IMPLEMENTING AND SUSTAINING DATA LIFECYCLE BEST PRACTICES: A FRAMEWORK FOR RESEARCHERS AND REPOSITORIES

Emerging data management mandates in conjunction with cross-domain international interoperability are posing new challenges for researchers and repositories. Domain repositories are serving in this critical, growing role monitoring and leading data management standards and capability within their own repository and working on mappings between repositories internationally. Leading research institutions and companies will also be important as they develop and expand data curation efforts.

This landscape poses a number of challenges for developing and ensuring the use of best practices in curating research data, enabling discovery, elevating quality across diverse repositories, and helping researchers collect and organize it through the full data life cycle. This multidimensional challenge will continue to grow in complexity.

The American Geophysical Union (AGU) is developing two programs to help researchers and data repositories develop and elevate best practices and address these challenges. The goal is to provide tools for the researchers and repositories, whether domain, institutional, or other, that improve performance throughout the data lifecycle across the Earth and space science community.

For scientists and researchers, AGU is developing courses around handling data that can lead toward a certification in geoscience data management. Course materials will cover metadata management and collection, data analysis, integration of data, and data presentation. The course topics are being finalized by the advisory board with the first one planned to be available later this year.

AGU is also developing a program aimed at helping data repositories, large and small, domain-specific to general, assess and improve data management practices. AGU has partnered with the CMMI® Institute to adapt their Data Management Maturity (DMM)^SM framework within the Earth and space sciences.

A data management assessment using the DMM^SM involves identifying accomplishments and weaknesses compared to leading practices for data management. Recommendations can help improve quality and consistency across the community that will facilitate reuse in the data lifecycle. Through governance, quality, and architecture process areas the assessment can measure the ability for data to be discoverable and interoperable.