Sharing FAIR monitoring program data improves discoverability and reuse
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Abstract
Data resulting from environmental monitoring programs are valuable assets for natural resource managers, decision-makers, and researchers. These data are often collected to inform specific reporting needs or decisions with a specific timeframe. While program-oriented data and related publications are effective for meeting program goals, sharing well-documented data and metadata allows users to research aspects outside initial program intentions. As part of an effort to integrate data from four long-term large-scale US aquatic monitoring programs, we evaluated the original datasets against the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and offer recommendations and lessons learned. Differences in data governance across these programs resulted in considerable effort to access and reuse the original datasets. Requirements, guidance, and resources available to support data publishing and documentation are inconsistent across agencies and monitoring programs, resulting in various data formats and storage locations that are not easily found, accessed, or reused. Making monitoring data FAIR will reduce barriers to data discovery and reuse. Programs are continuously striving to improve data management, data products, and metadata; however, provision of related tools, consistent guidelines and standards, and more resources to do this work is needed. Given the value of these data and the significant effort required to access and reuse them, actions and steps intended on improving data documentation and accessibility are described.
Publication type | Article |
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Publication Subtype | Journal Article |
Title | Sharing FAIR monitoring program data improves discoverability and reuse |
Series title | Enviornmental Monitoring and Assessment |
DOI | 10.1007/s10661-023-11788-4 |
Volume | 195 |
Year Published | 2023 |
Language | English |
Publisher | Springer |
Contributing office(s) | Forest and Rangeland Ecosystem Science Center |
Description | 1141, 12 p. |
Google Analytic Metrics | Metrics page |