 2022Article
- Omicron (B.1.1.529) - A new heavily mutated variant: Mapped location and probable properties of its mutations with an emphasis on S-glycoprotein.International journal of biological macromolecules · 2022Article
- A comprehensive analysis of the mutational landscape of the newly emerging Omicron (B.1.1.529) variant and comparison of mutations with VOCs and VOIs.GeroScience · 2022Article
- Comparative genomics, evolutionary epidemiology, and RBD-hACE2 receptor binding pattern in B.1.1.7 (Alpha) and B.1.617.2 (Delta) related to their pandemic response in UK and India.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2022Article
- A Paradigm Shift in the Combination Changes of SARS-CoV-2 Variants and Increased Spread of Delta Variant (B.1.617.2) across the World.Aging and disease · 2022Article
- Sharing Time-to-Event Data with Privacy Protection.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
- Omicron variant (B.1.1.529) of SARS-CoV-2: understanding mutations in the genome, S-glycoprotein, and antibody-binding regions.GeroScience · 2022Article
- An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2.PloS one · 2022Article
- Evolution, Mode of Transmission, and Mutational Landscape of Newly Emerging SARS-CoV-2 Variants.mBio · 2021Article
- Asian-Origin Approved COVID-19 Vaccines and Current Status of COVID-19 Vaccination Program in Asia: A Critical Analysis.Vaccines · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitative analysis to answer questions related to dataset contents, accessibility and citations.
methodsWe downloaded COVID-19-related full-text articles published until 31 May 2020 from PubMed Central. Dataset URL links mentioned in full-text articles were extracted, and each dataset was manually reviewed to provide information on 10 variables: (1) type of the dataset, (2) geographic region where the data were collected, (3) whether the dataset was immediately downloadable, (4) format of the dataset files, (5) where the dataset was hosted, (6) whether the dataset was updated regularly, (7) the type of license used, (8) whether the metadata were explicitly provided, (9) whether there was a PubMed Central paper describing the dataset and (10) the number of times the dataset was cited by PubMed Central articles. Descriptive statistics about these seven variables were reported for all extracted datasets.
resultsWe found that 28.5% of 12 324 COVID-19 full-text articles in PubMed Central provided at least one dataset link. In total, 128 unique dataset links were mentioned in 12 324 COVID-19 full text articles in PubMed Central. Further analysis showed that epidemiological datasets accounted for the largest portion (53.9%) in the dataset collection, and most datasets (84.4%) were available for immediate download. GitHub was the most popular repository for hosting COVID-19 datasets. CSV, XLSX and JSON were the most popular data formats. Additionally, citation patterns of COVID-19 datasets varied depending on specific datasets.
conclusionPubMed Central articles are an important source of COVID-19 datasets, but there is significant heterogeneity in the way these datasets are mentioned, shared, updated and cited.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.