Bridging the OA Data Management Workflow Gap

Eugene F. Burger1, Kevin M. O’Brien2, Karl M. Smith2, Ansley Manke1, Roland Schweitzer3

1 NOAA/PMEL, Seattle, WA, 98112 USA
2 University Washington, Seattle, WA, 98112 USA
3 WeatherTop Consulting, Bryan, TX, 77801 USA

Effective use of data collected in support of Ocean Acidification research for analysis and synthesis product generation, it is desirable that the data are quality controlled, documented, and accessible by the applications scientists prefer to use. The processing requirements, along with increases in data volume now require a significant effort by OA scientists. Second level data processing and quality control is time-consuming, and reduces the resources available to scientists to perform their research. National data directives now require our scientific data to be documented, publically available and archived in two years or less, further adding to the scientists’ data management burden. Although procedures exist to submit data to archival centers, it is the data-workflow gap between initial data processing, known as level one processing, and data archival that has not been addressed for a significant amount of OA data.

We propose tools and processes that will streamline OA data processing and quality control. This vision suggests a solution that relies on a combination extending existing development and new development on tools that will allow users to span this data workflow gap; to streamline the processing, quality control, and archive submission of biogeochemical OA data and metadata. Workflow established by this software will reduce the data management burden for scientists while also creating data in interchangeable standards-based formats that promote easier use of the high-value data. Time savings gained by this streamlined data processing will also allow scientists to meet their obligations for data archival. This poster will present this vision and highlight the existing applications and tools, built for the SOCAT effort, which, if extended, can meet these OA data management requirements at a much-reduced development cost.