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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bembibre, Cecilia; Leemans, Inger; Elpers, Sophie;

    This resource presents an overview of intangible cultural heritage practices in which smells plays or can play a significant role. The list is compiled to support cultural heritage policy makers to identify the significance of olfactory heritage and help heritage communities to acknowledge the value of olfaction in heritage practices. The information is compiled by the Odeuropa project in collaboration with the Dutch Centre for Intangible Cultural Heritage (DICH), the Smell of Heritage group of University College London and the KNAW Meertens Institute.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Petram, Lodewijk; Koolen, Marijn; Wevers, Melvin; van Koert, Rutger; +1 Authors

    This is a dataset on 460,452 individuals employed by the Dutch East India Company (Verenigde Oost-Indische Compagnie, VOC) in the seventeenth and especially the eighteenth centuries, developed from 774,200 muster records in the ‘VOC-opvarenden’ collection. The original data has been enhanced through the disambiguation of individual records, the standardization of 44,152 unique place names, and the addition of wage details and rank structure. This collection includes the original ‘VOC-opvarenden’ dataset (comprising three files), enriched files (totaling nine), integrated external data, and Jupyter notebooks documenting the transformation from original to enriched datasets. The accompanying data paper provides an in-depth overview of the original dataset, the enhancement process, and potential applications. Additionally, it features appendices serving as codebooks, offering concise descriptions of each variable present in the enriched data files. Enabling research into career patterns, network structures, and migration trends, this resource is of significant value to the study of early modern history, social and economic history, and sociology.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Helfenstein, Anatol; Mulder, Vera L.; Hack-ten Broeke, Mirjam J.D.; van Doorn, Maarten; +3 Authors

    This dataset contains 206 spatially explicit environmental variables, also termed covariates, at 25m resolution that cover the entire Netherlands (national scale). The raster data are comprised of covariates related to the soil-forming factors (climate, organism/land use/land cover, relief/topography, parent material/geology) for the purpose of using them for digital soil mapping. However, since the covariates cover a wide range of environmental variables, they can potentially be used for spatial modelling in the Netherlands also outside the field of soil science. All covariates can also be found from the original source, but the potential strength and practicality of this dataset lies in the broad range of readily available, collected, prepared and harmonized raster data.The metadata of all the covariates in this dataset can be found in the "00_covariates_metadata.csv" file, including information about the names, category, value types, specific value types, type of geospatial data, file type, whether its static or dynamic, temporal coverage, date/version, resolution (all 25m), origin, source, access/license, description, processing steps and comments. The dataset includes 3 different types of files:GeoTIFF (.tif): the covariates as raster data at 25m resolution in the EPSG:28992 (Amersfoort / RD New) spatial projectionText (.txt): README files for each covariate with additional metadata information (filename ending in "_readme.txt")Tabular data (.csv): Classification and re-classification table for categorical covariates (filename ending in "_reclassify.csv")Note that the reclassification tables contain potential ways to reclassify the data provided, but can be altered by the user. Reclassification may be useful for categorical covariates with a large number of classes/categories. Note that covariates with CC BY-ND 4.0 licenses, covariates that are not open data or for which the license was unknown are not shared in this dataset.More information about these covariates can be found in the associated scientific paper "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). Different ways of pre-processing and preparing the covariates for subsequent modelling can be found in R scripts 20-25 in the associated code repository on GitLab. This includes assembling and preparing covariates using GDAL ("20_cov_prep_gdal.R"), computing digital elevation model (DEM) derivatives using SAGA GIS ("21_cov_dem_deriv_saga.R"), deriving spectral indices from RGBNIR bands of Sentinel 2 images ("22_cov_sensing_deriv.R"), preparing categorical covariates using GDAL ("23_cov_cat_recl_gdal.R"), deriving dynamic covariates ("24_cov_dyn_prep_gdal.R") and exploratory analysis of the covariates ("25_cov_expl_analysis_clorpt.Rmd", "25_cov_expl_analysis_cont_cat.Rmd").

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Research@WUR; 4TU.Re...arrow_drop_down
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    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC BY NC SA
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC BY NC SA
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC BY NC SA
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC BY NC SA
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: D. Reckien; M. Olazabal; A. Buzasi; P. Eckersley; +31 Authors

    This dataset contains information on the characteristics of Local Climate Adaptation Plans in Europe. A sample of 328 medium- to large-sized cities across the formerly EU-28 was investigated for the availability of Local Climate Plans and strategies on climate change adaptation. A set of 168 cities out of the 328 were identified to have at least one, if not more Local Climate Adaptation Plans. The contents of these plans were documented, using an elaborated framework combining indicators of state-of-the -art plan quality principles with indicators of justice/ equity theory. Used common plan quality principles are 1) Fact base - Climate change impact, risk and vulnerability assessment (related to risk, sectors, justice), 2) Adaptation goals (related to risk, quantitative); 3) Adaptation measures (distributed across 12 sectors, justice); 4) Implementation process (prioritization, responsibility, timeframe) & tools (budget); 5) Monitoring & evaluation (responsibility, timeframe, justice). Additionally to the information on these 5 plan quality principles information on the (potential) participation process, a communication strategy, the national and regional context, as well as with access information, access data, access type, and other meta data were retrieved and documented. The publication dates of the plans range from 2005 - 2020. The collection period ranges from March 2019 to June 2021, depending on the country and city, with the majority collected between May 2019 and June 2020. Date: Data collection: March 2019 to June 2021, depending on the country and city. (Publication) Date of local adaptation plans: 2020. Content analysis of EU Local Climate Adaptation Plans and Strategies Date Submitted: 2022-06-14

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ EASY; DANS Data Stat...arrow_drop_down
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    DANS-EASY
    Dataset . 2024
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ EASY; DANS Data Stat...arrow_drop_down
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      DANS-EASY
      Dataset . 2024
      Data sources: B2FIND
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    Authors: Liscio, Enrico; Araque, Oscar; Gatti, Lorenzo; Constantinescu, Ionut; +3 Authors

    Code for the paper "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric", published at ACL '23. This code implements Tomea, an Explainable AI method for investigating the difference in how language models represent morality across domains. Given a pair of datasets and models trained on the datasets, Tomea generates 10 m-distances and one d-distance to measure the difference between the datasets, based on the SHAP method. We make pairwise comparisons of seven models trained on the MFTC datasets (available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c).

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Liscio, Enrico; Araque, Oscar; Gatti, Lorenzo; Constantinescu, Ionut; +3 Authors

    Raw results and crowd task material for the paper "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric", published at ACL '23. This repository contains two main folders. First, the instructions and informed consent used to perform the crowd study described in the paper, where crowd workers are asked to compare word bubbles describing moral values in a domain. Second, the results of the pairwise comparisons performed with the seven models trained on the MFTC datasets and the comparison of the results with the crowd annotations.The code to generate the results is available at this DOI: 10.4121/1e71138c-be26-4652-971a-48a84837df8eThe seven models are available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
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    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
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      4TU.ResearchData
      Dataset . 2023
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      4TU.ResearchData
      Dataset . 2023
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      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
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    Authors: de Vos, Wiebe M.; Regenspurg, Jurjen; Wurm, Frederik; Peil, Stefan; +2 Authors

    The data includes the following datasets:dn_dc_for_Q_factor_determination: refractive indexes of lignin solutions measured at different concentrations (g/L) of lignin at different wavelengths (nm) of incoming light. The linear fit function in excel was used to determine the dn/dc from the presented data. The fit data can be found in the SI of the article.Filtration: Contains experimental results of crossflow filtration measurements for pure water permeability, 90% molecular weight cut off (MWCO) data based on polyethylene glycol (PEG) retentions (100-10000 g/mol) as measured by Gell permeation chromatography (GPC), and MgSO4 retention data. Measured quantities are: active fibre length (m), average [inlet and outlet] pressure (bar), permeate mass (g) [filled beaker-empty beaker], permeation time (s), feed and permeate conductivity (microS/cm).Fourier transform infrared spectroscopy data (FTIR): From the 3 different cationic lignin batches used in the article, the lignosulfonates and the unmodified Kraft lignin. batch number 1 was used for figure 2 in the article; batch number 2 was used for the salt stability, pH stability and biodegradability experiments (Figure 4, 5 and 6 of the main article and figure S4, S6, S7 and S9 of the SI); batch number 3 was used for the reflectometry and layer growth experiments at different NaCl concentrations (figure 3 in the article). the wavelength (cm-1) and signal intensity (a.u.) are presented.H-NMR: 1H-NMR data of the different cationic lignin batches, lignosulfonate and unmodified Kraft lignin. The chemical shift (ppm) and signal intensity (a.u.) are presentedP-NMR: 31P-NMR data of the unmodified Kraft lignin and lignosulfonate. To calculate the OH content of the lignin from this data the MestreNova software was used. The chemical shift (ppm) and signal intensity (a.u.) are presentedSEM: FESEM images of the bare support and the support coated with 9.5 or 10.0 bilayers of ligninReflectometry: Reflectometry data for the lignins coated from 5, 50 and 500 mM NaCl solutions onto a silicon waver. The Q factors as determined from the dn/dc data and the professor Huygens software are presented together with the signal (S) base signal (So) and calculated adsorption (Ads) in mg/m2.

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    4TU.ResearchData
    Dataset . 2023
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    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
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    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
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      4TU.ResearchData
      Dataset . 2023
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      4TU.ResearchData | science.engineering.design
      Dataset . 2023
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      Data sources: Datacite
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    Authors: Spanoghe, Geert; Desmet, Peter; Milotic, Tanja; Janssens, Kjell; +3 Authors

    MH_ANTWERPEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding near Antwerp (Belgium) is a bird tracking dataset published by the Research Institute for Nature and Forest (INBO). It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (http://lifewatch.be/en/gps-tracking-network-large-birds) for the project/study MH_ANTWERPEN, using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, http://www.uva-bits.nl). The study has been operational since 2018. In total 3 individuals of western marsh harriers (Circus aeruginosus) and 1 common buzzard (Buteo buteo) have been tagged in their breeding area near the city of Antwerp (Belgium), 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 https://github.com/inbo/bird-tracking). See Milotic et al. (2020, https://doi.org/10.3897/zookeys.947.52570) for a more detailed description of this dataset. Files Data in this package are exported from Movebank study 938783961. Fields in the data follow the Movebank Attribute Dictionary and are described in datapackage.json. Files are structured as a Frictionless Data Package. You can access all data in R via https://zenodo.org/records/10054153/files/datapackage.json using frictionless. datapackage.json: technical description of the data files. MH_ANTWERPEN-reference-data.csv: reference data about the animals, tags and deployments. MH_ANTWERPEN-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year. MH_ANTWERPEN-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year. Acknowledgements This dataset was collected using infrastructure provided by INBO and funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch. This version adds alt-project-id to the reference-data and references the latest Movebank Attribute Dictionary.

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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
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    ZENODO
    Dataset . 2021
    License: CC 0
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    ZENODO
    Dataset . 2023
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      ZENODO
      Dataset . 2019
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      ZENODO
      Dataset . 2019
      License: CC 0
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      ZENODO
      Dataset . 2023
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      ZENODO
      Dataset . 2023
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
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    Authors: Koks, Ben; Schlaich, Almut; Schaub, Tonio; Klaassen, Raymond; +6 Authors

    H_GRONINGEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding in Groningen (the Netherlands) is a bird tracking dataset collected by the Grauwe kiekendief - Kenniscentrum Akkervogels (GKA) / Dutch Montagu's Harrier Foundation and published by the Research Institute for Nature and Forest (INBO). It contains animal tracking data collected for the project/study H_GRONINGEN, using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, http://www.uva-bits.nl). The study was operational from 2012 until 2018. In total 4 individuals of western marsh harriers (Circus aeruginosus) 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 https://github.com/inbo/bird-tracking). No new data are expected. See Milotic et al. (2020, https://doi.org/10.3897/zookeys.947.52570) for a more detailed description of this dataset. Files Data in this package are exported from Movebank study 922263102. Fields in the data follow the Movebank Attribute Dictionary and are described in datapackage.json. Files are structured as a Frictionless Data Package. You can access all data in R via https://zenodo.org/records/10053658/files/datapackage.json using frictionless. datapackage.json: technical description of the data files. H_GRONINGEN-reference-data.csv: reference data about the animals, tags and deployments. H_GRONINGEN-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year. H_GRONINGEN-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year. This version adds alt-project-id to the reference-data and references the latest Movebank Attribute Dictionary.

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    ZENODO
    Dataset . 2023
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    ZENODO
    Dataset . 2023
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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC 0
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    ZENODO
    Dataset . 2021
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    ZENODO
    Dataset . 2023
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Blin, Inès; Stork, Lise; Spillner, Laura; Santagiustina, Carlo Romano Marcello Alessandro;

    The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality. We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023. In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization. Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person with the following information: A description of the use case Affiliation of the researchers involved How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d

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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: ZENODO; Sygma
    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: ZENODO; Sygma
      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bembibre, Cecilia; Leemans, Inger; Elpers, Sophie;

    This resource presents an overview of intangible cultural heritage practices in which smells plays or can play a significant role. The list is compiled to support cultural heritage policy makers to identify the significance of olfactory heritage and help heritage communities to acknowledge the value of olfaction in heritage practices. The information is compiled by the Odeuropa project in collaboration with the Dutch Centre for Intangible Cultural Heritage (DICH), the Smell of Heritage group of University College London and the KNAW Meertens Institute.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Petram, Lodewijk; Koolen, Marijn; Wevers, Melvin; van Koert, Rutger; +1 Authors

    This is a dataset on 460,452 individuals employed by the Dutch East India Company (Verenigde Oost-Indische Compagnie, VOC) in the seventeenth and especially the eighteenth centuries, developed from 774,200 muster records in the ‘VOC-opvarenden’ collection. The original data has been enhanced through the disambiguation of individual records, the standardization of 44,152 unique place names, and the addition of wage details and rank structure. This collection includes the original ‘VOC-opvarenden’ dataset (comprising three files), enriched files (totaling nine), integrated external data, and Jupyter notebooks documenting the transformation from original to enriched datasets. The accompanying data paper provides an in-depth overview of the original dataset, the enhancement process, and potential applications. Additionally, it features appendices serving as codebooks, offering concise descriptions of each variable present in the enriched data files. Enabling research into career patterns, network structures, and migration trends, this resource is of significant value to the study of early modern history, social and economic history, and sociology.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Helfenstein, Anatol; Mulder, Vera L.; Hack-ten Broeke, Mirjam J.D.; van Doorn, Maarten; +3 Authors

    This dataset contains 206 spatially explicit environmental variables, also termed covariates, at 25m resolution that cover the entire Netherlands (national scale). The raster data are comprised of covariates related to the soil-forming factors (climate, organism/land use/land cover, relief/topography, parent material/geology) for the purpose of using them for digital soil mapping. However, since the covariates cover a wide range of environmental variables, they can potentially be used for spatial modelling in the Netherlands also outside the field of soil science. All covariates can also be found from the original source, but the potential strength and practicality of this dataset lies in the broad range of readily available, collected, prepared and harmonized raster data.The metadata of all the covariates in this dataset can be found in the "00_covariates_metadata.csv" file, including information about the names, category, value types, specific value types, type of geospatial data, file type, whether its static or dynamic, temporal coverage, date/version, resolution (all 25m), origin, source, access/license, description, processing steps and comments. The dataset includes 3 different types of files:GeoTIFF (.tif): the covariates as raster data at 25m resolution in the EPSG:28992 (Amersfoort / RD New) spatial projectionText (.txt): README files for each covariate with additional metadata information (filename ending in "_readme.txt")Tabular data (.csv): Classification and re-classification table for categorical covariates (filename ending in "_reclassify.csv")Note that the reclassification tables contain potential ways to reclassify the data provided, but can be altered by the user. Reclassification may be useful for categorical covariates with a large number of classes/categories. Note that covariates with CC BY-ND 4.0 licenses, covariates that are not open data or for which the license was unknown are not shared in this dataset.More information about these covariates can be found in the associated scientific paper "BIS-4D: Mapping soil properties and their uncertainties at 25m resolution in the Netherlands" (Helfenstein et al., 2024, under review). Different ways of pre-processing and preparing the covariates for subsequent modelling can be found in R scripts 20-25 in the associated code repository on GitLab. This includes assembling and preparing covariates using GDAL ("20_cov_prep_gdal.R"), computing digital elevation model (DEM) derivatives using SAGA GIS ("21_cov_dem_deriv_saga.R"), deriving spectral indices from RGBNIR bands of Sentinel 2 images ("22_cov_sensing_deriv.R"), preparing categorical covariates using GDAL ("23_cov_cat_recl_gdal.R"), deriving dynamic covariates ("24_cov_dyn_prep_gdal.R") and exploratory analysis of the covariates ("25_cov_expl_analysis_clorpt.Rmd", "25_cov_expl_analysis_cont_cat.Rmd").

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    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2024
    License: CC BY NC SA
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC BY NC SA
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2024
    License: CC BY NC SA
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2024
      License: CC BY NC SA
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC BY NC SA
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2024
      License: CC BY NC SA
      Data sources: Datacite
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    Authors: D. Reckien; M. Olazabal; A. Buzasi; P. Eckersley; +31 Authors

    This dataset contains information on the characteristics of Local Climate Adaptation Plans in Europe. A sample of 328 medium- to large-sized cities across the formerly EU-28 was investigated for the availability of Local Climate Plans and strategies on climate change adaptation. A set of 168 cities out of the 328 were identified to have at least one, if not more Local Climate Adaptation Plans. The contents of these plans were documented, using an elaborated framework combining indicators of state-of-the -art plan quality principles with indicators of justice/ equity theory. Used common plan quality principles are 1) Fact base - Climate change impact, risk and vulnerability assessment (related to risk, sectors, justice), 2) Adaptation goals (related to risk, quantitative); 3) Adaptation measures (distributed across 12 sectors, justice); 4) Implementation process (prioritization, responsibility, timeframe) & tools (budget); 5) Monitoring & evaluation (responsibility, timeframe, justice). Additionally to the information on these 5 plan quality principles information on the (potential) participation process, a communication strategy, the national and regional context, as well as with access information, access data, access type, and other meta data were retrieved and documented. The publication dates of the plans range from 2005 - 2020. The collection period ranges from March 2019 to June 2021, depending on the country and city, with the majority collected between May 2019 and June 2020. Date: Data collection: March 2019 to June 2021, depending on the country and city. (Publication) Date of local adaptation plans: 2020. Content analysis of EU Local Climate Adaptation Plans and Strategies Date Submitted: 2022-06-14

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    DANS-EASY
    Dataset . 2024
    Data sources: B2FIND
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      DANS-EASY
      Dataset . 2024
      Data sources: B2FIND
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    Authors: Liscio, Enrico; Araque, Oscar; Gatti, Lorenzo; Constantinescu, Ionut; +3 Authors

    Code for the paper "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric", published at ACL '23. This code implements Tomea, an Explainable AI method for investigating the difference in how language models represent morality across domains. Given a pair of datasets and models trained on the datasets, Tomea generates 10 m-distances and one d-distance to measure the difference between the datasets, based on the SHAP method. We make pairwise comparisons of seven models trained on the MFTC datasets (available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c).

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Liscio, Enrico; Araque, Oscar; Gatti, Lorenzo; Constantinescu, Ionut; +3 Authors

    Raw results and crowd task material for the paper "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric", published at ACL '23. This repository contains two main folders. First, the instructions and informed consent used to perform the crowd study described in the paper, where crowd workers are asked to compare word bubbles describing moral values in a domain. Second, the results of the pairwise comparisons performed with the seven models trained on the MFTC datasets and the comparison of the results with the crowd annotations.The code to generate the results is available at this DOI: 10.4121/1e71138c-be26-4652-971a-48a84837df8eThe seven models are available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: de Vos, Wiebe M.; Regenspurg, Jurjen; Wurm, Frederik; Peil, Stefan; +2 Authors

    The data includes the following datasets:dn_dc_for_Q_factor_determination: refractive indexes of lignin solutions measured at different concentrations (g/L) of lignin at different wavelengths (nm) of incoming light. The linear fit function in excel was used to determine the dn/dc from the presented data. The fit data can be found in the SI of the article.Filtration: Contains experimental results of crossflow filtration measurements for pure water permeability, 90% molecular weight cut off (MWCO) data based on polyethylene glycol (PEG) retentions (100-10000 g/mol) as measured by Gell permeation chromatography (GPC), and MgSO4 retention data. Measured quantities are: active fibre length (m), average [inlet and outlet] pressure (bar), permeate mass (g) [filled beaker-empty beaker], permeation time (s), feed and permeate conductivity (microS/cm).Fourier transform infrared spectroscopy data (FTIR): From the 3 different cationic lignin batches used in the article, the lignosulfonates and the unmodified Kraft lignin. batch number 1 was used for figure 2 in the article; batch number 2 was used for the salt stability, pH stability and biodegradability experiments (Figure 4, 5 and 6 of the main article and figure S4, S6, S7 and S9 of the SI); batch number 3 was used for the reflectometry and layer growth experiments at different NaCl concentrations (figure 3 in the article). the wavelength (cm-1) and signal intensity (a.u.) are presented.H-NMR: 1H-NMR data of the different cationic lignin batches, lignosulfonate and unmodified Kraft lignin. The chemical shift (ppm) and signal intensity (a.u.) are presentedP-NMR: 31P-NMR data of the unmodified Kraft lignin and lignosulfonate. To calculate the OH content of the lignin from this data the MestreNova software was used. The chemical shift (ppm) and signal intensity (a.u.) are presentedSEM: FESEM images of the bare support and the support coated with 9.5 or 10.0 bilayers of ligninReflectometry: Reflectometry data for the lignins coated from 5, 50 and 500 mM NaCl solutions onto a silicon waver. The Q factors as determined from the dn/dc data and the professor Huygens software are presented together with the signal (S) base signal (So) and calculated adsorption (Ads) in mg/m2.

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
    4TU.ResearchData | science.engineering.design
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
      4TU.ResearchData | science.engineering.design
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Spanoghe, Geert; Desmet, Peter; Milotic, Tanja; Janssens, Kjell; +3 Authors

    MH_ANTWERPEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding near Antwerp (Belgium) is a bird tracking dataset published by the Research Institute for Nature and Forest (INBO). It contains animal tracking data collected by the LifeWatch GPS tracking network for large birds (http://lifewatch.be/en/gps-tracking-network-large-birds) for the project/study MH_ANTWERPEN, using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, http://www.uva-bits.nl). The study has been operational since 2018. In total 3 individuals of western marsh harriers (Circus aeruginosus) and 1 common buzzard (Buteo buteo) have been tagged in their breeding area near the city of Antwerp (Belgium), 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 https://github.com/inbo/bird-tracking). See Milotic et al. (2020, https://doi.org/10.3897/zookeys.947.52570) for a more detailed description of this dataset. Files Data in this package are exported from Movebank study 938783961. Fields in the data follow the Movebank Attribute Dictionary and are described in datapackage.json. Files are structured as a Frictionless Data Package. You can access all data in R via https://zenodo.org/records/10054153/files/datapackage.json using frictionless. datapackage.json: technical description of the data files. MH_ANTWERPEN-reference-data.csv: reference data about the animals, tags and deployments. MH_ANTWERPEN-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year. MH_ANTWERPEN-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year. Acknowledgements This dataset was collected using infrastructure provided by INBO and funded by Research Foundation - Flanders (FWO) as part of the Belgian contribution to LifeWatch. This version adds alt-project-id to the reference-data and references the latest Movebank Attribute Dictionary.

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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Koks, Ben; Schlaich, Almut; Schaub, Tonio; Klaassen, Raymond; +6 Authors

    H_GRONINGEN - Western marsh harriers (Circus aeruginosus, Accipitridae) breeding in Groningen (the Netherlands) is a bird tracking dataset collected by the Grauwe kiekendief - Kenniscentrum Akkervogels (GKA) / Dutch Montagu's Harrier Foundation and published by the Research Institute for Nature and Forest (INBO). It contains animal tracking data collected for the project/study H_GRONINGEN, using trackers developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, http://www.uva-bits.nl). The study was operational from 2012 until 2018. In total 4 individuals of western marsh harriers (Circus aeruginosus) 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 https://github.com/inbo/bird-tracking). No new data are expected. See Milotic et al. (2020, https://doi.org/10.3897/zookeys.947.52570) for a more detailed description of this dataset. Files Data in this package are exported from Movebank study 922263102. Fields in the data follow the Movebank Attribute Dictionary and are described in datapackage.json. Files are structured as a Frictionless Data Package. You can access all data in R via https://zenodo.org/records/10053658/files/datapackage.json using frictionless. datapackage.json: technical description of the data files. H_GRONINGEN-reference-data.csv: reference data about the animals, tags and deployments. H_GRONINGEN-gps-yyyy.csv.gz: GPS data recorded by the tags, grouped by year. H_GRONINGEN-acceleration-yyyy.csv.gz: acceleration data recorded by the tags, grouped by year. This version adds alt-project-id to the reference-data and references the latest Movebank Attribute Dictionary.

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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2019
    License: CC 0
    Data sources: ZENODO
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    ZENODO
    Dataset . 2021
    License: CC 0
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2019
      License: CC 0
      Data sources: ZENODO
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      ZENODO
      Dataset . 2021
      License: CC 0
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC 0
      Data sources: Datacite
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    Authors: Blin, Inès; Stork, Lise; Spillner, Laura; Santagiustina, Carlo Romano Marcello Alessandro;

    The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality. We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023. In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization. Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person with the following information: A description of the use case Affiliation of the researchers involved How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d

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    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: ZENODO; Sygma
    ZENODO
    Dataset . 2023
    License: CC BY NC
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: ZENODO; Sygma
      ZENODO
      Dataset . 2023
      License: CC BY NC
      Data sources: Datacite
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