<scp>D</scp>ata <scp>P</scp>ractices and <scp>C</scp>uration <scp>V</scp>ocabulary (<scp>DPCV</scp>ocab): An empirically derived framework of scientific data practices and curatorial processes

Chao, Tiffany C.; Cragin, Melissa H.; Palmer, Carole L.

doi:10.1002/asi.23184

Cited by 21 publications

(25 citation statements)

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“…The curation framework was developed primarily through iterative phases of stakeholder analysis and participatory engagement, drawing on principles from natural resource management [ 31 – 32 ] and methods adapted from our previous data practices research [ 33 – 34 ]. Our stakeholder analysis approach is rooted in the Delphi technique that enlists a panel of experts to solve problems through a process of consensus development [ 35 ].…”

Section: Methods: Stakeholder Analysis and Engagementmentioning

confidence: 99%

Site-based data curation based on hot spring geobiology

et al. 2017

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Site-Based Data Curation (SBDC) is an approach to managing research data that prioritizes sharing and reuse of data collected at scientifically significant sites. The SBDC framework is based on geobiology research at natural hot spring sites in Yellowstone National Park as an exemplar case of high value field data in contemporary, cross-disciplinary earth systems science. Through stakeholder analysis and investigation of data artifacts, we determined that meaningful and valid reuse of digital hot spring data requires systematic documentation of sampling processes and particular contextual information about the site of data collection. We propose a Minimum Information Framework for recording the necessary metadata on sampling locations, with anchor measurements and description of the hot spring vent distinct from the outflow system, and multi-scale field photography to capture vital information about hot spring structures. The SBDC framework can serve as a global model for the collection and description of hot spring systems field data that can be readily adapted for application to the curation of data from other kinds scientifically significant sites.

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Section: Methods: Stakeholder Analysis and Engagementmentioning

confidence: 99%

Site-based data curation based on hot spring geobiology

et al. 2017

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“…For instance, interview protocols developed through the Data Curation Profiles project can be used to guide information professionals in gathering necessary data provenance from data creators (Witt, Carlson, Brandt, & Cragin, ). The DCPVocab was developed to provide specific terms for representing relationships among research practices, types of data, and curation roles and activities (Chao, Cragin, & Palmer, ). Additionally, the many data lifecycle models developed by curation communities may be thought of as an approach to noncomputational process representation (for example, CCSDS, ; Faundeen et al, ; Higgins, ).…”

Section: Background: Provenance and Reproducibility Through Workflow mentioning

confidence: 99%

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

Thomer

Wickett

Baker

et al. 2018

Asso for Info Science & Tech

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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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“…Considering (3) that the majority of tool stacks build for BigData assume operation on cloud or at least mains powered computing resources, in many cases there is a need for real-time sUAS data processing on low power or low bandwidth edge compute devices. Finally (4), research has shown [14,75,76], that the range of data practices utilized by smaller teams should be considered a feature rather than a bug; this is because the data workflows and practices must be customized to the unique contexts and goals of a given group, project, and organizational structure. Standardized workflows across all smaller research teams are neither achievable nor desirable.…”

Section: Discussionmentioning

confidence: 99%

Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development

et al. 2019

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The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.

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Data Practices and Curation Vocabulary (DPCVocab): An empirically derived framework of scientific data practices and curatorial processes

Cited by 21 publications

References 57 publications

Site-based data curation based on hot spring geobiology

Site-based data curation based on hot spring geobiology

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

Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development

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