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dc.contributor.authorDamerow, J.E.
dc.contributor.authorVaradharajan, C.
dc.contributor.authorBoye, K.
dc.contributor.authorBrodie, E.L.
dc.contributor.authorBurrus, M.
dc.contributor.authorChadwick, K.D.
dc.contributor.authorCrystal-Ornelas, R.
dc.contributor.authorElbashandy, H.
dc.contributor.authorEloy Alves, R.J.
dc.contributor.authorEly, K.S.
dc.contributor.authorGoldman, A.E.
dc.contributor.authorHaberman, T.
dc.contributor.authorHendrix, V.
dc.contributor.authorKakalia, Z.
dc.contributor.authorKemner, K.M.
dc.contributor.authorKersting, A.B.
dc.contributor.authorMerino, N.
dc.contributor.authorO'Brien, F.
dc.contributor.authorPerzan, Z.
dc.contributor.authorRobles, E.
dc.contributor.authorSorensen, P.
dc.contributor.authorStegen, J.C.
dc.contributor.authorWalls, R.L.
dc.contributor.authorWeisenhorn, P.
dc.contributor.authorZavarin, M.
dc.contributor.authorAgarwal, D.
dc.date.accessioned2021-06-17T01:09:33Z
dc.date.available2021-06-17T01:09:33Z
dc.date.issued2021
dc.identifier.citationDamerow, J. E., Ely, K. S., Varadharajan, C., Boye, K., Brodie, E. L., Burrus, M., ... & Agarwal, D. (2021). Sample Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary Ecosystem Sciences. Data Science Journal, 20(BNL-221088-2021-JAAM).
dc.identifier.issn1683-1470
dc.identifier.doi10.5334/dsj-2021-011
dc.identifier.urihttp://hdl.handle.net/10150/659941
dc.description.abstractPhysical 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. (IGSN); physical samples; soil; water; plant; leaf; microbial communities; related identifiers; persistent identifiers. © 2021 The Author(s).
dc.language.isoen
dc.publisherUbiquity Press
dc.rightsCopyright © 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectGlobal Sample Number
dc.titleSample identifiers and metadata to support data management and reuse in multidisciplinary ecosystem sciences
dc.typeArticle
dc.typetext
dc.contributor.departmentBio5 Institute, University of Arizona
dc.identifier.journalData Science Journal
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleData Science Journal
refterms.dateFOA2021-06-17T01:09:33Z


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Copyright © 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Except where otherwise noted, this item's license is described as Copyright © 2021 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0).