ArticleOpen research Europe2022
FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research.
Article in Open research Europe, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Perceptions and Awareness of Healthcare Professionals Regarding FAIR Data Principles and Health Data Sharing in Saudi Arabia.Healthcare (Basel, Switzerland) · 2025Article
- Web-Based Dashboard for Tracking Cryptococcosis-Related Deaths in Brazil (2000-2022).Tropical medicine and infectious disease · 2025Article
- Privacy-preserving federated machine learning on FAIR health data: A real-world application.Computational and structural biotechnology journal · 2024Article
- A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis.Scientific data · 2024Article
- Article
- A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study.Journal of medical Internet research · 2023Article
- Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach.Journal of multidisciplinary healthcare · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
37 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.
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.