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”
H_GRONINGEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding in Groningen (the Netherlands)
<p><em>H_GRONINGEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding in Groningen (the Netherlands)</em> is a bird tracking dataset collected by the <a href="https://grauwekiekendief.nl/">Grauwe kiekendief - Kenniscentrum Akkervogels (GKA)</a> / Dutch Montagu's Harrier Foundation and published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal tracking data collected for the project/study <strong>H_GRONINGEN</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 was operational from 2012 until 2018. In total 4 individuals of western marsh harriers (<em>Circus aeruginosus</em>) have been tagged in their breeding area in the province Groningen (the Netherlands) close to the Netherlands-Germany border, mainly to study their habitat use and migration behaviour. 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.</p> <p>See Milotic et al. (2020, <a href="https://doi.org/10.3897/zookeys.947.52570">https://doi.org/10.3897/zookeys.947.52570</a>) for a more detailed description of this dataset.</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=study922263102">922263102</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/10053658/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>H_GRONINGEN-reference-data.csv</strong>: reference data about the animals, tags and deployments.</li> <li><strong>H_GRONINGEN-gps-yyyy.csv.gz</strong>: GPS data recorded by the tags, grouped by year.</li> <li><strong>H_GRONINGEN-acceleration-yyyy.csv.gz</strong>: acceleration data recorded by the tags, grouped by year.</li> </ul>
Dataset and analysis file for 3-factor solution for heat pump perception study using Q-methodology in Groningen, the Netherlands
<p>Dataset and analysis using KEN-Q method for a 3-factor solution for heat pump perception study using Q-methodology in Groningen, the Netherlands</p>
An inbreeding perspective on the effectiveness of wildlife population defragmentation measures: A case study on wild boar (Sus scrofa) of Veluwe, The Netherlands
<p>Pervasive inbreeding is a major genetic threat of population fragmentation and can undermine the efficacy of population connectivity measures. Nevertheless, few studies have evaluated whether wildlife crossings can alleviate the frequency and length of genomic autozygous segments. Here, we provided a genomic inbreeding perspective on the potential effectiveness of mammal population defragmentation measures. We applied a SNP-genotyping case study on the ~2500 wild boar Sus scrofa population of Veluwe, The Netherlands, a 1000-km<sup>2 </sup>Natura 2000 protected area with many fences and roads but also, increasingly, fence openings and wildlife crossings. We combined a 20K genotyping assessment of genetic status and migration rate with a simulation that examined the potential for alleviation of isolation and inbreeding. We found that Veluwe wild boar subpopulations are significantly differentiated (FST-values of 0.02-0.07) and have low levels of gene flow. One noteworthy exception was the Central and Southeastern subpopulation, which were nearly panmictic and appeared to be effectively connected through a highway wildlife overpass. Estimated effective population sizes were at least 85 for the meta-population and ranging from 31 to 52 for the subpopulations. All subpopulations, including the two connected subpopulations, experienced substantial inbreeding, as evidenced through the occurrence of many long homozygous segments. Simulation output indicated that whereas one or few migrants per generation could undo genetic differentiation and boost effective population sizes rapidly, genomic inbreeding was only marginally reduced. The implication is that ostensibly successful connectivity restoration projects may fail to alleviate genomic breeding of fragmented mammal populations. We put forward that defragmentation projects should allow for (i) monitoring of levels of differentiation, migration and genomic inbreeding, (ii) anticipation of the inbreeding status of the meta-population, and, if inbreeding levels are high and/or haplotypes have become fixed, (iii) consideration of enhancing migration and gene flow among meta-populations, possibly through translocation.</p>
Figure 11 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 11. Typhlodromus (Anthoseius) suecicus (Sellnick) (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermatheca; (D) Chelicera; (E) Tarsus leg IV.
Figure 12 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 12. Typhlodromus (Typhlodromus) baccettii Lombardini (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermatheca; (D) Chelicera; (E) Tarsus leg IV.
Figure 6 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 6. Proprioseiopsis sharovi (Wainstein) (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermathecae; (D) Chelicera; (E) Leg IV.
Figure 5 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 5. Proprioseiopsis gallus Karg (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermathecae; (D) Chelicera; (E) Leg IV.
Figure 4 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 4. Amblyseius meridionalis Berlese (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermatheca; (D) Chelicera; (E) Leg IV.
Figure 10 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 10. Typhlodromus (Anthoseius) kerkirae Swirski & Ragusa (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermathecae; (D) Chelicera; (E) Leg IV.
Figure 9 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 9. Typhloseiulus peculiaris (Kolodochka) (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermatheca; (D) Leg IV.
Figure 8 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 8. Neoseiulus insularis (Athias-Henriot) (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermatheca; (D) Chelicera; (E) Leg IV.
Figure 1 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 1. Kampimodromus florinensis Papadoulis, Emmanouel & Kapaxidi (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermathecae; (D) Chelicera; (E) Ventrianal shield, a variation; (F) Leg IV.
Figure 13 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 13. Metaseiulus (Metaseiulus) smithi (Schuster) (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermathecae; (D) Chelicera; (E) Leg IV.
Figure 3 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 3. Amblyseius herbicolus (Chant) (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Chelicera; (D) Spermatheca; E – Leg IV.
Figure 2 in Some new species records of the predatory mite family Phytoseiidae (Acari: Mesostigmata) from The Netherlands
Figure 2. Kampimodromus langei Wainstein & Arutunjan (Female): (A) Idiosoma, dorsal view; (B) Idiosoma, ventral view; (C) Spermathecae; (D) Chelicera; (E) Leg IV.
Data for 'In temperate Europe, fire is already here: the case of the Netherlands'
<p><strong>Dataset about recent wildfire statistics for the Netherlands, 2017-2022</strong></p> <p><strong>Stoof, C.R., Kok, E., Cardil Forradellas, A., M. van Marle<em>.</em> In temperate Europe, fire is already here: The case of The Netherlands. <em>Ambio</em> (2024). https://doi.org/10.1007/s13280-023-01960-y</strong></p> <p>This dataset contains:</p> <p>1) Data for Figure 1: Historic patterns of a) annual fire occurrence and b) annual area burned (1945-1993) and c) distribution of fire size (1978-1993) in The Netherlands. Source: IKC (1995). File names: burned_area_distribution_historic.csv, fires_1945-1993.csv</p> <p>2) Raw data for individual fire records, because of privacy reasons this file is limited to information regarding fire date, time, municipality, area burned by EU vegetation class, and detailed country-specific vegetation classification. File name: All_fires.csv. For information regarding the availability of other raw data please contact the corresponding author.</p> <p>2) KMZ files for the five largest fires since 1970 (Figure 2)</p> <p>3) The aggregated data based on which Figure 3 in the article is based:</p> <p>Temporal characteristics and other metrics of recent landscape fires in The Netherlands (2017-2022): number of fires by month and year (a), year (b), month (c), weekday (d), and time of day (e, time that fire was reported), mean fire size (f), vegetation type affected following EU classification (g), detailed vegetation type (h), and fire size distribution (i). </p> <p>4) The aggregated data based on which Figure 4 in the associated article is based:</p> <p>Presumed fire cause (a, n=3667), number of fire engines and water tenders requested (b, for n=3583 and n=2270 fires, respectively), and estimate of suppression and restoration costs (c). **Note that while the fire cause classification in (a) follows the EU classification system, fire cause was only informally assessed, hence these data consider presumed cause only.</p> <p>5) A metadata file about all csv files, explaining all column names in each file </p> <p> </p> <p> </p>
National Checklists: The Netherlands Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - The Netherlands
<p>This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of The Netherlands. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health.</p> <p>For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005. Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken.</p> <p>Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC.</p> <p>The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles.</p> <p>Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance.</p> <p>Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product.</p> <p>Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food.</p> <p>Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation.</p> <p>Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation.</p> <p>Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances.</p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>VMPR_2023 – Netherlands Food and Consumer Product Safety Authority</p> <p>VMPR_2022 – Netherlands Food and Consumer Product Safety Authority</p> <p>VMPR_2021 – Netherlands Food and Consumer Product Safety Authority</p> <p>VMPR_2020 – National Institute for Public Health and the Environment</p> <p>VMPR_2019 – National Institute for Public Health and the Environment </p> <p>VMPR_2018 – National Institute for Public Health and the Environment</p> <p>VMPR_2017 – National Institute for Public Health and the Environment</p>
Figs 27–33 in Lemonia batavorum sp. nov. from the Netherlands, an overlooked sibling of L. dumi (Lepidoptera: Brahmaeidae)
Figs 27–33. Comparative presentation of larvae and adults in natural position. 27–30 – Lemonia batavorum sp. nov.; 31–33 – L. dumi (Linnaeus, 1761) (a – larva in dorsal view; b – larva in lateral view; c – adult, female; d – adult, male).
Figs 20–23 in Lemonia batavorum sp. nov. from the Netherlands, an overlooked sibling of L. dumi (Lepidoptera: Brahmaeidae)
Figs 20–23. Comparison of uncus (a) and juxta (b) in male genitalia. 20–21 – Lemonia batavorum sp. nov.; 22–23 – L. dumi (Linnaeus, 1761).
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.