trends data 3. Grid- and visualization control and integrity and archiving resources computational accounting identification 3. Distributed 4. Data analyzed models traceability back to data sources, incrementally initial observational including and/or in real time data observation and at varying rates 3. Processed monitoring owing to variations (secondary) data facilities, sensor in source processes storage (in addition network, and 5. A variety of data to original source satellites and analytical and data) for future uses 4. Wide variety of modeling tools to 4. Provenance (and data: satellite support analytics persistent images/ for diverse identification [PID]) information, scientific control of data, climate and communities algorithms, and weather data, 6. Parallel data workflows photos, video, streams and 5. Curated sound recordings, streaming analytics (authorized) etc. 7. Access and reference data (e.g. 5. Multi-type data integration of species name lists), combination and multiple algorithms, software linkage, distributed code, workflows potentially databases unlimited data variety 6. Data streaming -- -- 1. GPU -- -- -- -- 6 Large-Scale 2. Deep Learning MPI and HPC Infiniband cluster 3. Libraries for single-machine or single-GPU computation –
trends data archiving 3. Distributed data sources, including observation and monitoring facilities, sensor network, and satellites 4. Wide variety of data: satellite images/ information, climate and weather data, photos, video, sound recordings, etc. 5. Multi-type data combination and linkage, potentially unlimited data variety 6. Data streaming 3. Grid- and resources 4. Data analyzed incrementally and/or in real time at varying rates owing to variations in source processes 5. A variety of data and analytical and modeling tools to support analytics for diverse scientific communities 6. Parallel data streams and streaming analytics 7. Access and integration of multiple distributed databases visualization computational models control and accounting integrity and identification traceability back to initial observational data 3. Processed (secondary) data storage (in addition to original source data) for future uses 4. Provenance (and persistent identification [PID]) control of data, algorithms, and workflows 5. Curated (authorized) reference data (e.g. species name lists), algorithms, software code, workflows Large-Scale Deep Learning -- -- 1. GPU 2. MPI and HPC Infiniband cluster 3. Libraries for single-machine or single-GPU computation – -- -- -- -- 6