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opencc-by-4.0Dec 2018View details →

A new Geo-Lithological Map (Geo-LiM) for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy)

We introduce a new geo-lithological map of Central Europe (Geo-LiM) elaborated adopting a lithological classification compliant to the methods more used in the litterature for estimating the consumption of atmospheric CO2 due by chemical weathering.  Geo-LiM represents a novelty if compared with published global geo-lithological maps. The first novelty is due by the attention paid in discriminating metamorphic rocks that were classified according to the chemistry of protoliths. The second novelty is that the procedure used for the definition of the map is made available on the web to allow the replicability and reproducibility of the product.

opencc-zeroNov 2019View details →

NOAA NCCOS Assessment: Prioritizing Areas for Future Seafloor Mapping, Research, and Exploration Offshore of California, Oregon, and Washington from 2019-03-01 to 2019-04-01

Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys offshore of the West Continental United States Coast (WCC). Twenty-six participants from NOAA’s West Coast Deep Sea Coral Initiative (WCDSCI) and Expanding Pacific Research and Exploration of Submerged Systems (EXPRESS) entered their priorities in an online application, using virtual coins to denote their priorities in 10x10 minute grid cells. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Ten high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations were distributed throughout the WCC, primarily in depths less than 1,000 m. Participants consistently selected (1) Exploration, (2) Biota/Important Natural Area and (3) Research as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Bathymetry and Backscatter as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable NOAA WCDSCI, EXPRESS and other WCC organization to more efficiently leverage resources and coordinate their mapping of high priority locations along California, Oregon and Washington.  This effort was funded by NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) through its WCDSCI. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys for the WCDSCI and EXPRESS. The results are expected to help WCDSCI, EXPRESS and other organizations on the WCC to identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals. There were four main steps in the WCC spatial prioritization process. The first step was to identify the technical advisory team, which included the 11 members of the DSCRTP WCDSCI Steering Committee and all of the participants involved in the EXPRESS campaign. This advisory team invited 37 participants for the prioritization. Step two was to develop the spatial framework and an online application. To do this, the WCC was divided into five subregions and 3,265 square grid cells approximately 10x10 minutes in size. Existing relevant spatial datasets (e.g., bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 26 participants to enter their priorities in each subregion of interest. Participants allocated virtual coins in the 10x10 minute grid cells to denote their priorities. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the subregions and used to identify spatial patterns across the WCC region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 10x10 minute grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analyses please see: Costa et al. 2019.

Public database of geological-paleontological mapping in the surroundings of Vălioara

The database contains the coordinates of the geological-paleontological mapping sites and measurements in the area of Vălioara (Romania) from 2019 onwards. The Excel format file data tables contain in separate worksheets the localities, the measurements and the explanation of the mapping units. The coordinates are given in UTM34 coordinate system and also with latitude-longitude data (WGS84 datum).

Phantom measurement data for 'Fast bias-corrected conductivity mapping using stimulated echoes', Iyyakkunnel et al. (2024)

This dataset contains the phantom measurement data used in the article by Iyyakkunnel et al., titled "Fast Bias-Corrected Conductivity Mapping Using Stimulated Echoes," published in MAGMA, 2024 (doi: 10.1007/s10334-024-01194-3). In this study, the feasibility of using a stimulated echo sequence for electrical properties tomography (EPT) is demonstrated. The data were acquired with a 3T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany). The dataset includes magnitude and phase measurements for the proposed Double-Angle Stimulated Echo (DA-STE) sequence, as well as reference measurements, including Double Angle measurements using a Gradient Echo sequence (GRE-DAM) for the B1+ magnitude, and a Single Echo Spin Echo sequence (SE) for the transceive phase. For both the DA-STE and SE sequences, each measurement was repeated with inverted readout gradient polarities, denoted as LR (left-right) and RL (right-left) in the respective measurement folders. For each measurement, magnitude and phase data are provided in separate folders (in dicom (.dcm) format). Please note that for DA-STE, the two echo acquisitions are sequentially stored in the same measurement folder. For further measurement details, please refer to the mentioned original article.

GBIF map data latest examples: species Gadus morhua

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GBIF map data latest examples: genus Gadus

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GBIF map data latest examples: order Gadiformes

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GBIF map data latest examples: family Gadidae

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Data for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."

Data and scripts for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains." Presented at the "JOINT VARENNA - LAUSANNE INTERNATIONAL WORKSHOP: THEORY OF FUSION PLASMAS, 2024" and published in PPCF.

Reducing cardiac-induced noise in brain maps of R2* and magnetic susceptibility

This high resolution dataset contain MR images of one participant acquired with a standard linear sampling and a cartesian pseudo-spiral sampling, with 3 repetitions each. It also contain the corresponding R2* and QSM maps shown in the paper. The data are presented both as .nii and .mat files.

ForestPaths: European tree genus map

<h2>Abstract</h2> This dataset provides an early access version of the European tree genus map at 10 m resolution for the year 2020, derived from Sentinel-1 and Sentinel-2 satellite data. The map distinguishes eight classes (Larix, Picea, Pinus, Fagus, Quercus, other needleleaf, other broadleaf, and no trees) and is distributed as Cloud Optimized GeoTIFFs (COGs) over a 100 km grid in EPSG:3035 (ETRS89 / LAEA Europe).  The map was generated using a CatBoost model trained on forest plot inventories, citizen science observations, orthophoto interpretation, and LUCAS data, with additional features from DEM and climate datasets. Labels were filtered and aggregated to genus level to reduce noise.    <h2>Early access notice</h2> This release is provided as an early access version. The map is still undergoing validation and fine-tuning, and a formal publication is planned. Updates and improvements may therefore be made in future releases.  We welcome feedback and contributions of additional training data to further improve the map.    <h2>Dataset description</h2> <ul> <li>Resolution: 10m</li> <li>Format: Cloud Optimized GeoTIFFs (COGs)</li> <li>Tiling: 100km grid</li> <li>Coordinate reference system: EPSG: 3035 (ETRS89 / LAEA Europe)</li> </ul> <h3>Legend</h3> 0 – Larix  1 – Picea  2 – Pinus  3 – Fagus  4 – Quercus  5 – Other needleleaf  6 – Other broadleaf  7 – No trees  <h2>Methodology summary</h2> The classification was performed using a CatBoost model trained on diverse reference sources [1-10]:  -    National and regional plot inventories  -    Citizen science observations  -    Orthophoto interpretation  -    LUCAS data  Training labels were filtered to reduce noise and aggregated to genus level. Predictor variables include annual statistics from Sentinel-1 and Sentinel-2, combined with auxiliary datasets on altitude (DEM) and climate.  <h2>Further details on the methodology will be made available in the product publication, which will follow this early access release.    Usage Notes </h2> <ul> <li>CRS: EPSG:3035 (ETRS89 / LAEA Europe). Reprojection may be required for use with other datasets.</li> <li>Tiling scheme: Provided as 100 km × 100 km COG tiles. Users may mosaic tiles if needed. </li> <li>Classes: See legend above. Class 7 (“No trees”) includes cropland, grassland, built-up, and other non-tree areas. </li> <li>Early access status: Not yet fully validated. Regional inconsistencies and misclassifications may be present.</li> </ul> Feedback & contributions: We invite users to share validation results and contribute additional reference data to improve future releases.  <h2> How to cite </h2> If you use this dataset, please cite as:  De Keersmaecker, W., Zanaga, D., Senf, C., Viana-Soto, A., Klapper, J., Blickensdörfer, L., Govaere, L., Lerink, B., Leyman, A., Schelhaas, M.-J., Teeuwen, S., Verkerk, P. J., & Van De Kerchove, R. (2025). European Tree Genus Map 2020 (Early Access Release) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13341104  BibTeX  @dataset{dekeersmaecker2025_treegenus,    author       = {De Keersmaecker, Wanda and Zanaga, Daniele and Senf, Cornelius                     and Viana-Soto, Alba and Klapper, Johanna and Blickensdörfer, Lukas                     and Govaere, Leen and Lerink, Bas and Leyman, Anja                     and Schelhaas, Mart-Jan and Teeuwen, Sander and Verkerk, Pieter Johannes and Van De Kerchove, Ruben},    title        = {European Tree Genus Map 2020 (Early Access Release)},    year         = {2025},    publisher    = {Zenodo},    version      = {early-access},    doi          = {10.5281/zenodo.13341104},    url          = {https://doi.org/10.5281/zenodo.13341104}  }  <h2>References</h2> [1] Alberdi, I., Bombín, R. V., González, J. G. Á., Ruiz, S. C., Ferreiro, E. G., García, S. G., Mateo, L. H., Jáuregui, M. M., Pita, F. M., & de Oliveira Rodríguez, N. (2017). The multi-objective Spanish national forest inventory. Forest systems, 26(2), 14.     [2] Álvarez-González, J. G., Canellas, I., Alberdi, I., Gadow, K. V., & Ruiz-González, A. (2014). National Forest Inventory and forest observational studies in Spain: Applications to forest modeling. Forest Ecology and Management, 316, 54-64.   [3] Finnish Forest Centre (Metsäkeskus). (2025). Forest resource lattice data (Hila-aineisto) [2019–2021]. Retrieved from https://www.metsakeskus.fi. [4] Fridman, J., Holm, S., Nilsson, M., Nilsson, P., Ringvall, A. H., & Ståhl, G. (2014). Adapting National Forest Inventories to changing requirements–the case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fennica, 48(3).   [5] Govaere L. & Leyman A. (2023). Vlaamse bosinventarisatie Agentschap Natuur en Bos (VBI1: 1997-1999; VBI2: 2009-2018; VBI3: 2019-2021, v2023-03-17). [6] Heisig, J., & Hengl, T. (2020). Harmonized Tree Species Occurrence Points for Europe (0.2). https://doi.org/https://doi.org/10.5281/zenodo.5524611  [7] IGN. (2016). BD Forêt Version 2.0. January 2016  [8] Riedel T., Hennig P., Kroiher F., Polley H., Schmitz F., Schwitzgebel F. (2017): Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertemethoden, 124 S. [9] Schelhaas MJ, Teeuwen S, Oldenburger J, Beerkens G, Velema G, Kremers J, Lerink B, Paulo MJ, Schoonderwoerd H, Daamen W, Dolstra F, Lusink M, van Tongeren K, Scholten T, Pruijsten L, Voncken F, Clerkx APPM (2022). Zevende Nederlandse Bosinventarisatie; Methoden en resultaten. Wettelijke Onderzoekstaken Natuur & Milieu, WOt-rapport 142. https://edepot.wur.nl/571720 [10] Villaescusa, R. & Díaz, R. (1998) Segundo inventario forestal nacional (1986–1996). Ministerio de Medio Ambiente, ICONA, Madrid. <h2>Acknowledgements</h2> We are very grateful for access to the forest plot inventories. We thank the Ministerio para la Transición Ecológica y Reto Demográfico (MITECO) for open access of the Spanish Forest Inventory (https://www.miteco.gob.es/). Finally, we would like to acknowledge the ForestPaths project (Co-designing Holistic Forest-based Policy Pathways for Climate Change Mitigation), that receives funding from the European Union's Horizon Europe Research and Innovation Programme (ID No 101056755), as well as from the United Kingdom Research and Innovation Council (UKRI).

opencc-by-4.0Jun 2024View details →

Database for GWAS SVatalog: a visualization tool to aid fine-mapping of GWAS loci with structural variations.

GWAS SVatalog is a novel visualization tool and database for structural variants (SV) found in a predominantly European population of 101 individuals with Cystic Fibrosis (CF). Aside from the CF-causing variants on chromosome 7 and the LD block in which they lie, the remainder of the genome is comparable to a the 1000 Genomes healthy European population. This data is a collection of SV calls and their linkage disequilibrium (LD) statistics with GWAS-significant SNPs reported in the GWAS Catalog.   The goal of this project is to provide a resource to aid fine mapping of GWAS loci using SVs. GWAS loci are generally identified by SNPs which account for an incomplete proportion of genetic variation and phenotypic heritability. Their relevance to the phenotype might be limited, tagging other polymorphisms, such as SVs, that could be the cause of the association signal. To leverage this data to its full potential, visit the <a href="https://svatalog.research.sickkids.ca/" target="_blank" rel="noopener">GWAS SVatalog</a> web tool. Here, interactive visualizations can illustrate SVs identified in high LD with GWAS-significant SNPs, suggesting putative causal variation that could guide additional functional investigation.   For more information on how to use GWAS SVatalog, visit the<a href="https://gwas-svatalog-docs.readthedocs.io/en/latest/index.html" target="_blank" rel="noopener noreferrer"> documentation</a>.   This project was accomplished in collaboration with the <a href="https://lab.research.sickkids.ca/strug/" target="_blank" rel="noopener">Strug Lab</a> at <a href="https://www.sickkids.ca/en/" target="_blank" rel="noopener">The Hospital for Sick Children (SickKids)</a>, <a href="https://www.tcag.ca/" target="_blank" rel="noopener">The Center for Applied Genomics (TCAG)</a>, and <a href="https://www.utoronto.ca/" target="_blank" rel="noopener">University of Toronto</a>.

Hyperspectral environmental illumination maps for outdoor and indoor scenes

This repository contains a dataset of hyperspectral illumination maps collected from 6 outdoor and 4 indoor scenes.   If you use this dataset in your research, please cite:   Takuma Morimoto, João M. M. Linhares, Sérgio M. C. Nascimento, and Hannah E. Smithson, “How many surfaces can you distinguish by color? Real environmental lighting increases discriminability of surface colors,” Optics Express (in press).   Technical details about data acquisition are described in:   Takuma Morimoto, Sho Kishigami, João M.M. Linhares, Sérgio M.C. Nascimento, and Hannah E. Smithson, “Hyperspectral environmental illumination maps: characterizing directional spectral variation in natural environments,” Optics Express, 27, 22, 32277 - 32293. (2019). <a href="https://doi.org/10.1364/OE.27.032277">https://doi.org/10.1364/OE.27.032277</a>   Each file includes the following formats:   <ol> <li>png: RGB image for visualization. </li> <li>mat: Hyperspectral image with wavelengths from 400 nm to 700 nm in 10 nm steps. Each pixel value represents spectral radiance in W m−2 sr−1 nm−1. The image and wavelength range are stored in the variables ‘radiance’ and ‘wls’, respectively.</li> </ol> The images have an average spatial resolution of 1019 (height) × 2035 (width) across 10 scenes. File names follow the format X_sceneY, where X is the scene type (outdoor or indoor) and Y is the scene number.   For Python users, the mat file can be loaded using e.g. scipy.io.loadmat. More information: <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html</a>   Note: To use for hyperspectral renderings (e.g., Mitsuba), convert the hyperspectral image to the OpenEXR format.

opencc-by-sa-4.0Nov 2023View details →

Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning

This data is supplementary to the paper titled "Mapping a novel metric for Flash Flood Recovery using Interpretable Machine Learning". The file contains the main results. For any queries, please visit <a href="https://hydrosense.iitd.ac.in" target="_blank" rel="noopener">Hydrosense Lab (IIT Delhi)</a>.

opencc-zeroSep 2024View details →

EOL full taxon identifier map

A mapping of taxon identifiers from EOL resources, of the form: node_id, resource_pk, resource_id, page_id, preferred_canonical_for_page <ul> <li>node_id: internal to EOL; useful for some API calls</li> <li>resource_pk: identifier according to the classification provider </li> <li>resource_id: identifies the classification provider (see below) </li> <li>page_id: EOL taxon concept identifier; official, for sharing </li> <li>preferred_canonical_for_page: canonical name preferred by EOL for this taxon concept </li> </ul> commas within entries are "escaped, by, quoting", and quotes-within-quotes are ""double quoted"" resource_ids, their names and descriptions, are available at: <a href="https://eol.org/resources.json">https://eol.org/resources.json </a> To view one at a time: https://eol.org/resources/[enter ID here]  There is also a summary file with resources ids, links, and resource names here: <a href="https://github.com/KatjaSchulz/eolResources">https://github.com/KatjaSchulz/eolResources</a> And there is a smaller mapping file just for the major EOL classification sources: <a href="../doi/10.5281/zenodo.13769681">EOL taxon identifier map</a>

Supplemental data for: Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study

<div> The dataset was used in the paper “Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study”. The article is currently under review for publication. DOI to be inserted. </div> <div> A data-in-brief article is to be published to give in-depth information about the data collected to improve reproducibility "Dataset for: Lifestyle Factors and Blood Glucose Variability in Adolescents with Type 1 Diabetes Mellitus". DOI to be inserted.    The aim of the study was to assess whether adolescents with T1D in Ireland meet current nutrition and physical activity (PA) guidelines and to explore the impact of nutrition and PA on glycaemic variability (GV). The dataset includes continuous glucose monitoring (CGM) data, dietary intake records, and PA metrics, providing a comprehensive view of the participants' glucose levels and associated lifestyle behaviours. </div>

SPIRE HeRS/HeLMS Combined SHIM Maps

This repository contains sub-mm wavelength sky images of the combined Herschel Stripe 82 Survey (HerS; [1]) and Herschel Large Mode Survey (HeLMS; [2]) fields as observed by the SPIRE photometer [3] on the Herschel Space Observatory [4] at 250, 350, and 500 microns.  These regions were selected to overlap with the SDSS-Stripe82 [5] and ACT-CMB [6] fields in a contiguous fashion.  The maps cover areas of approximately 360 square degrees at 6, 8.3, and 12 arcsec/pixel. The data in this repository improve on previous analyses of these data, which were heterogeneously analyzed, served on non-permanent archives, and occasionally had errors discovered by the user community.  The previous analyses include: - HerS survey maps constructed ~2012 with the SHIM map maker, described in [1] - HeLMS maps constructed ~2015 with the SANEPIC map maker, described in [7] - HerS+HeLMS+XMM maps constructed ~2016 with the SHIM map maker, described in [8]. - HerS and HeLMS individual maps constructed ~2019 with the CADE map maker, described in [9]. While all of these maps are currently circulating in the community, we expect their availability to wane as time goes on.  Further, in many cases the detailed reduction steps and provenance of the served products are unclear.  In this repository we have uploaded vetted and error-free fits files constructed in a consistent way as follows. To start, we use the Herschel Interactive Processing Environment (HIPE, version 15.0.1) and the most recent calibration files (version 14.3) to reduce the level zero observations to level one data products.  The code and calibration tree are the last released by the Herschel team, and we do not expect improvements or updates to them in the future.  For the products available in this download, our processing consists of the standard scanline calibration pipeline for SPIRE that comes pre-packaged in HIPE (Photometer Large Map Pipeline) with a few changes. The pipeline converts the detector timelines from products in engineering units (satellite pointing and voltages) to science products (sky pointing and flux units) with corrections for cross-talk, signal jumps, glitches, cooler burps, the low pass filter response, and the bolometer time response. We chose to turn off the temperature drift correction as we found that the SHIM map-maker performs better without it and we opt to use the sigma-kappa deglitcher rather than the wavelet deglitcher.  We also do not perform any destriping or baseline subtraction of the scan lines with HIPE.  The HIPE script is available as part of this download for inspection. We apply this HIPE reduction to the 33 observation IDs listed below (21 from proposal ID OT2_mviero_2 [HerS; 10] and 12 from proposal ID GT2_mviero_1 [HeLMS; 11]), which are retrieved with HIPE from the Herschel Science Archive (HSA; http://archives.esac.esa.int/hsa/whsa/). <table> <tbody> <tr> <td>Observation IDs</td> </tr> <tr> <td>1342247220</td> </tr> <tr> <td>1342247993</td> </tr> <tr> <td>1342247994</td> </tr> <tr> <td>1342247995</td> </tr> <tr> <td>1342247996</td> </tr> <tr> <td>1342247997</td> </tr> <tr> <td>1342247998</td> </tr> <tr> <td>1342248000</td> </tr> <tr> <td>1342248001</td> </tr> <tr> <td>1342248491</td> </tr> <tr> <td>1342248492</td> </tr> <tr> <td>1342248493</td> </tr> <tr> <td>1342248494</td> </tr> <tr> <td>1342248495</td> </tr> <tr> <td>1342248496</td> </tr> <tr> <td>1342248497</td> </tr> <tr> <td>1342248498</td> </tr> <tr> <td>1342248499</td> </tr> <tr> <td>1342248500</td> </tr> <tr> <td>1342249103</td> </tr> <tr> <td>1342249105</td> </tr> <tr> <td>1342234749</td> </tr> <tr> <td>1342236232</td> </tr> <tr> <td>1342236234</td> </tr> <tr> <td>1342236240</td> </tr> <tr> <td>1342237550</td> </tr> <tr> <td>1342237553</td> </tr> <tr> <td>1342237563</td> </tr> <tr> <td>1342238251</td> </tr> <tr> <td>1342246580</td> </tr> <tr> <td>1342246632</td> </tr> <tr> <td>1342247216</td> </tr> <tr> <td>1342257362</td> </tr> </tbody> </table> The level one observations are saved as HIPE-format fits files that are input into the map-maker as follows.  Spatial maps of the astrophysical emission are produced using the SHIM map maker [12, with additional processing details provided in 13], which has been found to out-perform other map makers in terms of retaining fidelity on all angular scales [14].  SHIM is a Herschel-SPIRE specific implementation of an iterative baseline removal and detector noise weighting algorithm originally described in [15].  The data are processed scan-wise, i.e. baselines and weights are calculated per one pass of the photometer across a region of sky.  A noise map is created by propagating detector noise as estimated by the variance of the residuals and computing the weighted inverse sum in the map pixels.  This detector noise propagation methodology gives a better measure of noise variations across the map and is more robust when the number of samples per pixel is low and the standard deviation becomes a poor estimate of the statistical error in the measurement.  Also included in the fits files are exposure and flag information.  The images are in units of [Jy/beam], which can be converted to units of surface brightness in [MJy/Sr] by applying the multiplicative conversion factors 90.646, 51.181, and 23.580 at 250, 350, and 500 microns, respectively.  The error maps are reported as the square root of the variance of the statistical noise in each pixel, and do not include the (large) effect of confusion noise in these maps [see eg 16].  Though we do not explicitly provide map transfer function estimates, the transfer function is 1% to angular scales of ~1 degree, and thereafter falls exponentially with a 3dB point of ~6 degrees. The FITS files provided in this repository are labeled with the SPIRE band names PSW, PMW, and PLW, which correspond to 250, 350, and 500 microns, respectively. Each FITS file contains a header data unit list (HDU, HDUL) with a primary HDU containing only very basic header info and four image HDUs with more extensive header info and the image data. The table below gives some information on the HDUL.  <table> <tbody> <tr> <td>Ext number</td> <td>Name</td> <td>Type</td> <td>Format</td> <td>Units</td> </tr> <tr> <td>0</td> <td>PRIMARY</td> <td>PrimaryHDU</td> <td>N/A</td> <td>N/A</td> </tr> <tr> <td>1</td> <td>image</td> <td>ImageHDU</td> <td>float64</td> <td>Jy/Beam</td> </tr> <tr> <td>2</td> <td>error</td> <td>ImageHDU</td> <td>float64</td> <td>Jy/beam</td> </tr> <tr> <td>3</td> <td>exposure</td> <td>ImageHDU</td> <td>float64</td> <td>s</td> </tr> <tr> <td>4</td> <td>mask</td> <td>ImageHDU</td> <td>int32</td> <td>N/A</td> </tr> </tbody> </table> The 'image' extension contains the science mosaics calibrated in F_nu, the 'error' extension contains the errors corresponding to the science mosaic (and are derived as described above), the 'exposure' extension is the effective number of seconds of integration in each map pixel, and finally the 'mask' extension contains the image mask, which is currently an array of zeros as no masking has been applied to these images. Each image extension also contains information to reference to the world coordinate system.

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types probabilities (part 1)</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types probabilities (part 2)</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>

Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data

<h2>Sub-dataset: WRB soil types classification and relative entropy</h2> <h2>Disclaimer</h2> This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk. <h2>Description</h2> This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics. This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset. This data set includes: <ul> <li>Soil types classification and relative entropy: This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li>Soil types probabilities (part 1): This data includes 92 averaged probabilities (0-1) maps for classes from abruptic.acrisols to gleyic.arenosols. The probabilites were scaled 100x (0-100). </li> <li>Soil types probabilities (part 2): This data includes 93 averaged probabilities maps for classes from gleyic.cambisols to vitric.andosols. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li>Time period: long term.</li> <li>Type of data: Soil types classification and model probabilities.</li> <li>How the data was collected or derived: The data was derived using ensemble ML models.</li> <li>Statistical methods used: Relative entropy (Kullback-Leibler divergence)</li> <li>Limitations or exclusions in the data: The dataset does not include data for Svalbard. </li> <li>Coordinate reference system: EPSG:3035</li> <li>Bounding box (Xmin, Ymin, Xmax, Ymax): (900,000, 899,000, 7,401,000, 5,501,000)</li> <li>Spatial resolution: 30m</li> <li>Image size: 216,700P x 153,400L</li> <li>File format: Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc) <h3>Name convention</h3> To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif, the fields are: <ol> <li>generic variable name: soil.types = soil types</li> <li>variable procedure combination: ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li>Position in the probability distribution/variable type: m = mean | c = class | p = probability</li> <li>Spatial support: 30m</li> <li>Depth reference: s = surface</li> <li>Time reference begin time: 20220101 = 2022-01-01</li> <li>Time reference end time: 20221231 = 2022-12-31</li> <li>Bounding box: eu = pan-Europe</li> <li>EPSG code: epsg.3035</li> <li>Version code: v20240917 = version from 2024-09-17</li> </ol>