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181 results for “SENTINEL-2”

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nasa24/100

OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 provisional product (Version 1)

This dataset contains Level-3 Dynamic OPERA provisional surface water extent product version 1. The data are provisional surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.

restrictednotspecifiedMar 2025View details →

nasa24/100

OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 provisional product (Version 0)

The OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned on April 25, 2025. Users are encouraged to use the [OPERA_L3_DIST-ALERT-HLS V1](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product which was released on March 14, 2024, and has achieved stage 1 validation.The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product Version 0 maps vegetation disturbance alerts from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). Vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes to provide information about more general disturbance trends. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration, or small magnitude/long duration. The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within in the DIST-ALERT product: vegetation disturbance status, current vegetation cover indicator, current vegetation anomaly value, historical vegetation cover indicator, max vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, and vegetation disturbance duration. See the Product Specification for a more detailed description of the individual layers provided in the DIST-ALERT product. Known Issues* Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).

restrictednotspecifiedJun 2025View details →

zenodo20/100

Sentinel-1 and Sentinel-2 database for refugee camps detection

This database was generated by AGENIUM Space in the framework of the CORTEX project (https://esacortexproject.agenium-space.com/) funded by ESA. Database of pairs of Sentinel-1 and Sentinel-2 images (https://www.copernicus.eu/en, https://scihub.copernicus.eu/) which  contain the same region where a refugee camp (from those identified by the UNHCR) is present. Original Copernicus images were acquired between 2017 and 2019. The Sentinel-1 images come from the IW mode and are GRD product from both ascending and descending orbits. The Sentinel-2 images are surface reflectance products (L2A). The initial targeted application is to perform style transfer to generate a Sentinel-2 lookalike from a Sentinel-1 image. The database focuses on refugees’ camps in the Middle East area. As said, the locations of 39 camps were retrieved from the UNHCR (United Nations refugee agency) website (https://www.unhcr.org/). More details are provided in attached document S1-S2-RefugeeCamps-DB-description.pdf This work is funded by a contract in the framework of the EO SCIENCE FOR SOCIETY PERMANENTLY OPEN CALL FOR PROPOSALS EOEP-5 BLOCK 4 issued by the European Space Agency.

restrictedJun 2020View details →

zenodo20/100

Data set: Yearly RACMO2.3p2 variables, threshold temperature and Sentinel-2 melt pond volume

Data set of yearly regional atmospheric climate model RACMO2.3p2 data over Antarctica. At the lateral and ocean boundaries the model is forced by ERA5 reanalysis data every 6 hours from 1979-2021. The model is run at 27 km horizontal resolution for the entire Antarctic ice sheet, which constitutes an update of the simulation forced from 1979-2018 by ERA-Interim reported in van Wessem et al., 2018.  Upper air relaxation is also active. To simulate the recent past (1950-2014) and the future (2015-2100) we use RACMO2.3p2 at 27 km resolution to dynamically downscale one historical and three future projections emission scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Detailed CESM2  description and latest updates are provided in Danabasoglu et., al 2020 and an evaluation over Greenland in Van Kampenhout et al., 2020. These data are then used to fit exponential and power-law relations of annual total liquid water production (melt + rain) and snow accumulation (snowfall - sublimation)  to calculate MoA sensitivity as a function of annual average 2 m temperature. A comparison with Sentinel-2 melt pond observations can then be performed. Data set includes: RACMO2.3p2-ERA5-3H - Yearly average precipitation, snowfall, snowmelt and sublimation for the period 1979-2021 in mm w.e per year, and yearly average 2 m temperature in K, forced by 3 hourly ERA5 reanalysis data. RACMO2.3p2-hist_r567 - Yearly average precipitation, snowfall, snowmelt and sublimation for the period 1950-2014 in mm w.e per year, and yearly average 2 m temperature in K, forced by 6 hourly CESM2 historical data. RACMO2.3p2-SSP''126,245,585''_r567 - Yearly average precipitation, snowfall, snowmelt and sublimation for the period 2015-2100 in mm w.e per year, and yearly average 2 m temperature in K, forced by 6 hourly CESM2 future scenario (SSP1-2.6, SSP2-4.5, SSP5-8.5) data. TT_ALL.nc - Antarctic threshold temperature \(T_T\) for MoA = 0.7 as described in the Methods section. dT_ALL.nc - Uncertainty in Antarctic threshold temperature \(T_T\) for MoA = 0.7 as described in the Methods section. TT_IS.nc - Antarctic threshold temperature \(T_T\) for MoA = 0.7 of 56 selected ice shelves as described in the Methods section. dT_IS.nc - Uncertainty in Antarctic threshold temperature \(T_T\) for MoA = 0.7 of 56 selected ice shelves as described in the Methods section. S2_meltpondvolume_v3.nc - Sentinel-2 melt pond volume as described in the Methods section. Height_latlon_ANT27.nc   - Grids of latitude, longitude, surface elevation (height), land/ice mask (mask2d), grounded land/ice mask (maskgrounded2d), aspect ratio, and surface slope.

openApr 2022View details →

nasa20/100

Sentinel-3B OLCI Level-2 Regional Earth-observation Full Resolution (EFR) Ocean Color (OC) - Near Realtime Data, version R2022.0

The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.

restrictednotspecifiedMar 2025View details →

nasa20/100

Sentinel-3A OLCI Level-2 Regional Earth-observation Full Resolution (EFR) Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version R2022.0

restrictednotspecifiedMar 2025View details →

nasa20/100

Sentinel-3B OLCI Level-2 Earth-observation Reduced-Resolution (ERR) Inherent Optical Properties (IOP), Near Real-time (NRT) Data, version R2022.0

restrictednotspecifiedMar 2025View details →

nasa20/100

Sentinel-3A OLCI Level-2 Regional Earth-observation Full Resolution (EFR) Ocean Color (OC) - Near Realtime Data, version R2022.0

restrictednotspecifiedMar 2025View details →

nasa20/100

Sentinel-3B OLCI Level-2 Regional Earth-observation Full Resolution (EFR) Inherent Optical Properties (IOP) - Near Real-time (NRT) Data, version R2022.0

restrictednotspecifiedMar 2025View details →

nasa20/100

Sentinel-3A OLCI Level-2 Earth-observation Reduced-Resolution (ERR) Inherent Optical Properties (IOP), Near Real-time (NRT) Data, version R2022.0

restrictednotspecifiedMar 2025View details →

nasa20/100

Sentinel-3B OLCI Level-2 Earth-observation Reduced Resolution (ERR) Ocean Color (OC) - Near Real-time (NRT) Data, version R2022.0

restrictednotspecifiedApr 2025View details →

nasa20/100

Sentinel-3A OLCI Level-2 Earth-observation Reduced Resolution (ERR) Ocean Color (OC) - Near Real-time (NRT) Data, version R2022.0

restrictednotspecifiedMar 2025View details →

zenodo16/100

Urban monthly land dynamics Sentinel-2 benchmark dataset

The public data set of the paper "Time-series land cover change detection using deep learning-based temporal semantic segmentation" uses monthly synthesized Sentinel-2 for time series semantic change detection. A total of 32894 samples were collected. Each timestamp has a land cover type annotation. Anyone can use this data set to conduct further research. We will add more areas in the future.  Data description: <ol> <li>The time series length of the sample is 48, 48 months.</li> <li>Label mapping: 0 is water body, 1 is woodland, 2 is grassland, 3 is bare soil, 4 is impervious surface, 5 is cropland.</li> <li>For any implementation details, you can refer to the paper or github.</li> </ol> Paper citations: He H, Yan J, Liang D, Sun Z, Li J, Wang L. Time-series land cover change detection using deep learning-based temporal semantic segmentation. Remote Sensing of Environment. 2024, 305:114101.

restrictedcc-by-4.0Dec 2023View details →

zenodo16/100

Improved Arctic Melt Pond Fraction Estimation Using Sentinel-2 Imagery

The dataset is part of the "Improved Arctic Melt Pond Fraction Estimation Using Sentinel-2 Imagery" research article.

restrictedcc-by-4.0Jul 2024View details →

zenodo16/100

CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2

Description CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs). Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates; we manually choose the images to guarantee that each IP inside an ROI matches one of the following cloud cover groups: - clear (0%) - low-cloudy (1% - 25%)  - almost clear (25% - 45%) - mid-cloudy (45% - 65%) - cloudy (65% >) An IP is the core unit in CloudSEN12. Each IP contains data from Sentinel-2 optical levels 1C and 2A, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from eight cutting-edge cloud detection algorithms. Besides, in order to support standard, weakly, and self-/semi-supervised learning procedures, cloudSEN12 includes three distinct forms of hand-crafted labelling data: high-quality, scribble, and no annotation. Consequently, each ROI is randomly assigned to a different annotation group: <ul> <li> 2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group). </li> <li> 2000 ROIs with scribble level annotation, where the annotation time is 15 minutes (scribble group). </li> <li> 5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group). </li> </ul> For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, a system that combines human photo-interpretation and machine learning. For scribble, ground truth pixels were drawn using IRIS but without ML support. Finally, the no annotation dataset is generated automatically, with manual annotation only in the clear image patch. The dataset is already available here: <a href="https://shorturl.at/cgjtz">https://shorturl.at/cgjtz</a>. Check out our website <a href="https://cloudsen12.github.io/">https://cloudsen12.github.io/</a> for examples of how to download the dataset via STAC.

restrictedAug 2022View details →

zenodo16/100

Gridded Global Revisit Periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their Combination

This is a global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid. Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2  and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020. The following equation is used to calculate revisit periods: Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured) Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times.

restrictedMar 2023View details →

zenodo16/100

Sentinel-1/-2 UK NIRv daily 100m 2020 Jan - 2020 Jun (inc.)

Daily 100 Sentinel-1/-2 NIRv over the UK. The production method is Xgboost-based, estimating NIRv from Sentinel-1

restrictedOct 2023View details →

zenodo12/100

National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2020)

This data set contains information on the agricultural land use in Germany for the year 2020. The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in <a href="https://www.sciencedirect.com/science/article/pii/S0034425721005514">Blickensdörfer et al. 2022</a> and can be seen as a continuation of the dataset provided under: <a href="http://zenodo.org/record/5153047#.YWFyXn1CREZ">https://zenodo.org/record/5153047#.YWFyXn1CREZ</a>. The maps can be explored online in a <a href="https://ows.geo.hu-berlin.de/webviewer/landwirtschaft/">webviewer</a>. Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.   Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022. <table> <tbody> <tr> <td> New class code (V2) </td> <td> Class name (V2) </td> <td> Class code (V1) </td> <td> Class name (V1) </td> </tr> <tr> <td> 1101 </td> <td> Winter wheat </td> <td> 31 </td> <td> Winter wheat </td> </tr> <tr> <td>   </td> <td>   </td> <td> 34 </td> <td> Other winter cereals </td> </tr> <tr> <td> 1102 </td> <td> Winter barley </td> <td> 33 </td> <td> Winter barley </td> </tr> <tr> <td> 1103 </td> <td> Winter rye </td> <td> 32 </td> <td> Winter rye </td> </tr> <tr> <td> 1201 </td> <td> Spring barley </td> <td> 41 </td> <td> Spring barley </td> </tr> <tr> <td> </td> <td> </td> <td> 43 </td> <td> Other spring cereals </td> </tr> <tr> <td> 1202 </td> <td> Oat </td> <td> 42 </td> <td> Spring oat </td> </tr> <tr> <td> 1300 </td> <td> Maize </td> <td> 91 </td> <td> Maize (silage) </td> </tr> <tr> <td> </td> <td> </td> <td> 92 </td> <td> Maize (grain) </td> </tr> <tr> <td> 1401 </td> <td> Potatoe </td> <td> 100 </td> <td> Potatoe </td> </tr> <tr> <td> 1402 </td> <td> Sugar beet </td> <td> 80 </td> <td> Sugar beet </td> </tr> <tr> <td> 1501 </td> <td> Rapeseed </td> <td> 50 </td> <td> Winter rapeseed </td> </tr> <tr> <td> 1502 </td> <td> Sunflower </td> <td> 70 </td> <td> Sunflower </td> </tr> <tr> <td> 1611 </td> <td> Peas </td> <td> 60 </td> <td> Legume </td> </tr> <tr> <td> 1612 </td> <td> Broad beans </td> <td> </td> <td> </td> </tr> <tr> <td> 1613 </td> <td> Lupine </td> <td> </td> <td> </td> </tr> <tr> <td> 1614 </td> <td> Soy </td> <td> </td> <td> </td> </tr> <tr> <td> 1603 </td> <td> Vegetables </td> <td> 120 </td> <td> Strawberry </td> </tr> <tr> <td> </td> <td> </td> <td> 130 </td> <td> Asparagus </td> </tr> <tr> <td> </td> <td> </td> <td> 140 </td> <td> Onion </td> </tr> <tr> <td> </td> <td> </td> <td> 181 </td> <td> Carrot </td> </tr> <tr> <td> </td> <td> </td> <td> 182 </td> <td> Other leafy vegetables </td> </tr> <tr> <td> 1602 </td> <td> Cultivated grassland </td> <td> 10 </td> <td> Grassland </td> </tr> <tr> <td> 200 </td> <td> Permanent grassland </td> <td> </td> <td> </td> </tr> <tr> <td> 3003 </td> <td> Fallow land </td> <td> </td> <td> </td> </tr> <tr> <td> 3001 </td> <td> Small woody features </td> <td> 555 </td> <td> Small woody features </td> </tr> <tr> <td> 3002 </td> <td> Other areas </td> <td> 999 </td> <td> Other agricultural areas </td> </tr> <tr> <td> 4001 </td> <td> Grapevine </td> <td> 110 </td> <td> Grapevine </td> </tr> <tr> <td> 4002 </td> <td> Hops </td> <td> 150 </td> <td> Hops </td> </tr> <tr> <td> 4003 </td> <td> Orchard </td> <td> 160 </td> <td> Orchards </td> </tr> </tbody> </table>   All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube.  The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map).    The maps are provided as GeoTiff files together with QGIS legend files for visualization.    References: Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831 BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).  BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.  https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.   <a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a>

restrictedApr 2022View details →

zenodo12/100

National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019)

Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches.   We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model.   All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube.   For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensdörfer et al. (2022).  The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © GeoBasis-DE / BKG (2018)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map).  The maps are provided as GeoTiff files together with a QGIS legend file for visualization.  Class catalogue: 10     Grassland 31     Winter wheat 32     Winter rye 33     Winter barley 34     Other winter cereal 41     Spring barley 42     Spring oat 43     Other spring cereal 50     Winter rapeseed 60     Legume 70     Sunflower 80     Sugar beet 91     Maize 92     Maize (grain) 100    Potato 110    Grapevine 120    Strawberry 130    Asparagus 140    Onion 150    Hops 160    Orchard 181    Carrot 182    Other vegetables 555    Small woody features 999    Other agricultural areas   Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831 BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021).  BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell.  https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.   <a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a>

restrictedAug 2021View details →

zenodo12/100

Dataset related to article "Preservation of Axillary Lymph Nodes Compared with Complete Dissection in T1-2 Breast Cancer Patients Presenting One or Two Metastatic Sentinel Lymph Nodes: The SINODAR-ONE Multicenter Randomized Clinical Trial "

This record contains raw data related to article “Preservation of Axillary Lymph Nodes Compared with Complete Dissection in T1-2 Breast Cancer Patients Presenting One or Two Metastatic Sentinel Lymph Nodes: The SINODAR-ONE Multicenter Randomized Clinical Trial" Abstract Background: The SINODAR-ONE trial is a prospective noninferiority multicenter randomized study aimed at assessing the role of axillary lymph node dissection (ALND) in patients undergoing either breast-conserving surgery or mastectomy for T1-2 breast cancer (BC) and presenting one or two macrometastatic sentinel lymph nodes (SLNs). The endpoints were to evaluate whether SLN biopsy (SLNB) only was associated with worsening of the prognosis compared with ALND in terms of overall survival (OS) and relapse. Methods: Patients were randomly assigned (1:1 ratio) to either removal of ≥ 10 axillary level I/II non-SLNs followed by adjuvant therapy (standard arm) or no further axillary treatment (experimental arm). Results: The trial started in April 2015 and ceased in April 2020, involving 889 patients. Median follow-up was 34.0 months. There were eight deaths (ALND, 4; SNLB only, 4), with 5-year cumulative mortality of 5.8% and 2.1% in the standard and experimental arm, respectively (p = 0.984). There were 26 recurrences (ALND 11; SNLB only, 15), with 5-year cumulative incidence of recurrence of 6.9% and 3.3% in the standard and experimental arm, respectively (p = 0.444). Only one axillary lymph node recurrence was observed in each arm. The 5-year OS rates were 98.9% and 98.8%, in the ALND and SNLB-only arm, respectively (p = 0.936). Conclusions: The 3-year survival and relapse rates of T1-2 BC patients with one or two macrometastatic SLNs treated with SLNB only, and adjuvant therapy, were not inferior to those of patients treated with ALND. These results do not support the use of routine ALND.

restrictedNov 2022View details →

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Allen Brain Atlas

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allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

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abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record