Source Code

A unique opportunity for USGS to collaborate with IRIS-PASSCAL (the national seismic instrument facility) has presented itself to develop a geophysical data archive format that follows FAIR principles. IRIS-PASSCAL is extending facility to include magnetotelluric (MT) instruments prescribing the need for them to archive collected MT data by extending their existing protocol. Concurrently, Congre

In the mid-1800s, tile-drains were installed in poorly-drained soils of topographic lows as water management to protect cropland during wet conditions; consequently, estimations of tile-drain location have been based on soil series. Most tile drains are in the Midwest, however each state has farms with tile and tile-drain density has increased in the last decade. Where tile drains quickly remove w

Increasingly, USGS scientists seek to share and collaborate while working on data and code. Furthermore, these scientists often require advanced computing resources. Jupyter Notebooks are one such tool for creating these workflows. The files are interactive, code “notebooks” which allow users to combine code and text in one document, enabling scientists to share the stories held within their data.

Computational models are important tools that aid process understanding, hypothesis testing, and data interpretation. The ability to easily couple models from various domains such as, surface-water and groundwater, to form integrated models will aid studies in water resources. This project investigates the use of the Community Surface Dynamics Modeling System (CSDMS) Modeling Framework (CMF) to

Access to up-to-date geospatial data is critical when responding to natural hazards-related crises, such as volcanic eruptions. To address the need to reliably provide access to near real-time USGS datasets, we developed a process to allow data managers within the USGS Volcano Hazard Program to programmatically publish geospatial webservices to a cloud-based instance of GeoServer hosted on Amazon

Global climate models are a key source of climate information and produce large amounts of spatially explicit data for various physical parameters. However, these projections have substantial uncertainties associated with them, and the datasets themselves can be difficult to work with. The project team created the first version (cst 0.1.0) of the Climate Futures Toolbox, an open source workflow in

Deep learning is a computer analysis technique inspired by the human brain’s ability to learn. It involves several layers of artificial neural networks to learn and subsequently recognize patterns in data, forming the basis of many state-of-the-art applications from self-driving cars to drug discovery and cancer detection. Deep neural networks are capable of learning many levels of abstraction, an

Insect pests cost billions of dollars per year globally, negatively impacting food crops and infrastructure and contributing to the spread of disease. Timely information regarding developmental stages of pests can facilitate early detection and control, increasing efficiency and effectiveness. To address this need, the USA National Phenology Network (USA-NPN) created a suite of “Pheno Forecast” ma

Inventories of landslides and liquefaction triggered by major earthquakes are key research tools that can be used to develop and test hazard models. To eliminate redundant effort, we created a centralized and interactive repository of ground failure inventories that currently hosts 32 inventories generated by USGS and non-USGS authors and designed a pipeline for adding more as they become availabl

U.S. Geological Survey (USGS) scientists are at the forefront of research that is critical for decision-making, particularly through the development of models (Bayesian networks, or BNs) that forecast coastal change. The utility of these tools outside the scientific community has been limited because they rely on expensive, technical software and a moderate understanding of statistical analyses. W

Executive Summary Traditionally in the USGS, data is processed and analyzed on local researcher computers, then moved to centralized, remote computers for preservation and publishing (ScienceBase, Pubs Warehouse). This approach requires each researcher to have the necessary hardware and software for processing and analysis, and also to bring all external data required for the workflow over the int

USGS scientists often face computationally intensive tasks that require high-throughput computing capabilities. Several USGS facilities use HTCondor to run their computational pools but are not necessarily connected to the larger USGS pool. This project demonstrated how to connect HTCondor pools by flocking, or coordinating, within the USGS. In addition to flocking the Upper Midwest Environmental