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325 results for “ESA”
Linked collectors and determiners for: ESA - Herbário da Escola Superior de Agricultura Luiz de Queiroz.
Natural history specimen data linked to collectors and determiners held within, "ESA - Herbário da Escola Superior de Agricultura Luiz de Queiroz". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/cbf8c968-37d6-45be-bf5c-e22914a384dc">https://bionomia.net/dataset/cbf8c968-37d6-45be-bf5c-e22914a384dc</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/cbf8c968-37d6-45be-bf5c-e22914a384dc">https://gbif.org/dataset/cbf8c968-37d6-45be-bf5c-e22914a384dc</a>. Formatted as a Frictionless Data package.
ESA StormSurgeCastNet Dataset
<blockquote> <p>This is the dataset for the storm surge forecasting work of Ebel et al (2024), featuring the curation of a global and multi-decadal dataset of extreme weather induced storm surges as well as the implementation of neural networks for addressing the associated forecasting task. The provided dataset is multi-modal and features preprocessed in-situ tide gauge records as well as atmospheric reanalysis products and ocean state simulations. The models applied in this work learn to fuse the sparse yet accurate in-situ measurements with the global ocean and atmosphere state products. This way, more accurate storm surge forecasts are achieved, with predictions broadcasted to sites missing well-maintained tidal gauge infrastructure.</p> </blockquote> <p> </p> <ul> <li>The publication is available in the proceedings <a href="https://openaccess.thecvf.com/content/CVPR2024W/EarthVision/html/Ebel_Implicit_Assimilation_of_Sparse_In_Situ_Data_for_Dense__CVPRW_2024_paper.html" rel="nofollow">https://openaccess.thecvf.com/content/CVPR2024W/EarthVision/html/Ebel_Implicit_Assimilation_of_Sparse_In_Situ_Data_for_Dense__CVPRW_2024_paper.html</a></li> <li>For the associated code, please see <a href="https://github.com/PatrickESA/StormSurgeCastNet">https://github.com/PatrickESA/StormSurgeCastNet</a></li> <li>For any further questions, please reach out to me here or via the credentials on my <a href="https://pwjebel.com" rel="nofollow">website</a>.</li> </ul> <p><br><strong>Reference:</strong></p> <p><br><em>P. Ebel, B. Victor, P. Naylor, G. Meoni, F. Serva, R. Schneider Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2024.</em></p>
Linked collectors and determiners for: ESA herbarium - Universidade de São Paulo - Herbário Virtual REFLORA.
Natural history specimen data linked to collectors and determiners held within, "ESA herbarium - Universidade de São Paulo - Herbário Virtual REFLORA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/02cb399f-3283-49fa-bce0-627c7f83083f">https://bionomia.net/dataset/02cb399f-3283-49fa-bce0-627c7f83083f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/02cb399f-3283-49fa-bce0-627c7f83083f">https://gbif.org/dataset/02cb399f-3283-49fa-bce0-627c7f83083f</a>. Formatted as a Frictionless Data package.
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)
ESA WorldCover 10 m 2020 v100
<p><strong>ESA WorldCover 10 m 2020 v100</strong></p> <p>The European Space Agency (ESA) WorldCover 10 m 2020 product provides a global land cover map for 2020 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project.</p> <p>The WorldCover product is developed by a consortium lead by VITO Remote Sensing together with partners Brockmann Consult, CS SI, Gamma Remote Sensing AG, IIASA and Wageningen University</p> <p><a href="https://viewer.esa-worldcover.org/worldcover/">Click here to view the maps</a></p> <p><a href="https://esa-worldcover.org">More information about the land cover maps</a></p> <p><a href="https://esa-worldcover.org/en/data-access">Product User Manual & Product Validation Report</a></p>
ESA WorldCover 10 m 2021 v200
<p><strong>ESA WorldCover 10 m 2021 v200</strong></p> <p>The European Space Agency (ESA) WorldCover 10 m 2021 product provides a global land cover map for 2021 at 10 m resolution based on Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes, aligned with UN-FAO's Land Cover Classification System, and has been generated in the framework of the ESA WorldCover project.</p> <p>The ESA WorldCover 10m 2021 v200 product updates the existing <a href="https://doi.org/10.5281/zenodo.5571936">ESA WorldCover 10m 2020 v100</a> product to 2021 but is produced using an improved algorithm version (v200) compared to the 2020 map. Consequently, since the <strong>WorldCover maps for 2020 and 2021 were generated with different algorithm versions</strong> (v100 and v200, respectively), <strong>changes between the maps</strong> should be treated with caution, as they i<strong>nclude both real changes in land cover and changes due to the algorithms used.</strong></p> <p>The WorldCover 2021 v200 product is developed by a consortium lead by VITO Remote Sensing together with partners Brockmann Consult, Gamma Remote Sensing AG, IIASA and Wageningen University</p> <p><a href="https://viewer.esa-worldcover.org/worldcover/">Click here to view the maps</a></p> <p><a href="https://esa-worldcover.org">More information about the land cover maps</a></p> <p><a href="https://esa-worldcover.org/en/data-access">Product User Manual & Product Validation Report</a></p>
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>
FIG. 31 -36. — Esada esa Dist. 31 34 in Classification raisonnée des Platypleures africaines (HomopteraCicadidae)
FIG. 31 -36. — Esada esa Dist. 31 34: F emelle. Complexe génital ectodermique vu de profil (31), de face (32) et en coupe transversale au niveau des voies (33); mise en évidence des orifices génitaux externes (34). 3536: Mâle. Terminalia en vues postérodorsale (35), puis de profil (36). (Légende des lettres: voir fig. 28).
Sentinel2 RGB chips over Colombia (NE) with ESA World Cover for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the east - northeast region of Colombia covering<br> parts of Santander, Norte de Santander, Boyacá, Bolívar, Antioquia and Cundinamarca.</strong></p> <p>We use the communes administrative division defined by DANE (Departamento Administrativo<br> Nacional de Estadística) under "municipios" in the MGN2021 at <br> <a href="https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/">https://geoportal.dane.gov.co/geovisores/territorio/mgn-marco-geoestadistico-nacional/</a></p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a><br> <br> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a> </p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
Sentinel2 RGB chips over BENELUX with ESA World Cover for Learning with Label Proportions
<p><strong>Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg</strong></p> <p>We use the communes administrative division which is standardized across Europe by EUROSTAT at:<br> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a><br> This is roughly equivalent to the notion municipalities in most countries.</p> <p>From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders<br> are taken from NUTS_RG_01M_2021_3035.shp.</p> <p><strong>images: Sentinel2 RGB from 2020-01-01 to 2020-31-12</strong><br> filtered out pixels with clouds during the observation period according to QA60 band following the example<br> given in GEE dataset info page, and took the median of the resulting pixels</p> <p> see <a href="https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED">https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED</a></p> <p> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py</a></p> <p><strong>labels: ESA WorldCover 10m V100</strong><br> labels mapped to the interval [1,11] according to the following map<br> { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 }<br> pixel value zero is reserved for invalid data.<br> see <a href="https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100">https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100</a><br> <br> see also <a href="https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py">https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py</a></p> <pre><code>_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc. _communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc. splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split. data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to </code></pre>
SGS-LTER Historical LTER Soil water - neutron probe: field data (1983-1992) in ungrazed ESA (Ecosystem Stress Area) on the Central Plains Experimental Range, Nunn, Colorado, USA, ARS Study Number 10
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Long-term (1985-1992) dynamics and spatial variations in soil water below the evaporative zone were evaluated for a shortgrass steppe with a low and variable precipitation regime. Each of sandy loam, clay loam, and two sandy clay loam sites compromised a toposequence with upland, midslope, and lowland positions. Soil water was monitored at 15cm intervals providing estimates covering 22.5 to 97.5 cm depths. Soil water throughout the profile was highest in the clay loam and lowest in the sandy loam. However, stored soil water did not vary systematically among slope positions. Additional information and referenced materials can be found: http://hdl.handle.net/10217/82912
InSAR 2015 Nepal EQ Sentinel 1 ESA
<p>InSAR for the 215 Nepal EQ using Sentinel 1 Data.</p> <p> </p> <p>Processed with DIAPASON on GEP. </p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from MIOST geostrophic velocities (1/24°).
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from MIOST geostrophic velocities (1/72°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the MIOST algorithm (Ubelmann et al., 2019; https://doi.org/10.1029/2020JC016560) to altimetry L3-data (https://doi.org/10.5281/zenodo.10648981) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/24°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET20 geostrophic velocities (1/72°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/20°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10912777) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
ESA 4DMED-Sea - Finite-size Lyapunov Exponents in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/72°)
<p>This product provides the Finite-size Lyapunov Exponents derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Size Lyapunov Exponents was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>fsle (2D)</p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/24°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/24° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/24º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</p>
ESA 4DMED-SEA - Mediterranean Multivariate Optimal Interpolated Salinity and Density fields
<p>Daily Mediterranean gap-free Level-4 (L4) analyses of the Sea Surface Salinity (SSS) and Sea Surface Density (SSD) at 1/24° of resolution from 2016 to 2022, obtained through a multivariate optimal interpolation algorithm that combines sea surface salinity images from multiple satellite sources as NASA’s Soil Moisture Active Passive (SMAP) and ESA’s Soil Moisture Ocean Salinity (SMOS) satellites with in situ salinity measurements and satellite UHR SST information. </p>
ESA 4DMED-Sea - Finite-Time Lagrangian Vorticity in the Mediterranean Sea derived from 4DVARNET8 geostrophic velocities (1/72°)
<p>This product provides the Finite-Time Lagrangian Vorticity derived from surface geostrophic velocities result from the application of the 4DVARNET algorithm (<a href="https://isprs-annals.copernicus.org/articles/V-3-2021/295/2021/isprs-annals-V-3-2021-295-2021.html" target="_blank" rel="noopener">Fablet et al., 2021</a>; resolution of the dynamical model used for the learning/training (<a href="https://github.com/ocean-next/eNATL60">eNATL60-BLB02</a>) downgraded to 1/8°) to altimetry L3-data (https://doi.org/10.5281/zenodo.10908416) at a resolution of 1/72° over the Mediterranean Sea and for the period from April 2016 to July 2022. </p> <p>Algorithm used to compute Finite-Time Lagrangian Vorticity was developed by I. Hernandez-Carrasco (Hernandez-Carrasco et al, 2011, Ocean modelling. https://doi.org/10.1016/j.ocemod.2010.12.006).</p> <p>----------------------------------------------------------------------------------------</p> <p>Geographical coverage: Mediterranean Sea</p> <p>Grid and horizontal spatial resolution: Evenly spaced 1/72º grid</p> <p>Vertical levels: Only surface level</p> <p>Temporal resolution: Daily (April 2016 - July 2022)</p> <p>-----------------------------------------------------------------------------------------</p> <p>Variables:</p> <p>lon (1D)</p> <p>lat (1D)</p> <p>time (1D)</p> <p>ftlv (2D)</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.