Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

• DOI: 10.1038/s41597-022-01606-w
• Publication Date: 2022-11-14T20:16:16Z
• Authors: Robert Crystal-Or...; Charuleka Varadha...; Dylan O’Ryan; Kathleen Beilsmith; Benjamin Bond-Lam...; Kristin Boye; Madison Burrus; Shreyas Cholia; Danielle S. Chris...; Michael Crow; Joan Damerow; Kim S. Ely; Amy E. Goldman; Susan L. Heinz; Valerie C. Hendrix; Zarine Kakalia; Kayla Mathes; Fianna O’Brien; Stephanie C. Penn...; Emily Robles; Alistair Rogers; Maegen Simmonds; Terri Velliquette; Pamela Weisenhorn; Jessica Nicole Welch; Karen Whitenack; Deborah A. Agarwal
• Keywords: Metadata; 550; Science; Data Science; Q; Applied Computing; 15. Life on land; 01 natural sciences; Article; Workflow; Research Design; 13. Climate action; Information and Computing Sciences; Environmental Science; 0105 earth and related environmental sciences

AbstractResearch can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.