Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
33
datasets available to search
ShareScore release 0.9.0
Dataset results
33 results for “level statistics”
Precipitation, water level and descriptive statistics
Open the record for dataset details and reuse information.
S-NPP CrIS IMG: Collocated VIIRS level 1 / cloud mask statistical summary V2 (SNDRSNCrISL1BIMG) at GES DISC
The Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution, after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution (FSR). The NSR files have 1,317 channels: 163 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 437 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.The Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint..
MERRA-2 statM_2d_slv_Nx: 2d,Monthly,Aggregated Statistics,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree V5.12.4 (M2SMNXSLV) at GES DISC
M2SMNXSLV (or statM_2d_slv_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of monthly mean of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period. The collection also includes the variance of parameters.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from SNPP CrIMSS and Aqua AIRS, V2 (SNDR13IML3SSDFCVPD)
The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Atmospheric Temperature from Aqua AIRS, V2 (SNDRAQIL3SSDFCNSAT)
This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
JPSS-1 CrIS IMG: Collocated VIIRS level 1 / cloud mask statistical summary V2 (SNDRJ1CrISL1BIMG) at GES DISC
The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also know as NOAA-20 (National Oceanic and Atmospheric Administration). The JPSS-1 mission with CrIS instrumentation is a follow-on to the Suomi National Polar-orbiting Partnership (SNPP) mission. The CrIS instrumentation and data processing system is nearly identical to that of the SNPP satellite. /The FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries.The Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Atmospheric Temperature from SNPP CrIMSS and Aqua AIRS, V2 (SNDR13IML3SSDFCNSAT)
This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from Aqua AIRS, V2 (SNDRAQIL3SSDFCVPD)
This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS).The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
MERRA-2 statD_2d_slv_Nx: 2d,Daily,Aggregated Statistics,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree V5.12.4 (M2SDNXSLV) at GES DISC
M2SDNXSLV (or statD_2d_slv_Nx) is a 2-dimensional daily data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
Descriptive Statistics and Town level Geospatial Distribution of Archaeological Settlements of Turkey in Iron Age (1200 –330 BCE)
<p><strong>Context</strong></p> <p>This dataset is a byproduct of my phd thesis. It combines the Archaeological Settlements of Turkey (TAY) Project data with geo spatial data obtained from openstreetmaps.</p> <p><strong>Content</strong></p> <p>For each archaeological settlement, the data contains:</p> <ul> <li>active dates:</li> <li>geo spatial data which points to the town containing the settlement.</li> <li>information with respect to site type and its research status/methodology.<br> These are all contained in the file <code>taydata.json</code>.</li> </ul> <p>The associated <a href="https://www.kaggle.com/dkaane/data-extraction-protocol-for-iaasot">notebook</a> to this dataset gives how each file is produced.</p> <p>We give several important statistics with respect to regions, and cities of Turkey for the Iron Age.</p> <p>If you want to visualize the data on a map. You can use the <code>1200___330_bce_sites_of_turkey.umap</code> file.<br> Just download the file and visualize it on <a href="https://umap.openstreetmap.fr/en/">umap</a> or on <a href="https://www.kaggle.com/dkaane/framacarte.org/">framacarte</a></p> <p><strong>Acknowledgements</strong></p> <p>Without the immense effort of TAY Project and its researchers, this dataset would not be possible.</p>
Statistical Association Between Bicarbonate Levels and Death or Admission to Intensive Care Within 48 Hours in Patients Presenting to the Emergency Department
ClinicalTrials.gov study NCT07340060. IPD Sharing: NO. Countries: 0. Publications: 0.
JPSS-2 CrIS IMG: Collocated VIIRS level 1 / cloud mask statistical summary V3.0 (SNDRJ2CrISL1BIMG) at GES DISC
The Cross-track Infrared Sounder (CrIS) Level 1B Full Spectral Resolution (FSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Joint Polar Satellite System-2 (JPSS-2) platform. This platform is also know as NOAA-21 (National Oceanic and Atmospheric Administration). The IMG product supplements the CrIS Level 1B (L1B) hyperspectral radiance product by providing collocated high-spatial resolution data from the Visible Infrared Imaging Radiometer Suite (VIIRS) imager located on the same platform. VIIRS radiance and cloud mask values are grouped and aggregated for every CrIS field of view (FOV) and made available in a format intended for use alongside the CrIS L1B data. The collocated VIIRS level 1 / cloud mask statistical summary is the main product and consists of collocated CrIS field of views with the VIIRS cloud mask and radiances/reflectances. This can be thought of as a match-up between CrIS and VIIRS. The supplementary product, array indices for collocated VIIRS observations (collection name SNDRJ2CrISL1BIMGC), consists of array indices for collocated VIIRS observations and provides the collocated indices of the VIIRS pixels within each CrIS footprint. The FSR files have 2,223 channels (*2211 apodized channels): 637 (*633) shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 869 (*865) midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 (*713)longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries. The Visible Infrared Imaging Radiometer Suite (VIIRS) has 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns. It provides the sensor data records for clouds, sea surface temperature, ocean color, and others. This IMG product primarily contains statistics of the VIIRS cloud mask and VIIRS L1B data within each CrIS footprint.
ACE Solar Wind Ion Composition Spectrometer (SWICS) Solar Wind Plasma Elemental Charge State Distributions with Statistical Uncertainties and Data Quality Flags, Level 2 (L2), 2 h Data
This ACE SWICS/SWIMS Data Set contains actual Charge State, Q, Distributions of Carbon, Oxygen, Neon, Magnesium, Silicon and Iron as opposed to the averaged Charge States that have been available on CDAWeb for some time. The Solar Wind Ion Composition Spectrometer (SWICS), prior to August 23, 2011 and denoted as SWICS Version 1.1, determines uniquely the Chemical and Ionic Charge State Composition of the Solar Wind, the Temperatures and Mean Speeds of major Solar Wind Ions at all Speeds above 300 km/s for Protons and 170 km/s for Fe+16, and resolves Protons and Helium Isotopes of Solar and Interstellar Sources. SWICS 1.1 measures the Distribution Functions of Interstellar Cloud Pick-Up Ions and Interplanetary Dust Cloud Pick-Up Ions up to Energies of 100 keV/e. The ACE SWICS 1.1 Data Products represent a new Release of the Data with significantly improved Time Series Measurements for the Elemental Abundance, Charge State Composition, and Kinetic Properties of Heavy Ions in the Solar Wind. It is a major new Update produced with completely redesigned Analysis Methods to account more rigorously for Instrumental and Statistical Effects (Shearer et al., 2014). Rare Elements are now identified more reliably and Estimates of Statistical Error are provided. Release Notes are available that describe the Data, the Methods used to determine the Data Values, and Issues concerning Data Quality and Measurement Uncertainty. The Quality of ACE Level 2 Data is such that it is suitable for serious Scientific Study. However, to avoid Confusion and Misunderstanding, it is recommended that Users consult with the appropriate ACE Team Members before publishing Work derived from the Data. The ACE Team has worked hard to ensure that the Level 2 Data are free from Errors, but the Team cannot accept Responsibility for Erroneous Data, or for Misunderstandings about how the Data may be used. This is especially true if the appropriate ACE Team Members are not consulted before Publication. At the very least, Preprints should be forwarded to the ACE Team before Publication. For more Information about the SWICS Instrument, visit the SWICS Home Page at http://solar-heliospheric.engin.umich.edu/ace.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.