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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.

BackgroundThe European Commission is funding projects that aim to establish data-sharing platforms. These platforms are envisioned to enhance and facilitate the international sharing of cohort data. Nevertheless, broad data sharing may be restricted by the lack of adequate recognition for those who share data. ObjectiveThe aim of this study is to describe in depth the concerns about acquiring credit for data sharing within epidemiological research. MethodsA total of 17 participants linked to European Union–funded data-sharing platforms were recruited for a semistructured interview. Transcripts were analyzed using inductive content analysis. ResultsInterviewees argued that data sharing within international projects could challenge authorship guidelines in multiple ways. Some respondents considered that the acquisition of credit for articles with extensive author lists could be problematic in some instances, such as for junior researchers. In addition, universities may be critical of researchers who share data more often than leading research. Some considered that the evaluation system undervalues data generators and specialists. Respondents generally looked favorably upon alternatives to the current evaluation system to potentially ameliorate these issues. ConclusionsThe evaluation system might impede data sharing because it mainly focuses on first and last authorship and undervalues the contributor’s work. Further movement of crediting models toward contributorship could potentially address this issue. Appropriate crediting mechanisms that are better aligned with the way science ought to be conducted in the future need to be developed.

Various data sharing platforms are being developed to enhance the sharing of cohort data by addressing the fragmented state of data storage and access systems. However, policy challenges in several domains remain unresolved. The euCanSHare workshop was organized to identify and discuss these challenges and to set the future research agenda. Concerns over the multiplicity and long-term sustainability of platforms, lack of resources, access of commercial parties to medical data, credit and recognition mechanisms in academia and the organization of data access committees are outlined. Within these areas, solutions need to be devised to ensure an optimal functioning of platforms.

Various data sharing platforms are being developed to enhance the sharing of cohort data by addressing the fragmented state of data storage and access systems. However, policy challenges in several domains remain unresolved. The euCanSHare workshop was organized to identify and discuss these challenges and to set the future research agenda. Concerns over the multiplicity and long-term sustainability of platforms, lack of resources, access of commercial parties to medical data, credit and recognition mechanisms in academia and the organization of data access committees are outlined. Within these areas, solutions need to be devised to ensure an optimal functioning of platforms.

BackgroundInfrastructures are being developed to enhance and facilitate the sharing of cohort data internationally. However, empirical studies show that many barriers impede sharing data broadly.PurposeTherefore, our aim is to describe the barriers and concerns for the sharing of cohort data, and the implications for data sharing platforms.MethodsSeventeen participants involved in developing data sharing platforms or tied to cohorts that are to be submitted to platforms were recruited for semi-structured interviews to share views and experiences regarding data sharing.ResultsCredit and recognition, the potential misuse of data, loss of control, lack of resources, socio-cultural factors and ethical and legal barriers are elements that influence decisions on data sharing. Core values underlying these reasons are equality, reciprocity, trust, transparency, gratification and beneficence.ConclusionsData generators might use data sharing platforms primarily for collaborative modes of working and network building. Data generators might be unwilling to contribute and share for non-collaborative work, or if no financial resources are provided for sharing data.