Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review

Inau, Esther Thea; Sack, Jean; Waltemath, Dagmar; Zeleke, Atinkut Alamirrew

doi:10.2196/22505

Cited by 38 publications

(28 citation statements)

References 33 publications

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“…Bioinformatics and biology as a whole have been early adopters of FAIR, and data management systems like the FAIRDOMHub [ 35 ] aid the creation of fair scientific data. In addition, the biomedical and health domains recently started to investigate FAIRification approaches [ 36–39 ].…”

Section: Interoperability Through Standards’ Implementation In Tools ...mentioning

confidence: 99%

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Niarakis

Waltemath

Glazier

et al. 2022

Briefings in Bioinformatics

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Computational models are often employed in systems biology to study the dynamic behaviours of complex systems. With the rise in the number of computational models, finding ways to improve the reusability of these models and their ability to reproduce virtual experiments becomes critical. Correct and effective model annotation in community-supported and standardised formats is necessary for this improvement. Here, we present recent efforts toward a common framework for annotated, accessible, reproducible and interoperable computational models in biology, and discuss key challenges of the field.

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Section: Interoperability Through Standards’ Implementation In Tools ...mentioning

confidence: 99%

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Niarakis

Waltemath

Glazier

et al. 2022

Briefings in Bioinformatics

Self Cite

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“…FAIR (findable, accessible, interoperable and reusable) health data principles, initiatives, implementation practices, and lessons learned in the FAIRification process can meaningfully support both evidence based clinical practice and research transparency [ 8 ]. Data driven policy relies on the creation of universal guidelines for the architecture of strong health information systems which should include citizen associations in their governance structure, as is the case of the French Health Data Hub [ 9 ] at the national level or sought by regional projects, such as TEHDAS (Towards European Health Data Space).…”

Section: Resultsmentioning

confidence: 99%

Evidence based policy making during times of uncertainty through the lens of future policy makers: four recommendations to harmonise and guide health policy making in the future

et al. 2022

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The Covid-19 pandemic has not only outlined the importance of using evidence in the healthcare policy making process but also the complexity that exists between policymakers and the scientific community. As a matter of fact, scientific data is just one of many other concurrent factors, including economic, social and cultural, that may provide the rationale for policy making. The pandemic has also raised citizens’ awareness and represented an unprecedented moment of willingness to access and understand the evidence underpinning health policies.This commentary provides policy recommendations to improve evidence-based policy making in health, through the lens of a young generation of public policy students and future policymakers, enrolled in a 24-hour course at Sciences Po Paris entitled “Evidence-based policy-making in health: theory and practice(s)”.Four out of 11 recommendations were prioritised and presented in this commentary which target both policymakers and the scientific community to make better use of evidence-based policy making in health. First, policy makers and scientists should build trusting partnerships with citizens and engage them, especially those facing our target health care issues or systems. Second, while artificial intelligence raises new opportunities in healthcare, its use in contexts of uncertainty should be addressed by policymakers in terms of liability and ethics. Third, conflicts of interest must be disclosed as much as possible and effectively managed to (re) build a trust relationship between policymakers, the scientific community and citizens, implying the need for risk management tools and cross border disclosure mechanisms. Last, well-designed and secure health information systems need to be implemented, following the FAIR (findable, accessible, interoperable and reusable) principles for health data. This will take us a step further from data to ‘policy wisdom’.Overall, these recommendations identified and formulated by students highlight some key issues that need to be rethought in the health policy cycle through elements like institutional incentives, cultural changes and dialogue between policy makers and the scientific community. This input from a younger generation of students highlights the importance of making the conversation on evidence-based policy making in health accessible to all generations and backgrounds.

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“…FAIR is the combination of different small practices that make the data easier to find, easier to understand, less likely to be lost, and more likely to be usable during the project time and years later [16]. FAIR principles [11] are guidelines for data management and stewardship that are valid for both machines and humans: Findable: (meta)data should be discoverable, identifiable and searchable via the assignment of metadata and unique identifiers.…”

Section: Fair and Beyondmentioning

confidence: 99%

“…Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that personal data should be limited to only what is necessary to the purposes for which the data is processed [29]. Linking back to the discussion of FAIR, we note that in [5,16], authors suggested that FAIR data and metadata can facilitate compliance with data minimization principle since FAIR principles allow for an assessment of which data to reuse.…”

Section: Data Minimizationmentioning

confidence: 99%

Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems

Slokom,

Larson

2021

Preprint

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We present a case that the newly emerging field of synthetic data in the area of recommender systems should prioritize 'doing data right'. We consider this catchphrase to have two aspects: First, we should not repeat the mistakes of the past, and, second, we should explore the full scope of opportunities presented by synthetic data as we move into the future. We argue that explicit attention to dataset design and description will help to avoid past mistakes with dataset bias and evaluation. In order to fully exploit the opportunities of synthetic data, we point out that researchers can investigate new areas such as using data synthesize to support reproducibility by making data open, as well as FAIR, and to push forward our understanding of data minimization.

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Initiatives, Concepts, and Implementation Practices of FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles in Health Data Stewardship Practice: Protocol for a Scoping Review

Cited by 38 publications

References 33 publications

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology

Evidence based policy making during times of uncertainty through the lens of future policy makers: four recommendations to harmonise and guide health policy making in the future

Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems

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