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Science Applications employs mdEditor for metadata creation and editing. Metadata for contacts of other DOI agencies and centers are used frequently across the USFWS regions. In an effort to reduce redundancy and increase efficiency, SA headquarters maintains a master mdJSON metadata file aka seed file. for use across SA regions and the Service.If you would like to add commonly used contacts to this list, please contact the SA HQ data manager at sadatamanager@fws.gov
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Predicted population cores (25%, 50% and 75%) of breeding Baird’s sparrow (Ammodramus bairdii) based on a range-wide random forest distribution model. We summed the probability of occurrence across all pixels in the study region to generate an index of total population. We placed each grid cell prediction in the context of the study area by dividing the individual pixel probability by the total index. Starting with the highest-value pixels, we cumulatively summed the probabilities until a given threshold was met. We set 25, 50 and 75% thresholds to delineate cores as the smallest possible areas containing the highest concentrations of predicted birds.
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Predicted population cores (25%, 50% and 75%) of breeding thick-billed longspur (Rhynchophanes mccownii) based on a range-wide random forest distribution model. We summed the probability of occurrence across all pixels in the study region to generate an index of total population. We placed each grid cell prediction in the context of the study area by dividing the individual pixel probability by the total index. Starting with the highest-value pixels, we cumulatively summed the probabilities until a given threshold was met. We set 25, 50 and 75% thresholds to delineate cores as the smallest possible areas containing the highest concentrations of predicted birds.
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CalHABMAP provides updates on current algal blooms and facilitates information exchange among HAB researchers, managers and the general public in California. A major component of this program is regional HAB monitoring. Water samples and net tows are collected once per week to monitor for HAB species and naturally occurring algal toxins. Water quality data including temperature, salinity, and nutrients are also collected.
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Description of Work U.S. Geological Survey (USGS) will provide easily accessible, centrally located, USGS biological, water resources, geological, and geospatial datasets for Great Lakes basin restoration activities coordinated with GLOS. Managers, partners and the public will be able to readily access this information in usable interactive formats to help plan and implement restoration activities. Building tools and infrastructure to support standard data access, efficient data discovery and dynamic mapping of watersheds and their hydrologic properties. Developing decision support tools to enhance scientific investigation or disseminate project findings, for example integrating hydrologic models with real-time...
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Fishery and aquatic scientists often assess habitats to understand the distribution, status, stressors, and relative abundance of aquatic resources. Due to the spatial nature of aquatic habitats and the increasing scope of management concerns, using traditional analytical methods for assessment is often difficult.However, advancements in the geographic information systems (GIS) field and related technologies have enabled scientists and managers to more effectively collate, archive, display, analyze, and model spatial and temporal data. For example, spatially explicit habitat assessment models allow for a more robust interpretation of many terrestrial and aquatic datasets, including physical and biological monitoring...
This is the contact list for LCC funded projects for use in mdEditor. Please pay attention to the last update field. After adding contacts to the list please export and replace the file associated with this record and update the last update date accordingly.
Categories: Data; Tags: Metadata
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Predicted population cores (25%, 50% and 75%) of breeding chestnut-collared longspurs (Calcarius ornatus) based on a range-wide random forest distribution model. We summed the probability of occurrence across all pixels in the study region to generate an index of total population. We placed each grid cell prediction in the context of the study area by dividing the individual pixel probability by the total index. Starting with the highest-value pixels, we cumulatively summed the probabilities until a given threshold was met. We set 25, 50 and 75% thresholds to delineate cores as the smallest possible areas containing the highest concentrations of predicted birds.
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Fishery and aquatic scientists often assess habitats to understand the distribution, status, stressors, andrelative abundance of aquatic resources. Due to the spatial nature of aquatic habitats and the increasingscope of management concerns, using traditional analytical methods for assessment is often difficult.However, advancements in the geographic information systems (GIS) field and related technologies haveenabled scientists and managers to more effectively collate, archive, display, analyze, and model spatial andtemporal data. For example, spatially explicit habitat assessment models allow for a more robustinterpretation of many terrestrial and aquatic datasets, including physical and biological monitoring...
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Predicted population cores (25%, 50% and 75%) of breeding Sprague’s pipit (Anthus spragueii) based on a range-wide random forest distribution model. We summed the probability of occurrence across all pixels in the study region to generate an index of total population. We placed each grid cell prediction in the context of the study area by dividing the individual pixel probability by the total index. Starting with the highest-value pixels, we cumulatively summed the probabilities until a given threshold was met. We set 25, 50 and 75% thresholds to delineate cores as the smallest possible areas containing the highest concentrations of predicted pipits.
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Fishery and aquatic scientists often assess habitats to understand the distribution, status, stressors, andrelative abundance of aquatic resources. Due to the spatial nature of aquatic habitats and the increasingscope of management concerns, using traditional analytical methods for assessment is often difficult.However, advancements in the geographic information systems (GIS) field and related technologies haveenabled scientists and managers to more effectively collate, archive, display, analyze, and model spatial andtemporal data. For example, spatially explicit habitat assessment models allow for a more robustinterpretation of many terrestrial and aquatic datasets, including physical and biological monitoring...
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This data product contains estimates of habitat quality and connectivity for mountain lion, mule deer, desert bighorn sheep, and black bear, and combined estimates of high habitat and connectivity areas for all species. The analysis area was a 236,000 square kilometers that encompassed the Navajo Nation, which includes portions of Arizona, New Mexico, and Utah. The estimates of habitat quality were created with spatially explicit habitat variables and either an expert-based linear combination process (for mountain lion and mule deer) or a generalized linear mixed model-based estimation that used radio-collar telemetry data (for desert bighorn sheep, black bear, and pronghorn; collected between 2005-2011). Habitat...
Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Antilocapra americana, Antilocapra americana, Arizona, Arizona, EARTH SCIENCE > LAND SURFACE > LANDSCAPE, All tags...
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Fishery and aquatic scientists often assess habitats to understand the distribution, status, stressors, and relative abundance of aquatic resources. Due to the spatial nature of aquatic habitats and the increasing scope of management concerns, using traditional analytical methods for assessment is often difficult.However, advancements in the geographic information systems (GIS) field and related technologies have enabled scientists and managers to more effectively collate, archive, display, analyze, and model spatial and temporal data. For example, spatially explicit habitat assessment models allow for a more robust interpretation of many terrestrial and aquatic datasets, including physical and biological monitoring...
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The Alaska Division of Geological and Geophysical Surveys provides FGDC metadata records for many different geoscience collections, databases, and individual artifacts. Those are harvested for access through ScienceBase along with records from sample data preservation activities. This collection is in the process of being replaced by a new collection of all geoscience publications. https://www.sciencebase.gov/catalog/item/61522455d34e0df5fb9bd804
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This product results from one of 5 subprojects of the North Atlantic LCC funded NROC project, “Demonstrations & Science Delivery Networks for Coastal Resilience Information in the Northeast”. Coastal storm and flood risk data were generated through the North Atlantic Coast Comprehensive Study (NAACS), an initative of the U.S. Army Corps of Engineers. In order to make this data widely available, the Northeast Regional Ocean Council (NROC) funded the development of a database and web services that provide streamlined access to high-resolution data on coastal storm and flood risk in the Northeast. Produced by a team from RPS ASA, the database includes projections for future climate scenarios and is a valuable resource...
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Fishery and aquatic scientists often assess habitats to understand the distribution, status, stressors, andrelative abundance of aquatic resources. Due to the spatial nature of aquatic habitats and the increasingscope of management concerns, using traditional analytical methods for assessment is often difficult.However, advancements in the geographic information systems (GIS) field and related technologies haveenabled scientists and managers to more effectively collate, archive, display, analyze, and model spatial andtemporal data. For example, spatially explicit habitat assessment models allow for a more robustinterpretation of many terrestrial and aquatic datasets, including physical and biological monitoring...
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Fishery and aquatic scientists often assess habitats to understand the distribution, status, stressors, andrelative abundance of aquatic resources. Due to the spatial nature of aquatic habitats and the increasingscope of management concerns, using traditional analytical methods for assessment is often difficult.However, advancements in the geographic information systems (GIS) field and related technologies haveenabled scientists and managers to more effectively collate, archive, display, analyze, and model spatial andtemporal data. For example, spatially explicit habitat assessment models allow for a more robustinterpretation of many terrestrial and aquatic datasets, including physical and biological monitoring...
A major challenge with communicating potential climate change impacts to general audiences is that many people have difficulty understanding how projected changes in temperature and precipitation affect the climate they are accustomed to and their lives in general. Climate analogs are an alternative tool that can be used to communicate potential climate change impacts by comparing locations with similar climates to illustrate changes that models project. The approach works by comparing the future climate at a location of interest to the historic climate of all locations (Figure 1). We identify the location with the most similar historic climate (analog site) to the future climate at the location of interest. The...
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Fishery and aquatic scientists often assess habitats to understand the distribution, status, stressors, andrelative abundance of aquatic resources. Due to the spatial nature of aquatic habitats and the increasingscope of management concerns, using traditional analytical methods for assessment is often difficult.However, advancements in the geographic information systems (GIS) field and related technologies haveenabled scientists and managers to more effectively collate, archive, display, analyze, and model spatial andtemporal data. For example, spatially explicit habitat assessment models allow for a more robustinterpretation of many terrestrial and aquatic datasets, including physical and biological monitoring...


map background search result map search result map Geoscience publications with digital data harvested from the Alaska Division of Geological and Geophysical Surveys - (Deprecated) Enabling Discovery and Access to USGS Great Lakes Scientific Data Through Web-Based Applications Metadata North Atlantic Coast Comprehensive Study (NACCS) Coastal Storm and Flood Risk Data Harmful Algal Bloom (HAB) data collected by the Scripps Institution of Oceanography and made available by the Southern California Coastal Observing System (SCCOOS) Baird’s Sparrow Cores GPFHP_Turbid_River_Guild_Restoration_Priorities Sprague’s Pipit Cores GPFHP_Southern_Headwaters_Restoration_Priorities GPFHP_Southern_Headwaters_Protection_Priorities GPFHP_Darter_Guild_Protection_Priorities Chestnut-collared Longspur Cores GPFHP_Northern_Headwaters_Guild_Restoration_Priorities Thick-billed Longspur Cores GPFHP_Madtom_Guild_Protection_Priorities GPFHP_Madtom_Guild_Restoration_Priorities Metadata Thick-billed Longspur Cores Sprague’s Pipit Cores Harmful Algal Bloom (HAB) data collected by the Scripps Institution of Oceanography and made available by the Southern California Coastal Observing System (SCCOOS) Baird’s Sparrow Cores Enabling Discovery and Access to USGS Great Lakes Scientific Data Through Web-Based Applications Chestnut-collared Longspur Cores North Atlantic Coast Comprehensive Study (NACCS) Coastal Storm and Flood Risk Data GPFHP_Turbid_River_Guild_Restoration_Priorities GPFHP_Southern_Headwaters_Restoration_Priorities GPFHP_Southern_Headwaters_Protection_Priorities GPFHP_Darter_Guild_Protection_Priorities GPFHP_Northern_Headwaters_Guild_Restoration_Priorities GPFHP_Madtom_Guild_Protection_Priorities GPFHP_Madtom_Guild_Restoration_Priorities Geoscience publications with digital data harvested from the Alaska Division of Geological and Geophysical Surveys - (Deprecated)