10A.4 Data Ecosystem for the Joint ESA–NASA Multimission Algorithm and Analysis Platform

Wednesday, 15 January 2020: 2:15 PM
157C (Boston Convention and Exhibition Center)
Kaylin Bugbee, Univ. of Alabama, Huntsville, AL; and A. Whitehurst, A. Kaulfus, J. Le Roux, A. Barciauskas, L. Duncanson, M. Lavalle, R. Ramachandran, and M. Maskey

The scientific method within the Earth sciences is rapidly evolving. Ever increasing data volumes require new methods for processing and interpreting data while an almost 60 year Earth observation record makes more data-intensive retrospective analyses possible. Through emerging technologies and collaborations, scientists are adapting research methods to be more efficient and scalable. However both the data information infrastructure and the supporting data stewardship practices have been slow to adapt to meet these rapidly evolving needs. Each step in the data development process occurs within independent, siloed components. For example, generation of standard data products occurs at processing systems which are then ingested into local archive centers. These local archive centers then provide metadata to a centralized repository for data search and discovery. Similarly, the data stewardship process has a well-established but narrow view of data publication that may be too limiting in an ever-changing data environment. A new approach to both the data information infrastructure and the stewardship model is needed to meet the challenges of the evolving scientific environment. The data ecosystem approach offers a solution to these challenges by placing an emphasis on the relationships between data, technologies and people. In this presentation, we present the Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform’s (MAAP) data system as a forward-looking ecosystem solution. We will present the key components needed to support the MAAP data ecosystem along with the capabilities the MAAP data ecosystem supports, including the ability for users to share data and software within the MAAP, the creation of analysis ready data (ARD) and analytics optimized data stores (AODs), and the creation of an aggregated catalog for data discovery.
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