Details

Autor(en) / Beteiligte

• Damerow, Joan E
• Varadharajan, Charuleka
• Boye, Kristin
• Brodie, Eoin L
• Burrus, Madison
• Chadwick, K. Dana
• Crystal-Ornelas, Robert
• Elbashandy, Hesham
• Alves, Ricardo J. Eloy
• Ely, Kim S
• Goldman, Amy E
• Haberman, Ted
• Hendrix, Valerie
• Kakalia, Zarine
• Kemner, Kenneth M
• Kersting, Annie B
• Merino, Nancy
• O'Brien, Fianna
• Perzan, Zach
• Robles, Emily
• Sorensen, Patrick
• Stegen, James C
• Walls, Ramona L
• Weisenhorn, Pamela
• Zavarin, Mavrik
• Agarwal, Deborah

Titel

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

Ist Teil von

• Data science journal, 2021-03, Vol.20 (1), p.11

Ort / Verlag

United States: Ubiquity Press Ltd

Erscheinungsjahr

2021

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.