Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park.

A comprehensive record of research data provenance is essential for the successful curation, management, and reuse of data over time. However, creating such detailed metadata can be onerous, and there are few structured methods for doing so. In this case study of data curation in support of geobiolo...

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Publicado en:Journal of the Association for Information Science & Technology Vol. 69; no. 10; pp. 1234 - 1246
Autores principales: Thomer, Andrea K., Wickett, Karen M., Baker, Karen S., Fouke, Bruce W., Palmer, Carole L.
Formato: case study research tables/charts Journal Article
Publicado: Wiley-Blackwell Oct2018
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2018
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        10.1002/asi.24039
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        atl: Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park.
      aug:
        au:
          Thomer, Andrea K.
          Wickett, Karen M.
          Baker, Karen S.
          Fouke, Bruce W.
          Palmer, Carole L.
        affil: School of Information, University of Michigan, 105 S. State Street, Ann Arbor, Michigan 48109 USA
      sug:
        subj:
          Documentation
          Workflow
          Data Analytics Methods
          Data Curation
          Human
          Systems Analysis
          Metadata
          Interprofessional Relations
          Research Personnel
          Information Management
          Research Methodology
          Field Studies
          Funding Source
      ab: A comprehensive record of research data provenance is essential for the successful curation, management, and reuse of data over time. However, creating such detailed metadata can be onerous, and there are few structured methods for doing so. In this case study of data curation in support of geobiology research conducted at Yellowstone National Park, we describe a method of “Research Process Modeling” for documenting noncomputational data provenance in a structured yet flexible way. The method combines systems analysis techniques to model research activities, the World Wide Web Consortium Provenance (PROV) ontology to illustrate relationships between data products, and simple inventory methods to account for research processes and data products. It also supports collaborative data curation between information professionals and researchers, and is therefore a significant step toward producing more useable and interpretable research data. We demonstrate how this method describes data provenance more robustly than “flat” metadata alone and fills a critical gap in the documentation of provenance for field‐based and noncomputational workflows. We discuss potential applications of this approach to other research domains.
      pubtype: Academic Journal
      doctype:
        case study
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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