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Recent open data policies of the Office of Science and Technology Policy (OSTP) and Office of Management and Budget (OMB), which were fully enforceable on October 1, 2016, require that federally funded information products (publications, etc.) be made freely available to the public, and that the underlying data on which the conclusions are based must be released. A key and relevant aspect of these policies is that data collected by USGS programs must be shared with the public, and that these data are subject to the review requirements of Fundamental Science Practices (FSP). These new policies add a substantial burden to USGS scientists and science centers; however, the upside of working towards compliance with...
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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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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, and thus outperform many other types of automated classification algorithms. This project developed software tools, resources, and two training workshops that will allow USGS scientists to apply deep learning to remotely sensed imagery and to better understand natural hazards and habitats across the Nation. The tools and...
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We are working to incorporate environmental DNA (eDNA) data into the Nonindigenous Aquatic Species (NAS) database, which houses over 570,000 records of nonindigenous species nationally, and already is used by a broad user-base of managers and researchers regularly for invasive species monitoring. eDNA studies have allowed for the identification and biosurveillance of numerous invasive and threatened species in managed ecosystems. Managers need such information for their decision-making efforts, and therefore require that such data be produced and reported in a standardized fashion to improve confidence in the results. As we work to gain community consensus on such standards, we are finalizing the process for submitting...
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Droughts are becoming more frequent and severe and this trend is expected to continue in the coming century. Drought effects on natural resources include reduced water availability for plants and humans, as well as increased insect, disease, and vegetation mortality. Land managers need more information regarding how water availability may change and how drought will affect their sites in the future. We developed an online, interactive application that allows natural resource managers to access site-specific, observed historical and predicted future water availability. Users are able to set information that affects water balance, including soil texture and vegetation composition. With these inputs, as well as site-specific...
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ScienceCache was originally developed as a mobile device data collection application for a citizen science project. ScienceCache communicates with a centralized database that facilitates near real-time use of collected data that enhances efficiency of data collection in the field. We improved ScienceCache by creating a flexible, reliable platform that reduces effort required to set up a survey and manage incoming data. Now, ScienceCache can be easily adapted for citizen science projects as well as restricted to specific users for private internal research. We improved scEdit, a web application interface, to allow for creation of more-complex data collection forms and survey routes to support scientific studies....
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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” map products relevant to science and management. Pheno Forecasts indicate, for a specified day, the status of the insect’s target life cycle stage in real time across the contiguous United States. These risk maps enhance decision-making and short-term planning by both natural resource managers and members of the public. ...
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Autonomous Underwater Vehicles (AUVs) are instruments that collect water-quality, depth, and other data in waterbodies. They produce complex and massive datasets. There is currently no standard method to store, organize, process, quality-check, analyze, or visualize this data. The Waterbody Rapid Assessment Tool (WaterRAT) is aPython application that processes and displays water-quality data with interactive two-dimensional and three-dimensional figures, but it runs offline with few capabilities and for just one study site. This project will transition WaterRAT to an online application that the public can easily use to view all AUV data. A database of all AUV datasets will be developed to improve accessibility,...
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The U.S. network of 160 weather radars known as NEXRAD (NEXt generation RADar) is one of the largest and most comprehensive terrestrial sensor networks in the world. To date, the National Climatic Data Center (NCDC) has archived about 2 petabytes data from this system. Although designed for meteorological applications, these radars readily detect the movements of birds, bats, and insects. Many of these movements are continental in scope, spanning the entire range of the network. It is unclear whether biological or meteorological data comprise the bulk of the archive. Regardless, the biological portion is sufficiently large that it likely represents one of the largest biological data archives in the world, perhaps...
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Note 9/22/18: The Adopt a Pixel concept has been incorporated into NASA's Globe Observer App (Land Cover Tool). Find out more and download the app at https://observer.globe.gov/. *** Adopt a Pixel-Data Infrastructure (AaP-DI) provides the basis for a new data acquisition system for ground reference data. These data will be used to complement existing and future remote sensing collections by providing geospatiallytagged ground-based landscape imagery and landcover of an exact location from 6 different viewing aspects. The goal is for AaP-DI to enable citizen participation in Landsat science. Deliverables: The Adopt a Pixel web interface (http://adoptapixel.cr.usgs.gov) is a data upload portal that allows citizen...
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The National Land Cover Database (NLCD) serves as the definitive Landsat-based, 30-meter resolution, land cover database for the Nation. NLCD supports a wide variety of Federal, State, local, and nongovernmental applications that seek to assess ecosystem status and health, understand the spatial patterns of biodiversity, predict effects of climate change, and develop land management policy. However, access to NLCD products for the USGS community and the public is a concern due to large file sizes, limited download options, and the expectation that users must download and analyze multiple land cover products in order to answer even basic land cover change questions. Therefore, the goal of the NLCD Evaluation, Visualization...
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Geotagged photographs have become a useful medium for recording, analyzing, and communicating Earth science phenomena. Despite their utility, many field photographs are not published or preserved in a spatial or accessible format—oftentimes because of confusion about photograph metadata, a lack of stability, or user customization in free photo sharing platforms. After receiving a request to release about 1,210 geotagged geological field photographs of the Grand Canyon region, we set out to publish and preserve the collection in the most robust (and expedient) manner possible (fig. 6). We leveraged and reworked existing metadata, JavaScript, and Python tools and developed a toolkit and proposed workflow to display...
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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,...
Detailed information about past fire history is critical for understanding fire impacts and risk, as well as prioritizing conservation and fire management actions. Yet, fire history information is neither consistently nor routinely tracked by many agencies and states, especially on private lands in the Southeast. Remote sensing data products offer opportunities to do so but require additional processing to condense and facilitate their use by land managers. Here, we propose to generate fire history metrics from the Landsat Burned Area Products for the southeastern US. We will develop code for a processing pipeline that utilizes USGS high-performance computing resources, evaluate Amazon cloud computing services,...
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Land-use researchers need the ability to rapidly compare multiple land-use scenarios over a range of spatial and temporal scales, and to visualize spatial and nonspatial data; however, land-use datasets are often distributed in the form of large tabular files and spatial files. These formats are not ideal for the way land-use researchers interact with and share these datasets. The size of these land-use datasets can quickly balloon in size. For example, land-use simulations for the Pacific Northwest, at 1-kilometer resolution, across 20 Monte Carlo realizations, can produce over 17,000 tabular and spatial outputs. A more robust management strategy is to store scenario-based, land-use datasets within a generalized...
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This project will assess the accuracy of climate drivers (precipitation and temperature) from different sources for current and future conditions. The impact of these drivers on hydrologic response will be using the monthly water balance model (MWBM). The methodology for processing and analysis of these datasets will be automated for when new climate datasets become available on the USGS Geo Data Portal ( http://cida.usgs.gov/climate/gdp/). This will ensure continued relevancy of project results, future opportunities for research and assessment of potential climate change impacts on hydrologic resources, and comparison between generations of climate data. To share and distribute the results with scientists and...
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How can the public discover opportunities for participation in USGS scientific research? What citizen science projects are currently active within the USGS? How may PIs increase public engagement in and awareness of their citizen science projects? To address these questions, a web application leveraging existing Community for Data Integration (CDI) and USGS work was created to allow unprecedented public access to USGS citizen science project metadata and highlights of key science outcomes. Such an application enables, for the first time, high-visibility, unified open access to information about projects and practices related to citizen participation in USGS research. The need for such information was identified...
Wetland soils are vital to the Nation because of their role in sustaining water resources, supporting critical ecosystems, and sequestering significant concentrations of biologically-produced carbon. The United States has the world’s most detailed continent-scale digital datasets for soils and wetlands, yet scientists and land managers have long struggled with the challenge of integrating these datasets for applications in research and in resource assessment and management. The difficulties include spatial and temporal uncertainties, inconsistencies among data sources, and inherent structural complexities of the datasets. This project’s objective was to develop and document a set of methods to impute wetland...
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The Community for Data Integration (CDI) Risk Map Project is developing modular tools and services to benefit a wide group of scientists and managers that deal with various aspects of risk research and planning. Risk is the potential that exposure to a hazard will lead to a negative consequence to an asset such as human or natural resources. This project builds upon a Department of the Interior project that is developing geospatial layers and other analytical results that visualize multi-hazard exposure to various DOI assets. The CDI Risk Map team has developed the following: a spatial database of hazards and assets, an API (application programming interface) to query the data, web services with Geoserver (an open-source...
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Large amounts of data are being generated that require hours, days, or even weeks to analyze using traditional computing resources. Innovative solutions must be implemented to analyze the data in a reasonable timeframe. The program HTCondor (https://research.cs.wisc.edu/htcondor/) takes advantage of the processing capacity of individual desktop computers and dedicated computing resources as a single, unified pool. This unified pool of computing resources allows HTCondor to quickly process large amounts of data by breaking the data into smaller tasks distributed across many computers. This project team implemented HTCondor at the USGS Upper Midwest Environmental Sciences Center (UMESC) to leverage existing computing...