:A “Digital Divide” in data representation exists between the preferred way of data access by the hydrology community (i.e., as time series of discrete spatial objects) and the common way of data archival by NASA earth science data centers (i.e., as continuous spatial fields, one file per time step).
This Divide has been an obstacle between hydrology data users (e.g., CUAHSI HIS, HydroShare) and the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC).
The GES DISC (one of 12 NASA Earth Observing System (EOS) data centers) processes, archives, documents, and distributes data from Earth science missions and related projects, including hydrologic land surface data. The latter are part of the GES DISC Water & Energy Cycle data holdings.
Of the many related data services available to users, the NASA Giovanni (Geospatial Interactive Online Visualization and Analysis Infrastructure) is the best known and most used (cited in more than 2000 peer-reviewed research publications).
Giovanni provides a relatively simple way for researchers to conduct exploratory investigations with a variety of NASA Earth observation data and related data sets. Among Giovanni’s suite of plotting options, the time series is probably of the most interest to hydrology data users (and is the second most popular among users in general).
However, for optimal access to GES DISC data along the time dimension, the data as archived must be reorganize to some way that is optimal for that mode of access. Given the importance of bridging the Digital Divide, the GES DISC has
(1) developed “Data Rods,” a set of REST endpoints for long time series;
(2) improved the performance of Giovanni’s time series plotting option; and
(3) assisted the University of Texas-Austin in developing and supporting the Data Rods Explorer (DRE), a HydroShare app that combines data from the first two sources.
As part of NASA data centers’ overall transitioning to the cloud, the GES DISC has been investigating “Giovanni in the cloud.” Though still under development, the prototype Giovanni Cloud-Optimized Data Store (CODS) has already demonstrated a significant performance increase in time series capabilities—5-10 times faster than the current Data Rods endpoints.
The GES DISC aims to continually explore and implement appropriate technologies to improve its data services, in response to user needs of the hydrology community.