ArticleAmerican journal of epidemiology2023
Moving Toward Findable, Accessible, Interoperable, Reusable Practices in Epidemiologic Research.
Article in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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
16 citing papers in PubMed.
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Transdiagnostic Assessment of Mental Health and Sleep Registry: Protocol for a Cross-Sectional Study.Alpha psychiatry · 2026Article
- From neuroinflammation to neural inference: computational psychiatry meets precision medicine.Schizophrenia (Heidelberg, Germany) · 2026Review
- Leveraging large language models for structured information extraction from pathology reports.Journal of pathology informatics · 2025Article
- ClusterRadar: An interactive web-tool for the multi-method exploration of spatial clusters over time.PloS one · 2025Article
- Erratum: "Moving toward findable, accessible, interoperable, reusable practices in epidemiologic research".American journal of epidemiology · 2024Article
- Risk factors for breast cancer subtypes by race and ethnicity: a scoping review.Journal of the National Cancer Institute · 2024Article
- Building transparency and reproducibility into the practice of pharmacoepidemiology and outcomes research.American journal of epidemiology · 2024Article
- Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models.JAMIA open · 2024Article
- Risk factors for breast cancer subtypes by race and ethnicity: A scoping review of the literature.medRxiv : the preprint server for health sciences · 2024Article
- Policy instruments for the governance of the social drivers of health data in clinical and research settings: a policy mapping brief.Frontiers in public health · 2024Article
- EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization.Scientific reports · 2023Article
- Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models.ArXiv · 2023Article
- Understanding disparities in the burden of mortality as a step towards health equity in the USA.Lancet (London, England) · 2023Article
- PRScalc, a privacy-preserving calculation of raw polygenic risk scores from direct-to-consumer genomics data.Bioinformatics advances · 2023Article
- MedicaidJS: a FAIR approach to real-time drug analytics.Bioinformatics advances · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible ("Cloud") data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.
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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.