Ten Principles to Improve Dataset Discoverability
Creators
- Wu, Mingfang
- Gregory, Kathleen
- Löffler, Felicitas
- Mathiak, Brigitte
- Psomopoulos, Fotis
- Schindler, Uwe
- Aryani, Amir
- Bodera, Jordi
- Jael Castro, Leyla
- Culina, Antica
- Czerniak, Andreas
- Erdmann, Chris
- Grethe, Jeffrey
- Hellström, Maggie
- Henzen, Christin
- Hunter, Chris
- Juty, Nick
- Kvale, Live
- Lister, Allyson
- Liu, Ying-Hsang
- Madon, Bénédicte
- Medina-Smith, Andrea
- Parton, Graham
- Pearman-Kanza, Samantha
- Pörsch, Andrea
- Söding, Emanuel
- Szabo, Dimitri
- van der Meer, Lucas
- Weisweiler, Nina
- Widmann, Heinrich
- Woodford, CJ
Contributors
Description
The FAIR (meta) data principles provide overarching guidelines to make metadata and data
Findable, Accessible, Interoperable and Reusable. While significant effort has been
dedicated to specific recommendations that enable best practices in implementing FAIR
principles, particularly from a data curation perspective, this document focuses primarily on
enhancing the discoverability of data from the perspectives of data searchers and data
users, including both human users and machine-based systems.
The document outlines ten principles, along with practical implementation recommendations
for each principle, for improving dataset discoverability. The ten principles and
recommendations are not confined to a singular community. Instead, they are applicable
across a spectrum of communities since providing a data discovery service and good data
discovery experience involves a shared responsibility among various stakeholders.
Files
Ten Principles to Improve Dataset Discoverability- Published.pdf
Files
(3.4 MB)
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Additional details
Dates
- Accepted
-
2024-08-28