1.Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world, setting baselines and establishing trajectories of change. Although immensely valuable, these data are ultimately at risk of being lost unless they are actively managed, curated, and eventually archived on data repositories. 2.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. Improvements in policies and best practices around data management will hopefully limit the future need for data rescue; these changes, however, do not apply retroactively. While the concept of rescuing data is not new, the term lacks a formal definition, is often conflated with other terms (i.e., data reuse), and lacks general recommendations. 3.Here, we outline seven key guidelines for effective rescue of historically-collected and unmanaged datasets. We discuss how to prioritize which datasets to rescue, form effective data rescue teams, prepare the data and related metadata, and ultimately archive and share the rescued data. 4.In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science.
Thematic harmonization of environmental data: Facilitating interoperability of data within and among repositories in support of data reuse and scientific synthesis
