In their unusable and 'at-risk' state, these data represent an egregious waste of resources expended on their collection ( box 1). Yet, despite their substantial value, data are often misplaced, filed away or otherwise rendered unusable, often through poor data management practices. aggregation, collation and synthesis) of data from different contexts is essential to establishing broader ecological knowledge and informing conservation management. While data collection is often targeted to particular populations, communities or locations, the reuse (i.e. Observational and experimental data derived from ecology, evolution, conservation, and environmental sciences (hereafter, environmental data) are essential to establishing historical trajectories of ecosystems (baselines), understanding how species and communities respond to environmental change and designing and evaluating the outcomes of management efforts. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science.ĭata are among the most valuable outputs of research and scholarship beyond helping answer important questions, they inform new lines of inquiry, new testable hypotheses and future data collection efforts. In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and associated metadata, and archiving and sharing the rescued materials. Here, we outline seven key guidelines for effective rescue of historically collected and unmanaged datasets. data reuse), and lacks general recommendations. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e. Improvements in policies and best practices around data management will hopefully limit future need for data rescue these changes, however, do not apply retroactively. The practice of data rescue, which we define as identifying, preserving, and sharing valuable data and associated metadata at risk of loss, is an important means of ensuring the long-term viability and accessibility of such datasets. Although immensely valuable, these data are at risk of being lost unless actively curated and archived in data repositories. Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world.
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