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Community for Data Integration

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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...
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...
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.
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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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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