The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.
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Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
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The Biodiversity Heritage Library (BHL) is the world’s largest open access digital library for biodiversity literature and archives. BHL operates as a worldwide consortium of natural history, botanical, research, and national libraries working together to digitize the natural history literature held in their collections and make it freely available for open access.
Released to the public as part of the Department of Energy's Open Energy Data Initiative, this is the highest resolution publicly available long-term wave hindcast dataset that – when complete – will cover the entire U.S. Exclusive Economic Zone (EEZ).
The Encyclopedia of DNA Elements (ENCODE) Consortium is an international collaboration of research groups funded by the National Human Genome Research Institute (NHGRI). The goal of ENCODE is to build a comprehensive parts list of functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells and circumstances in which a gene is active. ENCODE investigators employ a variety of assays and methods to identify functional elements. The discovery and annotation of gene elements is accomplished primarily by sequencing a diverse range of RNA sources, comparative genomics, integrative bioinformatic methods, and human curation. Regulatory elements are typically investigated through DNA hypersensitivity assays, assays of DNA methylation, and immunoprecipitation (IP) of proteins that interact with DNA and RNA, i.e., modified histones, transcription factors, chromatin regulators, and RNA-binding proteins, followed by sequencing.
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This dataset contains modeled hourly streamflow at each about eighteen thousand selected operational and water quality stream gage locations. It was assembled from publicly available retrospective and operational V1.2 National Water Model outputs. The streamflow variable was extracted from model output files and the data were reshaped to optimize read performance. The stream gage locations were derived from several ongoing USGS projects using gages for evaluation of streamflow, water quality, and real-time monitoring however only National Water Model identifiers and NHDPlus catchment outlet locations are used to identify model results. Relationships between NWIS gages and National Water Model prediction locations were not reviewed for release at the time of publication of this data. Please contact the author for up to date information. All processing resources used for data reformatting and extraction can be found in this repository: https://code.usgs.gov/water/nwm_subset. This supporting code also contains Docker images that are capable of processing real-time National Water Model outputs into similar formats as are in this dataset and providing data services via the THREDDS Data Server. The retrospective is available in one NetCDF file. The operational model run archives are available in .tar.gz archives that contain daily Forecast Model Run Collection files. These data conform as much as possible to the NetCDF-CF Discrete Sampling Geometry conventions and are designed to be aggregated along the reference-time dimension allowing creation of a complete collection of forecast model runs with the THREDDS data server. The retrospective data are available from public cloud data outlets (Such as: https://registry.opendata.aws/nwm-archive/) and the operational outputs were retrieved from an archive maintained at the NOAA National Water Center in Tuscaloosa, AL. Note that some data were missing from this archive, on 5-8-2018 for all operational outputs and 7-17-2017 for long range outputs. The operational model runs include analysis and assimilation, short range, medium range, and an ensemble of four long range model runs. More information on these data are available from the National Water Model v1.2 release notes here. http://www.nws.noaa.gov/os/notification/scn18-16national_water_model.htm. The contents of that html page have been archived with this dataset. The retrospective model run is available from: https://docs.opendata.aws/noaa-nwm-pds/readme.html. The precise data included here should match that exactly but was sourced from NOAA-OWP systems at the National Water Center prior to availability of data from Amazon Public Datasets.
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These products are a subset of the ECMWF real-time forecast data and are made available to the public free of charge. They are based on the medium-range (high-resolution and ensemble) and seasonal forecast models. Products are available at 0.4 degrees resolution in GRIB2 format unless stated otherwise.
Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM for Europe at 30 m in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/). Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt The pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.
NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.
Terrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together with ancillary calibration data.
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This HydroShare resource contains Jupyter Notebooks with instructions and code for accessing and subsetting the NOAA Analysis of Record for Calibration (AORC) dataset. The resource includes two Jupyter Notebooks: 1. AORC_Point_Data_Retrieval.ipynb: Retrieves data for a specific point within the U.S. AORC coverage area, specified using geographic coordinates. 2. AORC_Zone_Data_Retrieval.ipynb: Retrieves data for an area defined by an uploaded polygon shapefile. These notebooks programmatically retrieve the data from Amazon Web Services (https://registry.opendata.aws/noaa-nws-aorc/) , aggregate data at user-defined time scales (which may differ from NOAA’s original time steps), and, in the case of shapefile-based data retrieval, compute the average over the shapes in the given shapefile. The provided notebooks are coded to retrieve data from AORC version 1.1 released in ZARR format in December 2023.
The Analysis Of Record for Calibration (AORC) is a gridded record of near-surface weather conditions covering the continental United States and Alaska and their hydrologically contributing areas (https://registry.opendata.aws/noaa-nws-aorc/). It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour. Elements include hourly total precipitation, temperature, specific humidity, terrain-level pressure, downward longwave and shortwave radiation, and west-east and south-north wind components. It spans the period from 1979 across the Continental U.S. (CONUS) and from 1981 across Alaska, to the near-present (at all locations). This suite of eight variables is sufficient to drive most land-surface and hydrologic models and is used as input to the National Water Model (NWM) retrospective simulation. While the original NOAA process generated AORC data in netCDF format, the data has been post-processed to create a cloud optimized Zarr formatted equivalent that NOAA also disseminates.
https://cdla.dev/permissive-1-0/https://cdla.dev/permissive-1-0/
Amazon-PQA is a product question-answer dataset. The Amazon-PQA dataset includes questions and their answers that are published on Amazon website, along with the public product information and category (Amazon Browse Node name). It contains more than 8M questions from 1M+ products.
This datasets contains the coverage of automatic Weather Stations (AWS). Eight AWS are strategically located in the northern parts of the country including Hargeysa, Borama, Aburin, Dacarbudhug, Xumbaweyne, Ceerigaabo, Garowe and Gaalckacyo . The nineth AWS is located in the south at the border of Kenya, Somalia and Ethiopia in Mandera town. It is hoped that when the situation allows more automatic weather stations will be installed in the southern regions. The stations record a variety of weather elements including; rainfall, temperature, relative humidity, atmospheric pressure, wind speed, wind direction and solar radiation. Data from these automatic stations is received in SWALIM Nairobi office daily in near-real-time through satellite at a frequency of every four hours. The data is then transmitted to the public though a client service platform on the SWALIM website.
This dynamic data release presents an aquatic reflectance product with 20-meter spatial resolution derived from Sentinel-2 satellite imagery for the conterminous United States using the Atmospheric Correction for OLI “lite” (ACOLITE). Aquatic reflectance, noted Rhow in ACOLITE documentation, is defined here as unitless water-leaving radiance reflectance and represents the ratio of water-leaving radiance (units of watts per square meter per steradian per nanometer) to downwelling irradiance (units of watts per square meter per nanometer) multiplied by π. This is also known as remote sensing reflectance (units of per steradian) multiplied by π. These data are intended for use in remote sensing of water color and differ from other satellite imagery products by using an atmospheric correction approach that is designed for aquatic applications. Level-1C top of atmosphere imagery collected with Multispectral Instrument (MSI) on the Sentinel-2A and Sentinel-2B satellites was atmospherically corrected using the dark spectrum fitting (DSF) algorithm and the Atmospheric Correction for OLI ‘lite’ (ACOLITE) version 20221114.0 processing software (https://github.com/acolite/acolite/releases/tag/20221114.0). The DSF atmospheric correction largely avoids glint and adjacency effects making the technique an improvement over other atmospheric correction for inland waters. Current default ACOLITE settings were used with the exception that; 1) output pixel size (S2_TARGET_RES) was set to 20 m, 2) the tiling dimensions (DSF_TILE_DIMENSIONS) was set to 300 pixels, 3) the non-water pixel mask (L2W_MASK THRESHOLD) was set to 0.05 and, 4) the Digital Elevation Model (DEM) to compute pressure (DEM_SOURCE) was set to the Shuttle Radar Topography Mission (SRTM). The optional residual glint correcting using the SWIR bands was not applied. Users are directed to the metadata file for a complete description of the atmospheric correction process. A Normalized Difference Water Index (NDWI) product is calculated as the difference of Band 3 (560 nm) and Band 8a (865 nm) divided by the sum of Band 3 and Band 8a, that is: NDWI = (Band 3 – Band 8a)/(Band 3 + Band a8a). The aquatic reflectance and NDWI products are stored as Cloud Optimized GeoTIFFs (COGs). Each Sentinel-2 scene includes cloud optimized geotiffs (COG) of aquatic reflectance raters for band 1, 2, 3, 4, 5, 6, 7, 8, 8a, 11, and 12, a computed Normalized Difference Water Index (NDWI) COG, and an ACOLITE settings file. The file names follow the same naming conventions as the Sentinel-2 products: MMM_MSIAQR_YYYYMMDDHHMMSS_Nxxyy_ROOO_Txxxxx_
https://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdfhttps://spacedata.copernicus.eu/documents/20126/0/CSCDA_ESA_Mission-specific+Annex.pdf
The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. GLO-90 provides worldwide coverage at 90 meters (refers to 30 rad seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters. The Copernicus DEM GLO-30 is mirrored from Open Data on AWS, dataset managed by Sinergise. https://registry.opendata.aws/copernicus-dem/. Naming convention: Copernicus_DSM_COG_[resolution]_[northing]_[easting]_DEM/ *[resolution] = resolution in arc seconds (not meters!), which is 10 for GLO-30, and 30 for GLO-90. * [northing] = e.g. S50_00 - decimal degrees where the decimal part is always 00. In original files, this is the northing of the center of the bottom-most pixels, while in our files, because we removed the bottom-most pixels, the center of the new bottom-most pixels is one pixel-length (resolution) away to the north. * [easting] = e.g. w125_00 - decimal degrees where the decimal part is always 00. The easting of of the center of the left-most pixels. For example: //copernicus-dem-90m/Copernicus_DSM_COG_30_S90_00_W178_00_DEM/
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Data to reproduce the experiments described in the paper: Image2Reg: Linking Chromatin Images to Gene Regulation using Genetic Perturbations Screens by Daniel Paysan, Adityanarayanan Radhakrishnan, G.V. Shivashankar and Caroline Uhler. The raw imaging data used in the study was obtained from IDR0033 available at http://idr-demo.openmicroscopy.org/webclient/?show=screen-1751 and cpg0016 available at https://registry.opendata.aws/cellpainting-gallery which need to be appropriately referenced if this dataset is used. Please refer to above mentioned paper for a complete list of the used data sets.
This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary
This report provides the status of each agency’s progress in meeting milestones within the Open Data Program. These include appointing a data coordinator; creating an agency open data inventory; completing an open data plan; and publishing open data.
An incomplete collection of open data domains throughout the U.S. (intended for comparison with King County open data)
The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.