AndreaBozzo/ceres-open-data-index huggingface dataset:
Specified System Requirements for the HydroAir Power Take Off System

Includes Annual Energy Production of the system, Target Availability of the system, the system's PWR (power to weight ratio), Critical Dimensions and Weights (e.g. center of gravity and center of buoyancy, capture length) of the system, the system's Noise Levels, and All Grid Requirements (e.g. tolerance to variation in frequency output, flicker, etc.) for the system.

Tags: availability, dimensions, energy, energy-production, grid-requirements, hydrokinetic, hydrokinetic-marine-energy-power, marine, mhk, noise-attenuation, noise-levels, power, power-take-off, power-to-weight-ratio, pto, pwr, turbine, wave-energy, wec
Organization: Department of Energy
Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis (Sup3rWind)

The Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis (Sup3rWind) data is a collection of high-resolution historical wind, temperature, humidity, and pressure fields. Sup3rWind data is produced by downscaling ECMWF Reanalysis Version 5 data (ERA5) to 2-km spatial and 5-minute temporal resolution (hourly for temperature, humidity, and pressure). The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). It improves the representation of terrain driven wind flows, extreme wind events, and preserves important spatiotemporal patterns for use in energy system planning and operations.

Coverage:
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Ukraine, Moldova, and part of Romania: This data is accessed through the "ukraine" folder in the "Sup3rWind Data and Models in S3" resource below. Sup3r software v0.1.2, phygnn v0.0.28, and the models in "models/sup3rwind_models_202401", were used to generate this data.

South America: This data is accessed through the "south_america" folder in the "Sup3rWind Data and Models in S3" resource below. Sup3r software v0.2.4, phygnn v0.0.33, and the models in "models/sup3rwind_models_202501", were used to generate this data.

Tags: data, data-assimilation, downscaling, energy, energy-planning, energy-systems, era5, gan, generative, generative-adversarial-learning, high-resolution, machine-learning, ml, model, power, power-systems, processed-data, renewable-energy, resource-data, south-america, sup3r, sup3rwind, temperature, ukraine, weather, wind, windspeed
Organization: Department of Energy
Technical Reference for Hydrogen Compatibility of Materials

Sandia National Laboratories is conducting an extensive review of reports and journal publications to gather existing materials data for inclusion in the Technical Reference for Hydrogen Compatibility of Materials. Additionally, Sandia is working internationally with collaborators to acquire newly generated data for inclusion in the Technical Reference. SAND2012-7321 is an archival report issued by Sandia National Laboratories representing the reference information compiled as of September 2012. Updates and additions of individual sections of this report are available at the "Technical Reference for Hydrogen Compatibility of Materials" website linked below.

The Technical Database for Hydrogen Compatibility of Materials is intended to be a complement to the Technical Reference for Hydrogen Compatibility of Materials. The database is formulated in the Granta MI framework and available at the "Hydrogen Materials Technical Database" link below and includes curated data on strength, fatigue and fracture measured in gaseous hydrogen environments.

Guidance on materials selection for hydrogen service is needed to support the deployment of hydrogen as a fuel as well as the development of codes and standards for stationary hydrogen use, hydrogen vehicles, refueling stations, and hydrogen transportation. Materials property measurement is needed on deformation, fracture and fatigue of metals in environments relevant to this hydrogen economy infrastructure. The identification of hydrogen-affected material properties such as strength, fracture resistance and fatigue resistance are high priorities to ensure the safe design of load-bearing structures.


Tags: compatibility, compatibility-of-materials, data, energy, fatigue, fracture, hydrogen, hydrogen-compatibility, materials, materials-science, nrel, sandia, snl, stregth, technical-database, technical-reference
Organization: Department of Energy
GHRSST Level 4 OSPO Global Nighttime Foundation Sea Surface Temperature Analysis (GDS version 2)

A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis at the Office of Satellite and Product Operations (OSPO) using optimal interpolation (OI) on a global 0.054 degree grid. The Geo-Polar Blended Sea Surface Temperature (SST) Analysis combines multi-satellite retrievals of sea surface temperature into a single analysis of SST. This analysis includes only nighttime data from sensors that include the Advanced Very High Resolution Radiometer (AVHRR), the Visible Infrared Imager Radiometer Suite (VIIRS), the Geostationary Operational Environmental Satellite (GOES) imager, the Japanese Advanced Meteorological Imager (JAMI) and in situ data from ships, drifting and moored buoys. This analysis was specifically produced to be used as a lower boundary condition in Numerical Weather Prediction (NWP) models. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.

Tags: oceanography
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
GHRSST Level 2P Gridded Global Subskin Sea Surface Temperature from the Advanced Scanning Microwave Radiometer - Earth Observing System (AMSR-E) on the NASA Aqua Satellite (GDS version 1)

The Advanced Microwave Scanning Radiometer (AMSR-E) was launched on 4 May 2002, aboard NASA's Aqua spacecraft. The National Space Development Agency of Japan (NASDA) provided AMSR-E to NASA as an indispensable part of Aqua's global hydrology mission. Over the oceans, AMSR-E is measuring a number of important geophysical parameters, including sea-surface temperature (SST), wind speed, atmospheric water vapor, cloud water, and rain rate. A key feature of AMSR-E is its capability to see through clouds, thereby providing an uninterrupted view of global SST and surface wind fields. Remote Sensing Systems (RSS, or REMSS) is the provider of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project. Although the product designation is "L2P_GRIDDED" it is in actuality a Level 3 Collated (L3C) product as defined in the GHRSST Data Processing Specification (GDS) version 2.0. Its "L2P_GRIDDED" name derives from a deprecated specification in the early Pilot Project phase of GHRSST (pre 2008) and has remained for file naming continuity. In this dataset, both ascending (daytime) and descending (daytime) gridded orbital passes on packaged into the same daily file.

Tags: oceanography
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Design Specifications for Manufacturer for the HydroAir Power Take Off System

Includes Design Specs for Manufacturer, PTO Weight, PTO Dimensions, Turbine (Rotor and Ductwork) Design, CAD Drawings of the turbine and rotor, List of components and weights, Materials Used, Generator, List of components and weights, Structural Design, CAD drawings of the structure, List of components and weights, Materials Used, Electrical System, Schematics of electrical system, List of components, weights, and placement.

Tags: cad, components, design, drawings, energy, generator, hydrokinetic, hydrokinetic-marine-energy-power, manufacture, marine, materials, mhk, power, power-take-off, pto, schematics, specification, specifications, turbine, wave-energy, wec
Organization: Department of Energy
Tree Canopy Cover (TCC) Science Standard Error (SE) Conterminous United States

<div style='text-align:Left;'><div><div><p><span>The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 1985 through 2023 are available. The Science data were produced using a random forest regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. </span><span>Data Download and Methods Documents: </span><span> - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ </span></p></div></div></div>

Tags: 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, basemaps, continuous, digital-spatial-data, earthcover, environment, gis, imagery, national-geospatial-data-asset, ngda, nlcd, percent-tree-canopy, remote-sensing, science, standard-error, tcc, tree-canopy-cover
Organization: Department of Agriculture
Firefighter Estimated Ground Evacuation (Image Service)

<div style='text-align:Left;'><div><div><p><span>The estimated ground evacuation time layer is intended to provide a broad view of medical care accessibility to inform incident- and regional-level strategic risk assessment and mitigation planning. The estimated ground evacuation times account for hospital locations, driving speeds on the current road network, and off-road walking speeds influenced by topography, vegetation, slash/blowdown, and waterbodies. Evacuation times are binned into broad classes (0-1 hours, 1-2 hours, 2-4 hours, 4-6 hours, and &amp; &gt;6 hours) commensurate with the precision of the analysis. There are several limitations of this estimated ground evacuation time dataset that users should understand. The analysis assumes that the closest general acute care or critical access hospital will be the first stop to stabilize the patient. Incident level safety and medical planning may identify different preferred medical facilities based on their actual travel times and/or capabilities. These estimated evacuation times only account for the travel portion of an evacuation and should therefore be viewed as best-case estimates. Complicated injuries may require additional time for patient evaluation, care, packaging for transport, and/or transfer to a vehicle. Travel times are calculated under the assumptions that the litter crew can travel at the median pedestrian rate (influenced by landscape factors) and that the ambulance or other transport vehicle can travel at the designated road speed limits. Users of this dataset should be thoughtful of factors not accounted for in this analysis that could slow evacuations such as additional landscape characteristics, crew condition, or dynamic factors like weather, traffic, or road closures.</span></p></div></div></div>

Tags: conus, environment, estimated, evacuation, firefighter, ground, health, planning, safety, time, transportation, usfs
Organization: Department of Agriculture
National Riparian Area Base Map

<div style='text-align:Left;'><div><div><p><span>This imagery layer shows national riparian areas for the conterminous United States. Riparian areas are an important natural resource with high biological diversity. These ecosystems contain specific vegetation and soil characteristics which support irreplaceable values and multiple ecosystem functions and are very responsive to changes in land management activities. Delineating and quantifying riparian areas is an essential step in riparian monitoring, planning, management, and policy decisions. USDA Forest Service supports the development and implementation of a national context framework with a multi-scale approach to define riparian areas utilizing free available national geospatial datasets.</span></p><p><span>Why was this layer created? </span></p><p><span>To estimate 50-year flood height riparian areas to support statistical analysis, map display, and model parameterization.</span></p><p><span>Provide a framework and an end product to stakeholders and apply the information into management actions and strategies.</span></p><p><span>Multi-scale approach to provide a national and regional report map. Create a product for managers to easily understand where to apply the information at various scales.</span></p><p><span>Develop a national context inventory of riparian areas and their condition within national forests and rangelands.</span></p><p><span>How was this layer created? </span></p><p><span>Using freely available data.</span></p><p><span>Develop cost effective modeling approach &amp; technique.</span></p><p><span>Multi-scale (national, regional, &amp; local).</span></p><p><span>Promote technology transfer to train/reach out to our partners.</span></p><p><span>Fifty-year flood heights were estimated using U.S. Geological Survey (USGS) stream gage information. NHDPlus version 2.1 was used as the hydrologic framework to delineate riparian areas. The U.S. Fish and Wildlife Service's National Wetland Inventory and USGS 10-meter digital elevation models were also used in processing these data.</span></p><p><span>The data are '1' if in the riparian zone and 'NoData' if outside the riparian zone. When displayed on a map, riparian zone cells are color-coded 'blue' with 25% transparency.</span></p><p><span>For additional information regarding methodologies for modeling and processing these data, see Abood et al. (2012) and the National Riparian Areas Base Map StoryMap</span></p><p><span>Data Download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0030</span></p></div></div></div>

Tags: conus, ecosystems, riparian
Organization: Department of Agriculture
Forest Health Protection Tree Species Metrics Stand Density Index

<div style='text-align:Left;'><div><div><p><span style='font-size:12pt'>These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002</span></p><p><span /></p></div></div></div>

Tags: alabama, arizona, arkansas, california, colorado, connecticut, conus, coterminous-us, cubist, delaware, density, district-of-columbia, fhtet-host-maps, fhtet-modeled, florida, forest-parameters, georgia, health, host-maps, host-model, idaho, illinois, indiana, interior-west-region, intermountain-region, iowa, kansas, kentucky, louisiana, maine, maryland, massachusetts, michigan, minnesota, mississippi, missouri, montana, na, nebraska, nevada, new-hampshire, new-jersey, new-mexico, new-york, north-carolina, north-dakota, northeastern-area, northern-region, ohio, oklahoma, oregon, pacific-northwest-region, pacific-southwest-region, pennsylvania, r1, r2, r3, r4, r5, r6, r8, r9, region-1, region-2, region-3, region-4, region-5, region-6, region-8, region-9, rhode-island, rocky-mountain-region, see-5, south-carolina, south-dakota, southern-region, southwestern-region, stand-density, stand-density-index, stocking, tennessee, texas, utah, vermont, virginia, washington, west-region, west-virginia, wisconsin, wyoming
Organization: Department of Agriculture
Forest Service Research Data Archive - Index

This archive publishes and preserves short and long-term research data collected from studies funded by:<p></p><li>Forest Service Research and Development (<a href='https://www.fs.usda.gov/research/' style='color: rgb(0, 0, 153); text-decoration-line: none;' target='_blank' rel='nofollow ugc noopener noreferrer'>FS R&amp;D</a>)</li><li>Joint Fire Science Program (<a href='https://www.firescience.gov/' style='color: rgb(0, 0, 153); text-decoration-line: none;' target='_blank' rel='nofollow ugc noopener noreferrer'>JFSP</a>)</li><li>Aldo Leopold Wilderness Research Institute (<a href='https://leopold.wilderness.net/' style='color: rgb(0, 0, 153); text-decoration-line: none;' target='_blank' rel='nofollow ugc noopener noreferrer'>ALWRI</a>)</li><p></p>Of special interest, our collection includes data from a number of Forest Service Experimental Forests and Ranges.<p>Each archived data set (i.e., &quot;data publication&quot;) contains at least one data set, complete metadata for the data set(s), and any other documentation the researcher deemed important to understanding the data set(s). The data catalog entries present the metadata and a link to the data. In some cases the data link is to a different archive.</p>

Tags: forest-service, index, open-data, rda, research-data-archive, usda-forest-service, usfs
Organization: Department of Agriculture
Extent of U.S. Rangelands

<div style='text-align:Left;'><div><div><p><span>This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [&gt; 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention). The dataset can be downloaded from the following website: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php</span></p><p><span>Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option. An image service that depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands.</span></p></div></div></div>

Tags: afforested-rangeland, agriculture, barren, conus, developed, extent-of-us-rangelands, forest, ice, rangeland, rangelands, snow, transitional-rangeland, water
Organization: Department of Agriculture
Productivity of U.S. Rangelands, Annual Data lbs/acre (Image Service)

<div style='text-align:Left;'><div><div><p><span>Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources.</span></p><p><span>This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.</span></p></div></div></div>

Tags: 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, biota, climate, drought, forage, forest-service, office-of-sustainability-and-climate, osc, rangeland-productivity, rangelands, rpa-assessment, usda, usfs
Organization: Department of Agriculture
Productivity of U.S. Rangelands, Annual Data Z-scores (Image Service)

<div style='text-align:Left;'><div><div><p><span>This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [&gt; 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention).</span></p><p><span>This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.</span></p><p><span>Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. These results were then converted to Z-scores for easier comparison of annual relative productivity in coterminous U.S. rangelands, and for rapid display in online time-enabled applications.</span></p></div></div></div>

Tags: 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, biota, climate, drought, forage, forest-service, office-of-sustainability-and-climate, osc, rangeland-productivity, rangelands, rpa-assessment, usda, usfs, z-scores
Organization: Department of Agriculture