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.
79
datasets available to search
ShareScore release 0.9.0
Dataset results
79 results for “CCI”
Global GFED-based monthly burned area time series (1996-2016) at 1 km and ESA CCI MODIS-based long-term monthly P90 burned area occurrence at 500 m
<p>Contains two separate datasets:</p> <ol> <li>Global <a href="https://www.globalfiredata.org/data.html">GFED-based monthly burned area</a> (in ha) <a href="https://youtu.be/kBJcP8mL2Qs">time series (1996-2016)</a> at 1 km (downscaled using cubic-splines from 25 km);</li> <li>Global burned area long term (2000-2012) P90 (quantile probability = 0.9) based on the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI burned area accumulated weekly product</a>;</li> </ol> <p>Original GFED monthly data is provided as HDF4 files (ftp.fuoco.geog.umd.edu/data/GFED/GFED4). Dataset is described in detail in <a href="https://doi.org/10.1002/jgrg.20042">Giglio et al. (2013)</a>. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/GFED"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a> or watch <a href="https://youtu.be/kBJcP8mL2Qs"><strong>this video</strong></a>.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>nhz = theme: natural hazards,</li> <li>monthly.burned.ha = variable: estimated monthly burned area in ha,</li> <li>gfed = data source GFED data,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000.02 = time reference aggregated: month Feb of year 2000,</li> <li>v4 = version number: GFEDv4,</li> </ul>
SM2RAIN-CCI (1 Jan 1998 – 31 December 2015) global daily rainfall dataset
<p>A NEW GLOBAL SCALE RAINFALL PRODUCT obtained from satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014</em>), at 0.25 degree/daily spatial-temporal resolution, has been delivered (Ciabatta et al., 2018). The SM2RAIN method was applied to the ESA CCI soil moisture Active and Passive products (<em>Liu et al., 2011, 2012; Wagner et al., 2012</em>) for the period from January 1998 to December 2015 (18 years).</p> <p>The CCI-derived rainfall datasets (in mm/day) is gridded over a 0.25-degree grid on a global scale. The number of dates is 6574 (1998/01/01 – 2015/12/31). The product represents the cumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. A climatological correction has been applied to the data at monthly scale.</p> <p>The rainfall dataset is provided in netCDF format. A total of 18 netCDF files, one per year, are provided.</p> <p>The rainfall dataset is obtained by applying the SM2RAIN algorithm to the ESA CCI soil moisture Active and Passive products at version 03.1 separately. Then, an integration procedure based on a weighted average is applied in order to obtain the rainfall estimate. The algorithm has been calibrated during three different periods (1998-2001, 2002-2006 and 2007-2013) against the Global Precipitation Climatology Centre Full-Data daily dataset (GPCC-FDD, Schamm et al., 2015). The quality flag provided within the raw soil moisture observations has been used to mask out low quality data, as well as the areas characterized by high topographic complexity, high frozen soil and snow probability and presence of tropical forests.</p> <p><strong>References</strong></p> <p>Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. <em>Journal of Geophysical Research</em>, 119(9), 5128-5141, doi:10.1002/2014JD021489.</p> <p>Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst. Sci. Data, 10, 267-280, https://doi.org/10.5194/essd-10-267-2018, 2018.</p> <p>Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436, doi:10.5194/hess-15-425-2011.</p> <p>Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M., Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014.</p> <p>Schamm, K., Ziese, M., Raykova, K., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U. (2015). GPCC Full Data Daily Version 1.0 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. DOI: 10.5676/DWD_GPCC/FD_D_V1_100.</p> <p>Wagner, W., Dorigo, W., de Jeu, R., Fernandez, D., Benveniste, J., Haas, E., Ertl, M. (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321.</p>
Marine heatwaves and cold spells events based on ESA-CCI SSTs (experimental product)
<p>This repository contains an extension of the catalogues of marine heatwaves (MHWs) and cold spells (MCSs) prepared by the National Research Council - Institute of Marine Sciences (CNR-ISMAR, Italy) within the ESA-funded CAREHeat project. The catalogues are based on the ESA-CCI sea surface temperature (SST) dataset (available from https://doi.org/10.24381/cds.cf608234) for the period 1982-2022, on a regular 1°x1° longitude-latitude grid.</p> <p>Events are identified for each pixel following the methodology of Hobday et al. (2016) after preprocessing. Event categories are provided as daily maps and metrics are given by event. Results are <strong>experimental</strong> since the post-processing procedure effectively removes interannual variability from the SST record, so please use having consulted the documentation and not for operational purposes. </p> <p><br>Please cite the reference paper "Serva, F., et al.: Detection of Satellite Sea Surface Temperature Extremes: Low Frequency Variability and Climate Change, JGR:Oceans, 10.1029/2025JC022886, 2025" when using the dataset in your work.</p>
Validation of ESA CCI SM combined v04.7 vs ESA CCI SM combined v05.2 vs ISMN 20191211 global
QA4SM validation of soil moisture data: ESA CCI SM combined v04.7 vs ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/ccfcee99-1d2a-4ee6-9919-8a3d87601d0b/. Produced on QA4SM (https://qa4sm.eu)
Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - Anomalies and no ISMN flags
QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/5f6ae4c5-5115-4022-b489-99f4dda1089f/. Produced on QA4SM (https://qa4sm.eu)
Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - Anomalies and ISMN flagged
QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/d8b5f409-2bb3-4580-be0b-0c9c2d71c968/. Produced on QA4SM (https://qa4sm.eu)
Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - without Anomalies and ISMN flagged
QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/73ed1e31-eaa5-469a-ab9b-4451e4e4d4df/. Produced on QA4SM (https://qa4sm.eu)
Validation of ESA CCI SM combined v05.2 vs ISMN 20191211 global - without anomalies and without ISMN flags
QA4SM validation of soil moisture data: ESA CCI SM combined v05.2 vs ISMN 20191211 global. URL: https://qa4sm.eu/result/bbf7f693-74ff-4b03-8f5d-792b7b846b40/. Produced on QA4SM (https://qa4sm.eu)
Derivation of plant functional type (PFT) maps from the ESA CCI Land Cover product
<p><em>This package supplements the following paper submitted to ESSD: <strong>Gross and net land cover changes of the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015).</strong></em></p> <p><em>Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A. and Peng, S.: Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps, Earth Syst. Sci. Data Discuss., 1–23, doi:10.5194/essd-2017-74, 2017.</em></p> <p><em>This package contains the protocol of converting the original annual ESA CCI Land Cover product into plant functional types (PFTs) that can be used by land surface models and the corresponding cross-walking table.</em></p> <p><em>The original ESA LC class data and translated PFTs in 2000 as an example are attached in the .zip file. The annual ESA CCI PFT maps from 1992 to 2015 at half degree resolution are also added in a .zip file.</em></p>
Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI
<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>
SST CCI Auxiliary Datasets
<p>These auxiliary datasets are used in the generation of SST CCI ATSR and AVHRR products. Each set of files is contained in a separate tar archive and their application is briefly described below. For more information on the SST CCI algorithms please refer to the publicly available Algorithm Theoretical Basis Document.</p> <p><strong>Retrieval coefficients:</strong></p> <p>ATSR_Retrieval_Coeffs.tar</p> <p>These files contain the retrieval coefficients used to retrieve SST for the ATSR instruments. These are banded by total column water vapour and viewing angle. Coefficients are provided for all applicable retrievals - nadir two channel (n2), nadir three channel (n3), dual-view two channel (d2) and dual-view three channel (d3).</p> <p><strong>Spectral response functions:</strong></p> <p>AATSR_SRF.tar, ATSR2_SRF.tar, ATSR1_SRF.tar, AVHRR_SRF.tar</p> <p>These files contain the spectral response functions for each channel of each ATSR and AVHRR instrument. Note that all the AVHRR instruments are bundled in a single tar file including NOAA6 - NOAA19 and MetopA. All files are in plain text format containing two columns, the first is the wavelength and the second the filter response at that wavelength.</p> <p><strong>Probability Density Functions:</strong></p> <p>ATSR_PDFs.tar, AVHRR_PDFs.tar</p> <p>These are netCDF format files which include spectral and textural PDFs for cloudy conditions and textural PDFs only for clear-sky conditions. Dimensions are defined also as variables which give metadata describing their bounds and bin widths. The PDFs themselves are multi-dimensional variables constrained by the definitions of their given dimensions.</p> <p><strong>Sea Surface Emissivity:</strong></p> <p>ATSR_sse.tar, AVHRR_sse.tar</p> <p>Within SST CCI, the sea surface emissivity is calculated offline, and then passed to RTTOV for use within the radiative transfer calculation (by-passing the internal RTTOV surface emissivity model). These files are in ascii format and constructed in such a way as to be readable by RTTOV.</p> <p><strong>Level 4 Land/Sea Mask</strong></p> <p>mask_ostia20Tnolakes.nc</p> <p>This is a netCDF format file containing the land/sea mask used when generating Level 4 data on the OSTIA grid.</p> <p> </p>
Validation of ESA CCI SM active v06.1 vs ISMN 20210131 global
QA4SM validation of soil moisture data: ESA CCI SM active v06.1 vs ISMN 20210131 global. URL: https://qa4sm.eu/result/4fc5718a-db09-4748-953d-341f5127485e/. Produced on QA4SM (https://qa4sm.eu)
Mediterranean Marine Heatwaves (MHWs) as detected from ESA CCI SST 0.05°x0.05° covering 1982-2021
<p>Daily records of Mediterranean Marine Heatwaves (MHW) resulting from detection applied to the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature (SST) satellite product on a regular 0.05°x0.05° grid, covering the period 01/01/1982-31/12/2021.</p> <p>The MHW detection has been carried out via Hobday's method (Hobday et al. 2016).</p> <p>The <strong>mhw_original</strong> field provides the intensity of anomaly [°C] of MHW events detected on the original SST data, while the <strong>mhw_detrended</strong> field describes the intensity of anomaly [°C] of MHW events detected on detrended SST data, via X-11 seasonal adjustment procedure. </p> <p>The production of this dataset has been sustained with the support of the European Space Agency (ESA) "deteCtion and threAts of maRinE Heat waves" project (CAREHeat; grant number: 4000137121/21/I-DT).</p> <p>References:<br> Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C., ... & Wernberg, T. (2016). <br> A hierarchical approach to defining marine heatwaves. Progress in Oceanography 141:227–238, https://doi.org/10.1016/j.pocean.2015.12.014.</p>
Code list used to generate cohorts in ICD-10 CCI US validation study
<p>Code list used to generate cohorts in validating the Charlson comorbidity index (CCI) for ICD-10 in the United States.</p>
Surface and subsurface summer marine heatwave output from CMCC-SPS3.5 seasonal forecast system, GREP reanalysis and ESA CCI SST.
<p>The attached data files were used to produce the figures and analysis in the following study:</p> <p>McAdam, R., Masina, S., Gualdi. S<em>.</em> Seasonal forecasting of subsurface marine heatwaves. <em>Nature Comms. Earth & Env. </em>(2023). </p>
Study Evaluating Temsirolimus (CCI-779) In Mantle Cell Lymphoma (MCL)
ClinicalTrials.gov study NCT00117598. IPD Sharing: Not stated. Countries: 18. Publications: 1.
Study Evaluating Interferon And CCI-779 In Advanced Renal Cell Carcinoma
ClinicalTrials.gov study NCT00065468. IPD Sharing: Not stated. Countries: 25. Publications: 5.
Combination CCI-779 (Temsirolimus) and Bortezomib (Velcade) in Relapsed and/or Relapsed/Refractory Multiple Myeloma
ClinicalTrials.gov study NCT00483262. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Kidney Response to Sepsis Affects Angiogenic Balance and Likelihood of CCI and PICS
ClinicalTrials.gov study NCT02276066. IPD Sharing: Not stated. Countries: 1. Publications: 1.
CCI-779 and Rituximab in Treating Patients With Relapsed or Refractory Non-Hodgkin's Lymphoma
ClinicalTrials.gov study NCT00109967. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.