ArticlePatterns (New York, N.Y.)2021
An automated solution for measuring the progress toward FAIR research data.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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
18 citing papers in PubMed.
- Five views on the foundations of data science.Patterns (New York, N.Y.) · 2026Article
- Assessing the FAIRness of survey data collected during the COVID-19 pandemic in Germany.PloS one · 2026Article
- Suggestions for extending the FAIR Principles based on a linguistic perspective on semantic interoperability.Scientific data · 2025Article
- FAIR data for optical tweezers experiments.Biophysical journal · 2025Review
- Article
- Publicly Available Dental Image Datasets for Artificial Intelligence.Journal of dental research · 2024Observational
- Making Metadata Machine-Readable as the First Step to Providing Findable, Accessible, Interoperable, and Reusable Population Health Data: Framework Development and Implementation Study.Online journal of public health informatics · 2024Article
- [FAIR health data in the national and international data space].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Article
- Understanding the provenance and quality of methods is essential for responsible reuse of FAIR data.Nature medicine · 2024Article
- Advancing healthcare through data: the BETTER project's vision for distributed analytics.Frontiers in medicine · 2024Article
- Current state of data stewardship tools in life science.Frontiers in big data · 2024Review
- COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.PloS one · 2024Article
- FAIR EVA: Bringing institutional multidisciplinary repositories into the FAIR picture.Scientific data · 2023Article
- FAIR-Checker: supporting digital resource findability and reuse with Knowledge Graphs and Semantic Web standards.Journal of biomedical semantics · 2023Article
- FAIR+E pathogen data for surveillance and research: lessons from COVID-19.Frontiers in public health · 2023Article
- Modeling community standards for metadata as templates makes data FAIR.Scientific data · 2022Article
- Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies.Patterns (New York, N.Y.) · 2022Review
- Current state and call for action to accomplish findability, accessibility, interoperability, and reusability of low carbon energy data.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With a rising number of scientific datasets published and the need to test their Findable, Accessible, Interoperable, and Reusable (FAIR) compliance repeatedly, data stakeholders have recognized the importance of an automated FAIR assessment. This paper presents a programmatic solution for assessing the FAIRness of research data. We describe the translation of the FAIR data principles into measurable metrics and the application of the metrics in evaluating FAIR compliance of research data through an open-source tool we developed. For each metric, we conceptualized and implemented practical tests drawn upon prevailing data curation and sharing practices, and the paper discusses their rationales. We demonstrate the work by evaluating multidisciplinary datasets from trustworthy repositories, followed by recommendations and improvements. We believe our experience in developing and applying the metrics in practice and the lessons we learned from it will provide helpful information to others developing similar approaches to assess different types of digital objects and services.
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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.