Documenting provenance in noncomputational workflows : research process models based on geobiology fieldwork in Yellowstone National Park
Thomer, Andrea K.; Wickett, Karen M.; Baker, Karen S.; Fouke, Bruce W.; Palmer, Carole L. (2018-07-05)
Thomer, A.K., Wickett, K.M., Baker, K.S., Fouke, B.W. and Palmer, C.L. (2018), Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park. Journal of the Association for Information Science and Technology, 69: 1234-1245. doi:10.1002/asi.24039
© 2018 ASIS&T. This is the peer reviewed version of the following article: Thomer, A.K., Wickett, K.M., Baker, K.S., Fouke, B.W. and Palmer, C.L. (2018), Documenting provenance in noncomputational workflows: Research process models based on geobiology fieldwork in Yellowstone National Park. Journal of the Association for Information Science and Technology, 69: 1234-1245, which has been published in final form at https://doi.org/10.1002/asi.24039. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe202002104994
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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.
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