Data rescue: saving environmental data from extinction.

Area of Science:

• Environmental Science
• Data Science
• Archival Science

Background:

• Historical and long-term environmental datasets are vital for understanding natural systems' responses to global change.
• These valuable data are at risk of loss without active curation and archiving.
• Data rescue, the process of identifying, preserving, and sharing at-risk data, ensures long-term dataset viability.

Purpose of the Study:

• To formally define data rescue and differentiate it from related terms like data reuse.
• To establish general recommendations and best practices for effective data rescue.
• To address the urgent need for preserving historical environmental data in the face of rapid environmental change.

Main Methods:

• Outlining seven key guidelines for rescuing historically collected and unmanaged datasets.
• Discussing the prioritization of datasets for rescue efforts.
• Detailing the preparation of data and metadata, and the archiving and sharing of rescued materials.

Main Results:

• The study proposes a formal definition and framework for data rescue.
• Seven comprehensive guidelines are presented to facilitate effective data rescue practices.
• The research emphasizes the importance of data rescue teams and proper data/metadata preparation.

Conclusions:

• Data rescue is an essential practice for safeguarding critical historical environmental information.
• Implementing the proposed guidelines will enhance the long-term accessibility and scientific utility of valuable datasets.
• Preserving at-risk environmental data is imperative for informing evidence-based policy solutions in a changing world.