All Funded Projects

In practice, e.g., in model applications, data are rarely complete without workflow code and workflows are often treated as software that include data. This project aims to understand current practice and recommend future practices that better fit the needs of reproducible workflows and models.

We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.

We will develop reproducible workflows in R and Python to combine already existing and underutilized field data collected as part of the USGS streamgage network with remotely sensed data to map flood-prone areas for various recurrence intervals in both gaged and ungaged stream reaches.

This proposal would fund the development of Seg2Map, a new open-source, browser-accessible software deployed on the cloud that will apply Machine Learning to imagery and image time-series, to make highly customizable to study Earth’s changing surface for a range of scientific purposes.

We propose to develop a suite of Jupyter notebooks that leverage existing APIs, cloud storage, and open source tools to make it easier for users to efficiently access USGS 3DEP data and to produce data processing and visualization workflows. These notebooks will enhance data utilization, stimulate creative applications, and generate significant return on investment for 3DEP.

An open-source tool for 3D visualization of lidar point cloud data along a vector line network and output of related lidar metrics. This tool will make available the valuable attribute data of point clouds to enable research such as riparian zone and migration corridor vegetation structure analysis or characterizing the related built environment.

We propose a summit for USGS and Tribal nation partners to share critical knowledge of past and future flooding. The summit will build on USGS research investigating changing flood conditions, improve access to flood information, and aid in building climate resilient communities.

We propose to bring in expertise from multiple USGS environmental DNA (eDNA) labs to create a database to track samples from initial collection through analysis and reporting and to long-term storage. We will publish this tracking template so it can be implemented within eDNA labs across the USGS.

To help users connect and comprehend USGS data, we propose to develop an interactive viewer for multi-channel geophysical data using existing Python PyViz tools.

We synthesize local high-resolution urban landcover imagery with microclimate data and regional meteorology to determine landcover drivers of extreme urban heat. Resulting outputs are mappable items spatially describing urban temperatures at fine scales, and a web application to analyze changes in urban heat under different climate scenarios.

Effectively documenting and distributing information about restoration projects is essential for measuring progress towards national conservation goals. We will improve the National Fish Habitat Partnership Project Tracking database by creating a data pipeline to compile project information and link data with other decision support tools.

This project leverages the USGS's photo archive and Structure from Motion algorithms to derive historical elevation and geomorphic data to catalyze a long-term landscape change analysis of a conservation area. We propose to create a best practices workflow and establish suitable accuracy metrics.