This dataset 100 top-rated maps posted on Reddit’s r/MapPorn between June 2024 and June 2025 in the form of 50 most popular posts labeled “Europe” and 50 top-ranking maps using the word “European,” after excluding duplicates and maps explicitly limited to the EU or only parts of Europe. The data was collected using Reddit’s native search tool. The collected maps were then subjected to manual coding to determine their type of representation for Europe.

Spatial data from Schulp et al., 2014. Uncertainties in ecosystem service maps: A comparison on the European scale. PloS ONE 9, e109643. Safeguarding the benefits that ecosystems provide to society is increasingly included as a target in international policies. To support such policies, ecosystem service maps are made. However, there is little attention for the accuracy of these maps. We made a systematic review and quantitative comparison of ecosystem service maps on the European scale to generate insights in the uncertainty of ecosystem service maps and discuss the possibilities for quantitative validation. This data package contains maps of the ecosystem services climate regulation, erosion protection, flood regulation, pollination, and recreation. For each service, a map of the average supply according to all analyzed maps is included, as well as a map of the uncertainty of the service. The data package contains a detailed read-me.

An historical review of how the land borders between Europe and Asia kept changing on maps over the last five centuries. By analyzing the sequence of nine cartographic achievement milestones of their times, we observe how the continental borders became better and better defined. The analysis also includes the progress in the art of cartography by seeing the development in the geographic knowledge of Europeans (more and more accurate seashore delineation of the continent), cartographic graticules, and symbology. Something interesting about, or characteristic for each map, is also brought to the attention of the reader.

European Sold Production of Printed Maps, Hydrographic or Similar Charts Not in Book-Form by Country, 2023 Discover more data with ReportLinker!

Annual land cover mapping for continental Europe based on Ensemble Machine Learning (EML), samples obtained from LUCAS (Land Use and Coverage Area frame Survey) and CLC (CORINE Land Cover) Maps, and several harmonized raster layers (e.g. GLAD Landsat ARD imagery and Continental EU DTM). The EML predicted the dominant land cover, probabilities and uncertainties for 33 classes compatible with CLC over 20 years (2000–2019), and was implemented in R and Python (eumap library).

The raster layers were mainly composed by the GLAD Landsat ARD imagery, which were downloaded for the years 1999 to 2020 considering the Continental Europe extent (land mask area and tiling system), screened to reduce cloud cover (GLAD quality assessment band), aggregated by season according with three different quantiles (i.e. 25th, 50th and 75th), and gap-filled using the Temporal Moving Window Median approach available in the eumap library. The images for each season were selected using the same calendar dates for all period:

• Winter: December 2 of previous year until March 20 of current year
• Spring: March 21 until June 24 of current year
• Summer: June 25 until September 12 of current year
• Fall: September 13 until December 1 of current year

In addition to Landsat spectral data, the EML considered night lights (VIIRS/SUOMI NPP), Global surface water frequency, Continental EU DTM, Landsat spectral indices (SAVI, NDVI, NBR, NBR2, REI and NDWI) and the max/min. monthly geometric temperature, estimated on a pixel basis and for each month.

The training data were obtained from the geographic location of LUCAS (in-situ source) and the centroid of all polygons of CORINE (supplementary source), harmonized according to the 33 CLC and organized by year, where each unique combination of longitude, latitude and year was treated as a independent sample with the following classes (the class descriptions are here):

• 111: Urban fabric
• 122: Road and rail networks and associated land
• 123: Port areas
• 124: Airports
• 131: Mineral extraction sites
• 132: Dump sites
• 133: Construction sites
• 141: Green urban areas
• 211: Non-irrigated arable land
• 212: Permanently irrigated arable land
• 213: Rice fields
• 221: Vineyards
• 222: Fruit trees and berry plantations
• 223: Olive groves
• 231: Pastures
• 311: Broad-leaved forest
• 312: Coniferous forest
• 321: Natural grasslands
• 322: Moors and heathland
• 323: Sclerophyllous vegetation
• 324: Transitional woodland-shrub
• 331: Beaches, dunes, sands
• 332: Bare rocks
• 333: Sparsely vegetated areas
• 334: Burnt areas
• 335: Glaciers and perpetual snow
• 411: Inland wetlands
• 421: Maritime wetlands
• 511: Water courses
• 512: Water bodies
• 521: Coastal lagoons
• 522: Estuaries
• 523: Sea and ocean

The LUCAS points with a unique land cover class received a confidence rating of 100%, while CORINE points received 85%, values which were considered by EML as sample weight in the training phase. The points were used in a spacetime overlay approach, which considered the location and the year to retrieve the pixel values of all rasters. Some specific land cover samples (i.e. 111, 122, 131, 141, 211, 221, 222, 223, 231, 311, 312, 321, 411, 512) were screened according to convergence with pre-existing mapping products (OSM roads, OSM railways and Copernicus-OSM buildings; Copernicus high resolution layers), where, for example, “111: Urban fabric” samples located in low density building areas (> 50% according to Copernicus-OSM building layer) were removed from the final training data ( ~5.3 million samples and 178 covariates/features).

Using this training data, three ML models were trained to predict probabilities (i.e. Random Forest, XGBoost, Artificial Neural Network), which served as input to train a linear meta-model (i.e. Logistic regression classifier), responsable for predicting the final land cover probabilities of all classes. The hyperparameter optimization was conducted using a 5-fold spatial cross validation, based on a 30x30km tilling system. The uncertainties were calculated for all classes according to the standard deviation of the three predicted probabilities for each pixel, and the highest probability was selected as the dominant land cover class, resulting in 20 annual maps for continental Europe.

The training samples, covariates/features and fitted models are available through lcv_landcover.hcl_lucas.corine.eml_p_landmapper_full.lz4, a LandMapper class instance that can be loaded by eumap library (check the code demonstration). The production code used to generate the current version of the annual land cover maps is available in the spatial layer repository and considered a lighter LandMapper class instance (lcv_landcover.hcl_lucas.corine.eml_p_landmapper_light.lz4,), which not includes the training samples.

Only the dominant land cover classes are provided here. To access the probabilities and uncertainties use:

• Open Data Science Europe viewer: https://maps.opendatascience.eu
• S3 Cloud Object Service: https://medium.com/swlh/europe-from-above-space-time-machine-learning-reveals-our-changing-environment-1b05cb7be520

A publication describing, in detail, all processing steps, accuracy assessment and general analysis of land-cover changes in continental Europe is under preparation. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues

This is the authors’ version of the work. It is based on a poster presented at the Wageningen Conference on Applied Soil Science, http://www.wageningensoilmeeting.wur.nl/UK/ Cite as: Bosco, C., de Rigo, D., Dewitte, O., Montanarella, L., 2011. Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map. Wageningen Conference on Applied Soil Science “Soil Science in a Changing World”, 18 - 22 September 2011, Wageningen, The Netherlands. Author’s version DOI:10.6084/m9.figshare.936872 arXiv:1402.3847

Towards the reproducibility in soil erosion modeling:a new Pan-European soil erosion map

Claudio Bosco ¹, Daniele de Rigo ¹ ² , Olivier Dewitte ¹, Luca Montanarella ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy

Soil erosion by water is a widespread phenomenon throughout Europe and has the potentiality, with his on-site and off-site effects, to affect water quality, food security and floods. Despite the implementation of numerous and different models for estimating soil erosion by water in Europe, there is still a lack of harmonization of assessment methodologies. Often, different approaches result in soil erosion rates significantly different. Even when the same model is applied to the same region the results may differ. This can be due to the way the model is implemented (i.e. with the selection of different algorithms when available) and/or to the use of datasets having different resolution or accuracy. Scientific computation is emerging as one of the central topic of the scientific method, for overcoming these problems there is thus the necessity to develop reproducible computational method where codes and data are available. The present study illustrates this approach. Using only public available datasets, we applied the Revised Universal Soil loss Equation (RUSLE) to locate the most sensitive areas to soil erosion by water in Europe. A significant effort was made for selecting the better simplified equations to be used when a strict application of the RUSLE model is not possible. In particular for the computation of the Rainfall Erosivity factor (R) the reproducible research paradigm was applied. The calculation of the R factor was implemented using public datasets and the GNU R language. An easily reproducible validation procedure based on measured precipitation time series was applied using MATLAB language. Designing the computational modelling architecture with the aim to ease as much as possible the future reuse of the model in analysing climate change scenarios is also a challenging goal of the research.

References [1] Rusco, E., Montanarella, L., Bosco, C., 2008. Soil erosion: a main threats to the soils in Europe. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 37-45 [2] Casagrandi, R. and Guariso, G., 2009. Impact of ICT in Environmental Sciences: A citation analysis 1990-2007. Environmental Modelling & Software 24 (7), 865-871. DOI:10.1016/j.envsoft.2008.11.013 [3] Stallman, R. M., 2005. Free community science and the free development of science. PLoS Med 2 (2), e47+. DOI:10.1371/journal.pmed.0020047 [4] Waldrop, M. M., 2008. Science 2.0. Scientific American 298 (5), 68-73. DOI:10.1038/scientificamerican0508-68 [5] Heineke, H. J., Eckelmann, W., Thomasson, A. J., Jones, R. J. A., Montanarella, L., and Buckley, B., 1998. Land Information Systems: Developments for planning the sustainable use of land resources. Office for Official Publications of the European Communities, Luxembourg. EUR 17729 EN [6] Farr, T. G., Rosen, P A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D., 2007. The Shuttle Radar Topography Mission. Review of Geophysics 45, RG2004, DOI:10.1029/2005RG000183 [7] Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M., 2008. A European daily high-resolution gridded dataset of surface temperature and precipitation. Journal of Geophysical Research 113, (D20) D20119+ DOI:10.1029/2008jd010201 [8] Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder, D. C., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture handbook 703. US Dept Agric., Agr. Handbook, 703 [9] Bosco, C., Rusco, E., Montanarella, L., Panagos, P., 2009. Soil erosion in the alpine area: risk assessment and climate change. Studi Trentini di scienze naturali 85, 119-125 [10] Bosco, C., Rusco, E., Montanarella, L., Oliveri, S., 2008. Soil erosion risk assessment in the alpine area according to the IPCC scenarios. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 47-58 [11] de Rigo, D. and Bosco, C., 2011. Architecture of a Pan-European Framework for Integrated Soil Water Erosion Assessment. IFIP Advances in Information and Communication Technology 359 (34), 310-31. DOI:10.1007/978-3-642-22285-6_34 [12] Bosco, C., de Rigo, D., Dewitte, O., and Montanarella, L., 2011. Towards a Reproducible Pan-European Soil Erosion Risk Assessment - RUSLE. Geophys. Res. Abstr. 13, 3351 [13] Bollinne, A., Laurant, A., and Boon, W., 1979. L’érosivité des précipitations a Florennes. Révision de la carte des isohyétes et de la carte d’erosivite de la Belgique. Bulletin de la Société géographique de Liége 15, 77-99 [14] Ferro, V., Porto, P and Yu, B., 1999. A comparative study of rainfall erosivity estimation for southern Italy and southeastern Australia. Hydrolog. Sci. J. 44 (1), 3-24. DOI:10.1080/02626669909492199 [15] de Santos Loureiro, N. S. and de Azevedo Coutinho, M., 2001. A new procedure to estimate the RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve region, Portugal. J. Hydrol. 250, 12-18. DOI:10.1016/S0022-1694(01)00387-0 [16] Rogler, H., and Schwertmann, U., 1981. Erosivität der Niederschläge und Isoerodentkarte von Bayern (Rainfall erosivity and isoerodent map of Bavaria). Zeitschrift fur Kulturtechnik und Flurbereinigung 22, 99-112 [17] Nearing, M. A., 1997. A single, continuous function for slope steepness influence on soil loss. Soil Sci. Soc. Am. J. 61 (3), 917-919. DOI:10.2136/sssaj1997.03615995006100030029x [18] Morgan, R. P C., 2005. Soil Erosion and Conservation, 3rd ed. Blackwell Publ., Oxford, pp. 304 [19] Šúri, M., Cebecauer, T., Hofierka, J., Fulajtár, E., 2002. Erosion Assessment of Slovakia at regional scale using GIS. Ecology 21 (4), 404-422 [20] Cebecauer, T. and Hofierka, J., 2008. The consequences of land-cover changes on soil erosion distribution in Slovakia. Geomorphology 98, 187-198. DOI:10.1016/j.geomorph.2006.12.035 [21] Poesen, J., Torri, D., and Bunte, K., 1994. Effects of rock fragments on soil erosion by water at different spatial scales: a review. Catena 23, 141-166. DOI:10.1016/0341-8162(94)90058-2 [22] Wischmeier, W. H., 1959. A rainfall erosion index for a universal Soil-Loss Equation. Soil Sci. Soc. Amer. Proc. 23, 246-249 [23] Iverson, K. E., 1980. Notation as a tool of thought. Commun. ACM 23 (8), 444-465. DOI:10.1145/358896.358899 [24] Quarteroni, A., Saleri, F., 2006. Scientific Computing with MATLAB and Octave. Texts in Computational Science and Engineering. Milan, Springer-Verlag [25] The MathWorks, 2011. MATLAB. http://www.mathworks.com/help/techdoc/ref/ [26] Eaton, J. W., Bateman, D., and Hauberg, S., 2008. GNU Octave Manual Version 3. A high-level interactive language for numerical computations. Network Theory Limited, ISBN: 0-9546120-6-X [27] de Rigo, D., 2011. Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modeling. The Mastrave project. http://mastrave.org/doc/MTV-1.012-1 [28] de Rigo, D., (exp.) 2012. Semantic array programming for environmental modelling: application of the Mastrave library. In prep. [29] Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P.: Modelling Soil Erosion at European Scale. Towards Harmonization and Reproducibility. In prep. [30] R Development Core Team, 2005. R: A language and environment for statistical computing. R Foundation for Statistical Computing. [31] Stallman, R. M., 2009. Viewpoint: Why “open source” misses the point of free software. Commun. ACM 52 (6), 31–33. DOI:10.1145/1516046.1516058 [32] de Rigo, D. 2011. Multi-dimensional weighted median: the module "wmedian" of the Mastrave modelling library. Mastrave project technical report. http://mastrave.org/doc/mtv_m/wmedian [33] Shakesby, R. A., 2011. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Reviews 105 (3-4), 71-100. DOI:10.1016/j.earscirev.2011.01.001 [34] Zuazo, V. H., Pleguezuelo, C. R., 2009. Soil-Erosion and runoff prevention by plant covers: A review. In: Lichtfouse, E., Navarrete, M., Debaeke, P Véronique, S., Alberola, C. (Eds.), Sustainable Agriculture. Springer Netherlands, pp. 785-811. DOI:10.1007/978-90-481-2666-8_48

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Stress Map of the Mediterranean and Central Europe 2016 displays 5011 A-C quality stress data records of the upper 40 km of the Earth’s crust from the WSM database release 2016 (Heidbach et al, 2016, http://doi.org/10.5880/WSM.2016.001). Focal mechanism solutions determined as being potentially unreliable (labelled as Possible Plate Boundary Events in the database) are not displayed. Further detailed information on the WSM quality ranking scheme, guidelines for the various stress indicators, and software for stress map generation and the stress pattern analysis is available at www.world-stress-map.org.

To support the ecological model of the study Rodríguez et al. (2020, in review), five BIOCLIM variables (BIO1, BIO6, BIO10, BIO11 and BIO12) were computed from the Oscillayers dataset, for 11 subdivisions of the Marine Isotope Stages MIS 14 to MIS 11, as defined in (Rodríguez et al 2020, in review).
Oscillayers is a global‐scale and region‐specific BIOCLIM paleoclimatic datasets with high temporal resolution spanning the Plio‐Pleistocene, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc‐minutes) and temporal (10 kyr time periods) resolution (Gamisch, 2019).
BIOCLIM is a model designed for Species Distribution Modelling (SDM) that defines a set of 19 bioclimatic variables derived from monthly temperature and rainfall values in order to obtain biologically meaningful variables that are commonly used in ecology to model species or biome distributions (Booth et al., 2014; Nix, 1986).
The GIS computation was conducted using GRASS GIS map algebra (Shapiro & Westervelt, 1991) scripted via its Python API. The according Python scripts are attached to this dataset.

Our Europe Zip Code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas for numerous European countries. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

This digitally compiled map includes geology, geologic provinces, and oil and gas fields of Europe including Turkey. The maps are part of a worldwide series of maps on CD-ROM released by the U.S. Geological Survey's World Energy Project. The goal of the project is to assess the undiscovered, technically recoverable oil and gas resources of the world. For data management purposes the world was divided into eight energy regions corresponding approximately to the economic regions of of the world as defined by the U.S. Department of State. Europe (Region 4) includes Albania, Andorra, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Liechtenstein, Luxembourg, The Former Yugoslav Republic of Macedonia, Malta, Monaco, Netherlands, Norway, Poland, Portugal, Romania, San Marino, Serbia and Montenegro, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom and Vatican. The depicted portion of Region 2 includes Turkey. Each region is divided into geologic provinces. Each province has a set of geologic characteristics that distinguish it from surrounding provinces. These characteristics may include the predominant lithologies, the age of the strata, and the structural style. Some provinces include multiple genetically-related basins. Geologic province boundaries are delineated using data from a number of geologic maps and other tectonic and geographic data (see References). Offshore province boundaries are defined by the 2000 meter bathymetric contour. Each province is assigned a unique number. Because geologic trends are independent of political boundaries, some provinces overlap two regions. The code of those provinces that lie entirely within Europe begin with the number 4 and those provinces that lie entirely within Turkey begin with the number 2. The code of those provinces that lie partly within another region may start with a 1, for the Former Soviet Union (Persits and others 1998) or a 2, for Middle East and North Africa (Pollastro , 1998; Persits and others, 1997). The centerpoint locations of oil and gas fields are plotted based on the locations in the Petroconsultants International Data Corp. (1996) database with permission. Selected provinces are currently being investigated, by Total Petroleum System analysis, and assessments are being made of the undiscovered oil and gas resource potential of these provinces. Klett and others (1997) discuss the worldwide geologic provinces and their relative ranking in terms of total known petroleum volume. Specific details of the data sources and map compilation are given in the metadata files on this CD-ROM. Some stratigraphic units are combined to simplify the map and to ensure consistency across the region. All rocks are colored by age. Igneous and metamorphic rocks are identified with fill patterns and colors. These maps are compiled using Environmental Systems Research Institute Inc. (ESRI) ARC/INFO software. Political boundaries and cartographic representations on this map are taken, with permission from ESRI's ArcWorld 1:3M digital coverage; they have no political significance and are displayed as general reference only. Portions of this database covering the coastline and country boundaries contain intellectual property of ESRI. (© 1992 and 1996, Environmental Systems Research Institute Inc. All rights reserved.)

This dataset series shows the distribution map (raster format: geotiff) of Abies alba. The distribution map is provided for Europe (EU28 plus part of other countries within the spatial extent), computed using the FISE harmonised European dataset of taxa presence/absence (based on the integration and harmonisation of the datasets by European National Forestry Inventories; BioSoil; Forest Focus/Monitoring; EUFGIS; GeneticDiversity). The distribution is estimated by means of statistical interpolation (Constrained Spatial Multi-Frequency Analysis, C-SMFA). Available years: 2006. The maps are available in the Forest Information System for Europe (FISE). FISE is run by the European Commission, Joint Research Centre. See the field Lineage for further information.

When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• Various authors, 2016. Abies alba in Europe: an outline on distribution, habitat, importance and threats. In: Online European Atlas of Forest Tree Species. FISE Comm. Publications Office of the European Union. pp. e01493b+. (Under review: please, check the current status at: https://w3id.org/mtv/FISE-Comm/v01/e01493b)

• de Rigo, D., Caudullo, G., Houston Durrant, T., San-Miguel-Ayanz, J., 2016. The European Atlas of Forest Tree Species: modelling, data and information on forest tree species. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e01aa69+. https://w3id.org/mtv/FISE-Comm/v01/e01aa69

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This dataset provides values for CORRUPTION INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

European Directive 2002/49/EC of 25 June 2002 on the assessment and management of environmental noise entails for EU Member States an assessment of environmental noise in the vicinity of major transport infrastructure (land and air) and in large agglomerations. This assessment is carried out in particular through the development of ‘so-called’ noise maps, the first series of which were drawn up in 2007 (1st deadline of the Directive) and 2012 (2nd deadline). Article L572-5 of the Environmental Code states that these maps are “reviewed, and if necessary revised, at least every five years”. Thus, the implementation of this review leads, in 2017 and as appropriate, to revise or renew the maps previously developed.

Strategic Noise Maps (CBS) are designed to allow for the overall assessment of exposure to noise and to forecast its evolution.

CBS are required in particular for road infrastructure with annual traffic of more than 3 million vehicles per year. For major road and rail transport infrastructure, the CBS are established, decided and approved under the authority of the prefect of the department.

Noise maps are developed according to the indicators established by the European Directive, namely Lden (Day Evening Night Level) and Ln (Night Level). • Day/day: [6h-18h] • Evening/evening: [18h-22h] • Night/night: [22h-6h] The Lden and Ln indicators correspond to a defined energy average over the periods (Day/Black/Night) for Lden and (Night) for Ln. The corresponding results are expressed in A or dB(A) weighted decibels.

Type B maps correspond to the areas affected by noise in accordance with the noise classification of land transport infrastructure which has been drawn up and adopted by the Prefect pursuant to Article L571-10 of the Environmental Code.

This classification defines, for future residential, educational, health and hotel buildings located in these areas affected by noise, a minimum sound isolation of buildings. These requirements are laid down in the Decree of 30 May 1996 as amended by Decree of 23 July 2013.

For a list of pathways, please refer to the non-technical summary (page 9).

European Sold Production of Printing Services for Books, Maps, Hydrographic, Pictures, Designs and Photographs, and Postcards Share by Country (Euros), 2023 Discover more data with ReportLinker!

This dataset series shows the distribution map (raster format: geotiff) of Populus tremula. The distribution map is provided for Europe (EU28 plus part of other countries within the spatial extent), computed using the FISE harmonised European dataset of taxa presence/absence (based on the integration and harmonisation of the datasets by European National Forestry Inventories; BioSoil; Forest Focus/Monitoring; EUFGIS; GeneticDiversity). The distribution is estimated by means of statistical interpolation (Constrained Spatial Multi-Frequency Analysis, C-SMFA). Available years: 2006. The maps are available in the Forest Information System for Europe (FISE). FISE is run by the European Commission, Joint Research Centre. See the field Lineage for further information.

When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• Various authors, 2016. Populus tremula in Europe: an outline on distribution, habitat, importance and threats. In: Online European Atlas of Forest Tree Species. FISE Comm. Publications Office of the European Union. pp. e01f148+. (Under review: please, check the current status at: https://w3id.org/mtv/FISE-Comm/v01/e01f148)

• de Rigo, D., Caudullo, G., Houston Durrant, T., San-Miguel-Ayanz, J., 2016. The European Atlas of Forest Tree Species: modelling, data and information on forest tree species. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e01aa69+. https://w3id.org/mtv/FISE-Comm/v01/e01aa69

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This dataset series shows the distribution map (raster format: geotiff) of Pinus mugo. The distribution map is provided for Europe (EU28 plus part of other countries within the spatial extent), computed using the FISE harmonised European dataset of taxa presence/absence (based on the integration and harmonisation of the datasets by European National Forestry Inventories; BioSoil; Forest Focus/Monitoring; EUFGIS; GeneticDiversity). The distribution is estimated by means of statistical interpolation (Constrained Spatial Multi-Frequency Analysis, C-SMFA). Available years: 2006. The maps are available in the Forest Information System for Europe (FISE). FISE is run by the European Commission, Joint Research Centre. See the field Lineage for further information.

When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• Various authors, 2016. Pinus mugo in Europe: an outline on distribution, habitat, importance and threats. In: Online European Atlas of Forest Tree Species. FISE Comm. Publications Office of the European Union. pp. e012d81+. (Under review: please, check the current status at: https://w3id.org/mtv/FISE-Comm/v01/e012d81)

• de Rigo, D., Caudullo, G., Houston Durrant, T., San-Miguel-Ayanz, J., 2016. The European Atlas of Forest Tree Species: modelling, data and information on forest tree species. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e01aa69+. https://w3id.org/mtv/FISE-Comm/v01/e01aa69

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IntroductionCoercion is frequently used in mental health practice. Since it overrides some patients’ fundamental human rights, adequate use of coercion requires legal and ethical justifications. Having internationally standardised datasets to benchmark and monitor coercion reduction programs is desirable. However, only a few countries have specific, open, publicly accessible registries for this issue.MethodsThis study aims to assemble expert opinions regarding strategies that might be feasible for promoting, developing, and implementing an integrated and differentiated coercion data collection system in Europe at national and international levels. A concept mapping methodology was followed, involving 59 experts from 27 countries in generating, sorting and rating strategies regarding relevance and feasibility. The experts were all researchers and/or practitioner members of an EU-COST-Action focused on coercion reduction Fostering and Strengthening Approaches to Reducing Coercion in European Mental Health Services (FOSTREN).ResultsA hierarchical cluster analysis revealed a conceptual map of 41 strategies organized in seven clusters. These clusters fit into two higher-order domains: “Advancing Global Health Research: Collaboration, Accessibility, and Technological Innovations/Advancing International Research” and “Strategies for Comprehensive Healthcare Data Integration, Standardization, and Collaboration.” Regarding the action with the higher priority, relevance was generally rated higher than feasibility. No differences could be found regarding the two domains regarding the relevance rating or feasibility of the respective strategies in those domains. The following strategies were rated as most relevant: “Collection of reliable data”, “Implementation of nationwide register, including data on coercive measures”, and “Equal understanding of different coercive measures”. In analysing the differences in strategies between countries and their health prosperity, the overall rating did not differ substantially between the groups.ConclusionThe strategy rated as most relevant was the collection of reliable data in the nationwide health register, ensuring that countries share a standard understanding/definition of different coercive measures. Respondents did not consider the feasibility of establishing a shared European database for coercive measures to be high, nor did they envision the unification of mental health legislation in the future. There is some consensus on the most suitable strategies that can be adopted to enable international benchmarking of coercion in mental health settings.

This dataset series shows the distribution map (raster format: geotiff) of Picea abies. The distribution map is provided for Europe (EU28 plus part of other countries within the spatial extent), computed using the FISE harmonised European dataset of taxa presence/absence (based on the integration and harmonisation of the datasets by European National Forestry Inventories; BioSoil; Forest Focus/Monitoring; EUFGIS; GeneticDiversity). The distribution is estimated by means of statistical interpolation (Constrained Spatial Multi-Frequency Analysis, C-SMFA). Available years: 2006. The maps are available in the Forest Information System for Europe (FISE). FISE is run by the European Commission, Joint Research Centre. See the field Lineage for further information.

When using these data, please cite the relevant data sources. A suggested citation is included in the following:

• Various authors, 2016. Picea abies in Europe: an outline on distribution, habitat, importance and threats. In: Online European Atlas of Forest Tree Species. FISE Comm. Publications Office of the European Union. pp. e012300+. (Under review: please, check the current status at: https://w3id.org/mtv/FISE-Comm/v01/e012300)

• de Rigo, D., Caudullo, G., Houston Durrant, T., San-Miguel-Ayanz, J., 2016. The European Atlas of Forest Tree Species: modelling, data and information on forest tree species. In: San-Miguel-Ayanz, J., de Rigo, D., Caudullo, G., Houston Durrant, T., Mauri, A. (Eds.), European Atlas of Forest Tree Species. Publ. Off. EU, Luxembourg, pp. e01aa69+. https://w3id.org/mtv/FISE-Comm/v01/e01aa69

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Refer to preprint here: https://arxiv.org/abs/2104.10922

A land cover classification for Europe at 10 m resolution produced with a machine learning workflow driven by Sentinel optical and radar satellite imagery. The classification model was trained on land cover reference data form the LUCAS (Land Use/Cover Area frame Survey) dataset. The map represents conditions in 2018.

The methodology is currently under review, but this will be updated as soon as the paper is available online. Please refer to the publication for accuracy estimates and usage guidelines.

The map is split up into a number of raster tiles with the coordinate reference system "EPSG:3035 - ERTS89 / LAEA Europe".The filename of each tile is in the form baseFilename-yMin-xMin where xMin and yMin are the coordinates of each tile within the overall bounding box of the entire ELC10 image.

The pixel values, their definitions and suggested hex color codes include: 0 (not mapped #000000), 1 (Artificial land, #CC0303), 2 (Cropland, #CDB400), 3 (Woodland, #235123), 4 (Shrubland, #B76124), 5 (Grassland, #92AF1F), 6 (Bare land, #F7E174), 7 (Water/permanent snow/ice, #2019A4), 8 (Wetland, #AEC3D6).