2022 journal article
Implementing FAIR data management practices in shellfish sanitation
AQUACULTURE REPORTS, 26.
author keywords: Water quality; Long-term monitoring; Fecal coliforms; Data sharing; Data archiving
doi.org (publisher)
Source: Web Of Science
Added: October 17, 2022
In the United States (U.S.), state agencies in charge of mariculture regulation are mandated under the U.S. Food and Drug Administration’s (FDA) National Shellfish Sanitation Program (NSSP) to monitor fecal indicator bacteria (FIB) concentrations, commonly of fecal coliforms, to determine the safety of coastal waters for supporting harvestable shellfish for human consumption. Many states have monitored bacteriological water quality for decades, creating impressive long-term records with the potential to advance foundational understanding of coastal systems and contribute to other complementary monitoring efforts. However, state shellfish sanitation programs differ in how they collect, manage, and share bacteriological monitoring data, resulting in their data typically being available in disparate state-level repositories with non-standardized database structures. Here, we outline three key recommendations as to how shellfish sanitation programs could implement practices to make their data more Findable, Accessible, Interoperable, and Reusable (FAIR), in turn creating new opportunities for the full potential of the data to be realized. We also offer sample materials of a standardized database, ShellBase, to provide an example of how diverse shellfish sanitation data may be integrated with a common data structure. • Shellfish sanitation programs have amassed impressive long-term records of bacterial quality in coastal waters. • Shellfish sanitation programs are currently not mandated to standardize or archive data across states. • FAIR data management practices could increase the value and usability of shellfish sanitation data. • To improve data standardization, programs could adopt common data vocabularies, quality standards, and file formats. • To improve data discoverability, programs could consider leveraging existing data repositories for data sharing.
