Data Rescue at the U.S. National Agricultural Library: Case Studies of 3 Hybrid Collections

Abstract

While the open science movement facilitates discussions in managing, preserving and curating data from active and ongoing research, data rescue efforts respond to the growing recognition of the value and reuse potential of data biding in unpublished records and collections of legacy research materials. Making data embedded in these records available for reuse may support longitudinal analysis, meta-analysis, and cross-disciplinary research.

The data rescue project was led by graduate fellows in the University of Maryland College of Information Studies (iSchool) Digital Curation Fellowship, a cooperative agreement between iSchool and the U.S. National Agricultural Library. We identified 18 assessment factors after conducting three case studies to investigate potential issues of curating collections with the purpose of data recovery and reuse. Although there is no one-size-fit-all solution to process and appraise data-rich collections, the 18 assessment factors assist curators to navigate different issues and find most suitable methods to make research data available.