Policies to regulate data sharing of cohorts via data infrastructures: An interview study with funding agencies

Devriendt, Thijs; Shabani, Mahsa; Borry, Pascal

doi:10.1016/j.ijmedinf.2022.104900

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…This article focuses on creating incentives for data sharing through altering attribution and evaluation systems in academia. The results obtained from this interview study on policy measures that somewhat restrict decision-making options for researchers (e.g., mandates) are reported elsewhere [24]. These policy measures are complementary to the design of reputational and financial incentives within the overall science policy framework.…”

Section: Introductionmentioning

confidence: 86%

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Reward systems for cohort data sharing: An interview study with funding agencies

2023

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Data infrastructures are being constructed to facilitate cohort data sharing. These infrastructures are anticipated to increase the rate of data sharing. However, the lack of data sharing has also been framed as being the consequence of the lack of reputational or financial incentives for sharing. Some initiatives try to confer value onto data sharing by making researchers’ individual contributions to research visible (i.e., contributorship) or by quantifying the degree to which research data has been shared (e.g., data indicators). So far, the role of downstream evaluation and funding distribution systems for reputational incentives remains underexplored. This interview study documents the perspectives of members of funding agencies on, amongst other elements, incentives for data sharing. Funding agencies are adopting narrative CVs to encourage evaluation of diverse research outputs and display diversity in researchers’ profiles. This was argued to diminish the focus on quantitative indicators of scientific productivity. Indicators related to open science dimensions may be reintroduced if they are fully developed. Shifts towards contributorship models for research outputs are seen as complementary to narrative review.

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Section: Introductionmentioning

confidence: 86%

“…The study was approved by the Social and Societal Ethics Committee (SMEC) at KU Leuven (G-2021-3823). The full methodological details can be found in the Supplementary Data of the publication made on science policy measures based on this interview study [24].…”

Section: Methodsmentioning

confidence: 99%

Reward systems for cohort data sharing: An interview study with funding agencies

2023

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“…Moreover, in many cases, data are stored outside of a repository, hindering external access (Federer et al, 2018). Repercussions in the case of noncompliance for data sharing are often non-existent because many funding bodies simply lack robust monitoring systems (Devriendt et al, 2022b). Whereas it is out of the scope of this article to provide a comprehensive overview of initiatives directed toward FAIR implementation, these recent studies would suggest that further policy research and development is needed in this area.…”

Section: Discussionmentioning

confidence: 99%

FAIR data sharing: An international perspective on why medical researchers are lagging behind

Rainey¹,

Lutomski

Broeders

2023

Big Data & Society

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FAIR data, that is, Findable, Accessible, Interoperable, and Reusable data, and Big Data intersect across issues related to data storage, access, and processing. The solution-oriented FAIR principles serve an integral role in improving Big Data; yet to date, the implementation of FAIR in multiple sectors has been fragmented. We conducted an exploratory analysis to identify incentives and barriers in creating FAIR data in the medical sector using digital concept mapping, a systematic mixed methods approach. Thirty-eight principal investigators (PIs) were recruited from North America, Europe, and Oceania. Our analysis revealed five clusters rated according to perceived relevance: ‘Efficiency and collaboration’ (rating 7.23), ‘Privacy and security’ (rating 7.18), ‘Data management standards’ (rating 7.16), ‘Organization of services’ (rating 6.98), and ‘Ownership’ (rating 6.28). All five clusters scored relatively high and within a narrow range (i.e., 6.28–7.69), implying that each cluster likely influences researchers’ decision-making processes. PIs harbor a positive view of FAIR data sharing, as exemplified by participants highly prioritizing ‘Efficiency and collaboration’. However, the other four clusters received only modestly lower ratings and largely contained barriers to FAIR data sharing. When viewed collectively, the benefits of efficiency and collaboration may not be sufficient in propelling FAIR data sharing. Arguably, until more of these reported barriers are addressed, widespread support of FAIR data will not translate into widespread practice. This research lays the preliminary foundation for conducting targeted large-scale research into FAIR data practices in the medical research community.

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“…However, policy measures that restrict the authority of researchers to make data sharing decisions are often not supported. In this regard, incentive design is paramount if funding agencies do not wish to impose restrictions on the decision-making authority of researchers [ 13 ]. Additionally, the call for stronger health data governance is gaining significant movement with the progress of digital transformation [ 14 – 16 ].…”

Section: Introductionmentioning

confidence: 99%

Health data hubs: an analysis of existing data governance features for research

Alvarez-Romero

Martínez-García

Bernabéu-Wittel

et al. 2023

Health Res Policy Sys

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Background Digital transformation in healthcare and the growth of health data generation and collection are important challenges for the secondary use of healthcare records in the health research field. Likewise, due to the ethical and legal constraints for using sensitive data, understanding how health data are managed by dedicated infrastructures called data hubs is essential to facilitating data sharing and reuse. Methods To capture the different data governance behind health data hubs across Europe, a survey focused on analysing the feasibility of linking individual-level data between data collections and the generation of health data governance patterns was carried out. The target audience of this study was national, European, and global data hubs. In total, the designed survey was sent to a representative list of 99 health data hubs in January 2022. Results In total, 41 survey responses received until June 2022 were analysed. Stratification methods were performed to cover the different levels of granularity identified in some data hubs’ characteristics. Firstly, a general pattern of data governance for data hubs was defined. Afterward, specific profiles were defined, generating specific data governance patterns through the stratifications in terms of the kind of organization (centralized versus decentralized) and role (data controller or data processor) of the health data hub respondents. Conclusions The analysis of the responses from health data hub respondents across Europe provided a list of the most frequent aspects, which concluded with a set of specific best practices on data management and governance, taking into account the constraints of sensitive data. In summary, a data hub should work in a centralized way, providing a Data Processing Agreement and a formal procedure to identify data providers, as well as data quality control, data integrity and anonymization methods.

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Policies to regulate data sharing of cohorts via data infrastructures: An interview study with funding agencies

Cited by 6 publications

References 17 publications

Reward systems for cohort data sharing: An interview study with funding agencies

Reward systems for cohort data sharing: An interview study with funding agencies

FAIR data sharing: An international perspective on why medical researchers are lagging behind

Health data hubs: an analysis of existing data governance features for research

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