Data rescue: saving environmental data from extinction

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1098/rspb.2022.0938. This is version 4 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Ellen Bledsoe, Joseph Burant, Gracielle Higino , Dominique Roche, Sandra Ann Binning, Kerri Finlay, Jason Pither, Laura J. Pollock, Jennifer Sunday, Diane Srivastava

Abstract

Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world. Although immensely valuable, these data are at risk of being lost unless actively curated and archived on data repositories. 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 future need for data rescue; these changes, however, do not apply retroactively. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e., data reuse), and lacks general recommendations. Here, we outline seven key guidelines for effective rescue of historically-collected and unmanaged datasets. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and related metadata, and archiving and sharing the rescued data. 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.

DOI

https://doi.org/10.32942/osf.io/ra6ze

Subjects

Ecology and Evolutionary Biology, Life Sciences

Keywords

data archiving, historical data, long-term ecological research, open data, open science, reproducibility

Dates

Published: 2021-10-26 20:45

Last Updated: 2022-05-13 15:49

Older Versions
License

CC-By Attribution-ShareAlike 4.0 International