Billets de Michelle Dawson

Billet publié sur Twitter/X le 05/07/2020 11:40

Twitter/X Publication avec DOI crossref Extrait cité dans le billet Lien intégré au billet

Liens cités structurés

1 ressource citée

Publication avec DOI Récupéré Publication crossref

Tonya White, Elisabet Blok, Vince D. Calhoun (2022). Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Human Brain Mapping, 43(1), 278-291. Wiley.

Date de publication
04/07/2020
Identifiant
10.1002/hbm.25120
Auteurs
Tonya White, Elisabet Blok, Vince D. Calhoun
Source
Human Brain Mapping
Détails
43(1), 278-291
Type de référence
article
Éditeur
Wiley
Source de métadonnées
crossref

Résumé

Abstract Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who “give” their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.

Auteur·ices de l’étude dans cette référence citée