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FAIR is an international set of principles for improving the findability, accessibility, interoperability, and reusability of research data and other digital products. The PIs for this CDI project planned and hosted a workshop of USGS data stakeholders, data professionals, and managers of USGS data systems from across the Bureau’s Mission Areas. Workshop participants shared case studies that fostered collaborative discussions, resulting in recommended actions and goals to make USGS research data more FAIR. Project PIs are using the workshop results to produce a roadmap for adopting FAIR principles in USGS. The FAIR Roadmap will be foundational to FY2021 CDI activities to ensure the persistence and usability of...
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Advances in information technology now provide large volume, high-frequency data collection which may improve real-time biosurveillance and forecasting. But, big data streams present challenges for data management and timely analysis. As a first step in creating a data science pipeline for translating large datasets into meaningful interpretations, we created a cloud-hosted PostgreSQL database that collates climate data served from PRISM (https://climatedataguide.ucar.edu/climate-data) and water-quality data from the National Water Quality Portal (https://www.waterqualitydata.us/) and NWIS (https://waterdata.usgs.gov/nwis; fig 1). Using Python-based code, these data streams are queried and updated every 24 hours,...
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We developed an Internet of Things (IoT) prototype and associated cloud infrastructure for camera-based data collection and initial processing of river streamflow using the cloud (fig. 1). This pilot successfully created a hardware and cloud infrastructure to collect and upload video from a camera gage at San Pedro Creek in San Antonio, Texas. Using a ThingLogix Foundry instance in the Amazon Webservices Cloud, we have created a cloud framework that can auto-provision new camera-based gaging equipment, as well as process incoming videos into image frames for the computation of streamflow. Additionally, we began testing of serving timeseries data from a camera gage (water level and CPU temperature) using real-time...
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Sinkholes present hazards to humans due to subsidence and by focusing contaminated surface water runoff into groundwater. Sinkholes create instability in the foundations of buildings, roads and other infrastructure, resulting in damage and in some cases loss of life, but may also play an important role as vernal pools in some ecosystems. This project created a prototype nationwide subsidence susceptibility map using established USGS research, existing USGS authoritative data (National Elevation Dataset, National Hydrography Dataset), and innovative processing techniques using the USGS Yeti supercomputer. By creating both a national polygon dataset of closed features and a heatmap of regions characterized by dense...
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Environmental DNA (eDNA) testing allows for high sensitivity monitoring efforts of cryptic species in large, remote systems and is performed by investigating water and soil samples for sloughed DNA. Having access to eDNA datasets across multiple taxa and ecosystems is necessary for improved coordination among researchers and management. Additionally, quality control protocols are needed to vet incoming database submissions. We developed a mechanism to submit eDNA data to the USGS Nonindigenous Aquatic Species (NAS) database, which currently maps and displays visual identification or physical capture data for non-native aquatic species. We have been working within the invasive species and eDNA communities to establish...
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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 Web Services (AWS), using ScienceBase. To accomplish this, we developed a new process in the ScienceBase application, added new functionality to the ScienceBase Python library (sciencebasepy), and assembled a functioning Python workflow demonstrating how users can gather data from a web API and publish these data as a cloud-based...
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The goal of this project was to maximize the value of expensive animal tagging data. We developed an interactive web application to help scientists understand patterns in their own tagging datasets and to help scientists, funders and agencies communicate tagging data to decision-makers and to the general public. Interactive visualizations have emerged recently as a valuable tool for identifying patterns in complex datasets that are typical of ecological tagging studies. To make it easier and faster for users to gain access to interactive movement visualizations, we developed the algorithms and web-based software platform to allow users to upload their own data into a data visualization showing dynamic movement of...
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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 couple existing USGS hydrologic models into integrated models. The CMF provides a Basic Model Interface (BMI), in a range of common computer languages, that enables model coupling. In addition, the CMF also provides a Python wrapper for any model that adopts the BMI. In this project the Precipitation-Runoff Modeling...
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Rangeland systems are some of our nation’s largest providers of agro-ecological services, sustaining plant productivity that is highly variable across seasons and years. Although the ability to predict the upcoming growing season’s rangeland productivity would have enormous economic and management value – such as for making decisions about cattle stocking rates, fire, restoration, and wildlife – the ability to provide these forecasts has remained poor. New remote sensing and modeling technologies allow for dramatic improvements to near-term forecasts of rangeland productivity. With this project, our multi-disciplinary team has shown that, compared with traditional remote sensing greenness indices, NIRv-based (NIR...
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Identifying the leading edge of a biological invasion can be difficult. Many management and research entities have biological samples or surveys that may unknowingly contain data on nonindigenous species. The new Nonindigenous Aquatic Species (NAS) Database automated online tool “SEINeD” (Screen and Evaluate Invasive and Non-native Data) will allow a user to search for these nonindigenous occurrences at a push of a button. This new tool will enable stakeholders to upload a biological dataset of fishes, invertebrates, amphibians, reptiles, or aquatic plants collected anywhere in a U.S. State or Territory and screen that data for non-native aquatic species occurrences. In addition to checking for the nativity of species...
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Geochronological data provide essential information necessary for understanding the timing of geologic processes and events, as well as quantifying rates and timescales key to geologic mapping, mineral and energy resource and hazard assessments. The USGS’s National Geochronological Database (NGDB) contains over 30,000 radiometric ages, but no formal update has occurred in over 20 years. This project is developing a database with a web-based user interface and sustainable workflow to host all USGS-generated geochronological data. This new geochronological database consists of (1) data from the existing NGDB; (2) published literature data generated by the USGS; and (3) more recent data extracted from ScienceBase...
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Natural resources managers are regularly required to make decisions regarding upcoming restoration treatments, often based on little more than business as usual practices. To assist in the decision-making process, we created a tool that predicts site-specific soil moisture and climate for the upcoming year, and provides guidance on whether common restoration activities (i.e. seeding, planting) will be successful based on these conditions. This tool is hosted within the Land Treatment Exploration Tool (LTET), an application already used by land managers that delivers a report of site condition and treatment history. Incorporated within the short-term drought forecaster (STDF) is a rigorous statistical process,...
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Over the past 40 years the National Wildlife Health center has collected wildlife health information from around the U.S. and beyond, amassing the world’s largest repository of wildlife-disease surveillance data. This project identified, characterized, and documented NWHC’s locally stored wildlife health datasets, a critical first step to migrating them to new laboratory- and public-facing data systems, such as the Wildlife Health Information Sharing Partnership-event reporting system. To accomplish this, we developed a systematic, standardized approach for collaborating with laboratory scientists to locate, define, and classify their long-term datasets so that they can be cleansed, archived, and mapped to new...
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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 R that allows users to access downscaled climate projections data, clip data by spatial boundaries (shapefile), save the output, and generate summary tables and plots. A detailed R vignette guides users to easily generate derived variables in order to answer specific questions about their region of interest (e.g. how will...


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