Help Choose the Next Round of CDI Projects - FY20 Statements are In!

The USGS data community of practice, CDI, has received 24 statements of interest in this year's Request for Proposals.

The FY20 RFP themes are related to building elements of an Integrated Predictive Science Capacity for the USGS, as outlined in the USGS Director's Science Planning Strategy. This includes

• Integration of USGS data into a comprehensive data lake
• Development, integration, and application of a capability that incorporates data, interpretations, and knowledge that spans discipline boundaries, geographies, and sectors
• Actionable intelligence that can be used via dashboards and applications to enhance situational awareness, provide new operational capabilities, and inform decision making
• Tools and methods for promotion of smart and actionable data supporting wildland fire and water predictions
• Production of FAIR (Findable, Accessible, Interoperable, and Reusable) data and tools
• Reusing or repurposing modular tools such as thos developed by previous CDI projects

The CDI invites you to view and comment on these statements of interest from October 15 - November 8, 2019.

A Lightning Presentation Session on October 23 give all project teams a chance to pitch their idea before community voting starts on October 25, 2019.

See more information about the CDI Statements of Interest on the CDI wiki.

FY20 Statements of Interest

Building a framework to compute continuous grids of basin characteristics for the conterminous United States
Barnhart, Theodore    

Implementing FAIR Data Principles to a Ecohydrology Dataset Simulated at Fine Temporal and Spatial Scales for the Western United States
Bradford, John    

Integrating Two Foundational USGS Data Products: the Breeding Bird Survey (BBS) and the Bird Banding Lab (BBL) Data
Burnett, Jessica    

So you want to build a web-tool?: Assessing successes, pitfalls, and lessons learned in an emerging frontier of scientific visualization
Duniway, Michael  

Using Jupyter Notebooks to tell data stories and create reproducible workflows
Erickson, Richard    

USGS Cloud Environment Cookbook
Fox, Aaron    

Enabling AI for citizen science in fish ecology
Hitt, Nathaniel (Than)    

Implementing FAIR practices: Storing and displaying eDNA data in the USGS Nonindigenous Aquatic Species database
Hunter, Margaret    

Developing a "fire-aware" stream gage network by integrating USGS enterprise databases
Kolb, Katharine    

Visualizing environmental effects on animal movements
Letcher, Ben    

Communicate and Refine USGS Strategy for Crowdsourcing, Citizen Science, and Competitions through Workshops and Trainings
Liu, Sophia B    

A framework for incorporating management objectives into an integrative predictive model to direct future eDNA monitoring
Martin, Julien    

Waterbody Rapid Assessment Tool (WaterRAT): 3-dimensional Visualization of High-Resolution Spatial Data
Medenblik, Andrea    

Developing operational long-term forecasting capabilities at USGS: a test case from the USA National Phenology Network
Miller, Mark    

Development and distribution of standardized data products from small UAS photo collections
Nelson, Kurtis    

Development of a Flexible Multi-Channel Spatiotemporal Geophysical HDF5 Data Format Supporting FAIR
Peacock, Jared    

Real-time Coastal Salinity Index for monitoring coastal drought and ecological response to changing salinity values
Petkewich, Matthew    

GrassCast: A multi-agency tool using remote sensing, modeling, and on-the-ground science to forecast grassland productivity in the Southwest
Reed, Sasha    

Better data stewardship of historical flood estimates—A new database and interface
Ryberg, Karen    

Can deep learning leverage USGS occurrence data to predict invader dominance?
Sofaer, Helen    

Moving towards EarthMAP: Establishing linkages among USGS land use, water use, runoff, and recharge models
Sohl, Terry    

Research to Operations: Ensemble Modeling at Hillslope to Regional Scales
Webb, Richard    

A Prototype AWS Fit-For-Purpose Scale-Transform Service
Wellman, Tristan    

Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery
Williamson, Tanja N