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Folders: ROOT > ScienceBase Catalog > Community for Data Integration (CDI) ( Show direct descendants )

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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...
The purpose of this study is to understand how the USGS is using decision support, learning from successes and pitfalls in order to help streamline the design and development process across all levels of USGS scientific tool creation and outreach. What should researchers consider before diving into tool design and development? Our goal is to provide a synthesis of lessons learned and best practices across the spectrum of USGS decision support efforts to a) provide guidance to future efforts and b) identify knowledge gaps and opportunities for knowledge transfer and integration. Principal Investigator : Amanda E Cravens Co-Investigator : Nicole M Herman-Mercer, Amanda Stoltz
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Wildfires affect streams and rivers when they burn vegetation and scorch the ground. This makes floods more likely to happen and reduces water quality. Public managers, first responders, fire scientists, and hydrologists need timely information before and after a fire to plan for floods and water treatment. This project will create a method to combine national fire databases with the StreamStats water web mapping application to help stakeholders make informed decisions. When the project is finished, people will be able to use StreamStats to estimate post-wildfire peak flows in streams and rivers for most of the United States (where data is available). There will also be tools that allow users to trace upstream and...
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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 available. The repository consists of (1) a ScienceBase community page where the data are available for download and (2) an accompanying web application that allows users to browse and visualize the available datasets. We anticipate that easier access to these key datasets will accelerate progress in earthquake-triggered ground...
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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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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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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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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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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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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....
Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection.This project is one of the first attempts to apply AI towards fish population assessment with citizen science. Principal Investigator : Nathaniel P Hitt Co-Investigator : Natalya I Rapstine, Mona (Contractor) Arami, Jeff T Falgout, Benjamin Letcher, Nicholas Polys Cooperator/Partner : Sophia Liu, Fraser Hayes, Ky Wildermuth, Bryan...
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,...
The Total Water Level and Coastal Change Forecast delivers 6-day forecasts of hourly water levels and the probability of waves impacting dunes along 5000 km of sandy coasts along the Atlantic and Gulf of Mexico and will soon expand to the Pacific. These forecasts provide needed information to local governments and federal partners and are used by the USGS to place sensors before a storm. The forecast data are presented in a publicly accessible web tool and stored in a database. Currently, model data are only accessible to project staff. A growing user community is requesting direct access to the data, to conduct scientific analyses and share forecasts on other platforms. To address this need, we will develop an...
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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,...
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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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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 internet to their local computer. To explore a more efficient and effective scientific workflow, we explored an alternate model: storing scientific data remotely, and performing data analysis and visualization close to the data, using only a local web browser as an interface. Although this environment was not a good fit...
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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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In this age of rapidly developing technology, scientific information is constantly being gathered across large spatial scales. Yet, our ability to coordinate large-scale monitoring efforts depends on development of tools that leverage and integrate multiple sources of data. North American bats are experiencing unparalleled population declines. The North American Bat Monitoring Program (NABat), a multi-national, multi-agency coordinated monitoring program, was developed to better understand the status and trends of North American bats. Similar to other large-scale monitoring programs, the ultimate success of NABat relies on a unified web-based data system. Our project successfully developed a program interface...
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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 Science Center and the Wisconsin Water Science Center, we have flocked with the USGS Advanced Research Computing Yeti supercomputing cluster and other water science centers. We also developed tutorials on how to sandbox code using Docker within the USGS environment for use with high-throughput computing. A main accomplishment...