From ice cores to dinosaurs: physical collections managers’ research data curation perceptions and behaviors
Bradley Wade Bishop, Jaxx Fox, Sidney Wanda Taylor Gavel, Emily Grace Chapin, Sarah Kansa, Peter Biehl, Peter Biehl, Peter Biehl

TL;DR
This study explores how physical collection managers curate data from tangible objects, aiming to improve their discoverability and reuse across scientific fields.
Contribution
The study provides insights into the data curation practices of physical collection managers across disciplines, highlighting the need for better infrastructure to support data reuse.
Findings
Physical collections use universal metadata and data storage standards to improve discoverability.
Interdisciplinary reuse of physical collections and derived data requires additional investment in cyberinfrastructure.
Participants emphasized the importance of cross-disciplinary research data management.
Abstract
Physical collections provide the tangible objects that when analyzed become data informing all sciences. Physical collection managers aim to make physical objects discoverable, accessible, and reusable. The volume and variety of physical collections acquired, described, and stored across decades, and in some cases centuries, results from large public and private investments. The purpose of this study is to understand the curation perceptions and behaviors of physical collection managers across domains to inform cross-disciplinary research data management. Ten focus groups were conducted with thirty-two participants across several physical collection communities. Participants responded to open-ended questions about the data lifecycle of their physical objects. Results indicated that physical collections attempt to use universal metadata and data storage standards to increase…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsResearch Data Management Practices · Scientific Computing and Data Management · Data Quality and Management
