Dataset Information

Globally Accessible Distributed Data Sharing (GADDS): a decentralized FAIR platform to facilitate data sharing in the life sciences.

ABSTRACT:

Motivation

Technical advances have revolutionized the life sciences and researchers commonly face challenges associated with handling large amounts of heterogeneous digital data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles provide a framework to support effective data management. However, implementing this framework is beyond the means of most researchers in terms of resources and expertise, requiring awareness of metadata, policies, community agreements and other factors such as vocabularies and ontologies.

Results

We have developed the Globally Accessible Distributed Data Sharing (GADDS) platform to facilitate FAIR-like data-sharing in cross-disciplinary research collaborations. The platform consists of (i) a blockchain-based metadata quality control system, (ii) a private cloud-like storage system and (iii) a version control system. GADDS is built with containerized technologies, providing minimal hardware standards and easing scalability, and offers decentralized trust via transparency of metadata, facilitating data exchange and collaboration. As a use case, we provide an example implementation in engineered living material technology within the Hybrid Technology Hub at the University of Oslo.

Availability and implementation

Demo version available at https://github.com/pavelvazquez/GADDS.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Vazquez P

PROVIDER: S-EPMC9344842 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Globally Accessible Distributed Data Sharing (GADDS): a decentralized FAIR platform to facilitate data sharing in the life sciences.

Vazquez Pavel P Hirayama-Shoji Kayoko K Novik Steffen S Krauss Stefan S Rayner Simon S

Bioinformatics (Oxford, England) 20220801 15

<h4>Motivation</h4>Technical advances have revolutionized the life sciences and researchers commonly face challenges associated with handling large amounts of heterogeneous digital data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles provide a framework to support effective data management. However, implementing this framework is beyond the means of most researchers in terms of resources and expertise, requiring awareness of metadata, policies, community agreements and ot ...[more]

PMID: 35639939

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Project description:BackgroundThe meaningful sharing of health data between different stakeholders is central to the advancement of science and to improve care offered to individual patients. However, it is important that the interests of individual stakeholders involved in this data sharing ecosystem are taken into account to ensure fair data sharing practices. In this regard, this qualitative study investigates such practices from the perspectives of a subset of relevant Swiss expert stakeholders, using a distributive justice lens.MethodsUsing purposive and snowball sampling methodologies, 48 expert stakeholders from the Swiss healthcare and research domains were recruited for semi-structured interviews. After the experts had consented, the interviews were audio-recorded and transcribed verbatim, but omitting identifying information to ensure confidentiality and anonymity. A thematic analysis using a deductive approach was conducted to identify fair data sharing practices for secondary research purposes. Themes and subthemes were then identified and developed during the analysis.ResultsThree distributive justice themes were identified in the data sharing negotiation processes, and these are: (i) effort, which was subcategorized into two subthemes (i.e. a claim to data reciprocity and other reciprocal advantages, and a claim to transparency on data re-use), (ii) compensation, which was subcategorized into two subthemes (i.e. a claim to an academic compensation and a claim to a financial compensation), and lastly, (iii) contribution, i.e. the significance of data contributions should be matched with a corresponding reward.ConclusionsThis qualitative study provides insights, which could inform policy-making on claims and incentives that encourage Swiss expert stakeholders to share their datasets. Importantly, several claims have been identified and justified under the basis of distributive justice principles, whilst some are more debatable and likely insufficient in justifying data sharing activities. Nonetheless, these claims should be taken seriously and discussed more broadly. Indeed, promoting health research while ensuring that healthcare systems guarantee better services, it is paramount to ensure that solutions developed are sustainable, provide fair criteria for academic careers and promote the sharing of high quality data to advance science.

Dataset Information

Globally Accessible Distributed Data Sharing (GADDS): a decentralized FAIR platform to facilitate data sharing in the life sciences.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Globally Accessible Distributed Data Sharing (GADDS): a decentralized FAIR platform to facilitate data sharing in the life sciences.

Similar Datasets

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