Indigenous Data Discovery
New Topic March 28, 2025, revised August 29, 2025
The ESIP Discovery Cluster is interested in advancing methods for search and easy retrieval of Earth science data, which should accrue benefits to all, including Indigenous communities. However, the term Discovery itself has its roots in colonialism, and this manifests in Earth sciences when Western science describes natural phenomenon that are the subject of Indigenous Traditional Knowledge. Western science focuses on parts of the same phenomenon described in Traditional Knowledge systems, and the fruits of scientist’s labor are filed away in many different repositories and publication outlets, fragmenting the Earth science from the perspective of Indigenous communities and their Traditional Knowledge (see this ESIP guest blog). This presents a challenge for Indigenous communities as they seek to apply Earth science information to better manage their lands for the benefit of their people. Therefore, the discovery cluster will pursue promising practices and tools for the discovery of Earth science information that are respectful of different belief systems, and presents Earth science information in a holistic manner that is responsive to concerns and interests expressed by specific Indigenous communities. We will develop tools, like Virtual Collections (see this ESIP meeting session, and this EOS blog) and dynamic citation (see this white paper), to help Indigenous communities discover and access Earth science information, while at the same time acknowledging Indigenous Data Sovereignty and securing Traditional Knowledge to protect it from exploitation.
References:
Blythe J. 2024. Citing Virtual Collections. ESIP 2024 Winter Meeting. Wednesday Jan 24, 2024 11:00 AM - 12:30 PM EST. URL: https://wiki.esipfed.org/Citing_Virtual_Collections
Blythe J. 2025. Earth Science Data Should Relate Science to Society. ESIP Guest Blog. URL: https://www.esipfed.org/guest-blog-earth-science-data-should-relate-science-to-society/.
Bugbee K, Smith D, Wingo S, Foshee E. 2023. The art of scientific curation. Eos. 104. doi: 10.1029/2023EO230201
Rauber A, Gößwein B, Zwölf CM, Schubert C, Wörister F, Duncan J, Flicker K, Zettsu K, Meixner K, McIntosh LD, Pröll S, Miksa T, Parsons MA. 2021. Precisely and persistently identifying and citing arbitrary subsets of dynamic data. Harvard Data Science Review. 3(4): 1-30. doi: 10.1162/99608f92.be565013
