Abstract
Widespread investments in facilitating reuse and reproducibility of scientific research have spurred an increasing recognition of the potential value of data biding in unpublished records and legacy research materials, such as scientists’ papers, historical publications, and working files. Recovering usable scientific data from legacy collections constitutes one kind of data rescue: the usually urgent application of selected data curation processes to data at imminent risk of loss. Given growing interest in data-intensive research, and a concomitant movement toward computationally amenable collections in memory institutions, scientific data repositories and collecting institutions would benefit from systematic approaches to assessing and processing legacy collections with the specific goal of retrieving reusable or historically valuable scientific data. This paper suggests a preliminary framework for assessing legacy collections of research materials for the purpose of data rescue. Developed through three case studies of agricultural research collections held by the United States Department of Agriculture’s National Agricultural Library, this framework aims to guide data rescue initiatives in agricultural research centers, and to provide conceptual framing for emerging conversations around data rescue across disciplines.
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Acknowledgements
This research was conducted by Fellows in the Digital Curation Fellowship program, supported by Non-Assistance Cooperative Agreement #58–8260-6–003 between the University of Maryland and the United States Department of Agriculture (USDA), Agricultural Research Service (ARS), National Agricultural Library with funding provided by the USDA, ARS, Office of National Programs.
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Shiue, H.S.Y., Clarke, C.T., Shaw, M., Hoffman, K.M., Fenlon, K. (2021). Assessing Legacy Collections for Scientific Data Rescue. In: Toeppe, K., Yan, H., Chu, S.K.W. (eds) Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science(), vol 12646. Springer, Cham. https://doi.org/10.1007/978-3-030-71305-8_25
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