Sharing FAIR monitoring program data improves discoverability and reuse

Area of Science:

• Environmental Science
• Data Science
• Ecology

Background:

• Environmental monitoring data are crucial for resource management and decision-making.
• Current data practices often limit reuse due to program-specific goals and inconsistent documentation.
• Integrating data from multiple long-term monitoring programs presents significant challenges.

Purpose of the Study:

• To evaluate datasets from four US aquatic monitoring programs against FAIR data principles.
• To identify barriers to data accessibility and reusability.
• To provide recommendations for improving data sharing and integration.

Main Methods:

• Assessment of existing datasets against the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.
• Analysis of data governance structures and documentation practices across different monitoring programs.
• Identification of inconsistencies in data formats, storage, and available resources.

Main Results:

• Significant challenges were found in accessing and reusing data due to varying data governance and inconsistent documentation.
• Lack of standardized guidelines and resources hinders data discoverability and interoperability.
• Data formats and storage locations are often disparate, complicating integration efforts.

Conclusions:

• Improving data documentation and accessibility is essential for maximizing the value of environmental monitoring data.
• Implementing FAIR data principles can reduce barriers to data discovery and reuse.
• Consistent guidelines, standards, tools, and resources are needed to support data management and publishing efforts.