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762 results for “Belgium”
Moth trends and traits in Flanders (northern Belgium)
<p>This code is related to the investigation of species traits as a guidance for moth conservation in the highly anthropogenic European region of Flanders (northern part of Belgium) based on Multi-Species Change Indices (MSCIs).</p> <p><strong>Abstract</strong></p> <ol> <li>Insects appear to decline rapidly in recent decades. This so-called sixth mass extinction garnered significant media attention, raising public awareness.</li> <li>Macro-moths—a species-rich and ecologically diverse insect group—face severe declines, particularly in urbanised and intensively farmed areas.</li> <li>Flanders is a highly anthropogenic region, serving as a case study where the impact on macro-moths of stressors like intensive agriculture, industrialization and urbanization has been quantified through a recently compiled Red List. Here, for 717 macro-moth species, we calculated relative changes in distribution area between a reference period (1980-2012) and the subsequent period (2013-2022). By correlating these species-specific trends with ten key ecological and life-history traits, we calculated more general Multi-Species Change Indices (MSCIs).</li> <li>These MSCIs showed that species associated with wet biotopes and heathlands declined on average by 20-25%, while (sub)urban species increased by more than 60%. Species feeding on lichens or mosses increased by 31%, while grass-feeding species decreased by 20%. Both very small (+34%) and very large species (+15%) increased, whereas medium-sized species decreased by 5%. Monophagous (+17%), migrant (+88%), and colour-invariable species (+5%) increased, while colour-variable species decreased (-8%). Finally, Holarctic (-21%) and Palearctic species (-5%) decreased, while Mediterranean (+27%) and Western-Palearctic species (+9%) increased.</li> <li>Our trait-based approach identifies key threats and mitigation strategies for moths in anthropogenic regions, offering evidence-based insights for crafting efficient management recommendations and informed conservation policies to safeguard moth communities.</li> </ol>
Data from a cross-sectional study of fifth grade children in a sample of primary schools in Belgium that differ in amount of greenness at school and landscape level
<p>The data in this deposit were collected as part of the <code>B@SEBALL</code> project (Biodiversity at School Environments - Benefits for All). </p> <p>The project investigated how biodiversity in the school environment can positively affect children’s health and mental well-being. <code>B@SEBALL</code> also investigated the opportunities for reducing health inequalities among children via biodiversity at school environments.</p> <p>The data are organized according to the <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package standard</a>. All child-level and school-level data have been anonymized. Each data package is a collection of <code>csv</code> files and a <code>json</code> file. The <code>json</code> file holds descriptive information for all variables in all <code>csv</code> files. The <code>zip</code> file contains two frictionless data packages. The data packages contain information on 37 primary schools and 513 children. </p> <p>The data package, <code>data_package_an_zenodo_cleaned_data</code>, contains the original data in a tidied and cleaned format. It consists of 46 <code>csv</code> files. The files relate to the following contents:</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>landscape level variables</td> <td>wp1_landscape_level_data.csv</td> </tr> <tr> <td>metadata about participants</td> <td>wp2_participants_metadata.csv</td> </tr> <tr> <td>general school level data</td> <td>wp2_school_data.csv</td> </tr> <tr> <td>pollution data at school level</td> <td>wp3_ua_sirm_data.csv</td> </tr> <tr> <td>classroom data about air quality</td> <td>wp3_ucl_classroom_airquality.csv</td> </tr> <tr> <td>area of ecotopes in the school environment</td> <td>wp3_ucl_ecotope_categories.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_indicators.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_key.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenpatches.csv</td> </tr> <tr> <td>playground biodiversity indicators</td> <td>wp3_ucl_playground_biodiversity.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_child.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_line.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_linegroup.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_data.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_questions.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_data.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_questions.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_data.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_questions.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_data.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_key.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part1.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part2.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_key.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_data.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_key.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_data.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_key.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_data.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_key.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_data.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_key.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_data.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_key.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_data.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_key.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part1.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part2.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part3.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part4.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_key.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part1.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part2.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_key.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_data.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_key.csv</td> </tr> </tbody> </table> <p> </p> <p>The <code>data_package_an_zenodo_derived_data</code> data package, contains derived data that was calculated based on input from <code>data_package_an_zenodo_cleaned_data</code> at either child-level or at school-level.</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>derived data at child level</td> <td>wp1_child_level_key_variables.csv</td> </tr> <tr> <td>derived attention score based on d2-test data, aggregated to line-level</td> <td>wp1_d2_by_line_attention_score.csv</td> </tr> <tr> <td>derived data at school level</td> <td>wp1_school_level_key_variables.csv</td> </tr> </tbody> </table> <p>These data packages only store information for participants that gave consent for a particular part of the study and that gave consent for long-term storage of the data. There may therefore be slight differences between results published as part of the project consortium, which could make use of participant data that did not give consent for long-term data storage, and reproduction of these results based on the data in this data repository. We also note that the derived variables in the derived data package were calculated with these participants included and removal of participants for which we had no long-term storage consent was done after these calculations.</p> <p>As part of the project, microbiome data were also collected (both from cheek swabs on the children and from environmental samples), but this part of the data are not a part of this deposit and will be deposited in the European Nucleotide Archive (ENA).</p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Belgium
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> <li>TSE_2022_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> <li>TSE_2021_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> <li>TSE_2020_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> <li>TSE_2019_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> </ul>
UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands
<p><em>UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 24 weather radars in Belgium, Germany and the Netherlands, with varying coverage from 2008 to 2023. These data were created by processing weather radar data - provided by the Royal Meteorological Institute of Belgium (<a href="https://www.meteo.be/">RMI</a>), German Meteorological Service (<a href="https://www.dwd.de/">DWD</a>) and Royal Netherlands Meteorological Institute (<a href="https://www.knmi.nl/">KMNI</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=uva/">Aloft bucket</a>.</p> <p>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 3 radars in Belgium.</li> <li><strong>de.gz</strong>: VPTS data from 18 radars in Germany.</li> <li><strong>nl.gz</strong>: VPTS data from 3 radars in the Netherlands.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2019-2020
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2020-2021
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2018-2019
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2018-2019.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Airborne EM data (Belgium) from flight line 306025
<p>This dataset contains Airborne EM data from a SkyTEM instrument from the Flanders region, Belgium. </p> <p>Details about the instrument set-up can be found in the data report.</p> <p>Details about the region, geology, saltwater intrusion context can be found in </p> <p>Delsman, J., van Baaren, E., Vermaas, T., Karaoulis, M., Bootsma, H., de Louw, P. G. B., ... & Thofte, S. (2019). TOPSOIL Airborne EM kartering van zoet en zout grondwater in Vlaanderen (FRESHEM Vlaanderen: Deelopdrachten 1 tot en met 3.</p> <p><strong>When using this dataset, always cite the above reference. </strong></p> <p>The actual measured data is in "dat_skytem_306025_flightline.csv". Columns refer to either Low or High moment and time of measurement. Corresponding estimated relative errors can be found in rel_err_skytem_306025_flightline. Distances between soundings/measurment locations and their hieghts above the surface is found in "distances_between_soundings" and "altitudes_per_sounding" respectively.</p> <p>To use this data for the appraisal method, see https://github.com/WouterDls/AEM_appraisal. For more information, write to wouter.deleersnyder@kuleuven.be </p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Belgium
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
Party control, intra-party competition and the substantive focus of women's parliamentary questions: evidence from Belgium (replication data)
<p>Replication data for B. de Vet & R. Devroe, (2022). Party Control, Intraparty Competition, and the Substantive Focus of Women's Parliamentary Questions: Evidence from Belgium. <em>Politics & Gender,</em> 1-25. doi:10.1017/S1743923X21000490</p>
Hexagonal grid in Flanders, Belgium
<p>As part of the annual partridge spring counts in Flanders, hunters are required to count partridges in all areas that have <a href="https://doi.org/10.5281/zenodo.5814833">suitable habitat</a> for partridges (<em>Perdix perdix</em>). Since it is assumed that visibility is around 200 meters, this hexagonal grid with a distance of 400 meters between parallel sides, helps volunteers in choosing observation points.</p>
LBBG_ZEEBRUGGE - Lesser black-backed gulls (Larus fuscus, Laridae) breeding at the southern North Sea coast (Belgium and the Netherlands)
<p><em>LBBG_ZEEBRUGGE - Lesser black-backed gulls (Larus fuscus, Laridae) breeding at the southern North Sea coast (Belgium and the Netherlands)</em> is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (<a href="http://lifewatch.be/en/gps-tracking-network-large-birds">http://lifewatch.be/en/gps-tracking-network-large-birds</a>) for the project/study <strong>LBBG_ZEEBRUGGE</strong>, using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, <a href="http://www.uva-bits.nl">http://www.uva-bits.nl</a>). The study has been operational from 2013 until 2023. In total 162 individuals of lesser black-backed gull (<em>Larus fuscus</em>) have been tagged in or near their breeding area at the southern North Sea coast (Zeebrugge and Ostend in Belgium and Vlissingen in the Netherlands), mainly to study their habitat use and migration behaviour. Data are periodically uploaded from the UvA-BiTS database to Movebank and from there archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>). No new data are expected.</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study985143423">985143423</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/12336021/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json</strong>: technical description of the data files.</li> <li><strong>LBBG_ZEEBRUGGE-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>LBBG_ZEEBRUGGE-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> <li><strong>LBBG_ZEEBRUGGE-acceleration-yyyy.csv.gz</strong>: acceleration data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was collected using infrastructure provided by VLIZ and INBO funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch.</p>
BOP_RODENT - Rodent specialized birds of prey (Circus, Asio, Buteo) in Flanders (Belgium)
<p><em>BOP_RODENT - Rodent specialized birds of prey (Circus, Asio, Buteo) in Flanders (Belgium)</em> is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (<a href="http://lifewatch.be/en/gps-tracking-network-large-birds">http://lifewatch.be/en/gps-tracking-network-large-birds</a>) for the project/study <strong>BOP_RODENT</strong>, using trackers developed by Ornitela (<a href="https://www.ornitela.com">https://www.ornitela.com</a>). The study has been operational since 2020. In total 35 individuals of 5 bird of prey species have been tagged at several locations in Flanders (Belgium), mainly to study their habitat use and migration behaviour. Data are automatically synced with Movebank and from there periodically archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>).</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1278021460">1278021460</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/12567894/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json:</strong> technical description of the data files.</li> <li><strong>BOP_RODENT-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>BOP_RODENT-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was collected using infrastructure provided by INBO and funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch. Additional funding was provided by Agentschap voor Natuur en Bos (ANB).</p>
National Checklists 2017: Belgium Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Belgium collected using effechecka and geonames polygons
National Checklists 2019: Belgium Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Belgium collected using effechecka and geonames polygons
LBBG_ADULT - Lesser black-backed gulls (Larus fuscus, Laridae) breeding in Belgium
<p><em>LBBG_ADULT - Lesser black-backed gulls (Larus fuscus, Laridae) breeding in Belgium</em> is a bird tracking dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (<a href="http://lifewatch.be/en/gps-tracking-network-large-birds">http://lifewatch.be/en/gps-tracking-network-large-birds</a>) for the project/study <strong>LBBG_ADULT</strong>, using trackers developed by Ornitela (<a href="https://www.ornitela.com/">https://www.ornitela.com</a>). The study has been operational since 2022. In total 39 individuals of lesser black-backed gull (<em>Larus fuscus</em>) have been tagged in the breeding colony of Zeebrugge in Belgium, mainly to study their habitat use and migration behaviour. Data are automatically synced with Movebank and from there periodically archived on Zenodo (see <a href="https://github.com/inbo/bird-tracking">https://github.com/inbo/bird-tracking</a>).</p> <h2>Files</h2> <p>Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page%3Dstudies%2Cpath%3Dstudy2298738353">2298738353</a>. Fields in the data follow the <a href="http://vocab.nerc.ac.uk/collection/MVB">Movebank Attribute Dictionary</a> and are described in <code>datapackage.json</code>. Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/records/17292786/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <ul> <li><strong>datapackage.json</strong>: technical description of the data files.</li> <li><strong>LBBG_ADULT-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>LBBG_ADULT-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was collected using infrastructure provided by INBO and funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch.</p>
Observational rainfall data of the 2021 mid-July flood event in Belgium – Part 1. Rain gauges observations
<p>From July 13th to 16th 2021, a long period of sustained and heavy rainfall affected Central Europe producing extreme rainfall amounts in western Germany, eastern Belgium, Luxembourg and The Netherlands. In Belgium, this unusual event induced massive flooding on a large part of the country and was responsible for 39 fatalities and strong damages to buildings and infrastructures.</p><p>Such extremely rare event needs to be documented as much as possible and data must be made available for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming at identifying and understanding all factors leading to such disaster.</p><p>The observational rainfall data available for Belgium during the period from July 13th to July 16th 2021 are here shared with the scientific community. These data are twofold and provided in 2 parts:</p><p><br><strong>Part 1. </strong><a href="https://doi.org/10.5281/zenodo.7739983"><strong>Observations from high-quality rain gauges</strong></a></p><p>The dataset includes daily precipitation accumulation recorded by 323 weighing and manual rain gauges in Belgium as well as 5-min precipitation data recorded by 168 weighing rain gauges. These data were checked for possible errors and inconsistencies.</p><p>The rain gauges observations are provided in csv format in 2 files:</p><ul><li>RainGaugesData_FLOOD21_daily.csv</li><li>RainGaugesData_FLOOD21_5min.csv</li></ul><p><br><strong>Part 2. </strong><a href="https://doi.org/10.5281/zenodo.7740059"><strong>Radar-based quantitative precipitation estimation (RADFLOOD21)</strong></a></p><p>This product provides a quantitative precipitation estimation of the event at high spatial (i.e., 1 km) and temporal (i.e., 5 min and hourly) resolutions. It is obtained after a careful processing of the weather radar measurements and a merging with rain gauge measurements. The data is provided in hdf5 format. In addition, an animation of the 5-min RADFLOOD21 data is also made available.</p><p> </p><p>These data are exposed and discussed in <a href="https://hess.copernicus.org/articles/27/3169/2023/">https://hess.copernicus.org/articles/27/3169/2023/</a>. In particular, several analyses of these data are performed to describe the spatial and temporal distribution of rainfall during the event and to illustrate its exceptional character.</p><p> </p>
PDST sensor files European eel (Anguilla anguilla) Belgium
<p><strong>Brief data description</strong></p> <p>This data consists of the raw sensor data from a tagging study on European eel (<em>Anguilla anguilla</em> L.) caught and released in Nieuwpoort Belgium. The applied tags were pop-off G5 data storage tags (CEFAS Technology, Lowestoft, UK). Temperature was measured every 10 seconds and pressure (i.e. depth) every 2 seconds. The sensor data contains the raw data per eel with the tag ID as an individual eel. The tags were externally attached to eels and came off before or at a preprogrammed time, drifted to the surface, washed ashore and when found, the data could be downloaded when the tag was retrieved. Note that some recovered tags were reused hence a tag ID can occur more than once.</p> <p>This data is part of the eel-pdst-analysis GitHub repository: https://github.com/PieterjanVerhelst/eel-pdst-analysis. The sensor files have the following destination: eel-pdst-analysis\data\interim\sensorlogs</p> <p> </p> <p><strong>Files</strong></p> <p>Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/record/8398240/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <p> </p> <p><strong>More Info</strong></p> <p>For more information on the data collection and analysis, see the following two research papers:</p> <p>Scientific Reports: https://doi.org/10.1038/s41598-021-04052-7</p> <p>Science of The Total Environment: <a href="https://doi.org/10.1016/j.scitotenv.2023.167341">https://doi.org/10.1016/j.scitotenv.2023.167341</a></p>
Distribution of invasive alien species of Union concern (Regulation (EU) 1143/2014) in Belgium for the reporting period 2015-2018
<p><strong>Aims and scope</strong></p> <p>Member State authorities are required to report on the distribution in their territory of each of the invasive alien species (IAS) of Union concern. These are species with documented biodiversity impacts sensu the European Union Regulation on the prevention and management of the introduction and spread of Invasive Alien Species in Europe (IAS Regulation No 1143/2014) (European Union 2014). This distribution represents the official reporting under Article 24(1) of R.1143/2014 on invasive alien species for the period 2015–2018. Baseline distribution of these species has previously been reported and published (Adriaens et al. 2018, ).</p> <p>Data were compiled from various datasets holding invasive species observations such as data from research institutes and research projects (9%), citizen science observatories (68%) and a range of other sources (23%) such as governmental agencies, water managers etc. More specifically the dataset includes:</p> <ul> <li>The citizen science recording portals www.waarnemingen.be and www.observation.be which has a specific alert system for IAS where nature volunteers can report their observations (Adriaens et al. 2018);</li> <li>Data from the Research Institute for Nature and Forest (INBO), the Flemish government institute that coordinates N2000, WFD and BIrd Directive and IAS monitoring in the terrestrial, estuarine and freshwater environment;</li> <li>Data from the Flemish Environment Agency which performs management of muskrat and invasive water plants in Flanders, gathered with a dedicated smartphone app since 2015;</li> <li>Data from the Flemish provinces and Rato vzw that manage water plants, muskrat, giant hogweed etc.;</li> <li>Some smaller datasets from cities;</li> <li>Data from the Brussels Capital Region from the Brussels Environment data portal;</li> <li>Plant inventories of the ‘contrats de rivière’ along watercourses in Wallonia, making use of a dedicated application to collect data directly from the field (fulcrum);</li> <li>The government reporting portals for IAS of the ‘Observatoire wallon de la flore, de la faune et des habitats (Service Public de Wallonie)’;</li> <li>Some validated data from specific datasets on gbif (iNaturalist, Natusfera, Naturgucker).</li> </ul> <p>Data were normalized using a custom mapping of the original data files to Darwin Core (Wieczorek et al. 2012) where possible. Species names were mapped to the GBIF Backbone Taxonomy (GBIF 2016) using the species API (http://www.gbif.org/developer/species). The mapping was assisted by dedicated software (SMARTIE) which was specifically written for the purpose of aggregating IAS data from various sources. Appropriate selection of records was performed based on the cut-off dates (see data range) and record content validation (see validation procedure). Data were then joined with GRID10k layer Belgium based on GRID10k cellcodes (ETRS_1989_LAEA). The technical format is in line with the <a href="http://cdr.eionet.europa.eu/help/ias_regulation/material/IAS-species-distribution-user-manual">guidelines</a> provided to the member states for the compilation of reports on Species Distribution (SD) of Invasive Alien Species of Union concern.</p> <p><strong>File description</strong></p> <p>The dataset contains a shapefiles (<em>T1_Belgium_Union_List_Species.shp</em>) with the distribution of the species of Union Concern at 10km<sup>2</sup> (European Terrestrial Reference System projection - 1989 ETRS_1989_LAEA) level. The attributes table contains <em>Cellcode </em>(ETRS<sup> </sup>grid cell code) and <em>Species </em>(scientific name + authority).</p> <p><strong>Date range</strong></p> <p>The data reflects the distribution of the IAS of Union concern in Belgium in the first reporting period for the EU Regulation hence comprises observations of Union List invasive species between January 2015 (2015-01-01) and December 2018 (2018-12-31). </p> <p><strong>Validation procedure</strong></p> <p>Record validation was performed to exclude dubious records, wrong identifications etc. This was done based on the IdentificationVerificationStatus field (to which validation information from original data were mapped) if available. In general, non-validated data were not considered. Data were validated in the original datasets based on evidence (e.g. pictures), on the observer’s experience, or based on a set of predefined rules (e.g. automated validation based on geographic filtering). Data from research institutes were generally considered validated. A few casual records of EU list species that were clearly planted were discarded manually. When the original dataset did not mention any validation status, records were not considered validated and therefore not taken into account unless for Chinese mitten crab <em>Eriocheir sinensis</em>, ruddy duck <em>Oxyura jamaicensis</em>, raccoon <em>Procyon lotor</em>, Siberian ground squirrel <em>Tamias sibiricus</em>, sacred ibis <em>Threskiornis aethiopicus</em>, Egyptian goose <em>Alopochen aegyptiaca, </em>Himalayan balsam <em>Impatiens glandulifera</em>, giant hogweed <em>Heracleum mantegazzianum, </em>muskrat <em>Ondatra zibethicus </em>and red-eared slider <em>Trachemys spp</em>. For these species, it was assumed all records were correct as they originate from dedicated sampling (<em>E. sinensis</em>) within research projects, were gathered by public bodies (e.g. muskrat), or represent species that are readily recognizable by people in the field. Data provided by EASIN in the care package and GBIF data were carefully checked.</p> <p>A visual check was performed on the resulting distribution maps by representatives of the Belgian national scientific council on invasive alien species, an official consultative structure coordinating scientific input and data aggregation between Belgian regions and institutions with regards to technical implementation of the Regulation No 1143/2014 on invasive alien species.</p> <p><strong>Data providers</strong></p> <p>The providers of the invasive species data for this exercise (individuals and their respective organizations) are listed in the "data providers" section of the dataset metadata. Much of the primary occurrence data that formed the basis for this aggregated dataset will be published as open data on the Global Biodiversity Information Facility (GBIF).</p>
Species occurrence and occupancy in protected areas of the Natura2000 network in Belgium
<p><strong>Context</strong></p> <p>Invasive alien species have been pointed out as an important driver of biodiversity loss. Many policy responses are being developed to address this threat. Protected areas often represent and preserve hotspots of biological diversity and ensure the maintenance of ecosystem services crucial to human livelihoods. The impact of biological invasions can be particularly severe in protected areas and their occurrence and impact in such areas is an important element of the risk they pose. To address this, there is a need for data on the occurrence and extent of alien species invasions in protected areas.</p> <p><strong>Description</strong></p> <p>This dataset contains species occurrence and occupancy in protected areas of the Natura2000 network in Belgium (Special Conservation Areas sensu Habitat Directive and Special Protection Areas sensu Bird Directive). The dataset was generated using the <a href="https://doi.org/10.5281/zenodo.3637911">Belgian occurrence cube at species level</a> and the <a href="https://doi.org/10.5281/zenodo.3635510">Belgian occurrence cube for non-native taxa</a> (both containing GBIF data aggregated using Oldoni et al. 2020), the 1x1km <a href="https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2">EEA reference grid</a> and the <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-spatial-data/natura-2000-shapefile-1">Natura2000 protected areas shapefiles</a> from the European Environment Agency.</p> <p>Data are grouped by protected area (<code>SITECODE</code>), year (<code>year</code>) and (infra)species (<code>taxonKey</code>, <code>speciesKey</code>). For each group, it provides the number of occurrences found in GBIF (<code>n</code>), the area of occupancy (<code>aoo</code>: number of 1 km<sup>2</sup> squares), the coverage (<code>coverage</code>: % of 1 km<sup>2</sup> squares), the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (<code>min_coord_uncertainty</code>), and the alien status (<code>is_alien</code>) based on the <a href="https://doi.org/10.15468/xoidmd">Global Register of Introduced and Invasive Species - Belgium</a>. For infraspecific taxa in the latter, the <a href="https://github.com/trias-project/indicators/blob/00e1ae72df3fb98b2a215c3af8769e53fbcd0182/reference/species_of_infraspecific_alien_taxa.tsv">alien status of the species</a> is looked up and included.</p> <p>The dataset is built on open science principles and intended to be completely reproducible:</p> <ul> <li>The input data are publicly available on Zenodo, with the download DOIs listed in the related identifiers of this dataset package.</li> <li>The <a href="https://trias-project.github.io/indicators/10_species_observations_occupancy_in_protected_areas.html">code</a> to process the data is publicly available and documented on GitHub.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>protected_areas_species_occurrence.csv</strong>: number of occurrences (<code>n</code>), area of occupancy (<code>aoo</code>) and <code>coverage</code> of taxa (<code>taxonKey</code>) in Natura2000 areas of Belgium (<code>SITECODE</code>). Other columns included: <code>speciesKey</code> (for species is <code>speciesKey</code> = <code>taxonKey</code>), <code>SITETYPE</code> containing the site type of the Natura2000 area (one of <code>A</code>, <code>B</code> or <code>C</code>), <code>min_coord_uncertainty</code> with the lowest coordinate uncertainty in meters, <code>is_alien</code> containing the alien status (<code>TRUE</code> or <code>FALSE</code>) and <code>remarks</code> containing, if present, the infraspecific alien taxa whose occurrences contribute to the calculated <code>aoo</code> (only for species).</li> <li><strong>protected_areas_species_info.csv</strong>: taxonomic information of taxa in <code>protected_areas_species_occurrence.csv</code> as retrieved from <a href="https://www.gbif.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c">GBIF Backbone Taxonomy</a>. Columns: <code>taxonKey</code>, <code>speciesKey</code>, <code>scientificName</code>, <code>kingdom</code>, <code>phylum</code>, <code>order</code>, <code>class</code>, <code>genus</code>, <code>family</code>, <code>species</code>, <code>rank</code> and <code>includes</code>. The latter contains the infraspecific taxa and synonyms whose occurrences contribute to the number of occurrences at species level.</li> <li><strong>protected_areas_metadata.csv</strong>: protected area information for areas included in <code>protected_areas_species_occurrence.csv</code>. Columns: <code>SITECODE</code> as in <code>protected_areas_species_occurrence.csv</code> (<code>BE*******</code>), <code>SITENAME</code> containing the name of the protected area, <code>SITETYPE</code> as in <code>protected_areas_species_occurrence.csv</code>, <code>flanders</code>, <code>wallonia</code> and <code>brussels</code> containing whether the area is situated respectively in Flanders, Wallonia or Brussels-Capital Region (<code>TRUE</code> or <code>FALSE</code>). Field codes are in line with <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-tabular-data-12-tables">EEA element definitions</a> for Natura 2000 sites.</li> </ul> <p><strong>Potential use of the dataset</strong></p> <p>Currently, there is no comprehensive reporting system for invasive alien species in Natura 2000 sites. This dataset provides a baseline as to which species occur in which protected area. We envisage this dataset can be an interesting starting point for various types of analyses on alien species in protected areas in Belgium, but that it can also be used in complement to other data on alien species in protected areas to study more general patterns. Some examples of research questions:</p> <ul> <li>Which protected areas are most invaded by alien species</li> <li>Which alien species are most distributed in protected areas and which traits do they have</li> <li>How does the proportion of alien species in protected areas change in time</li> <li>How does the occurrence/occupancy of alien species in protected areas match lists of regulated species (e.g. Union List, EPPO lists)</li> <li>To what extent can the network of protected areas contribute to providing safe refuge to native species from the impacts of invasive alien species</li> <li>How widespread are the impacts of certain alien species on protected areas</li> </ul> <h2>Acknowledgements</h2> <p>This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS), the European Union's LIFE program (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).</p>
ScienceDex guides
Understand access before you commit
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.