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403 results for “satellite data”
Data for: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data
<p><span>1. Quantifying tree defoliation by insects over large areas is a major challenge in forest management, but it is essential in ecosystem assessments of disturbance and resistance against herbivory. However, the trajectory from leaf-flush to insect defoliation to refoliation in broadleaf trees is highly variable. Its tracking requires high temporal- and spatial-resolution data, particularly in fragmented forests. </span></p> <p><span>2. In a unique replicated field experiment manipulating gypsy moth <i>Lymantria dispar</i> densities in mixed-oak forests, we examined the utility of publicly accessible satellite-borne radar (Sentinel-1) to track the fine-scale temporal trajectory of defoliation. The ratio of backscatter intensity between two polarizations from radar data of the growing season constituted a canopy development index (CDI) and a normalized CDI (NCDI), which were validated by optical (Sentinel-2) and terrestrial laser scanning (TLS) data as well by intensive caterpillar sampling from canopy fogging. </span></p> <p><span>3. The CDI and NCDI strongly correlated with optical and TLS data (Spearman's ρ=0.79 and 0.84, respectively). The ∆NCDI<sub><sub>Defoliation</sub><sub> (</sub><sub>A</sub><sub>-</sub><sub>C</sub><sub>)<i> </i></sub></sub>significantly explained caterpillar abundance (R<sup>2</sup>=0.52). The NCDI at critical time-steps and ΔNCDI related to defoliation and refoliation well discriminated between heavily and lightly defoliated forests. </span></p> <p><span>4. We demonstrate that the high spatial and temporal resolution and the cloud independence of Sentinel-1 radar potentially enable spatially unrestricted measurements of the highly dynamic canopy herbivory. This can help monitor insect pests, improve the prediction of outbreaks, and facilitate the monitoring of forest disturbance, one of the high priority Essential Biodiversity Variables, in the near future.</span></p>
2021 UN Open GIS Challenge 1 - Training on Satellite Data Analysis and Machine Learning with QGIS (Satellite_QGIS)
<p>This dataset is part of the <a href="https://www.osgeo.org/foundation-news/2021-osgeo-un-committee-educational-challenge/?fbclid=IwAR0UvwkPO2pay7C0tJawb63eewjBGfeL9TIQpYUFccza9OIo6HAolmHXLWE">2021 UN Open GIS Challenge 1 - Training on Satellite Data Analysis and Machine Learning with QGIS (Satellite_QGIS)</a>,</p> <p>Exercise 1: Supervised Change Detection: Monitoring deglaciation in Huascaran, Peru.</p>
Satellite Sensor Relative Spectral Response data
<p>Satellite Spectral Response functions for a number of imaging sensors. Currently supports:</p> <ul> <li>Himawari-8 AHI</li> <li>Himawari-9 AHI</li> <li>GOES-16 ABI</li> <li>GOES-17 ABI</li> <li>GOES-18 ABI</li> <li>GOES-19 ABI</li> <li>NOAA AVHRR/1, AVHRR/2, AVHRR/3 (NOAA-6 errors in ch1 & 2 fixed)</li> <li>Metop AVHRR/3</li> <li>TIROS-N AVHRR/1</li> <li>Envisat AATSR</li> <li>Sentinel-3A, B, C & D SLSTR</li> <li>Sentinel-3A&B OLCI - mean rsr</li> <li>Meteosat SEVIRI</li> <li>Terra/Aqua MODIS</li> <li>Suomi-NPP VIIRS</li> <li>JPSS-1 (NOAA-20) VIIRS</li> <li>JPSS-2 (NOAA-21) VIIRS</li> <li>Sentinel-2A MSI</li> <li>Sentinel-2B MSI</li> <li>Landsat-8 OLI & TIRS</li> <li>Landsat-9 OLI & TIRS</li> <li>HY-1C COCTS</li> <li>Metop-SG-A1 MetImage - NB! simulated data - not measured!</li> <li>Sentinel-3B OLCI - mean rsr</li> <li>FY-3D MERSI-2</li> <li>FY-3F MERSI-3</li> <li>FY-4A AGRI</li> <li>FY-4B AGRI</li> <li>FY-3B VIRR</li> <li>FY-3C VIRR</li> <li>GEO-KOMPSAT-2A AMI</li> <li>Meteosat-12 FCI (version from EUMETSAT, April 2022)</li> <li>MTG-I1 FCI (same as above)</li> <li>Electro-L N2 MSU-GS</li> <li>Arctica-M 1 MSU-GS/A</li> <li>DSCOVR EPIC</li> <li>Sentinel-C MSI</li> </ul> <p> </p> <p>Changelog for the Relative Spectral Response data<br>=================================================</p> <p>Version v1.5.0 (Tue Sep 30 09:53:59 PM CEST 2025)</p> <p>-------------------------------------------</p> <p>* Added SLSTR RSRs for Sentinel-3C and -D. Updated with the latest versions from ESA for Sentinel-3A and -B.</p> <p> </p> <p>Version v1.4.1 (Tue Oct 29 03:45:40 PM CET 2024)</p> <p>-------------------------------------------</p> <p> * Added RSRs for Landsat-8 and -9 OLI & TIRS. Original RSR as provided by NASA. Previously we had only Landsat-8 OLI</p> <p> </p> <p>Version v1.4.0 (Tue Sep 24 03:35:15 PM CEST 2024)</p> <p>-------------------------------------------</p> <p> * Added RSRs for Sentinel-2C MSI and updated the MSI RSR for Sentinel 2A & B</p> <p> </p> <p>Version v1.3.2 (Mon Jul 15 12:32:12 PM CEST 2024)</p> <p>-------------------------------------------</p> <p> * Corrected the file name for the RSRs of Mersi-3 onboard FY-3F</p> <p> </p> <p>Version v1.3.1 (Mon Jul 15 11:12:10 AM CEST 2024)<br>-------------------------------------------</p> <p> * Added SRFs (RSRs) for the Mersi-3 sensor onboard FY-3F</p> <p> </p> <p>Version v1.3.0 (Fri May 3 04:11:43 PM CEST 2024)<br>-------------------------------------------</p> <p> * Added SRFs (RSRs) for the Mersi-1 sensor onboard FY-3A/B/C<br> * Added SRFs for the Mersi-RM sensor onboard FY-3G<br> * Added SRFs for the GHI (Geostationary High-speed Imager) sensor onboard FY-4B<br> * Added SRFs for GOCI-II (Geostationary Ocean Color Imager: Follow-on) sensor onboard GK-2B (GEO-KOMPSAT-2B)</p> <p> </p> <p>Version v1.2.4 (Sat Oct 21 12:25:00 PM CEST 2023)</p> <p>-------------------------------------------</p> <p>* Normalized RSR responses for the EPIC sensor. Now values are scaled to be</p> <p> between 0 and 1.</p> <p> </p> <p>Version v1.2.3 (Fri Oct 20 01:58:29 2023)</p> <p>-------------------------------------------</p> <p>* Added RSR file for EPIC on DSCOVR (responses not normalized)</p> <p> </p> <p>Version v1.2.2 (Tue Nov 15 14:44:03 2022)<br>-------------------------------------------</p> <p> * Added RSR file for AGRI onboard FY-4B<br> * Corrected RSR file for AGRI onboard FY-3B<br> Values were between 0 and 100, now scaled to between 0 and 1</p> <p> </p> <p>Version v1.2.1 (Mon Oct 24 16:26:14 2022)<br>-------------------------------------------</p> <p> * Changed name of the FY-3D MERSI-2 RSR file, removing the hyphen:<br> New name = rsr_mersi2_FY-3D.h5<br> </p> <p>Version v1.2.0 (Tue Sep 27 21:08:13 2022)<br>-------------------------------------------</p> <p> * Added VIIRS RSR for JPSS-2/NOAA-21:<br> Based on the J2_VIIRS_RSR_DAWG_At-Launch_Public_Release_V2_Jul2019.zip<br> The data read are the Detector wise files under J2_VIIRS_Detector_RSR_V2:</p> <p> J2_VIIRS_RSR_DNBLGS_Detector_Fused_V2FS.txt<br> J2_VIIRS_RSR_I1_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I2_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I3_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_I4_Detector_V2F.txt<br> J2_VIIRS_RSR_I5_Detector_V2F.txt<br> J2_VIIRS_RSR_M10_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M11_Detector_V2F.txt<br> J2_VIIRS_RSR_M12_Detector_V2F.txt<br> J2_VIIRS_RSR_M13_Detector_V2F.txt<br> J2_VIIRS_RSR_M14_Detector_V2F.txt<br> J2_VIIRS_RSR_M15_Detector_V2F.txt<br> J2_VIIRS_RSR_M16A_Detector_V2F.txt<br> J2_VIIRS_RSR_M1_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M2_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M3_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M4_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M5_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M6_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M7_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M8_Detector_Fused_V2F.txt<br> J2_VIIRS_RSR_M9_Detector_WVCOR_Fused_V2F.txt</p> <p> The two files here were not considered:<br> J2_VIIRS_RSR_DNBMGS_Detector_Fused_V2FS.txt<br> J2_VIIRS_RSR_M16B_Detector_V2F.txt</p> <p> * Correted errors concerning NOAA-6 channel 1 and 2. These proved to be wrong<br> (a factor of 10 scale error), as the files they were based on were<br> wrong. For AVHRR-1 we have been using the ascii files from NOAA STAR<br> (https://www.star.nesdis.noaa.gov/smcd/spb/fwu/homepage/AVHRR/spec_resp_func/index.html). Example<br> of the start of the file for NOAA-6 channel-1:</p> <p> %> cat NOAA_6_A103C001.txt<br> Wavelegth (nm) Normalized RSF<br> 5600.000000 0.071000<br> 5700.000000 0.449000<br> 5800.000000 0.739000<br> 5900.000000 0.813000<br> 6000.000000 0.806000<br> 6200.000000 0.919000<br> 6400.000000 1.000000<br> ...</p> <p> Here we have instead used the xls files on which the NOAA-STAR files are based: AVHRR1_SRF_only.xls</p> <p> For TIROS-N there seem to be a typo in the wavelengths array for channel-1<br> on TIROS-N: A 640 nm should most likely have been 840 nm. This has been<br> corrected in the hdf5 file.<br> </p>
Los Alamos National Laboratory (LANL) and Geostationary Operational Environmental Satellite (GOES)–R Geosynchronous Particle Data for Ferradas, C. P., et al. (2023)
<p>This repository contains data from a set of charged particle analyzers onboard the Los Alamos National Laboratory (LANL) and the Geostationary Operational Environmental Satellite (GOES)–R geosynchronous orbit satellites. The data are used in the following open access publication submitted to Frontiers in Astronomy and Space Sciences.</p> <p>Ferradas, C. P., M.-C. Fok, N. Maruyama, M. G. Henderson, S. Califf, S. A. Thaller, and B. T. Kress (2022), <strong>The effects of particle injections on the ring current development during the 7-8 September 2017 geomagnetic storm</strong>, <em>Frontiers in Astronomy and Space Sciences</em>, <em>submitted.</em></p>
Drone survey, time-lapse camera, and satellite SAR data covering the 2021 summit craters and late fractures at Tajogaite volcano, Cumbre Vieja, La Palma
<p>A new eruption started on 19 September 2021 at the Tajogaite volcano, which is at the western flank of the Cumbre Vieja, just 1•5km to the north of the vents of the 1949 eruption, and terminated after 85 days on 13 December 2021. The location of the 2021 eruption at the Cumbre Vieja was not foreseen, although a diffuse unrest was identified years before already. At the location of the eruption, the slope of the edifice was gentle, but a number of older vents, mostly open to the west, were evident. Here we present field and satellite data showing (a) the development of the craters at the summit of the evolving Tajogaite volcano, and (b) the formation of a pronounced structural trend interpreted to be related to tensile faulting during the late stage of the eruption.</p> <p>Data contains:</p> <ol> <li>Drone data acquired by DJI drones (Phantom RTK and Mavic2) during two periods showing the summit craters and the tensile fracture set in detail.</li> <li>Time-lapse camera records from the east and the north-northeast showing eruption and morphology changes.</li> <li>Satellite radar amplitude data acquired in three different geometries (2 ascending and 1 descending track) of the Cosmo Skymed Satellite constellation</li> </ol> <p>Use data without restriction but cite our work; for details on acquisition geometries and maps refer to the papers published by:</p> <ul> <li>Walter, T.R.; Zorn, E.Z.; Gonzalez, P.J.; Sansosti, E.; Munoz, V.; Shevchenko, A.V.; Plank, S.; Reale, D.; Richter, N. (in press) Late complex tensile fracturing interacts with topography at Cumbre Vieja, La Palma, VOLCANICA 5(2): 300–316. https://doi.org/10.30909/vol.05.02.300</li> <li>Muñoz, V.; Walter, T.R.; Zorn, E.U.; Shevchenko, A.V.; González, P.J.; Reale, D.; Sansosti, E. Satellite Radar and Camera Time Series Reveal Transition from Aligned to Distributed Crater Arrangement during the 2021 Eruption of Cumbre Vieja, La Palma (Spain). Remote Sens. 2022, 14, 6168. https://doi.org/10.3390/rs14236168</li> </ul> <p> </p>
Tables and Data for "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado"
<p>These are data sets and tables used in the paper "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado" to be submitted to Earth and Space Science. The monitor site 2016 and 2017 counts files have gridded HYSPLIT back trajectory counts for the 4 highest ozone concentration days at each site, as described in the manuscript.</p>
Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data."
<p>Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data." Additional information can be found at https://github.com/amtseismo/hrgnss_denoising.</p>
Plant functional traits and corresponding satellite multispectral data for two sites in the Czech Republic
<p>This data set contains measurements of plant functional traits and corresponding satellite multispectral data for two sites in the Czech Republic – Lanžhot and Štítná throughout multiple campaigns. Plant functional traits were collected from representative trees selected at both study sites. At the Lanžhot site, a total of 16 trees of the following species were sampled: Quercus robur (4 trees), Quercus cerris L. (2 trees), Carpinus betulus (4 trees), Fraxinus angustifolia (4 trees), Tilia cordata (2 trees). At Štítná, a total of 10 Fagus sylvatica trees were sampled. Field campaigns were timed to cover different phenological stages of deciduous tree leaf development, from fresh leaf emergence, through full leaf development at the peak of the growing season, to autumn leaf senescence. In total, six field campaigns at Lanžhot during 2019 and 2020 and five field campaigns at Štítná during 2020 and 2021 were conducted.</p>
Dataset of AI4ER MRes titled "Improving Urban Tree Management Using High-Resolution Satellite Data"
<p>This repository contains the data used in the Master's thesis titled "Improving Urban Tree Management Using High-Resolution Satellite Data" by Andrés C. Zúñiga-González as part of the AI4ER MRes 1st year project at the University of Cambridge.</p> <p>The folders are split into large and small training and testing datasets. These folders contain the tiles (in png and tif formats) used in the models. In addition, it includes the crowns in ESRI Shapefile format for the training and testing datasets. Finally, it contains the best model from the project (named urban_trees_Cambridge_20230630.pth).</p>
Satellite derived floodplains (GFD ) combined with spatially- explicit socio-economic data and DFO impacts
<p>These data is linked to the paper </p> <p><strong>Limited progress in global reduction of vulnerability to flood impacts over the past two decades</strong></p> <p><strong>Inga J. Sauer¹ ², Benedikt Mester¹ ³, Katja Frieler¹, Sandra Zimmermann1, Jacob Schewe¹, and Christian Otto¹</strong></p> <p><strong>¹ Potsdam Institute for Climate Impact Research, Potsdam, Germany </strong></p> <p><strong>² Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland</strong></p> <p><strong>³ Institute of Environmental Science and Geography, Potsdam University, Potsdam, Germany</strong></p> <p><span>DOI: 10.1038/s43247-024-01401-y</span></p> <p>It combines the satellite observed flooded areas from the <strong>global flood database (GFD) </strong>with socio-economic and infrastructure data and the socio-economic impacts recorded in the <strong>Global Active Archive of Large Flood Events provided by the Dartmouth Flood Observatory (DFO).</strong></p>
Raw FITS and AAV data of the Bluewalker 3 LEO communication satellite.
<p>Separate tar/zip files of raw images from each telescope used in the l2022 observing campaign of the AST SpaceMobile prototype Bluewalker 3 LEO communication satellite, led and organised by the International Astronomical Union (IAU) <a href="https://cps.iau.org/">Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference</a> (CPS). The images along with their associated calibration frames were used in the publication (Nandakumar+ <a href="https://doi.org/10.21203/rs.3.rs-2557594/v1">2023</a>) and are freely available to the community. The reduced data (e.g. apparent magnitude, TLE accuracy, and phase angles) are provided in (Nandakumar+ <a href="https://doi.org/10.21203/rs.3.rs-2557594/v1">2023</a>) as supplementary machine readable data tables.</p>
Data set for manuscript 'Quantifying geomorphically effective floods using satellite observations of river mobility'
<p>Data underlying the plots / used in the modelling work for the paper 'Quantifying geomorphically effective floods using satellite observations of river mobility', submitted to <em>Geophysical Review Letters.</em></p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
Open the record for dataset details and reuse information.
Data from: Satellite tracking of American Woodcock reveals a gradient of migration strategies
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Data from: Mapping coastal redwoods (<em>Sequoia sempervirens</em>) across their natural range: An updateable and field-validated distribution map using Sentinel satellite data and cloud computing
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Data for: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data
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Data from: Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes
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Satellite Data for Corn and Soybean Fields + Other Land Cover: Illinois, US, 2017-2019
<p>These are the datasets associated with the paper:<br> Hannah Kerner, Ritvik Sahajpal, Sergii Skakun, Inbal Becker-Reshef, Brian Barker, Mehdi Hosseini, Estefania Puricelli, and Patrick Gray. 2020. Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization. In <em>KDD ’20: ACM Special Interest Group (SIG) on Knowledge Discovery and Data Mining Conference Workshops</em>, August 23–27, 2020, San Diego, CA. </p> <p>The code that uses these datasets can be found at: <a href="https://github.com/nasaharvest/croptype-mapping-gsn/tree">https://github.com/nasaharvest/croptype-mapping-gsn</a></p>
Data accompanying "Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites"
<p>This dataset contains the spatial data underlying the statistical analysis in:</p> <p><em>Assmann et al. (in press) - Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites</em></p> <p>Together with the code and tabular data contained in <a href="https://github.com/jakobjassmann/qhi_phen_ts/">https://github.com/jakobjassmann/qhi_phen_ts/</a> the data in this repository are required to reproduce the figures, tables and statistics reported in the manuscript.</p> <p>This dataset consists of two components:</p> <ol> <li> <p>Multispectral drone observations from the growing seasons 2016 and 2017 for 8 study plots on Qikiqtaruk - Herschel Island in Canada collected with Parrot Sequioa sensors (62 sets of multispectral orhomosaics in total).</p> </li> <li> <p>Post-porcessed Sentinel-2 MSI L2A scenes covering the same 8 study plots, including all scenes for which the area of the plots and their immediate surroundings were cloud free between May and September in 2016 and 2017.</p> </li> </ol> <p>-----------------------------------------------------------------------------------------------------------------</p> <p><strong>Citation</strong>: Jakob J. Assmann, Isla H. Myers-Smith, Jeffrey T. Kerby, Andrew M. Cunliffe and Gergana N. Daskalova.<em> <strong>In press</strong>.</em> Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites. <a href="https://doi.org/10.32942/osf.io/tqekn">https://doi.org/10.32942/osf.io/tqekn</a></p> <p><strong>Legal notice:</strong> This dataset contains modified Copernicus Sentinel [2016, 2017] data.</p> <p><strong>Acknowledgements (from the manuscript):</strong></p> <p>We would like to thank the Team Shrub field crews of the 2016 and 2017 field seasons for their hard work and effort invested in collecting the data presented in this research, this includes Will Palmer, Santeri Lehtonen, Callum Tyler, Sandra Angers-Blondin and Haydn Thomas. Furthermore, we would like to thank Tom Wade and Simon Gibson-Poole from the University of Edinburgh Airborne GeoSciences Facility, as well as Chris McLellan and Andrew Gray from the NERC Field Spectroscopy Facility for their support in our drone endeavours. We also want to express our gratitude to Ally Phillimore, Ed Midchard, Toke Høye and two anonymous reviewers for providing feedback on earlier versions of this manuscript. Lastly, JJA would like to thank IMS, Ally Phillimore and Richard Ennos for academic mentorship throughout his PhD.</p> <p>We thank the Herschel Island—Qikiqtaruk Territorial Park Team and Yukon Government for providing logistical support for our field research on Qikiqtaruk including: Richard Gordon, Cameron Eckert and the park rangers Edward McLeod, Sam McLeod, Ricky Joe, Paden Lennie and Shane Goosen. We thank the research group of Hugues Lantuit at the Alfred Wegener Institute and the Aurora Research Institute for logistical support. Research permits include Yukon Researcher and Explorer permits (16-48S&E and 17-42S&E) and Yukon Parks Research permits (RE-Inu-02-16 and 17-RE-HI-02). All airborne activities were licensed under the Transport Canada special flight operations certificates ATS 16-17-00008441 RDIMS 11956834 (2016) and ATS 16-17-00072213 RDIMS 12929481 (2017).</p> <p>Funding for this research was provided by NERC through the ShrubTundra standard grant (NE/M016323/1), a NERC E3 Doctoral Training Partnership PhD studentship for Jakob Assmann (NE/L002558/1), a research grant from the National Geographic Society (CP-061R-17), a Parrot Climate Innovation Grant, the Aarhus University Research Foundation, and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (754513) for Jeffrey Kerby, a NERC support case for use of the NERC Field Spectroscopy Facility (738.1115), equipment loans from the University of Edinburgh Airborne GeoSciences Facility and the NERC Geophysical Equipment Facility (GEF 1063 and 1069).</p> <p>Finally, we would like to thank the Inuvialuit people for the opportunity to conduct research in the Inuvialuit Settlement Region.</p>
Data supplementing the article: Schultz, N.M., Lawrence P.J, Lee X., Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. Journal of Geophysical Research - Biogeosciences
<p>These data supplement the article: Schultz, N.M., Lawrence P.J, Lee X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation, under review at the Journal of Geophysical Research - Biogeosciences.</p> <p>contact: Natalie M. Schultz, natalie.schultz@yale.edu</p> <p>Below are descriptions of the data files included here:</p> <p><br> (1) Global LST data: globalLST_Forest_Open.YYYY.nc [2003-2013]</p> <p>- DayLST1/NightLST1 and DayLST2/NightLST2 are the final (after the DEM correction) LST values for forest, and open land cover classes, respectively.<br> - Count variables show the number of pixels of each land cover class in each 0.5 degree grid<br> - The average elevation of each class is given by the DEM1 and DEM2 vars<br> - The DEM correction is the dLSTdDEM vars</p> <p>(2) Global fluxes data: globalFluxes_Forest_Open.YYYY.nc [2003-2013]<br> - Again, forest class = var1, open class = var2<br> - SWRABS is absorbed solar radiation<br> - LE is the latent heat flux<br> - HP is the heating potential term, as defined in the manuscript<br> - As described for the LST data, class pixel counts and DEM data are included</p> <p>(3) climzones3.nc<br> - The delineation of the three climate zones defined in this paper</p> <p>(4) MERRA inversion data: MERRA_11yr_TS_T10M.mat [2003-2013]<br> - 11 years of daily 1am local data averaged over 8-day intervals for 2003-2013<br> - TS = surface temperature<br> - T10M = 10M air temperature (above d)</p> <p> </p> <p> </p>
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.