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

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  • Andrea K. Thomer
  • Karen M. Wickett
  • Karen S. Baker
  • Bruce W. Fouke
  • Carole L. Palmer

Abstract

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.

Suggested Citation

  • Andrea K. Thomer & Karen M. Wickett & Karen S. Baker & Bruce W. Fouke & Carole L. Palmer, 2018. "Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(10), pages 1234-1245, October.
    • Handle: RePEc:bla:jinfst:v:69:y:2018:i:10:p:1234-1245
    DOI: 10.1002/asi.24039
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    Cited by:

    1. Caifan Du & Johanna Cohoon & Patrice Lopez & James Howison, 2021. "Softcite dataset: A dataset of software mentions in biomedical and economic research publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 870-884, July.
    2. Andrea K. Thomer, 2022. "Integrative data reuse at scientifically significant sites: Case studies at Yellowstone National Park and the La Brea Tar Pits," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1155-1170, August.

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