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The implementation of the EU General Data Protection Regulation (GDPR) has had significant impacts on biomedical research, often complicating data sharing among researchers. The recently announced proposal for a new EU Data Governance Act is a promising step towards facilitating data sharing, if it can interplay well with the GDPR. Graphical Abstract The EU General Data Protection Regulation (GDPR) has affected biomedical research, often complicating data sharing. The recently announced proposal for a new EU Data Governance Act, is a promising step towards facilitating data sharing.

Genetic data contain sensitive health and non-health-related information about the individuals and their family members. Therefore, adopting adequate privacy safeguards is paramount when processing genetic data for research or clinical purposes. One of the major legal instruments for personal data protection in the EU is the new General Data Protection Regulation (GDPR), which has entered into force in May 2016 and repealed the Directive 95/46/EC, with an ultimate goal of enhancing effectiveness and harmonization of personal data protection in the EU. This paper explores the major provisions of the new Regulation with regard to processing genetic data, and assesses the influence of such provisions on reinforcing the legal safeguards when sharing genetic data for research purposes. The new Regulation attempts to elucidate the scope of personal data, by recognizing pseudonymized data as personal (identifiable) data, and including genetic data in the catalog of special categories of data (sensitive data). Moreover, a set of new rules is laid out in the Regulation for processing personal data under the scientific research exemption. For instance, further use of genetic data for scientific research purposes, without obtaining additional consent will be allowed, if the specific conditions is met. The new Regulation has already fueled concerns among various stakeholders, owing to the challenges that may emerge when implementing the Regulation across the countries. Notably, the provided definition for pseudonymized data has been criticized because it leaves too much room for interpretations, and it might undermine the harmonization of the data protection across the countries.

Infrastructures are being developed to enhance and facilitate the sharing of cohort data internationally. However, empirical studies show that many barriers impede sharing data broadly. Therefore, our aim is to describe the barriers and concerns for the sharing of cohort data, and the implications for data sharing platforms. Seventeen 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. Credit 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. Data 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.

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.

Data sharing platforms are being constructed to make clinical cohort data more findable, accessible, interoperable, and reusable. Their primary purpose is to enhance the sharing of data. However, the lack of incentives for data sharing has been conceptualized in both scientific literature and policy documents as a problem of science policy. As platforms can only facilitate data sharing through technical means, they may not be able of fully resolving the data sharing problem. In this article, it is shown how the design of platforms may help in addressing policy barriers to data sharing in the long-term. In essence, platforms can be made into policy instruments that generate information on the data sharing process and the functionality of data access committees. This allows platforms to be used to inform science policy development, to monitor data sharing practices and to steer funding prioritization for cohorts and data infrastructures themselves. In this way, the creation of data infrastructures is closely connected to the policy evolutions in the context of open science.

The 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. The aim of this study is to describe in depth the concerns about acquiring credit for data sharing within epidemiological research. A 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. Interviewees 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. The 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.

This paper explores the perspectives of stakeholders involved in federated networks for health data sharing, focusing on the legal and practical dimensions of data protection and governance under GDPR and EHDS in the development of such infrastructures. Using a qualitative approach centered on perspectives of 19 experts with experience in projects building federated networks, it investigates the perceived challenges in fulfilling specific obligations under the GDPR, as well as in establishing the contractual framework of a federated network, including the arrangements and mechanisms required to control data access and to define the conditions for lawful and effective data sharing and reuse. The study critically assesses the commonly cited assurance that “data never leaves the node” and evaluates the compatibility of federated approaches with data protection requirements. It highlights key tensions between legal theory and practical implementation, offering insights relevant to the design and governance of other federated architectures and emerging European data spaces. Study results indicate that while the advantages of a federated approach, such as data minimization, should not be discarded, there are also significant challenges of aligning federated networks architectures with data protection requirements in particular. Federated networks help initiate discussions about data sharing with new data holders, but they do not offer a straightforward solution to legal and technical challenges of data sharing.

Here, we argue that, in line with the dramatic increase in the collection, storage and curation of human genomic data for biomedical research, genomic data repositories and consortia have adopted governance frameworks to both enable wide access and protect against possible harms. However, the merits and limitations of different governance frameworks in achieving these twin aims are a matter of ongoing debate in the scientific community; indeed, best practices and points for consideration are notably absent in devising governance frameworks for genomic databases. According to our collective experience in devising and assessing governance frameworks, we identify five key functions of ‘good governance’ (or ‘better governance’) and three areas in which trade-offs should be considered when specifying policies within those functions. We apply these functions as a benchmark to describe, as an example, the governance frameworks of six large-scale international genomic projects.

Background Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts. Main text In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC’s scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC’s way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science. Conclusions We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.

Sharing genomic research data through controlled‐access databases has increased in recent years. Policymakers and funding organizations endorse genomic data sharing in order to optimize the use of public funds and to increase the statistical power of databases. Well‐established data access arrangements and data access committees (DACs)—responsible for reviewing and managing requests for access to genomic databases—are therefore central for implementing the policies and principles of data sharing. This article aims to investigate the functionality of DACs through the perspective of existing practices. Graphical Abstract Data access arrangements and data access committees (DACs) are central for implementing the policies and principles of data sharing. Their functionality is here discussed through the perspective of existing practices.