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Wednesday, January 16 • 11:00am - 12:30pm
ESIP Lab Innovation: Deep Learning, Data Streaming, Provenance, JupyterHub and MORE!

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Session Takeaways (post meeting):
1) ESIP Lab provides the support and framework for a variety of projects that span the realm of earth science data sciences.
2) Products/projects developed through the ESIP lab help to improve data management, organization, quality control, and/or accessibility for a number of different audiences/communities.
3) The next steps for these projects include securing further funding via proposal opportunities, advancing participants’ PhD research, and increase user count.

Brian Wee: Enabling the encoding and visualization of provenance metadata for better discovery and understanding of climate resilience strategies for agriculture-related decision-making
Keith Maull: ESIPhub: Developing and promoting an ESIP community resource for sharing and running scientific workflows via JupyterHub
Ziheng Sun: Geoweaver: a web-based prototype system for managing compound geospatial workflows of large-scale distributed deep networks
Mike Daniels: SensorDat: Real-time sensor testbed for improved provenance and data quality
Yunsoo Choi: A deep-learning driven improved ensemble approach for hurricane forecasting
Jessica Fayne: Building an Operational Network to Validate Novel Inland Water Swath Altimetry

Wednesday January 16, 2019 11:00am - 12:30pm EST
White Flint
  White Flint, Breakout Session