Sample Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary Ecosystem Sciences

Autor: Joan E. Damerow, Charuleka Varadharajan, Kristin Boye, Eoin L. Brodie, Madison Burrus, K. Dana Chadwick, Robert Crystal-Ornelas, Hesham Elbashandy, Ricardo J. Eloy Alves, Kim S. Ely, Amy E. Goldman, Ted Haberman, Valerie Hendrix, Zarine Kakalia, Kenneth M. Kemner, Annie B. Kersting, Nancy Merino, Fianna O'Brien, Zach Perzan, Emily Robles, Patrick Sorensen, James C. Stegen, Ramona L. Walls, Pamela Weisenhorn, Mavrik Zavarin, Deborah Agarwal
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Data Science Journal, Vol 20, Iss 1 (2021)
Druh dokumentu: article
ISSN: 1683-1470
DOI: 10.5334/dsj-2021-011
Popis: Physical samples are foundational entities for research across biological, Earth, and environmental sciences. Data generated from sample-based analyses are not only the basis of individual studies, but can also be integrated with other data to answer new and broader-scale questions. Ecosystem studies increasingly rely on multidisciplinary team-science to study climate and environmental changes. While there are widely adopted conventions within certain domains to describe sample data, these have gaps when applied in a multidisciplinary context. In this study, we reviewed existing practices for identifying, characterizing, and linking related environmental samples. We then tested practicalities of assigning persistent identifiers to samples, with standardized metadata, in a pilot field test involving eight United States Department of Energy projects. Participants collected a variety of sample types, with analyses conducted across multiple facilities. We address terminology gaps for multidisciplinary research and make recommendations for assigning identifiers and metadata that supports sample tracking, integration, and reuse. Our goal is to provide a practical approach to sample management, geared towards ecosystem scientists who contribute and reuse sample data.
Databáze: Directory of Open Access Journals