Recode: Policy Recommendations For Open Access To Research Data

Tsoukala, Victoria; Angelaki, Marina; Kalaitzi, Vasso; Wessels, Bridgette; Price, Lada Trifonova; Taylor, Mark J.; Smallwood, Rod; Linde, Peter; Sondervan, Jeroen; Reilly, Susan; Noorman, Merel; Wyatt, Sally; Bigagli, Lorenzo; Finn, Rachel; Sveinsdottir, Thordis; Wadhwa, Kush

doi:10.5281/zenodo.50863

Cited by 3 publications

(2 citation statements)

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“…Policies and guidelines are often drafted from perspectives that bury the complexity and cultural specificity of data sharing and reuse. 52,53 While guidelines such as the FAIR data principles recognize the importance of making data findable, accessible, interoperable, and reusable, the importance of embedded social communication and the relevance of data practices in domain-specific epistemic processes, particularly in reusability, still need to be made explicit and deserve further study.…”

Section: Conclusion: Ramifications For System Designmentioning

confidence: 99%

Understanding data search as a socio-technical practice

Gregory

Cousijn

Groth

et al. 2019

Journal of Information Science

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Open research data are heralded as having the potential to increase effectiveness, productivity, and reproducibility in science, but little is known about the actual practices involved in data search. The socio-technical problem of locating data for reuse is often reduced to the technological dimension of designing data search systems. We combine a bibliometric study of the current academic discourse around data search with interviews with data seekers. In this article, we explore how adopting a contextual, socio-technical perspective can help to understand user practices and behavior and ultimately help to improve the design of data discovery systems.

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Section: Conclusion: Ramifications For System Designmentioning

confidence: 99%

Understanding data search as a socio-technical practice

Gregory

Cousijn

Groth

et al. 2019

Journal of Information Science

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“…129 Institutional policy can reinforce open data sharing. 130,131 A trend towards more open data sharing and reuse is apparent among early career researchers. 132,133 Guidelines on properly presenting image data in publications exist (e.g., by Cromey 89 ), and members of QUAREP-LiMi developed comprehensive community checklists.…”

Section: Analysis Results Exploration Requires Dedicated Software Ins...mentioning

confidence: 99%

A practical guide to bioimaging research data management in core facilities

Schmidt,

Boissonnet,

Dohle

et al. 2024

Journal of Microscopy

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Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data‐driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well‐established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging – Society for Microscopy and Image Analysis (GerBI‐GMB), cross‐institutional working groups and third‐party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging‐core‐facility‐centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.

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Openness in Big Data and Data Repositories

Xafis

Labude

2019

ABR

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There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

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Recode: Policy Recommendations For Open Access To Research Data

Cited by 3 publications

References 0 publications

Understanding data search as a socio-technical practice

Understanding data search as a socio-technical practice

A practical guide to bioimaging research data management in core facilities

Openness in Big Data and Data Repositories

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