Abstract
The publication of resources for minority languages requires a balance between making data open and accessible and respecting the rights and needs of its language community. The FAIR principles were introduced as a guide to good open data practices and they have since been complemented by the CARE principles for indigenous data governance. This article describes how the DGS Corpus implemented these principles and how the two sets of principles affected each other. The DGS Corpus is a large collection of recordings of members of the deaf community in Germany communicating in their primary language, German Sign Language (DGS); it was created to be both as a resource for linguistic research and as a record of the life experiences of deaf people in Germany. The corpus was designed with CARE in mind to respect and empower the language community and FAIR data publishing was used to enhance its usefulness as a scientific resource.- Anthology ID:
- 2022.lrec-1.18
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 164–173
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.18
- DOI:
- Cite (ACL):
- Marc Schulder and Thomas Hanke. 2022. How to be FAIR when you CARE: The DGS Corpus as a Case Study of Open Science Resources for Minority Languages. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 164–173, Marseille, France. European Language Resources Association.
- Cite (Informal):
- How to be FAIR when you CARE: The DGS Corpus as a Case Study of Open Science Resources for Minority Languages (Schulder & Hanke, LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2022.lrec-1.18.pdf