cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.kke6y4
title: Occurrence Download

description: A dataset containing 277133 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "GadmGid is one of (IND.29.22_1, IND.29.18_1, IND.29.8_1)"
 ]
}

 The dataset includes 277133 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005396-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24416/uu01-l83i9c
title: Dataset of Bjurböle (L/LL4 S1) Ordinary Chondritic Meteorite, properties studied by Optical and Electron Microscopy (thin section A).

description: Optical and electron microscopy data for thin section A of the Bjurböle (L/LL4 S1) Ordinary Chondritic Meteorite, collected as part of the BSc Thesis by J.M.C. Reichard (2024) titled: "Bjurböle and Mezö-Madaras Ordinary Chondritic Meteorites, properties studied by Optical and Electron Microscopy.".

Dataset has the following contents:
	-- BjA_BSE_Atlasfullscan: Full VP BSD and SE scans in native .czi format and exported to png.
	-- BjA_BSE_closeups: BSE images used to locate the EDS scans.
		--- PLainImages: Plain images without metadata footer.
	-- BjA_BSE_EDS: Native .oip file that can be used to edit data and view data in higher quality.
		--- reports: Exported data to .xlsx format.
	-- BjA_PPL_XPL_10X: Full thin section PPL and XPL scans in native .czi format and exported to png. 
	-- BjA_PPL_XPL_closeups: Close-up PPL and XPL images.
	-- BjA_StraboMicro: StraboMicro is software for exploration microscopy data sets.
DOI: 10.24416/uu01-abvqr4
title: Central African Primate vocalization bioacoustics dataset

description: The dataset contains a train and test dataset for training and testing classification algorithms. The train dataset contains recordings from chimpanzees, red-capped mangabeys, mandrills and mixed species of guenons from the Mefou Primate Sanctuary in Cameroon, as well as background forest recordings from a primary wet tropical forest in South-West Gabon. 
The test dataset is fully independent from the training dataset and contains recordings from chimpanzees from the semi-natural Sanaga Sanctuary in Cameroon. Semi-natural here implies that enclosures contain a high degree of forest cover and are also surrounded by primary forests directly adjacent to the enclosures. An earlier version of this multi species dataset was submitted for the Interspeech 2021 challenge (Schuller et al. 2021; Zwerts et al. 2021).
All recordings were made using Audiomoth (v1.1.0) recorders [16]. Devices recorded 1- min segments continuously at 48 kHz and 30.6 dB gain, storing the data in one minute WAVE-files, with interruptions from two to five seconds between recordings for the recorder to save the files.

References:

Schuller, B.W., Batliner, A., Bergler, C., Mascolo, C., Han, J., Lefter, I., Kaya, H., Amiriparian, S., Baird, A., Stappen, L., Ottl, S., Gerczuk, M., Tzirakis, P., Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P., Rothkrantz, L.J.M., Zwerts, J.A., Treep, J., Kaandorp, C.S. (2021) The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates. Proc. Interspeech 2021, 431-435, doi: 10.21437/Interspeech.2021-19

Zwerts, J.A., Treep, J., Kaandorp, C.S., Meewis, F., Koot, A.C., Kaya, H. (2021) Introducing a Central African Primate Vocalisation Dataset for Automated Species Classification. Proc. Interspeech 2021, 466-470, doi: 10.21437/Interspeech.2021-154
DOI: 10.15468/dl.yg4nqn
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Andorra",
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus faginea"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005451-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24416/uu01-nf1v8z
title: Data supplement of Transformative potential of digital systems for promoting human-wildlife coexistence: A systematic literature review

description: This is the data set that was produced during the systematic literature review titled "Transformative potential of digital systems for promoting human-wildlife coexistence: A systematic literature review". The data set contains all articles that were included by means of using ASReview during the screening process, and therefore, every article has an ASReview ranking. A group of 11 reviewers then processed those articles and excluded articles based on the exclusion criteria as specified in the paper [EX1, EX2, EX3, EX4]. For the reviewed articles we collected 17 study characteristics (of which 14 were used in the paper), as well as a Digital Maturity score based on 3 digitalization characteristics [Visualizations, Statistical Modelling, Machine Learning] (Raghupathi and Raghupathi, 2021) and [Data Sharing/Storing] (Wilkinson, et al., 2016) and a Systemic Depth score based on 4 system characteristics [Parameters, Feedbacks, Design, Intent] (Abson et al., 2017) and one flag [Explains LP interactions] to mark whether the study explain the interactions between them. Both the Digital Maturity and Systemic Depth scores are then calculated using the equations explained in the paper, and the Transformative Potential is the geometric mean of the two scores.

The data is provided in 1 csv-file. Detailed information about this file is given in the explanatory file README.md. Contact person is Bas Michielsen - research@bshtmichielsen.eu
DOI: 10.24416/uu01-en1bwr
title: WIMBY D1.1 Bias correction ERA5 ratios

description: Bias correction ratios (U GWA/U ERA5) considering the microscale influences on the wind speed as computed by the Global Wind Atlas (GWA). Two datasets are provided for two versions of the GWA: GWA2 and GWA3.

This dataset is recommended for considering the microscale influences (terrain and vegetation) on the wind speed predicted by the ERA5 dataset, which has significant biases over mountains. 

The data were created by averaging microscale GWA wind speeds (250 m x 250 m) at three heights [50, 100 and 200 m] over a 0.025 degrees by 0.025 degrees grid and dividing by the corresponding value of the ERA5 data over the same period. Two versions of the GWA are considered (GWA2 and GWA3). Over Europe, studies have found an overestimation of the wind speed. Using GWA2 is recommended.
DOI: 10.24416/uu01-d9fhyg
title: WIMBY D1.6 Collision mortality for 22 bats and 320 birds: Summary maps at the assemblage (grid-cell) level

description: The data folders contain summary statistics at the assemblage (grid-cell) level of estimated collision-mortality rates, calculated across 320 bird species and 22 bat species known to occur in Europe. For birds only, the folder also contains species richness by estimated vulnerability classes (species grouping: highly vulnerable, high latent risk, possible persisters, possible adapters; for the detailed Methodology, see Deliverable D1.6 of the WIMBY project).

The summary statistics are provided in separate raster (.tif) files birds and bats.

The raster files are cropped around Europe; the resolution is 10 km by 10 km and the projection used is Lambert Azimuthal Equal Area projection.
See Deliverable D1.6 of the WIMBY project for details on the Methodology.

Note on contributions:
Martin Jung (IIASA) contributed to these outputs by providing suitability maps for 320 species of birds and 22 species of bats. 
The suitability maps were obtained following the Methodology described in Chapman et al. (2023), following Methods developed in Jung (2023). 
These maps were developed as part of the BIOCLIMA EU-funded project.
References:
Chapman, M., Jung, M., Leclère, D., Boettiger, C., D.Augustynczik, A. L., Gusti, M., Ringwald, L., & Visconti, P. (2023). Meeting European conservation and restoration targets under future land-use demands. OSF. [under review at Nature Sustainability]
https://doi.org/10.31219/osf.io/ynqfx
Jung, M. (2023). An integrated species distribution modelling framework for heterogeneous biodiversity data. Ecological Informatics, 76, 102127. 
https://doi.org/10.1016/j.ecoinf.2023.102127
DOI: 10.24416/uu01-aon211
title: WIMBY D1.7 Impact assessment on terrestrial and marine fauna (a)_Pantelleria

description: Habitat suitability of each species was modelled. The output from this is a habitat suitability map where the values range from 0.0 to 1.0 which represent lowest and highest suitability respectively. The species_RASTER.tif file is a raster in geotiff format with spatial resolution of 10 km and coordinate system of WGS 84 (EPSG:4326)
DOI: 10.24416/uu01-ruzkn8
title: WIMBY D1.7_Impact assessment on terrestrial and marine fauna (a)_Rogaland

description: The data represent seabird vulnerability through the Wind farm Sensitivity Index (WSI) in the pilot region Rogaland.
DOI: 10.24416/uu01-3gghfs
title: WIMBY D1.7 Impact assessment on terrestrial and marine fauna (a)_Styria

description: Habitat suitability of each species was modelled. The output from this is a habitat suitability map where the values range from 0.0 to 1.0 which represent lowest and highest suitability respectively. These proabilistic values were then transformed into binary values - 0 and 1 which represent unsuitable and suitable habitat using an optimal threshold. The threshold was selected using the sensitivity‐specificity sum maximisation approach. The binary habitat suitability were then summed to result in the 'summed_habitat suitability.tif' file. The value ranges from 0 to 3. Here, 0 indicates that the pixel does not contain suitable for any species while 3 indicates the pixel contains suitable habitat for 3 species. Note, currently only 3 species have been analysed. The 'summed_habitat suitability.tif' file is a raster of geotiff format with spatial resolution of 250 m and coordinate system of MGI / Austria Lambert (EPSG:31287).

Habitat connectivity of each species was modelled. The output from this is a habitat connectivity map where the values range from 0.0 to N. Here, N varies from species to species. Thus, the results of were scaled from their original values to 0.0-1.0. The rescaled habtiat connectivity were then averaged to result in the 'average_habitat connectivity.tif' file. The value ranges from 0.0 to 0.2386 which indicate lowest and highest connectivity. The 'average_habitat connectivity.tif' file is a raster of geotiff format with spatial resolution of 250 m and coordinate system of MGI / Austria Lambert (EPSG:31287).
DOI: 10.82209/rpd7-x122
title: The legacy effect of Cover Crops on soil structure amelioration: Cover crop (CC) mixtures show a higher mean weight diameter (MWD) increase (16%) than single species (12%).
DOI: 10.24416/uu01-sry0jn
title: WIMBY - Modelling habitat suitability for 23 species (amphibian, bat, bird, butterfly) in Styria, Austria

description: Habitat suitability of 23 species was modelled using the R package caret (Kuhn 2008) and the software R statistics (R Core Team 2023). The output from this are habitat suitability maps (geotiff format) with a spatial resolution of 250 m and coordinate system of MGI / Austria Lambert (EPSG:31287). Habitat suitability ranged from 0.0 to 1.0, which represent lowest and highest suitability, respectively. These probabilistic values were then transformed into binary values - 0 and 1, which represent unsuitable and suitable habitat using an optimal threshold (th). The threshold was selected using the sensitivity‐specificity sum maximisation approach (Liu et al. 2005). The accuracy and general fit of the models were assessed using the Area Under the Curve (AUC) of the Receiver Operating Characteristics curve (Fielding and Bell 1997) and the True Skilled Statistic (TSS, Allouche et al. 2006). For three amphibian species (Bufotes viridis, Rana dalmatina, Salamandra atra) the study area was limited to their IUCN distribution ranges within Styria because it was unlikely that these species occur outside their ranges. 

The files are consistently formatted as followed: 
Probabilistic values: "Species name.aXXXX.tX.XXXX.tif", where the numbers following "a" indicate the AUC values and the numbers following "t" indicate the TSS values. 
Binary values: "Species name.thX.XX.tif" 

References: 
Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43(6):1223-1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x 
Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24(1):38-49. https://doi.org/10.1017/S0376892997000088 
Kuhn M (2008) Building predictive models in R using the caret package. Journal of Statistical Software 28:1-26. https://doi.org/10.18637/jss.v028.i05 
Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28(3):385-393. https://doi.org/10.1111/j.0906-7590.2005.03957.x 
R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ 

For further details contact: Eva Maria Schöll, eva.schoell@boku.ac.at
DOI: 10.24416/uu01-j2iivr
title: National regulations and guidelines governing financial participation schemes in the wind energy sector

description: This dataset was developed as part of the WIMBY (Wind In My BackYard) research project, funded by the European Union's Horizon Europe programme from January 2023 - December 2025. It contributes to Deliverable D2.10, \"Data on Regulatory and Socioeconomic Conditions and Impacts (b),\" and supports the WIMBY Interactive Map, a tool for high-level planning of wind farms at any location in Europe. This sheet provides an overview of regulations, guidelines governing financial participation and compensation schemes in the wind energy sector in the EU27, CH, NO, and the UK. To find out more about our research project, visit www.wimby.eu.
DOI: 10.24416/uu01-hla9q3
title: National regulations and guidelines governing wind turbine setback distances

description: This dataset was developed as part of the WIMBY (Wind In My BackYard) research project, funded by the European Union's Horizon Europe programme from January 2023 - December 2025. It contributes to Deliverable D2.10, "Data on Regulatory and Socioeconomic Conditions and Impacts (b)," and supports the WIMBY Interactive Map, a tool for high-level planning of wind farms at any location in Europe. This sheet provides an overview of regulations, guidelines, and other national or industry documents specifying the setback distances that wind park developers must follow when constructing turbines in the EU27, CH, NO, and the UK. To find out more about our research project, visit www.wimby.eu.
DOI: 10.24416/uu01-b1xsbb
title: WIMBY D2.2 Lden measurement for populations exposed to wind turbines noise in Switzerland

description: The map in the file Lden_map_Switzerland_2023-12-04.html shows the results of the noise impact analysis of the 50 currently operating wind turbines in Switzerland. The maps find wind turbine installations and residence buildings within the noise propagation area of the wind turbines. Red and dark-red colours are used for populations exposed to an average noise level superior to the Lden value recommended by the WHO.
DOI: 10.24416/uu01-yoodaz
title: WIMBY D2.2 Location and health impact information of wind power accidents in Europe

description: The map in the file wind_turbine_accidents_map_europe.html shows the risk assessment for wind power accidents in Europe from 2000 to 2022.
DOI: 10.24416/uu01-91dbbc
title: WIMBY D2.4 - net noise (L_den) contribution of currently operating wind tubrines in Europe

description: The map WIMBY_D2.4_eu_map_with_turbines_02-2025.html shows the net contribution to noise levels (L_den) from currently operating wind turbines in Europe, considering other anthropogenic noise sources (i.e., highways, railways, industries and airports). Data generated by the WindWhisper model (https://github.com/Laboratory-for-Energy-Systems-Analysis/windwhisper). This dataset excludes wind turbines with incomplete metadata and focuses on turbines with validated geographic and technical details (e.g., hub height, and power).
DOI: 10.24416/uu01-sumr9e
title: WIMBY D2.4 Disability-Adjusted Life Years (DALY) from Onshore Wind Turbine Accidents in Europe

description: This dataset presents the results of a health impact assessment of onshore wind turbine accidents across Europe from 2000 to 2023. Using updated accident data linked to turbine and wind farm locations, Disability-Adjusted Life Years (DALYs) were estimated for each event. DALYs were computed based on fatality and injury data, adjusted using global burden of disease disability weights, and spatially disaggregated to the NUTS2 level using a Bayesian Hierarchical Model (BHM). The data were visualized as a risk map using QGIS and stored as GeoPackage files.
DOI: 10.24416/uu01-ezn7cw
title: WIMBY D2.5 Landscape impact metrics for Switzerland

description: This dataset contains the four adapted landscape metrics for Switzerland from the Swiss landscape wilderness study by Radford et al. (2018) : naturalness, human impact, remoteness, and ruggedness. 
They are calculated based on European-scale open-source data: Corine land cover, OpenStreetMap, and Copernicus Digital elevation model.
DOI: 10.24416/uu01-rysdzc
title: D2.6 European landscape scenicness dataset

description: The produced European landscape scenicness datasets are predictions by the machine learning model (Extreme Gradient Boosting classifier) trained on the ScenicOrNot data from GB. Two types of classification results are delivered: binary (0: unscenic to scenic, 1: highly scenic) and ternary (0: unscenic to medium, 1: scenic, 2: highly scenic). 
The used features are the four landscape wilderness indicators from D2.5 (naturalness, ruggedness, remoteness, and human impact) and land cover categories, elevation, global horizontal irradiance, population density, proximity to waterbodies, and slope aspect angle.
DOI: 10.24416/uu01-as60k5
title: WIMBY D1.6 Mean species-level collision mortality estimate, spatially explicit (plotted onto species' suitable areas)

description: Description:
These folders contain species-level collision mortality estimates plotted onto species' areas of suitability, for 22 bats and 320 birds. 
- The species-level collision-mortality estimates were obtained from models described in Deliverable D1.6 of the WIMBY project.
- The suitable areas for individual species were obtained following the Methodology described in Chapman et al. (2023), following Methods developed in Jung (2023). The raw suitability maps were obtained as part of the BIOCLIMA EU-funded project  and are currently unpublished. 

References:

Chapman, M., Jung, M., Leclère, D., Boettiger, C., D.Augustynczik, A. L., Gusti, M., Ringwald, L., & Visconti, P. (2023). Meeting European conservation and restoration targets under future land-use demands. OSF. [under review at Nature Sustainability]
https://doi.org/10.31219/osf.io/ynqfx


Jung, M. (2023). An integrated species distribution modelling framework for heterogeneous biodiversity data. Ecological Informatics, 76, 102127. 
https://doi.org/10.1016/j.ecoinf.2023.102127
DOI: 10.24416/uu01-kibtfk
title: WIMBY D1.6 Estimated species-level collision mortality rates

description: These .csv files contain the species-level estimates of collision-mortality rate (mean estimated number of collision victims per year and per turbine, and 95% credible intervals), estimated from the fitted models for birds and bats. For all species, the predictions were made on the basis of the fitted models, using species traits as predictors. For the subset of species that figured in the fitted data, the predictions also accounted for the random-effects of species' identity.
DOI: 10.15468/dl.cb3s9r
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Moenkhausia barbouri",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 11 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005456-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24416/uu01-uefhv2
title: WIMBY D2.3 - Shadow flicker testing result

description: These data provide the simulated shadow flicker results calculated by the WIMBY-SF calculator for the testing phase in the year 2023. 

There are three sets of shadow flicker testing result, at the location(53. 123084, 5.588264) in the Netherlands, shown by vector files (SFvec) and and raster files (SFraster):
1. Coarse resolution, with 30*30 meters plotting resolution, calculated by every 14 days, with timestamp every 4 minutes. 
2. Normal resolution, with 20*20 meters plotting resolution, calculated by every 7 days, with timestamp every 3 minutes. 
3. Fine resolution, with 10*10 meters plotting resolution, calculated by every 3 days, with timestamp every 2 minutes.

The "multi" in the file name reflects that these files were created with the version of the tool implemented in multi-core.

The files are in tiff file for raster results, in geojson file for vector result. Qmd and xml files for each .tiff and .geojson file are also provided for metadata information.
DOI: 10.24416/uu01-tnszbj
title: WIMBY D1.1 Mean wind speed by height, month, and time of the day

description: The data were created by averaging wind speeds at three heights [50, 100 and 200 m] over the month and hour of the day for the period (2013-2022). We select an area containing Europe from 35N-72N and 15W-35E. The resulting matrix has dimensions of (12,24,2,149,201). The horizontal grid spacing is 0.25 degrees by 0.25 degrees. We recommend that this data be biased corrected using the ratio_gwa2_era5.nc dataset for application models.
DOI: 10.24416/uu01-0q6su6
title: World Water Map Data Package

description: This data package contains the data associated with the World Water Map project (worldwatermap.nationalgeographic.org). The World Water Map informs on the state of the worlds water resources between 1980 and 2019: storages and fluxes in the terrestrial water cycle, human impacts on the water cycle (i.e. demand, withdrawal and consumption) and, most importantly, the gap between renewable water availability and human water demands (i.e. the water gap). The data behind the World Water Map is based on the global hydrological model PCR-GLOBWB (globalhydrology.nl/research/models/pcr-globwb-2-0), which was developed at Utrecht University. This data package contains all the maps associated with the World Water Map. For more information, please contact Bram Droppers (b.droppers@uu.nl) or Niko Wanders (n.wanders@uu.nl).

For more information on the global hydrological model PCR-GLOBWB and its components: Sutanudjaja, E. H., Van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López, P. L., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D. & Bierkens, M. F. (2018). PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geoscientific Model Development, 11(6), 2429-2453. doi.org/10.5194/gmd-11-2429-2018. The model code is publicly available at github.com/UU-Hydro/PCR-GLOBWB_model.