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The explosion of IoT devices and sensors in recent years has led to a demand for efficiently storing, processing and analyzing time-series data. Geoscience researchers use time-series data stores such as Hydroserver, VOEIS and CHORDS. Many of these tools require a great deal of infrastructure to deploy and expertise to manage and scale. Tapis's (formerly known as Agave) platform as a service provides a way to support researchers in a way that they are not responsible for the infrastructure and can focus on the science. The University of Hawaii (UH) and Texas Advanced Computing Center (TACC) have collaborated to develop a new API integration that combines Tapis with the CHORDS time series data service to support projects at both institutions for storing, annotating and querying time-series data. This new Streams API leverages the strengths of both the Tapis platform and CHORDS service to enable capabilities for supporting time-series data streams not available in either tool alone. These new capabilities may be leveraged by Tapis powered science gateways with needs for handling spatially indexed time-series data-sets for their researchers as they have been at UH and TACC. 

The massive surge in the amount of observational field data demands richer and more meaningful collab-oration between data scientists and geoscientists. This document was written by members of the Working Group on Case Studies of the NSF-funded RCN on Intelli-gent Systems Research To Support Geosciences (IS-GEO, https:// is-geo.org/ ) to describe our vision to build and enhance such collaboration through the use of specially-designed benchmark datasets. Benchmark datasets serve as summary descriptions of problem areas, providing a simple interface between disciplines without requiring extensive background knowledge. Benchmark data intend to address a number of overarching goals. First, they are concrete, identifiable, and public, which results in a natural coordination of research efforts across multiple disciplines and institutions. Second, they provide multi-fold opportunities for objective comparison of various algorithms in terms of computational costs, accuracy, utility and other measurable standards, to address a particular question in geoscience. Third, as materials for education, the benchmark data cultivate future human capital and interest in geoscience problems and data science methods. Finally, a concerted effort to produce and publish benchmarks has the potential to spur the development of new data science methods, while provid-ing deeper insights into many fundamental problems in modern geosciences. That is, similarly to the critical role the genomic and molecular biology data archives serve in facilitating the field of bioinformatics, we expect that the proposed geosciences data repository will serve as “catalysts” for the new discicpline of geoinformatics. We describe specifications of a high quality geoscience bench-mark dataset and discuss some of our first benchmark efforts. We invite the Climate Informatics community to join us in creating additional benchmarks that aim to address important climate science problems. 

The massive surge in the amount of observational field data demands richer and more meaningful collab- oration between data scientists and geoscientists. This document was written by members of the Working Group on Case Studies of the NSF-funded RCN on Intelli- gent Systems Research To Support Geosciences (IS-GEO, https://is-geo.org/) to describe our vision to build and enhance such collaboration through the use of specially- designed benchmark datasets. Benchmark datasets serve as summary descriptions of problem areas, providing a simple interface between disciplines without requiring extensive background knowledge. Benchmark data intend to address a number of overarching goals. First, they are concrete, identifiable, and public, which results in a natural coordination of research efforts across multiple disciplines and institutions. Second, they provide multi- fold opportunities for objective comparison of various algorithms in terms of computational costs, accuracy, utility and other measurable standards, to address a particular question in geoscience. Third, as materials for education, the benchmark data cultivate future human capital and interest in geoscience problems and data science methods. Finally, a concerted effort to produce and publish benchmarks has the potential to spur the development of new data science methods, while provid- ing deeper insights into many fundamental problems in modern geosciences. That is, similarly to the critical role the genomic and molecular biology data archives serve in facilitating the field of bioinformatics, we expect that the proposed geosciences data repository will serve as “catalysts” for the new discicpline of geoinformatics. We describe specifications of a high quality geoscience bench- mark dataset and discuss some of our first benchmark efforts. We invite the Climate Informatics community to join us in creating additional benchmarks that aim to address important climate science problems.