Find research datasets worth reusing

opencc-by-sa-4.0Nov 2022View details →

Annual land cover maps of Germany based on Sentinel-2 MSI Level 3A (WASP) data

Overview: This annual land cover product is available for the years 2016, 2019, 2020, 2021 for the whole of Germany. It was generated based on Sentinel-2 MSI L3A WASP Data provided by DLR (https://geoservice.dlr.de/data-assets/4hcq6dgkj648.html). For a complete description of the classification procedure please refer to Riembauer, G.; Weinmann, A.; Xu, S.; Eichfuss, S.; Eberz, C.; Neteler, M.: Germany-wide Sentinel-2 based land cover classification and change detection for settlement and infrastructure monitoring. In: Proceedings of the 2021 conference on Big Data from Space (doi:10.2760/125905), 2021. Source data: <ul> <li>Satellite data <ul> <li>German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648</li> </ul> </li> <li>Auxiliary data <ul> <li>European Union, Copernicus Land Monitoring Service, European Environment Agency (EEA), Copernicus High Resolution Layer: Imperviousness Status Map, 2018 (https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018)</li> <li>OpenStreetMap Planet dump retrieved from https://planet.osm.org, https://www.openstreetmap.org</li> <li>S2GLC Map of Europe (R. Malinowski, S. Lewiński, M. Rybicki, E. Gromny, M. Jenerowicz, M. Krupiński, A. Nowakowski, C. Wojtkowski, M. Krupiński, E. Krätzschmar, and P. Schauer, "Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery," Remote Sensing, vol. 12, no. 21, p. 3523, 2020.)</li> </ul> </li> </ul> File naming: classification_map_germany_[year].tif example: classification_map_germany_2020.tif Projection + EPSG code: WGS 84 / UTM zone 32N (EPSG: 32632) Spatial extent: north: 55:03:38.646483N south: 47:08:24.738401N west: 5:33:47.816647E east: 15:34:24.108516E Spatial resolution: 10 m Format: COG (Cloud-Optimized GeoTIFF) Pixel values: 10: forest 20: low vegetation 30: water 40: built-up 50: bare soil 60: agriculture Temporal coverage: Years 2016, 2019, 2020, 2021 Software used: GRASS 7.8, actinia Original dataset license: The Sentinel-2 level 3A data produced and distributed by DLR are based on Copernicus Sentinel-2 level 1C data, which are subject to the following license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf One of the following citations is mandatory for using the provided MAJA/WASP L3A product: German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648 or Contains modified Copernicus Sentinel data, processed by DLR, licensed under CC-BY 4.0 Processed by: mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)

Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.

opencc-by-4.0Jan 2023View details →

GIS Shapefiles for In Situ Geologic Map based on PMars 2020 Perseverance Rover Observations

These are shapefiles for in situ geologic maps illustrated in Crumpler et al., 2023. n Situ Geologic Context Mapping Transect on the Floor of Jezero Crater from Mars 2020 Perseverance Rover Observations. Journal of Geophysical Research-Planets, Mars 2020 Results Special Collection

opencc-by-4.0Jan 2023View details →

Fine-mapped summary statistics for protein coding regions (i.e. cis regions) based on the Olink Explore 1536 and Explore Expansion technologies

This data set contains fine-mapping results performed by SuSie for cis regions ±500kb around the protein coding gene) for protein targets as measured by the Olink Explore 1536 and Explore Expansion technologies in 1,180 individuals from EPIC Norfolk study (https://www.epic-norfolk.org.uk/). Only protein targets where fine-mapping predicted at least one credible set were included in the results.

Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

opencc-by-4.0May 2023View details →

FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach.

The products can be vizualized at <a href="https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer">https://martinschwartz0.users.earthengine.app/view/forms-height-biomass-volume-viewer</a> - FORMS-H: Canopy height map of France at 10 m resolution. The units are in centimeter (10^-2 m). - FORMS-B: Above-ground biomass density map of France at 30 m resolution. The units are in Mg ha-1 - FORMS-V: Wood volume density map of France at 30 m resolution. The units are in m3 ha-1 Please refer to the paper <a href="https://doi.org/10.5194/essd-15-4927-2023">https://doi.org/10.5194/essd-15-4927-2023</a> for further details.

opencc-by-4.0May 2023View details →

Geologic Map of Ceres [Dawn Mission] - Global dataset based on the 15 individual quadrangle maps

Background: Between 2011 and 2018, the NASA Dawn spacecraft visited asteroid (4) Vesta and dwarf planet (1) Ceres to investigate the surfaces of both protoplanets through optical and hyperspectral imaging and their composition through gamma-ray and neutron spectroscopy from orbit. For both Vesta and Ceres, a geologic mapping investigation was realized based on optical and hyperspectral data as well as a photogrammetrically derived digital terrain model. For the global mapping investigation, mappers employed Geographic Information System (GIS) software to map 15 quadrangles. The results were published as individual map sheets alongside research papers discussing the geologic evolution. The style of collaborative mapping to produce a consistent global view represented by individual quadrangle maps is comparably new despite abundantly available mapping experiences. Ongoing data acquisition during mapping created considerable challenges for the coordination and homogenization of mapping results. To handle this issue simultaniously to the active mission phase as best as possible a GIS-based environment was needed in order to conduct one homogenous dataset (w.r.t. geometrical and visual character) that represents one geologically-consistent map at the end. Therefore, the mapping team was supported by an predefined mapping template which was generated in the proprietary ArcGIS environment. The template contains different layers (called feature classes) for the different object/geomoetry types and contains predefined attribute values as well as cartographic symbols. The cartographic symbols follow international standards as far as possible. The colours for the geological units refering to established colour values used in geologic maps, e.g., standardized planetary maps generated by USGS, but considering individual needs and requests within the mapping team, too. The data product pubished here based on the mentioned GIS-based template and represents the merged global GIS-dataset of the 15 individually conducted geological maps of Ceres within the Dawn Mission. The detailed descriptions of all those scientific interpretions are published in the papers listed within the reference section. Based on team-internal decisions the dataset is provided within the properitary format of ESRIs ArcGIS environment. However, in order to use the data product also outside this software environment, single shapefiles with additional information about the symbology are also included. All available data are available within the compressed folder and the readme-file gives some informative remarks for the useage of the data Additional remark: The data set provided here does not represent a holistic (in term of topological and scientifical) unification of the 15 individual mapping data as primarily geometric and content-related inconsistencies at quadrangle boundaries prohibited a unified compilation. On the one side, this is due to the fact that the the aim of the mapping project was not to produce a uniform global map, but rather to gain a first impression of the geology of Ceres and publish associated scientific papers. On the other side, that the geological mapping project ran parallel to the regular mission phase, and a finalizing review process for creating a global geological dataset wasn´t scheduled in the mission planning. This deficiency cannot be remedied simply by merging topological missmatches or changing the visualisation. Rather it will require ongoing and detailed scientific discussion of the interpretation results, which could be solved within an updating version of the global map.

opencc-by-4.0Dec 2019View details →

A method for generating coherent spatially explicit maps of seasonal palaeoclimates from site-based reconstructions

Reconstruction of climate anomalies in southern Europe for the Last Glacial Maximum (LGM, ca 21,000 years ago), made by combining pollen based reconstructions (from Bartlein et al. 2011) and averaged outputs of LGM simulations from the 3rd round of the Palaeoclimate Model Intercomparison Project (PMIP, Braconnot et al. 2011), under a variational data assimilation technique. Reconstructions made using this technique are designed to be used for data-model comparison, specifically against the results of PMIP4. The dataset consists of 6 variables: moisture index (the ratio precipitation and equilibrium evapotranspiration), mean annual precipitation (mm), mean annual temperature (degrees C), mean temperature of the coldest month (degrees C), mean temperature of the warmest month (degrees C), growing degree days above 5 degrees C (day degrees C). The standard deviation of these variables is also given.

opencc-by-4.0Dec 2020View details →

Landsat-based maps of irrigated dry season cropping in Southeastern Anatolia, Turkey

Landsat-based maps of irrigated dry-season cropping in Southeastern Anatolia, Turkey Long-term monitoring of the extent and intensity of irrigation systems is needed to track crop water consumption and to optimize land use in a changing climate. We mapped the expansion and land use intensity of irrigated dry season cropping in Turkey´s Southeastern Anatolia Project annually from 1990 to 2018 using Landsat time series and Google Earth Engine. This dataset includes multiple maps documenting the expansion and land use intensity of irrigated dry season cropping in Turkey´s largest irrigation scheme. We aggregated all Landsat imagery acquired during the July through September for the period 1990 to 2018 into spectral-temporal metrics and predicted dry season cropping annually using a machine learning classifier. We performed several post-processing steps to derive multiple map products for all areas with at least two dry season cropping cycles in the study period. The dataset comes in .zip format and includes the following map products: <ul> <li>gap_dsc_fst.tif: first year of dry season cropping</li> <li>gap_dsc_yrs.tif: number of years with dry season cropping</li> <li>gap_dsc_frq.tif: dry season cropping frequency (% of years since first dry season cultivation):</li> <li>gap_dsc_trd.tif: five-year dry season cropping frequency trend magnitude</li> <li>gap_dsc_pvl.tif: significance level (p-values)</li> </ul> Spatial coverage The maps come in 30m spatial resolution and cover the Southeastern Anatolia Project (Güneydoğu Anadolu Projesi, GAP) region. The region consists of nine provinces which account for approximately 10% of the Turkish land area.  Temporal coverage The analyses cover the period 1990-2018. The first year of dry season cropping and the number of years with dry season cropping represent the time period 1990-2017. The temporal coverage of dry season cropping frequency varies on a pixel level, depending on the initial year of dry-season cultivation. The temporal coverage of the trend indicators also vary on a pixel level and furthermore have a constrained maximum temporal coverage of 1992-2012 due to the post-processing steps involved. Data format The data are delivered as 16bit single layer GeoTIFFs in EPSG:3035 projection. The images are LZW compressed, and have NoData value 0. Publication & further information Please see the publication for further information on the methodology and accuracy of the map products: Rufin, P.; Müller, D.; Schwieder, M.; Pflugmacher, D.; Hostert, P. (2020): Landsat time series reveal simultaneous expansion and intensification of irrigated dry season cropping in Southeastern Turkey. Journal of Land Use Science. DOI: http://dx.doi.org/10.1080/1747423X.2020.1858198 Acknowledgments This research contributes to the Landsat Science Team 2018-2023 (http://www.usgs.gov/land-resources/nli/landsat/landsat-science-teams) and the Global Land Programme (https://glp.earth/). We gratefully acknowledge the open cloud processing platform provided by Google.

opencc-by-4.0Jun 2024View details →

Land use-based Adaptation and Mitigation Solution (LAMS) suitability maps final version

<div>These maps provide the suitable area (maximum available area) for the potential implementation of the specific proposed LAMS. The suitability map includes 4 classes: not suitable (0), least suitable (1), moderately suitable (2) and most suitable (3). Maps for 13 different LAMS (from the LAMS catalogue V1-1) have been developed for the six RethinkAction case studies, when possible. This v2 includes the metadata.</div> <div>The codes and the names of the LAMS are the following:</div> <div>LAMS03-EstGra: Establishment (conversion to) of permanent grassland</div> <div>LAMS14-SpaPla: Spatial planning for the sustainable deployment of energy on land</div> <div>LAMS15-PhoPla: Photovoltaic plants</div> <div>LAMS21-AgrPla: Agrovoltaic farms</div> <div>LAMS22-IncFor: Increased portion of forests included under protected areas</div> <div>LAMS23-RefAff: Reforestation/afforestation</div> <div>LAMS31-UrbSpr: Limiting urban sprawl</div> <div>LAMS32-GreUrb: Establishment and maintenance of green urban ecosystems</div> <div>LAMS44-IncCul: Increase in cultivated area</div> <div>LAMS49-FloSol: Floating solar photovoltaic panels in water bodies</div> <div>LAMS50-SolPan: Solar panels in rooftops/buildings</div> <div>LAMS55-WatHar: Water harvesting: collect and store rain water in reservoirs</div> <div>LAMS59-LanMan: Land management of solar photovoltaic systems land</div>

opencc-by-4.0Sep 2023View details →

Sea-surface pCO2 maps for the Bay of Bengal based on machine learning algorithms

The dataset contains two products, the first being sea-surface pCO2 and the second being air-sea CO2 flux for the Bay of Bengal region. The data is climatological data, with 12 months of the climatological year. Each of these data has a spatial resolution of 1/12o. The positive value of CO2 flux indicates the outgassing of CO2, and the negative value shows the uptake of atmospheric CO2. This version contains an elaborate description in the attribute section of the NC files, which is missing from the previous versions.

opencc-by-4.0May 2024View details →

High-Resolution Canopy Fuel Maps Based on GEDI: A Foundation for Wildfire Modeling in Germany

Open access publication under review. Visit <a href="https://ee-forestfuels-ger.projects.earthengine.app/view/gedi-fuels">this Earth Engine app</a> to explore the data interactively.   Abstract: Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data. We propose a two-step approach where we first use measurements from NASA’s GEDI instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for Canopy (Base) Height (CH, CBH), Cover (CC), Bulk Density (CBD), and Fuel Load (CFL) by segmenting airborne LiDAR point clouds and processing tree-level metrics with allometric crown biomass models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold Nearest Neighbor Distance Matching. Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a Universal Kriging method to produce countrywide maps at 20-meter resolution. Agreement (RMSE/R²) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH) rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs. The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.   Data: This repository contains modeling data, model objects (R), and predicted maps. The TIFF-files each have six bands, which includes (1) the final Universal Kriging result, (2) the linear model prediction (3) the prediction of residual Kriging, (4) the Kriging variance, (5) the linear model prediction standard error, and (6) Universal Kriging standard error.   Disclaimer: Maps in this repository are predicted using canopy fuel estimates from GEDI measurements. These are limited the region between 51.6° North and South. Map predictions exceeding this range should be considered an extrapolation of the model to an unknown biophysical domain. Error maps (6) can aid in utilizing our canopy fuel maps.

opencc-by-4.0Nov 2024View details →

Mapping the DeFi Crime Landscape: An Evidence-based Picture

README - Crime Events Dataset This document provides a detailed overview of the structure of the dataset for the paper: "Mapping the DeFi crime landscape: An Evidence-based Picture", published in the Journal of Cybersecurity, and available here: https://academic.oup.com/cybersecurity/article/11/1/tyae029/7962044 The following fields are included, each representing different aspects of the events collected. Data Fields 1. unique_key     Description: A unique number assigned to identify each event in the dataset. 2. Agregators      Description: The sources where the event is listed. Aggregators include:      - De.Fi REKT      - SlowMist      - CryptoSec (rebranded to ChainSec as of February 2023) 3. DeFi actor involved      Description: The name of the DeFi actor involved in the event (target, perpetrator, or intermediary).     Sources:       - On De.Fi REKT: Found as the "Title" of the event’s listing.      - On SlowMist: Found under the “Hacked target” title.      - On CryptoSec: Found in the "Title" of the event’s listing with the date. 4. REKT URL       Description: The URL to the event's listing on De.Fi REKT.     Process: Found by searching for the DeFi actor involved in the REKT Database: https://de.fi/rekt-database 5. SlowMist URL       Description: The URL to the event's listing on SlowMist.     Process: Available via https://hacked.slowmist.io/search/. Note that searching the actor's name will lead to the event but without an individualized URL. 6. CryptoSec URL       Description: The URL to the event's listing on CryptoSec.     Process: Found at https://chainsec.io/defi-hacks/. Events are listed on a single page; use traditional keyboard search to locate specific events. 7. Aggregator Summary       Description: A summary of the event provided by the aggregator.     Sources:       - On De.Fi REKT: Found under "Quick Summary" and "Details of the Exploit".      - On SlowMist: Under "Description of the event".      - On CryptoSec: Below the title in quotation marks. 8.  Aggregator sources URL        Description: The URLs of references linked by the aggregator in the event’s listing.      Sources:      - On De.Fi REKT: Found at the bottom by clicking "Source" or "Archived link".      - On SlowMist: Found by clicking "View Reference Sources".      - On CryptoSec: Available by clicking the source’s name at the end of the summary. 9.  Event date        Description: The date the event occurred.      Sources:      - On De.Fi REKT: Listed under the "Date" field.      - On SlowMist: At the top right of the listing.      - On CryptoSec: Listed in parentheses behind the actor’s name. 10. Event year         Description: The year the event occurred, extracted from the Event date. 11. Stolen amount USD         Description: The total amount stolen, converted to USD.       Sources:       - On De.Fi REKT: Found under "Funds lost".       - On SlowMist: Under the title “Amount of loss”.       - On CryptoSec: Behind the title "Amount stolen".       Note: If needed, conversions were manually performed using CoinMarketCap’s historical data as explained in the paper.  12. Implication of actor         Description: Indicates whether the DeFi actor was a target, perpetrator, or intermediary in the event. Manually coded after reviewing the aggregator’s summary and linked sources. 13. Strategy         Description: The main approach used to steal funds. Six categories are possible: Technical vulnerability, Human risks, Undetermined, Malicious use of contract, Misappropriation of funds, and Imitation. This was manually coded from the event summary and sources. 14. General tactic         Description: The common techniques or methods used by malicious actors. Eleven categories are possible, defined in the appendix. Manually coded after reviewing the summary and linked sources. 15. Specific tactic         Description: The precise technique used to commit the crime. Thirty-seven categories are possible, defined in the appendix. This was manually coded based on the event summary and sources. 16. Paper category         Description: The main area of operation of the involved DeFi actor. Twelve categories are possible: Blockchain, Bridge, DApp, Derivatives, Exchange, Fungible Token (FT), Non-Fungible Token (NFT), Oracle, Yield, Staking, and Others. This was determined by the event summary and research on the actor. 17. Stack category         Description: The technical layer of the DeFi Stack Reference (DSR) model corresponding to the paper category. Five categories are possible: DeFi Compositions (CP), DeFi Protocols (P), Cryptoassets (CA), Distributed Ledger Technology (DLT), and Interfaces (INT). --- For more detailed information on the tactics, strategies, or categories used, please refer to the appendix of the dataset or the associated documentation. The previous version included duplicates. They have been removed in the second version.

opencc-by-4.0Jun 2020View details →

A 30-meter terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine

This dataset contains the China terrace map at 30 m resolution in 2018. The map values and their corresponding classes are as follows: 0: Non-terrace   1: Terrace   255: No data The 30 m China terrace map can also be viewed online at <a href="https://cbw.users.earthengine.app/view/chinaterracemap">https://cbw.users.earthengine.app/view/chinaterracemap</a> Citations: When using this dataset, please cite both the dataset and the following data description article: Cao, B., Yu, L., Naipal, V., Ciais, P., Li, W., Zhao, Y., Wei, W., Chen, D., Liu, Z., and Gong, P.: A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine, Earth Syst. Sci. Data, 13, 2437–2456, https://doi.org/10.5194/essd-13-2437-2021, 2021.

opencc-by-4.0Oct 2020View details →

Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform

(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a>  The data included: 1) Raster data of commodity maps (TIFF Compressed in ZIP) 2) READ ME for the dataset (DOCX) 3) Legend for raster data in ArcGIS Format (LYR)

Apertif drift scan based compound beam maps (1220-1520 MHz)

Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape. This data set contains Apertif drift scan based CB maps measured at the second Apertif imaging survey frequency setting (1220-1520 MHz). Apertif imaging observations were take at these frequencies between January 2021 and February 2022. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys. The dataset contains two types of fits files: 1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky. 2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to.  All CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets.  <table> <caption>Notes on the individual CB data sets</caption> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td> 210205 </td> <td>Cyg A</td> <td> RFI in channels 13-15 (1.46-1.48 GHz) </td> </tr> <tr> <td> 210402 </td> <td>Cas A</td> <td> RFI in channels 13-15 (1.46-1.48 GHz), no RTC and RTD, spline fit for CB01 failed for the per antenna maps </td> </tr> <tr> <td> 211103 </td> <td>Cas A</td> <td> RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps </td> </tr> <tr> <td> 211118 </td> <td>Cyg A</td> <td> RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps </td> </tr> </tbody> </table> For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662). For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739

Apertif drift scan based compound beam maps (1130-1430 MHz)

Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape. This data set contains Apertif drift scan based CB maps measured at the first Apertif imaging survey frequency setting (1130-1430 MHz). Apertif imaging observations were take at these frequencies between July 2019 and January 2021. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys. The dataset contains two types of fits files: 1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky. 2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to.  Wit the exception of two datasets (191008 and 191023), all CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets.  Notes on the individual CB data sets: <table> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td>190628</td> <td>Cyg A</td> <td>issue in channel 2 (1.31 GHz) with CBs 15-16, issue in channels 5-6 (1.36 - 1.37 Ghz) and 8-9 (1.4 - 1.41 GHz), spline fit for CBs 01-07 failed for the per antenna maps</td> </tr> <tr> <td>190722</td> <td>Cyg A</td> <td>RFI in chan 6 (1.37 GHz) for CB 1</td> </tr> <tr> <td>190821</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190826</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190912</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190916</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>191008</td> <td>Cyg A</td> <td>CBs 14, 19, 20 and 26 are distorted, no per antenna maps</td> </tr> <tr> <td>191023</td> <td>Cyg A</td> <td>RFI in channel 6 ( 1.37 GHz) for CBs 0, 3-4, 9-10, 15-17, 22, no per antenna maps</td> </tr> <tr> <td>191120</td> <td>Cyg A</td> <td>RFI in channels 8-9 (1.4 - 1.41 GHz)</td> </tr> <tr> <td>200130</td> <td>Cyg A</td> <td>no RT5, RFI in channel 6 (1.37 GHz) for CBs 1, 6-8, 14, 21, 33</td> </tr> <tr> <td>200430</td> <td>Cyg A</td> <td>no RTC and RTD</td> </tr> <tr> <td>200710</td> <td>Cyg A</td> <td>no RTB, CBs 0, 20-39 are affected by RFI</td> </tr> <tr> <td>200819</td> <td>Cyg A</td> <td>issue with CBs 37-39, RFI in channel 6 (1.37 GHz), spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201009</td> <td>Cyg A</td> <td>no RTB, antenna 9 (RTB) files are empty in the antenna based maps</td> </tr> <tr> <td>201028</td> <td>Cas A</td> <td>spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201218</td> <td>Cyg A</td> <td> </td> </tr> </tbody> </table> For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662). For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739

Building fraction map of Germany (Sentinel-1/-2 based, 10m and 100m resolution)

This dataset features a map of building fractions (as opposed to built-up fractions including other impervious surfaces such as roads) for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. The data were created by using machine learning regression and spectral unmixing, using synthetically mixed training data. The dataset is completely based on freely accessible satellite imagery, and was validated with freely available building footprint reference data for three federal states. We recommend to use data at an aggregated resolution of 20m, 50m, or 100m, and to clip data at about 20% building fraction when using 10m resolution maps (or roughly the corresponding RMSE at any other resolution). Temporal extent Used Sentinel-2 data were acquired in 2018, and Sentinel-1 data were acquired in 2017 (see publication). The map is, thus, representative for 2017/2018. Validation results can be affected by building footprint reference data from different years. Data format The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building fraction values are in percent, from 0 to 100. In the original dataset with 10m spatial resolution, fraction values are equivalent to area in m². In the aggregated dataset with 100m spatial resolution, the values must be multiplied with 100 in order to see area in m². Further information For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de). A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/building-area/">here</a>. Publication Schug, F.; Frantz, D.; Okujeni, A.; Hostert, P. (2022). Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and -2 data. Remote Sensing Letters. DOI: 10.1080/2150704X.2022.2088253 Acknowledgements The dataset was generated by FORCE v. 3.6.1 (<a href="https://doi.org/10.3390/rs11091124">paper</a>, <a href="https://github.com/davidfrantz/force">code</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the <a href="https://scihub.copernicus.eu/">European Space Agency and the European Commission</a>. Sentinel-1 data were provided by <a href="https://eodc.eu/">EODC</a>. We thank the providers of the building footprint reference data (see publication). Funding This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">MAT_STOCKS</a>, grant agreement No 741950).

Data from: Robust Estimation of Field Inhomogeneity Map Following Magnitude-Based Water-Fat Separation with Resolved Ambiguity

These data have been uploaded and shared as part of "Robust Estimation of Field Inhomogeneity Map Following Magnitude-Based Water-Fat Separation with Resolved Ambiguity". The data may be used for field inhomogeneity mapping and PDFF/R2* reconstruction.  These data were acquired by Perspectum Ltd (https://perspectum.com/) on a healthy volunteer. Informed consent was obtained from the participant. The dataset includes a localizer series and a multi-slice series (magnitude and phase) acquired from a volunteer covering the dome of the liver, heart and lungs: <ul> <li>1-localizer_haste_bh</li> <li>2-I_6_Echo_3D_32_Slice_IDEAL</li> <li>3-I_6_Echo_3D_32_Slice_IDEAL</li> </ul> These data were gathered using a Siemens Prisma 3 Tesla scanner. The main dataset comprises an acquisition with thirty-two slices including the abdominal region, with slices placed away from the isocenter. The acquisition consisted of a 6‐echo (TE1=1.3 ms, ΔTE=1 ms) gradient-recalled echo (GRE) protocol designed to minimize T1 bias (3° flip angle), Pixel Bandwidth = 1565 Hz, and 232 x 256 reconstructed image size, with 5 mm slice thickness and 1.72 x 1.72 mm^2 in-plane resolution.