Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

651

datasets available to search

ShareScore release 0.9.0

Dataset results

651 results for “The Netherlands”

Learn how ShareScore rates datasets ↗

zenodo48/100

Supplementary table 1 for 'Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands' (2015)

This supplementary table to Visser(2015) was not openly available.  This dataset provides the supplementary table in the open ODS-format and also as XLS and CSV. <div> <div>Publication: Visser, RM. 2015 Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands. Journal of Archaeological Science 53: 243–254. DOI: <a href="https://doi.org/10.1016/j.jas.2014.10.017">https://doi.org/10.1016/j.jas.2014.10.017</a>.</div> </div>

opencc-by-sa-4.0Oct 2014View details →

zenodo48/100

Data on the Netherlands and United Kingdom's Citizens Juries on New Plant Breeding Techniques

This dataset contains the codebooks, code references, and code items for the Netherlands and United Kingdom citizens' juries on new plant breeding techniques.  The main folders 01_NLJury_Codes & codebook and 02_UKJury_Codes contain the data for the Netherlands and United Kingdom citizens' juries and the codebook respectively. The juries were four days long and each main folder has four sub-folder which contains the code references and code items for each day of the citizens' jury. Both the main folders also contain a Word document that provides the codebooks for the respective citizens' jury.

opencc-by-4.0Dec 2022View details →

zenodo44/100

Data from a Large-Scale Experiment to Evaluate the Effects of Trapping to Control Muskrats (Ondatra zibethicus) in The Netherlands

This data set supports the publication 'A Large-Scale Experiment to Evaluate the Effects of Trapping to Control Muskrats (Ondatra zibethicus) in The Netherlands' by Daan Bos, Emiel van Loon, Erik Klop and Ron Ydenberg. (the paper was accepted for publication in Wildlife Society Bulletin in 2020) The Muskrat is an invasive species in Europe and in the Netherlands muskrat burrowing can compromise the integrity of dykes and hence poses a public safety threat. For that reason a control programme has been in effect since the arrival of the species in 1941. To investigate the relation between catch and effort and enhance prediction models, a large randomized controlled experiment was designed and conducted from 2013 till 2016. The publication by Bos et al. (2020) analyses the experimental results and here we present and document the experimental data. See the readme.md file for further information.

opencc-by-4.0May 2020View details →

zenodo44/100

Baseline data for SDM of the SIM4NEXUS case of the Netherlands

This dataset consists of the baseline data for the SDM of the SIM4NEXUS case of the Netherlands. Most data is based on year data that has been equally distributed over 12 months per year.  For optional extension to monthly data, the data display already monthly data starting at December 2009 until December 2050 i.e. 493 points in time. The data include time series for socio-economic indicators, land use, food production, energy use, climate and water. The socio-economic system includes data on population and gross domestic product (GDP) per capita. The land system includes data on four main land uses, namely built-up areas, agriculture (with areas for food, energy crops and fodder crops production) , nature areas (non-forest, forest not for biomass production, forest for biomass production) , and areas for renewable energy production like wind mills and solar power fields. The food system includes plant-based (food crops like cereals and vegetables and fruit) and animal-based food production (based on the herds of cattle, pigs and poultry) in terms of protein. In addition, the animal- and plant-based protein requirements of Dutch consumers are also estimated. The energy system has an energy production and an energy demand part. Energy demand is determined for the domestic sector (i.e. households) based upon population and households’ demands for renewable and non-renewable energy. For the other economic sectors (agriculture, manufacturing industry, transportation and services sector) the demands for renewable and non-renewable energy are determined by GDP per sector and the energy intensity of the sectors. Energy supply is divided into non-renewable energy production and renewable energy production. Non-renewable energy sources include energy from coal, natural gas, oil and nuclear. The renewable energy consist of energy from wind (onshore and offshore), solar (on buildings and solar power fields), biomass and other sources (innovations like hydrogen or geothermic power). The energy of biomass there are 7 sources of biomass: energy crops, crop residues, manure, organic household waste, organic waste from public areas, waste water and timber residues. Timber residues are largely imported for large-scale use of co-firing in coal power plants and bio-based activities in the manufacturing industries. The water system has two parts: water quality which are the agricultural emissions of nitrogen and phosphorus to water, and water quantity i.e. agricultural water demand for irrigation and livestock drinking water. Finally, the climate system is divided into non-agricultural GHG emissions, and agricultural emissions. The non-agricultural GHG emissions are based on the GHG emissions from non-renewable energy production and non-energy related GHG emissions per economic sector except for agriculture. The agricultural GHG emissions relate to GHG emissions from livestock production, crop production and wetlands.

opencc-by-4.0Dec 2019View details →

zenodo44/100

Gado2: multilingual newspapers from the Netherlands Indies

This Handwritten Text Recognition (HTR) xml-page file dataset contains the ground truths of the Gado2 named entity processing application for newspapers from the Netherlands Indies and Indonesia, see: https://github.com/KBNLresearch/gado2. Optical Character Recognition (OCR) resulted in high Character Error Rates (CER) due to the inferior quality of many scans. In contrast, HTR led to CERs below 0.5 percent thus increasing the efficiency of the NER engine. All uploaded files are free of errors and fully tagged. A relevant knowledge base of Indonesian persons, places and organisations is attached in json format for entity linking.

opencc-by-4.0May 2021View details →

zenodo44/100

Paulina Polder precise Elevation (Netherlands)

The dataset corresponds to a precise AHN-2 5m resolution Digital Elevation Model (DEM) of the Paulina Polder area in the Netherlands.

opencc-by-4.0May 2017View details →

zenodo44/100

Paulina Polder Elevation (Netherlands)

The dataset corresponds to a precise AHN-2 5m resolution Digital Elevation Model (DEM) of the Paulina Polder area in the Netherlands.

opencc-by-4.0May 2017View details →

zenodo44/100

Ground beetle (Coleoptera:Carabidae) species composition of three forests in the Netherlands

During this research the carabid fauna assemblage of two forests, the Amsterdamse Bos and Purmerendse Bos, was determined. Throughout the forests series (locations within the forests) were chosen to place pitfall traps. Each series consisted of five plastic cups that were dug into the soil in such a way that they were flush with the surface. Each cup was located five meters away from the subsequent one. The cups were filled with formaldehyde (diluted water 1: 10) as conservative and a small amount of soap in order to decrease water tension and thus let the organisms submerge. Afterwards the trap was covered with a wooden plate attached onto the soil with nails to protect it from rain and damage. The plates were covered up with plant material as camouflage. A small opening in between the soil and the plate was left so there was space for soil fauna to crawl into the cup (Picture 1). Because carabids are often dispersed throughout an area in small populations instead of being homologous spread (Raino & Niemelä, 2003) a diversity of locations was chosen. Therefore biotic and abiotic conditions were recorded (soil, light invasion, litter and dominant vegetation) to select the most diverse sites. Nine series in the Amsterdamse Bos and eleven series in the Purmerendse Bos were placed. These forests were sampled for a time span of 63 days. When traps were emptied the formaldehyde was refreshed. After the third time all of the traps were removed and holes filled up with soil. The content of emptied traps was washed with water and afterwards the ground beetles were selected and preserved in 95% ethanol. The ground beetles found were identified by making use of “De Loopkevers van Nederland & Vlaanderen” by Boeken, Desender, Drost, van Gijzen, Koese, Muilwijk, Turin & Vermeulen (2002. For each series the quantity of caught individuals was recorded. From the Eyserbos, data collected by supervisor B. Brugge in the years from 2012 to 2014 was used for analysis; during this research the same catching methods were used but the time scale was different. For four years, five series of pitfall traps were placed for one week halfway of June thus for a total of 28 days. Data of the species composition and the ecological characteristics and classification of the three forests was collected. Following classifications and ecological characters of the species that were used for analysis were documented: the status of the species in the Netherlands, Belgium, Denmark and Luxemburg, the status of the species in the Netherlands, the distribution in the Netherlands, how important the species’ population in the Netherlands is in its distribution in Europe (so called I-species), which type of habitats a species can migrate through to spread to other habitat patches, the classification of the species by Lindroth (1969), the degree of eurytopicity of the species and the flight capabilities of the species. Data obtained can be found in the file: 201703-05_groundbeetle_species_composition_Eyserbos_AmsterdamseBos_and_PurmerendseBos.txt The possible inputs for characteristics can be found in the file 201706_Legenda_data_groundbeetles_species_composition_Amsterdamse_and_Purmerendse_bos.txt

opencc-by-4.0Jul 2017View details →

zenodo44/100

Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands

This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders: 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: 5_Maps). All 25 LiDAR metrics were calculated using Laserfarm workflow  (<a href="https://laserfarm.readthedocs.io/en/latest/">https://laserfarm.readthedocs.io/en/latest/</a>) (building on the user-extendable features from the “Laserchicken” software: <a href="https://laserchicken.readthedocs.io/en/latest/#features">https://laserchicken.readthedocs.io/en/latest/#features</a>). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> and Laserfarm: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>), as well as on the “Laserchicken” documentation page (<a href="https://laserchicken.readthedocs.io/en/latest/">https://laserchicken.readthedocs.io/en/latest/</a>). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: 6_Masks).  To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: 7_Auxiliary_data). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: 8_Sensitivity_analysis). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: 9_Use_case). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: 10_Ground_classification). An overview of all the folders in the repository: 1.     AHN1 2.     AHN2 3.     AHN3 4.     AHN4 5.     Maps Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset. 6. Masks <ul> <li>ahn3_10m_mask_building_road_water.tif</li> <li>ahn4_10m_mask_building_road_water.tif</li> <li>ahn4_10m_mask_powerline.tif</li> <li>ahn1_10m_NA_mask.tif</li> <li>ahn2_10m_NA_mask.tif</li> <li>ahn3_10m_NA_mask.tif</li> <li>ahn4_10m_NA_mask.tif</li> </ul>   It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (<a href="https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl">https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl</a>). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0. 7. Auxiliary data (1) Point_density <ul> <li>ahn1_10m_point_density.tif</li> <li>ahn2_10m_point_density.tif</li> <li>ahn3_10m_point_density.tif</li> <li>ahn4_10m_point_density.tif</li> </ul> (2) Pulse_density <ul> <li>ahn3_10m_pulse_density.tif</li> <li>ahn4_10m_pulse_density.tif</li> </ul> (3) Flighttime <ul> <li>ahn3_10m_flighttime.tif</li> <li>ahn4_10m_flighttime.tif</li> </ul> (4) DTM_DSM <ul> <li>ahn2_10m_dtm.tif</li> <li>ahn2_10m_dsm.tif</li> <li>ahn3_10m_dtm.tif</li> <li>ahn3_10m_dsm.tif</li> <li>ahn4_10m_dtm.tif</li> <li>ahn4_10m_dsm.tif</li> </ul> It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2–AHN4. All raster layers are provide at 10 m resolution. 8. Sensitivity analysis <ul> <li>Dunes</li> <li>Marsh</li> <li>Grassland</li> <li>Shrubland</li> <li>Woodland</li> <li>Code</li> <li>Figure</li> </ul> It contains the 25 metrics generated from point clouds with the original and down-sampled pulse densities (original pulse density of the AHN4, pulse density of the AHN3, ½ of the pulse density of the AHN3, and ¼ of the pulse density of AHN3) for each habitat type (i.e. dunes, marsh, grassland, shrubland, and woodland). We also provided the code and the figures generated from this analysis. 9. Use_case (1) Multi-temporal_AHN <ul> <li>Data</li> <li>Usecase_multi-temporal_AHN.R</li> </ul> It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R). (2) Natura2000 <ul> <li>Data</li> <li>Natura2000_end2021_HABITATCLASS.csv</li> <li>Natura2000_NL_habitat_grouped.csv</li> <li>Usecase_Natura2000.R</li> </ul> It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation  structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. 10. Ground classification <ul> <li>Raw_point_cloud</li> <li>Computed_metrics </li> <li>Plottings_and_code</li> <li>ArcGIS_project</li> </ul> It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project). Code availability Jupyter Notebooks for processing AHN datasets: <a href="https://github.com/ShiYifang/AHN">https://github.com/ShiYifang/AHN</a> Laserfarm workflow repository: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a> Laserchicken software repository: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> Code for downloading AHN dataset: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_downloading">https://github.com/ShiYifang/AHN/tree/main/AHN_downloading</a> Code for generating masks for AHN datasets: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_masks">https://github.com/ShiYifang/AHN/tree/main/AHN_masks</a> Code for demonstration of ecological use cases: <a href="https://github.com/ShiYifang/AHN/tree/main/Use_case">https://github.com/ShiYifang/AHN/tree/main/Use_case</a>

opencc-by-4.0Oct 2024View details →

zenodo44/100

O_ASSEN - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding in Assen (the Netherlands)

O_ASSEN - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding in Assen (the Netherlands) is a bird tracking dataset published by the <a href="https://assen.knnv.nl/werkgroep/vogel-werkgroep/">Vogelwerkgroep Assen</a>, <a href="https://nioo.knaw.nl">Netherlands Institute of Ecology (NIOO-KNAW)</a>, <a href="http://www.sovon.nl">Sovon</a>, <a href="http://www.ru.nl">Radboud University</a>, the <a href="https://ibed.uva.nl">University of Amsterdam</a> and the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected for the study O_ASSEN 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 was operational from 2018 to 2019. In total 6 individuals of Eurasian oystercatchers (Haematopus ostralegus) have been tagged as a breeding bird in the city of Assen (the Netherlands), mainly to study space use of oystercatchers breeding in urban areas. Data are 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. See van der Kolk et al. (2022, <a href="https://doi.org/10.3897/zookeys.1123.90623">https://doi.org/10.3897/zookeys.1123.90623</a>) for a more detailed description of this dataset. <h2>Files</h2> Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1605797471">1605797471</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/10053903/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>. <ul> <li>datapackage.json: technical description of the data files.</li> <li>O_ASSEN-reference-data.csv: reference data about the animals, tags and deployments.</li> <li>O_ASSEN-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year.</li> <li>O_ASSEN-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> These data were collected by Bert Dijkstra and Rinus Dillerop from Vogelwerkgroep Assen, in collaboration with the Netherlands Institute of Ecology (NIOO-KNAW), Sovon, Radboud University and the University of Amsterdam (UvA). Funding was provided by the Prins Bernard Cultuurfonds Drenthe, municipality of Assen, IJsvogelfonds (from Birdlife Netherlands and Nationale Postcodeloterij) and the Waterleiding Maatschappij Drenthe. The dataset was published with funding from Stichting NLBIF - Netherlands Biodiversity Information Facility.

opencc-zeroJan 2022View details →

zenodo44/100

O_AMELAND - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding on Ameland (the Netherlands)

O_AMELAND - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding on Ameland (the Netherlands) is a bird tracking dataset published by <a href="http://www.sovon.nl">Sovon</a>, the <a href="https://ibed.uva.nl">University of Amsterdam</a> and the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data for the study O_AMELAND 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 was operational from 2010 to 2013. In total 15 individuals of Eurasian oystercatchers (Haematopus ostralegus) have been tagged as a breeding bird on the Wadden island Ameland (the Netherlands), mainly to study their space use during the breeding season. Data are 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. See van der Kolk et al. (2022, <a href="https://doi.org/10.3897/zookeys.1123.90623">https://doi.org/10.3897/zookeys.1123.90623</a>) for a more detailed description of this dataset. <h2>Files</h2> Data in this package are exported from Movebank study <a href="1605803389">1605803389</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/10053853/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>. <ul> <li>datapackage.json: technical description of the data files.</li> <li>O_AMELAND-reference-data.csv: reference data about the animals, tags and deployments.</li> <li>O_AMELAND-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year.</li> <li>O_AMELAND-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> These data were collected by Sovon and University of Amsterdam (UvA). Funding was provided by NAM and supported by the UvA-BiTS virtual lab on the Dutch national e-infrastructure, built with support of LifeWatch, the Netherlands eScience Center, SURFsara and SURFfoundation. The dataset was published with funding from Stichting NLBIF - Netherlands Biodiversity Information Facility.

opencc-zeroNov 2021View details →

zenodo44/100

O_SCHIERMONNIKOOG - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding on Schiermonnikoog (the Netherlands)

O_SCHIERMONNIKOOG - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding on Schiermonnikoog (the Netherlands) is a bird tracking dataset published by <a href="http://www.sovon.nl">Sovon</a>, the <a href="https://ibed.uva.nl">University of Amsterdam</a> and the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected during <a href="https://chirpscholekster.nl">CHIRP</a> (Cumulative Human Impact on biRd Populations) for the study O_SCHIERMONNIKOOG 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 was operational from 2008 to 2014. In total 43 individuals of Eurasian oystercatchers (Haematopus ostralegus) have been tagged as a breeding bird on the saltmarshes of the island Schiermonnikoog (the Netherlands), mainly to study their space use both during the breeding season and winter season. Data are 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. See van der Kolk et al. (2022, <a href="https://doi.org/10.3897/zookeys.1123.90623">https://doi.org/10.3897/zookeys.1123.90623</a>) for a more detailed description of this dataset. <h2>Files</h2> Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1605799506">1605799506</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/10053970/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>. <ul> <li>datapackage.json: technical description of the data files.</li> <li>O_SCHIERMONNIKOOG-reference-data.csv: reference data about the animals, tags and deployments.</li> <li>O_SCHIERMONNIKOOG-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year.</li> <li>O_SCHIERMONNIKOOG-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> These data were collected by Sovon in collaboration with the University of Amsterdam (UvA). Funding was provided by NAM and supported by the UvA-BiTS virtual lab on the Dutch national e-infrastructure, built with support of LifeWatch, the Netherlands eScience Center, SURFsara and SURFfoundation. The dataset was published with funding from Stichting NLBIF - Netherlands Biodiversity Information Facility.

opencc-zeroJan 2022View details →

zenodo44/100

O_VLIELAND - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding and wintering on Vlieland (the Netherlands)

O_VLIELAND - Eurasian oystercatchers (Haematopus ostralegus, Haematopodidae) breeding and wintering on Vlieland (the Netherlands) is a bird tracking dataset published by the <a href="https://nioo.knaw.nl">Netherlands Institute of Ecology (NIOO-KNAW)</a>, <a href="http://www.sovon.nl">Sovon</a>, <a href="http://www.ru.nl">Radboud University</a>, the <a href="https://ibed.uva.nl">University of Amsterdam</a> and the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected during <a href="https://chirpscholekster.nl">CHIRP</a> (Cumulative Human Impact on biRd Populations) for the study O_VLIELAND 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 was operational from 2016 to 2021. In total 103 individuals of Eurasian oystercatchers (Haematopus ostralegus) have been tagged either as a breeding bird or while overwintering on the Wadden island Vlieland (the Netherlands), mainly to study how they respond to disturbances from aircraft. Data are 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. See van der Kolk et al. (2022, <a href="https://doi.org/10.3897/zookeys.1123.90623">https://doi.org/10.3897/zookeys.1123.90623</a>) for a more detailed description of this dataset. <h2>Files</h2> Data in this package are exported from Movebank study <a href="https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study1605802367">1605802367</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/10053988/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>. <ul> <li>datapackage.json: technical description of the data files.</li> <li>O_VLIELAND-reference-data.csv: reference data about the animals, tags and deployments.</li> <li>O_VLIELAND-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year.</li> <li>O_VLIELAND-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year.</li> <li>O_VLIELAND-accessory-measurements-yyyy.csv.gz: behaviour categories derived from acceleration data, grouped by year.</li> </ul> <h2>Acknowledgements</h2> These data were collected by the Netherlands Institute of Ecology (NIOO-KNAW), in collaboration with Sovon, Radboud University and the University of Amsterdam (UvA) for the CHIRP (Cumulative Human Impact on biRd Populations) project. Funding was provided by the Applied and Engineering Sciences domain of the Netherlands Organisation for Scientific Research (NWO-TTW 14638) and co-funding via NWO-TTW by Royal Netherlands Air Force, Birdlife Netherlands, NAM gas exploration and Deltares. The dataset was published with funding from Stichting NLBIF - Netherlands Biodiversity Information Facility.

opencc-zeroJan 2022View details →

zenodo44/100

Pilot Radiation under Surveillance, Veluwe, Netherlands

Research on the effect of transmitter radiation intensity and frequency on bird and bat behavior around a transmitter mast in the Vierhouten forest on the National park the Veluwe in The Netherlands done in 2022 and 2023. This research has tested a protocol and taken a first step toward monitoring effects of radiation on nature in the Veluwe. To ensure the health of nature, the research will have to be continued. To this end, the data from this study are made available. Environmental radiation measurement data, bird behavior and bat counts.

opencc-by-4.0Oct 2023View details →

zenodo44/100

Towards an open pipeline for the detection of Critical Infrastructure from satellite imagery – A case study on electrical substations in The Netherlands

Abstract. Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI prevents their incorporation in large-scale risk assessment studies. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMaps, processes the imagery and assets' masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The best metrics achieved are an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable AI.

opencc-by-4.0Nov 2023View details →

zenodo44/100

Contact data of Pienter3 (2016-2017) and PiCo (2020-2023) studies in the Netherlands

Contact data of two cross-sectional, sero-epidemiological studies in the general population of the Netherlands:<ul><li>The Pienter3 study (2016-2017)</li><li>The PienterCorona (PiCo) study consisting of 10 rounds (2020-2023)</li></ul>

opencc-by-4.0Mar 2024View details →

zenodo44/100

Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - the Netherlands

This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001. Reporting authorities contributing to each data collection: <ul> <li>TSE_2023_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> <li>TSE_2022_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> <li>TSE_2021_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> <li>TSE_2020_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> <li>TSE_2019_NL: Netherlands Food and Consumer Product Safety (NVWA)</li> </ul>

opencc-by-4.0Nov 2020View details →

zenodo44/100

UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands

UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands 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>. 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. <h2>Files</h2> 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>. <ul> <li>coverage.csv: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li>vpts-csv-table-schema.json: technical description of the fields in the VPTS data.</li> <li>be.tgz: VPTS data from 3 radars in Belgium.</li> <li>de.gz: VPTS data from 18 radars in Germany.</li> <li>nl.gz: VPTS data from 3 radars in the Netherlands.</li> </ul> <h2>Acknowledgements</h2> 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.

opencc-zeroSep 2024View details →

zenodo44/100

AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - The Netherlands

This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of: <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>

opencc-by-4.0Sep 2022View details →

zenodo44/100

LBBG_ZEEBRUGGE - Lesser black-backed gulls (Larus fuscus, Laridae) breeding at the southern North Sea coast (Belgium and the Netherlands)

LBBG_ZEEBRUGGE - Lesser black-backed gulls (Larus fuscus, Laridae) breeding at the southern North Sea coast (Belgium and the Netherlands) 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 LBBG_ZEEBRUGGE, 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 (Larus fuscus) 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. <h2>Files</h2> 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>. <ul> <li>datapackage.json: technical description of the data files.</li> <li>LBBG_ZEEBRUGGE-reference-data.csv: reference data about the animals, tags and deployments.</li> <li>LBBG_ZEEBRUGGE-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year.</li> <li>LBBG_ZEEBRUGGE-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year.</li> </ul> <h2>Acknowledgements</h2> 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.

opencc-zeroDec 2018View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record