ReviewFrontiers in big data2022
Identifying and addressing data asymmetries so as to enable (better) science.
Review in Frontiers in big data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Unmasking the ethical dimensions of data-sharing in health research: perspectives from researchers at a public university in Uganda.BMC medical ethics · 2025Article
- Article
- Researchers' agency and the boundaries of global mental health: perspectives from and about Latin America.BMJ global health · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.