Reusing Data Technical and Ethical Challenges

Boté, Juan-José; Graells, Miquel Térmens i

doi:10.14429/djlit.39.06.14807

Cited by 26 publications

(19 citation statements)

References 52 publications

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“…Barriers and drivers of data reuse have been described as technical , economic and ethical (Bote and Termens 2019;Duke and Porter 2013;Poth 2019), legal (Dulong de Rosnay 2017), practical (Faniel and Jacobsen 2010), and cultural (Borgman 2015a). Studies document affinities between (data) reuse and willingness to share research outputs (Hsu et al 2015;Joo, Kim, and Kim 2017;Kim and Zhang 2015;Yoon andKim 2017, 2020).…”

Section: What Drives Data Reuse?mentioning

confidence: 99%

Data sharing for global, unspecified reuse

Reichmann¹

2022

Preprint

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This contribution has originally been conceived as the introduction to my dissertation, to be submitted in February 2022 to the University of Graz (Department of Sociology). In the dissertation, I take up the vast discourse on data sharing to argue that until now, the most influential positions (exemplified in the works of philosopher Sabina Leonelli and information scientists Christine L. Borgman) have construed their subject matter rather narrowly, both in terms of the dominant case studies as well as in focusing on the narrowly stated epistemic goals of the Open Science movement (of ensuring reproducibility, transparency, and data reuse, say). I take issue with both of these claims based on a series of empirical case studies, to show that data sharing is broader than has hitherto been assumed, especially in terms of its aims which have, by and large, been modelled along the lines of enabling unspecified, global data reuse. With this preprint, I explain what this means based on an analysis of three discourses, all pertaining to data sharing: Discussions predominantly found within information science documenting a tremendous variability of data types, formats, practices, and sharing behaviour, and an empirical literature that has frequently overlooked the difficulties in accounting for data practices at the level of faculties, research groups, departments, or universities, as well as a more critical discourse that has recently emerged around the factors that inhibit data sharing and reuse, such as the situatedness and contextuality of data practices. The contribution documents a growing awareness of the situated nature of data practices, in particular with respect to research activities in the global south. It further documents mounting evidence for what has been called data contextuality, discussions that ensued from the observation, already a decade old, that “data” are impossible to define, leading to a view now known as the relational view of data (data as potential evidence for knowledge claims).

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Section: What Drives Data Reuse?mentioning

confidence: 99%

Data sharing for global, unspecified reuse

Reichmann¹

2022

Preprint

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“…Reasons for reusing public research data include cost-effectiveness and efficiency, access to datasets that would be difficult or impossible to regenerate, an increased sense of community, greater transparency and clarity of research, ability to retest and validate a shared dataset, support for recognition of data ownership, and an increase in citations [11]. The use of public data provides individuals with a unique opportunity to leverage existing data sets in combination with other public data or their own.…”

Section: Use Public Data In Your Researchmentioning

confidence: 99%

Ten Simple Rules for Using Public Data for Your Research

Oza¹,

Whitlock²,

Wilk³

et al. 2022

Preprint

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With an increasing amount of "omics" data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public data are: 1) use public data in your research, 2) evaluate data for your use case, 3) check data reuse requirements and embargoes, 4) be aware of ethics for data reuse, 5) plan for data storage and compute requirements, 6) know what you are downloading, 7) download programmatically and verify integrity, 8) properly cite data, 9) make data FAIR and share, and 10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

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“…By depositing data in a repository for dissemination as open data, data producers contribute to open science and to the reuse of their data for new purposes. Data repositories, particularly specialist or domain repositories, facilitate the reuse of data within and across disciplines by offering extensive data curation and stewardship services (Boté & Térmens, 2019). Quality control, documentation, and peer-review also are necessary data curation functions of repositories that facilitate reuse (Koltay, 2020).…”

Section: Added-value Of Data Repositoriesmentioning

confidence: 99%

Improving Opportunities for New Value of Open Data: Assessing and Certifying Research Data Repositories

Downs¹

2021

Data Science Journal

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Investments in research that produce scientific and scholarly data can be leveraged by enabling the resulting research data products and services to be used by broader communities and for new purposes, extending reuse beyond the initial users and purposes for which the data were originally collected. Submitting research data to a data repository offers opportunities for the data to be used in the future, providing ways for new benefits to be realized from data reuse. Improvements to data repositories that facilitate new uses of data increase the potential for data reuse and for gains in the value of open data products and services that are associated with such reuse. Assessing and certifying the capabilities and services offered by data repositories provides opportunities for improving the repositories and for realizing the value to be attained from new uses of data. The evolution of data repository certification instruments is described and discussed in terms of the implications for the curation and continuing use of research data.

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Reusing Data Technical and Ethical Challenges

Cited by 26 publications

References 52 publications

Data sharing for global, unspecified reuse

Data sharing for global, unspecified reuse

Ten Simple Rules for Using Public Data for Your Research

Improving Opportunities for New Value of Open Data: Assessing and Certifying Research Data Repositories

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