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ForestPaths: European tree genus map
<h2>Abstract</h2> <p>This dataset provides an <strong>early access version</strong> of the European tree genus map at <strong>10 m resolution</strong> for the year 2020, derived from <strong>Sentinel-1 and Sentinel-2 </strong>satellite data. The map distinguishes eight classes (Larix, Picea, Pinus, Fagus, Quercus, other needleleaf, other broadleaf, and no trees) and is distributed as <strong>Cloud Optimized GeoTIFFs </strong>(COGs) over a 100 km grid in <strong>EPSG:3035 (ETRS89 / LAEA Europe)</strong>. </p> <p><br>The map was generated using a <strong>CatBoost model </strong>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. </p> <p> </p> <h2>Early access notice</h2> <p>This release is<strong> </strong>provided as an <strong>early access version</strong>. 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. </p> <p>We <strong>welcome feedback</strong> and contributions of additional training data to further improve the map. <br> </p> <h2>Dataset description</h2> <ul> <li><strong>Resolution</strong>: 10m</li> <li><strong>Format</strong>: Cloud Optimized GeoTIFFs (COGs)</li> <li><strong>Tiling</strong>: 100km grid</li> <li><strong>Coordinate reference system</strong>: EPSG: 3035 (ETRS89 / LAEA Europe)</li> </ul> <h3>Legend</h3> <p>0 – Larix <br>1 – Picea <br>2 – Pinus <br>3 – Fagus <br>4 – Quercus <br>5 – Other needleleaf <br>6 – Other broadleaf <br>7 – No trees </p> <h2>Methodology summary</h2> <p>The classification was performed using a <strong>CatBoost model</strong> trained on diverse reference sources [1-10]: <br>- National and regional plot inventories <br>- Citizen science observations <br>- Orthophoto interpretation <br>- LUCAS data </p> <p>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. </p> <h2>Further details on the methodology will be made available in the product publication, which will follow this early access release. <br> <br>Usage Notes </h2> <ul> <li><strong>CRS</strong>: EPSG:3035 (ETRS89 / LAEA Europe). Reprojection may be required for use with other datasets.</li> <li><strong>Tiling scheme</strong>: Provided as 100 km × 100 km COG tiles. Users may mosaic tiles if needed. </li> <li><strong>Classes</strong>: See legend above. Class 7 (“No trees”) includes cropland, grassland, built-up, and other non-tree areas. </li> <li><strong>Early access status</strong>: Not yet fully validated. Regional inconsistencies and misclassifications may be present.</li> </ul> <p><strong>Feedback & contributions</strong>: We invite users to share validation results and contribute additional reference data to improve future releases. </p> <h2> How to cite </h2> <p>If you use this dataset, please cite as: </p> <p><br>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 </p> <p><strong>BibTeX </strong></p> <p>@dataset{dekeersmaecker2025_treegenus, <br> author = {De Keersmaecker, Wanda and Zanaga, Daniele and Senf, Cornelius <br> and Viana-Soto, Alba and Klapper, Johanna and Blickensdörfer, Lukas <br> and Govaere, Leen and Lerink, Bas and Leyman, Anja <br> and Schelhaas, Mart-Jan and Teeuwen, Sander and Verkerk, Pieter Johannes and Van De Kerchove, Ruben}, <br> title = {European Tree Genus Map 2020 (Early Access Release)}, <br> year = {2025}, <br> publisher = {Zenodo}, <br> version = {early-access}, <br> doi = {10.5281/zenodo.13341104}, <br> url = {https://doi.org/10.5281/zenodo.13341104} <br>} </p> <h2>References</h2> <p>[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. <br> <br>[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. </p> <p>[3] Finnish Forest Centre (Metsäkeskus). (2025). Forest resource lattice data (Hila-aineisto) [2019–2021]. Retrieved from https://www.metsakeskus.fi.</p> <p>[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). </p> <p>[5] Govaere L. & Leyman A. (2023). Vlaamse bosinventarisatie Agentschap Natuur en Bos (VBI1: 1997-1999; VBI2: 2009-2018; VBI3: 2019-2021, v2023-03-17).</p> <p>[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 </p> <p>[7] IGN. (2016). BD Forêt Version 2.0. January 2016 </p> <p>[8] Riedel T., Hennig P., Kroiher F., Polley H., Schmitz F., Schwitzgebel F. (2017): Die dritte<br>Bundeswaldinventur (BWI 2012). Inventur- und Auswertemethoden, 124 S.</p> <p>[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</p> <p>[10] Villaescusa, R. & Díaz, R. (1998) Segundo inventario forestal nacional (1986–1996). Ministerio de Medio Ambiente, ICONA, Madrid.</p> <h2>Acknowledgements</h2> <p>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).</p> <p> </p>
Data supporting 'Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model'
<p>The dataset supporting our publication '<strong>Ice loss in the European Alps until 2050 using a fully assimilated, deep-learning-aided 3D ice-flow model</strong>' in <em>Geophysical Research Letters.</em></p> <p>The main .zip archive contains a set of NetCDF files detailing:</p> <ul> <li>Initial optimised glacier states (geology-optimized...)</li> <li>Simulation results (Prog20...)</li> </ul> <p>Initial states and results are given by cluster (see Figure 1 in the paper), as shown in all filenames (C1 through to C12). Prognostic simulation filenames additionally distinguish between runs between 1999 and 2019 (Prog2020) and between 2020 and 2050 (Prog2050). 'NV'/'NoVel' and 'NT'/'NoThk' refer to simulations using the partial optimisation (optimisation without including velocity/thickness observations) as detailed in the paper. 'AV' at the end of the filename denotes the integrated area/volume results file, as opposed to the 2D raster results file. A 'V' before the cluster designation shows that the simulation used the variable SMB as opposed to the fixed SMB (see the paper for details). 'ID' before the cluster designation shows that the simulation was using extrapolated SMB based on the trend in SMB since 2000, instead of assuming the continuation of the current SMB. 'ID' on its own denotes linear extrapolation and 'IDQ' denotes quadratic extrapolation (not used in the published paper). 'SMBF' in the filename shows that the simulation used the SMB-elevation feedback.</p> <p>The additional .zip archive contains the code of IGM v1.0 used to produce the model results. For details on installing and using IGM, please see the Github page at <a href="https://github.com/jouvetg/igm.The">https://github.com/jouvetg/igm</a>.</p> <p>A further .zip archive (in version 3 - Sims2010-2022.zip) contains the simulations based on linear extrapolation of the observed trend in SMB between 2010 and 2022, following the same nomenclature as in the principal archive (see above).</p> <p>Version 4 contains an additional mosaicked DEM of the results for the whole Alps with the ice removed to give the complete basal topography (kindly processed by T. Léger at UNIL) using the Japan Aerospace Exploration Agency (2021) ALOS World 3D 30 meter DEM. V3.2, Jan 2021. Distributed by OpenTopography. <a title="https://doi.org/10.5069/G94M92HB" href="https://doi.org/10.5069/G94M92HB" target="_blank" rel="noreferrer noopener">https://doi.org/10.5069/G94M92HB</a>. Accessed: 2024-09-09.</p>
SSP-aligned projected European water withdrawal/consumption at 5 arcminutes
<p><u><span>Release 0.9.1 – What is new?</span></u></p> <p><span><span>·<span> </span></span></span><span>Industrial water withdrawals were initially overestimated due to a problem with the input data, but they are now fixed.</span></p> <p><span><span>·<span> </span></span></span><span>Historical water withdrawal covering 1960-2020 added. Consistency between historical and projected water withdrawals is maintained.</span></p> <p><u><span>Contents and naming conventions</span></u></p> <p><span>Annual European water withdrawal: </span></p> <p><span>{scenario}_{sector} _year_millionm3_5min_Europe_{from_year}_{to_year}.nc</span></p> <p><span>Scenarios: historical, ssp1, ssp2, ssp3, ssp5</span></p> <p><span>Sector: dom, ind; stand for domestic/industrial</span></p> <p><span><span> </span>From/to_year: 1960/2020, 2020/2100; historical/ssp projections.</span></p> <p><span>Each file holds two variables: {sector}ww and {sector}wc representing gross and net (i.e. consumptive use) water withdrawal. For exmaple, for the industrial sector, there are two variables: <em>indww </em>and <em>indwc.</em><u> </u>The fraction ‘<em>1 – indwc/indww</em> ‘ represents the share of return flows. </span></p> <p>-------------------------</p> <p>The dataset provides annual water withdrawal and consumption estimates for Europe at a spatial resolution of 5 arcminutes, covering the periods 1960-2020 (historical) and 2020-2100 for four SSPs (1, 2, 3, and 5). Below, we outline the procedure used to downscale the population projections to a 5-arcminute resolution and describe the main equations applied to project water withdrawal and consumption under different SSPs.</p> <p>The development of the high-resolution (5 arcminute) projected water withdrawal and consumption for Europe follows the methodology outlined by Wada et al. (2011a, 2011b). This new release incorporates new projections for population, GDP per capita, and urbanization patterns from the latest SSP database (v3.0.1; available at <a href="https://data.ece.iiasa.ac.at/ssp/" target="_new">https://data.ece.iiasa.ac.at/ssp/</a>). Since this update is still in progress as of August 25<sup>th</sup>, 2024, some necessary input data are sourced from an earlier version of the SSP data (SSP 2013, see Table 1). <strong>All data and methods used to generate the results provided in this dataset are described in the Readme - Data and Methods file.</strong></p> <p><a name="_Ref175610392"></a>Table 1: Data availability in different versions of the SSP database as of August 25<sup>th</sup> 2024.</p> <table> <tbody> <tr> <td> <p><strong>Data</strong></p> </td> <td> <p><strong>SSP DatabaseVersion</strong></p> </td> <td> <p><strong>Module</strong></p> </td> </tr> <tr> <td> <p>Population</p> </td> <td> <p>SSP v3.0.1 2024</p> </td> <td> <p>Domestic</p> </td> </tr> <tr> <td> <p>GDP per capita</p> </td> <td> <p>SSP v3.0.1 2024</p> </td> <td> <p>Domestic/industrial</p> </td> </tr> <tr> <td> <p>Energy use per capita</p> </td> <td> <p>SSP 2013</p> </td> <td> <p>Industrial</p> </td> </tr> <tr> <td> <p>Electricity use per capita</p> </td> <td> <p>SSP 2013</p> </td> <td> <p>Industrial</p> </td> </tr> </tbody> </table>
A Comprehensive Dataset of Kurdish Body Part Terminology with Proto-Indo-European Roots
<p><strong>Overview</strong></p> <p>This dataset delves into the rich linguistic heritage of Kurdish body part terminology, tracing its roots back to the Proto-Indo-European (PIE) language. It comprises a comprehensive collection of 50 Kurdish body part terms, each meticulously analyzed through 24 linguistic features, uncovering their historical development and connections to other Indo-European languages.</p> <p><br><strong>Key Features</strong></p> <p>- Comprehensive Analysis: The dataset provides a detailed analysis of 1200 fundamental linguistic features, with each feature branching out to explore other related features. Additionally, it delves into the internal structure and phonetic evolution of Kurdish terminology throughout history.<br>- Categorization: The dataset categorizes the Kurdish body part terms into four distinct groups: external anatomical parts, upper torso and limbs, lower torso and limbs, and internal anatomical parts. This comprehensive categorization facilitates a nuanced exploration of the linguistic evolution and interconnections within the domain of body part terminology.<br>- Etymological Information: For each Kurdish term, the dataset provides in-depth etymological information, including its PIE root, Indo-Iranian and Indo-European cognates, and references to relevant scholarly sources.<br>- Linguistic Features: The dataset includes 24 linguistic features for each term, allowing for a comprehensive comparative analysis. These features include gender, morphological features, possessive form, etymological summary, syllabification, stress pattern, semantic field, example sentence, cultural significance, case, evolution of meaning, semantic shift, sound changes, phonetic changes, and Kurdish idiomatic proverbs.</p> <p><strong>Contact</strong><br>For any questions, please contact Marwan Hamay at marwan.hamay@rwth-aachen.de.</p>
Data from: External validation of prognostic and predictive gene signatures in 1097 European head and neck squamous cell carcinoma patients
<p><span>Anonymized data containing survival endpoints and gene signature scores for head and neck cancer patients.</span></p> <p><span>File <strong>data_os_gs.csv</strong> : data linking overall survival and gene signature scores</span></p> <p><span>File <strong>data_dfs_gs.csv</strong> : data linking disease-free survival and gene signature scores</span></p> <p><span><strong>Variables</strong>:</span></p> <ul> <li><span><em>supertreat_id</em>: patient ID</span></li> <li><span><em>GS_score_172GS</em>: gene signature score for the <em>172-GS</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_3clustersHPV</em>: gene signature score for the <em>3 clusters HPV</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_RSI</em>: gene signature score for the <em>radiosenstivity index (RSI) </em>signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_pancancerCisplatin</em>: gene signature score for the <em>pancancer-cisplatin</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span><em>GS_score_cl3Hypoxia</em>: gene signature score for the <em>Cl3-hypoxia</em> signature. The score is Z-score normalized with a mean of 0 and SD of 1. </span></li> <li><span>Variables only available in <strong>data_os_gs.csv: </strong></span> <ul> <li><span><em>overall_survival_days_2years</em>: Overall survival censored at 2 years since diagnosis. Number of days from diagnosis to death or censoring.</span></li> <li><span><em>overall_survival_days_5years</em>: Overall survival censored at 5 years since diagnosis. Number of days from diagnosis to death or censoring.</span></li> <li><span><em>overall_survival_status_2years</em>: Overall survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if dead. </span></li> <li><span><em>overall_survival_status_5years</em>: Overall survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if dead. </span></li> </ul> </li> </ul> <ul> <li><span>Variables only available in <strong>data_dfs_gs.csv:</strong></span> <ul> <li><span><em>disease_free_survival_days_2years</em>: Disease-free survival censored at 2 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</span></li> <li><span><em>disease_free_survival_days_5years</em>: Disease-free survival censored at 5 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</span></li> <li><span><em>disease_free_survival_status_2years</em>: Disease-free survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </span></li> <li><span><em>disease_free_survival_status_5years</em>: Disease-free survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </span></li> </ul> </li> </ul>
Accommodation Facilities and Ratings on Booking for 10 European Cities
<p>This dataset, collected on 11/11/2024, includes data from 4,806 accommodations across 10 European cities: Barcelona, Reykjavík, Amsterdam, Prague, Sofia, Porto, Edinburgh, Berlin, Dubrovnik, and Innsbruck. With prices corresponding to the 16/07/2025 night (2 adults and 1 room), it features global ratings, and category scores like cleanliness, comfort, staff, and location, along with details on available amenities and accommodation types. The dataset also covers check-in/check-out policies, pet policies, exact addresses, and review counts. It is designed to analyze key factors to a positive accommodation experience, and it allows comparisons across cities and types of accommodations.</p>
EERAdata D4.1 - General Building Stock Data for European Buildings
<p>This dataset fulfils the requirements for deliverable 4.1 of the EERAdata project and contains general data which models the building stock in three European cities - Andalusia, Copenhagen and Velenje. This dataset comprises local building data as well as research and scientific data. The dataset is still being built and will continue to be updated as more data is collected. A report describing this dataset in more detail has also been attached. </p>
Snow cover in the European Alps: Station observations of snow depth and depth of snowfall
<p>Auxiliary files, code, and data for paper published in The Cryosphere:</p> <p>Observed snow depth trends in the European Alps 1971 to 2019</p> <p> <a href="https://doi.org/10.5194/tc-15-1343-2021">https://doi.org/10.5194/tc-15-1343-2021</a></p> <p> </p> <p><strong>Auxiliary files:</strong></p> <ul> <li>aux_paper.zip: Auxiliary figures to the paper (time series showing the consistency of averaging monthly mean snow depth of stations within 500 m elevation bins; times of seasonal snow depth and snow cover duration indices).</li> <li>aux_paper_crocus_comparison.zip: Time series comparing spatial statistical gap filling from paper to gap filling using snow depth assimilation into Crocus snow model (only for subset of stations in the French Alps)</li> <li>aux_paper_monthly_time_series.zip: Plots of monthly time series of snow depth, for each station.</li> <li>aux_paper_spatial_consistency.zip: Aggregate results from spatial consistency (statistical simulation using neighboring stations), and time series of observed versus simulated monthly snow depths.</li> </ul> <p> </p> <p><strong>Code </strong>(working copy, not cleaned, all written in R statistical software): code.zip</p> <ul> <li>to read in the different data sources</li> <li>to do quality checks and data processing</li> <li>to perform statistical analyses as in paper</li> <li>to produce figures and tables as in paper</li> </ul> <p> </p> <p><strong>Data</strong>:</p> <ul> <li>> 2000 stations from Austria, Germany, France, Italy, Switzerland, and Slovenia</li> <li>Daily stations snow depth and depth of snowfall, as .zips, grouped by data provider. Information on column content is provided in "data_daily_00_column_names_content.txt".</li> <li>Monthly stations mean snow depth, sum of depth of snowfall, maximum snow depth, days with snow cover (1-100cm thresholds), as .zips, grouped by data provider. Information on column content is provided in "data_monthly_00_column_names_content.txt".</li> <li>Meta data (name, latitude, longitude, elevation) in "meta_all.csv", along with an interactive map "meta_interactive_map.html", and column information in "meta_00_column_names_content.txt".</li> <li>If you <strong>use the data you agree to adhere to the respective data provider's terms</strong> as listed in "00_DATA_LICENSE_AND_TERMS.PDF"</li> <li>The license terms especially (and additionally to any other terms of the single data providers) include: <strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a>] </li> </ul> <p> </p> <p> </p> <p><strong>Version history:</strong></p> <p>v1.3: added maxHS and SCD (with various 1-100cm thresholds) to monthly data</p> <p>v1.2: uploaded data</p> <p>v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript</p> <p>v1.0: initial upload</p>
Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020
<p>Seasonal composites of <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> imagery created as part of the <a href="https://opendatascience.eu/geo-harmonizer/">Geo-harmonizer project</a>, containing median of the blue, green, red, NIR, SWIR1 and SWIR2 bands, as well as pixel counts per season, produced in the ETRS89-extended / LAEA Europe (<a href="https://epsg.io/3035">EPSG:3035</a>) spatial reference system. Mosaics were produced from winter 2017 to spring 2020, with the imaging intervals per season being:</p> <ul> <li>winter: 02/12 of previous year to 20/03</li> <li>spring: 21/03 to 24/06</li> <li>summer: 25/06 to 12/09</li> <li>fall: 13/09 to 01/12</li> </ul> <p>Seamlessness of the composites was achieved through overlapping pixel averaging weighted by distance from the suborbital track.</p> <p>The data are provided as UINT8 values and were scaled with a common threshold (13712) chosen to minimize compression loss across the dataset. Data at the original (UINT16) scale can be obtained as follows:</p> <p><span>\(x_{\text{uint16}} = 13712 {x_{\text{uint8}} \over 254}\)</span></p> <p>For any additional questions regarding the data please contact the authors at <a href="mailto:multione@multione.hr?subject=S2L2A%20Europe%20mosaics">multione[at]multione.hr</a>.</p>
Setting files from: Spatially explicit paleogenomic simulations support cohabitation with limited admixture between Bronze Age Central European populations.
<p><strong>Simulated Data and Custom Scripts</strong></p> <p>This dataset release permits to simulate the expansion of populations from the Pontic Steppes to Central Europe with the version of SPLATCHE3 which is included. There are 2 main zipped folders: i) the one called "SPLATCHE3executableAndSettings" contains a "ReadMe.txt" file that contains all required information to make the simulations: the resulting ".prop" file contains proportions of genomic ancestry of the P2 layer, ancestry from P1 layer is equal to 1-(proportion from P2) ; ii) the other one called "HowToMakeFigure2" contains the R script and the tables necessary to reproduce Figure 2. See Rio J, Quilodrán CS & Currat M., Communications Biology (2021), for background.</p> <p><strong>Acknowledgments</strong></p> <p>This project was financially supported by the Swiss National Research Foundation grants n° 31003A_182577 to MC and n° P400PB_183930 to CQ, as well as the IGE3 Student Salary Award to JR.</p>
Qualitative dataset - Social justice-oriented narratives in European urban food strategies: Bringing forward redistribution, recognition and representation (Smaal et al., 2021)
<p>This qualitative dataset contains the English translations of the plain texts of the urban food strategy documents or webpages of 16 European medium-sized cities: Basel [CH]; Bristol [UK]; Bruges [BE]; Cordoba [ES]; Donostia - San Sebastián [ES]; Ede [NL]; Geneva [CH]; Ghent [BE]; Grenoble [FR]; Groningen [NL]; Montpellier [FR]; Nantes [FR]; Rennes [FR]; Tours [FR]; Uppsala [SE]; and Vitoria-Gasteiz [ES]. The search for and translation of the urban food strategy documents and webpages have been performed in early 2019. The files have been analysed in NVivo (qualitative data analysis software). The upload also includes figures and a table with the authors' assessments connected to the resources and services codes and radar diagram visualisations presented in the following paper: </p> <p>Smaal, S. A. L., Dessein, J., Wind, B. J., & Rogge, E. (2021). Social justice-oriented narratives in European urban food strategies: Bringing forward redistribution, recognition and representation. <em>Agriculture and Human Values</em>, 38(3), 709–727. <a href="http://doi.org/10.1007/s10460-020-10179-6">https://doi.org/10.1007/s10460-020-10179-6</a> </p> <p><strong>Abstract: </strong>More and more cities develop urban food strategies (UFSs) to guide their efforts and practices towards more sustainable food systems. An emerging theme shaping these food policy endeavours, especially prominent in North and South America, concerns the enhancement of social justice within food systems. To operationalise this theme in a European urban food governance context we adopt Nancy Fraser’s three-dimensional theory of justice: economic redistribution, cultural recognition and political representation. In this paper, we discuss the findings of an exploratory document analysis of the social justice-oriented ambitions, motivations, current practices and policy trajectories articulated in sixteen European UFSs. We reflect on the food-related resource allocations, value patterns and decision rules these cities propose to alter and the target groups they propose to support, empower or include. Overall, we find that UFSs make little explicit reference to social justice and justice-oriented food concepts, such as food security, food justice, food democracy and food sovereignty. Nevertheless, the identified resources, services and target groups indicate that the three dimensions of Fraser are at the heart of many of the measures described. We argue that implicit, fragmentary and unspecified adoption of social justice in European UFSs is problematic, as it may hold back public consciousness, debate and collective action regarding food system inequalities and may be easily disregarded in policy budgeting, implementation and evaluation trajectories. As a path forward, we present our plans for the RE-ADJUSTool that would enable UFS stakeholders to reflect on how their UFS can incorporate social justice and who to involve in this pursuit.</p> <p><em>This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 765389. </em></p> <p>Project webpage: <a href="https://recoms.eu/">https://recoms.eu/</a></p>
The arrival and spread of the European firebug Pyrrhocoris apterus in Australia as documented by citizen scientists
<p>Data and R script to reproduce analyses conducted in <strong>The arrival and spread of the European firebug <em>Pyrrhocoris apterus</em> in Australia as documented by citizen scientists</strong></p> <p><strong>Abstract</strong></p> <p>We present evidence of the recent introduction and quick spread of the European firebug <em>Pyrrhocoris apterus</em> in Australia, as documented on the citizen science platform iNaturalist. The first public record of the species was reported in December 2018 in the City of Brimbank (Melbourne, Victoria). Since then, the species distribution has quickly expanded into 15 local government areas surrounding this first observation, including areas in both Metropolitan Melbourne and regional Victoria. The number of records of the European firebug in Victoria has also seen a substantial increase, with a current tally of almost 100 observations in iNaturalist as of July 31<sup>st</sup>, 2021.</p> <p>The case of the European firebug in Australia adds to the list of examples of citizen scientists playing a key role in not only early detection of newly introduced species but in documenting their expansion across their non-native range. Citizen science presents an exciting opportunity to complement biosecurity efforts carried out by government agencies, which often lack resources to sufficiently fund detection and monitoring programs given the overwhelming number of current and potential invasive species. Recognising and supporting the invaluable contribution of citizen scientists to science and society can help reduce this gap by: (1) increasing the number of introduced species that are quickly detected; (2) gathering evidence of the species’ early expansion stage; and (3) prompting adequate monitoring and rapid management plans for potentially harmful species.</p> <p>Given the range expansion patterns of the European firebug worldwide, their adaptation ability, and future climate scenarios, we suspect this species will continue expanding beyond Victoria, including other parts of Australia, New Zealand, and the South Pacific. We firmly believe that most of the knowledge about how this expansion process continues to happen will be provided by citizen scientists.</p>
Data: The Role of Urban Trees in Reducing Land Surface Temperatures in European Cities
<p>Data on the LST differences between urban fabric, urban trees and urban green spaces for each city and the LST differences between urban fabric, rural forests and rural pastures (for hot days and JJA (June, July and August) average). In addition, estimates of the evapotranspiration of forests and pastures of each city and albedo estimates of urban fabric and forests are provided.</p> <p>The description of the column names is provided in the readme file.</p> <p> </p>
About ERIGrid 2.0 - Connecting European Smart Grid Research Infrastructures (IEA version)
<p>This video provides a brief overview of the activities and services of the <a href="https://ec.europa.eu/programmes/horizon2020/en">H2020</a> <a href="https://erigrid2.eu/">ERIGrid 2.0</a> research infrastructure project as well its links with the <a href="https://www.iea.org/">IEA</a>, especially its technology collaboration programme <a href="https://www.iea-isgan.org/">ISGAN</a> - Annex 5 <a href="https://www.iea-isgan.org/our-work/annex-5/">SIRFN</a>.</p>
Directory of the European Parliament members
<p>Over the past twenty-five years, a field of research into the careers of Members of European Parliament (MEPs) has developed. Drawing on a massive amount of accessible open data, we have assembled an updated database comprising all MEPs between 1979 and 2025.</p> <p>This dataset contains (some) socio-demographic informations about MEP’s and their carreer paths in EP. Data were scraped from the EP website.</p> <p>Metadata are filled separately in a rich text format (.rtf) document.</p>
Divergent evolution between sister species of European green lizards
<p>Annotation and variant calling files (VCFs, heffas) for <em>L. viridis </em>and<em> L. bilineata.</em> The variants have been called with <em>L. viridis</em> genome as reference.</p>
Primary vine varieties of European wine PDOs
<p>Primary varieties are the traditional vine cultivars of a region that are primarily used for making the wine products of a PDO region. In most cases, they are clearly defined in the legal document that regulate each PDO. We extracted primary varieties by analyzing the product specification files of European wine PDOs.</p>
List of validated primers of gilthead sea bream (Sparus aurata) and European seabass (DIcentrarchus labrax) developed in PerformFISH project (D2.3)
<p>The document contains all the primers identified for the screening of genes tested for their potential as biomarkers to predict quality performance in gilthead sea bream and European sea bass larvae and juveniles in the context of PERFORMFISH (WP2). The spreadsheet has the following information: Pathway, phisiologic process in which the gene is involved; name of protein that gene produces; gene code; acession nº, code given in the consulted databases and the sequence extracted for primer design; FW and RV primer, forward and reverse primer sequence specific for target gene; melt temperature, optimized temperature that primers work at ; amplicon size, size in base pairs of the product produced with the primers; eff%, efficency of primers; r2; source, the origin of the primers, "in house" or "literature" (including available DOI. Each pair of primers are classified using a "traffic light" system indicating their validation status.</p>
Research Data Repository Landscape infographic (European Research Data Landscape study)
<p>Infographic of the findings on research data repository landscape in the European Research Data Landscape study.</p>
Research Data Landscape infographic (European Research Data Landscape study)
<p>Infographic of the findings on research data landscape in the European Research Data Landscape study.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.