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Identifying and addressing data asymmetries so as to enable (better) science

Authors :
Stefaan Verhulst
Andrew Young
Source :
Frontiers in Big Data, Vol 5 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

As a society, we need to become more sophisticated in assessing and addressing data asymmetries—and their resulting political and economic power inequalities—particularly in the realm of open science, research, and development. This article seeks to start filling the analytical gap regarding data asymmetries globally, with a specific focus on the asymmetrical availability of privately-held data for open science, and a look at current efforts to address these data asymmetries. It provides a taxonomy of asymmetries, as well as both their societal and institutional impacts. Moreover, this contribution outlines a set of solutions that could provide a toolbox for open science practitioners and data demand-side actors that stand to benefit from increased access to data. The concept of data liquidity (and portability) is explored at length in connection with efforts to generate an ecosystem of responsible data exchanges. We also examine how data holders and demand-side actors are experimenting with new and emerging operational models and governance frameworks for purpose-driven, cross-sector data collaboratives that connect previously siloed datasets. Key solutions discussed include professionalizing and re-imagining data steward roles and functions (i.e., individuals or groups who are tasked with managing data and their ethical and responsible reuse within organizations). We present these solutions through case studies on notable efforts to address science data asymmetries. We examine these cases using a repurposable analytical framework that could inform future research. We conclude with recommended actions that could support the creation of an evidence base on work to address data asymmetries and unlock the public value of greater science data liquidity and responsible reuse.

Details

Language :
English
ISSN :
2624909X
Volume :
5
Database :
Directory of Open Access Journals
Journal :
Frontiers in Big Data
Publication Type :
Academic Journal
Accession number :
edsdoj.42d583406c3f4b44a4148b32c3171523
Document Type :
article
Full Text :
https://doi.org/10.3389/fdata.2022.888384