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.
13,117
datasets available to search
ShareScore release 0.9.0
Dataset results
13,117 results for “Maps”
Parsimonious machine learning for the global mapping of aboveground biomass density
<p>This repository hosts data and code presented in the article "Parsimonious machine learning for the global mapping of aboveground biomass potential". The repository contains a compressed file containing all the code needed to reproduce the methodology that we developed and to analyse its results. We did not upload all the temporary and intermediate data files that are created during the execution of the method. We rather uploaded "milestone" data, i.e. final results or important intermediate ones. This includes the final training dataset, model calibration data, the final trained model, the global data for prediction, the final global map of potential aboveground biomass density (AGBD) at present times (raster files at 1km2 and 10km2 resolution), maps depicting regions where climatic conditions are outside of the training range of positive AGBD instances and maps depicting world regions without trees. </p> <p><strong>Files:</strong></p> <p><a href="../api/records/11580414/draft/files/code.zip/content" target="_blank" rel="noopener noreferrer">code.zip</a> : Compressed directory with all the code needed to reproduce the methodology presented in the manuscript. Contains a README file. Also contains temporary data generated in the process, the training dataset, the trained model, and model calibration data.</p> <p><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">potential_AGBD_Mgha_1km_present_climate_1980_2010.tif</a> : the predicted global potential AGBD under contemporary climate conditions and at a resolution of 1 squared kilometer.</p> <p><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">potential_AGBD_Mgha_10km_</a><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">present_climate_1980_2010.tif</a> : the predicted global potential AGBD under contemporary climate conditions downsampled at a resolution of 10 squared kilometers.</p> <p><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">potential_AGBD_Mgha_10km_model_difference.tif</a> : the difference between our prediction of potential AGBD and the prediction from a complex state-of-the-art model from Walker et al. (2022). </p> <p><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">potential_AGB_Mg_1km_</a><a href="../api/records/11580414/draft/files/potential_AGBD_Mgha_1km2_contemporary_climate.tif/content" target="_blank" rel="noopener noreferrer">present_climate_1980_2010.tif</a> : the predicted global potential pixel-level AGB under contemporary climate conditions downsampled at a resolution of 1 squared kilometers.</p> <p><a href="../api/records/11580414/draft/files/number_predictors_out_of_range.zip/content" target="_blank" rel="noopener noreferrer">number_predictors_out_of_range.zip</a> : tiled maps representing the number of climatic predictors outside of the training range before including 0 AGBD instances in the training dataset. </p> <p><a href="../api/records/11580414/draft/files/tree_absence_map.zip/content" target="_blank" rel="noopener noreferrer">tree_absence_map.zip</a> : tiled maps representing world regions without trees. Based on Crowther et al. (2015) (https://elischolar.library.yale.edu/yale_fes_data/1/).</p> <p><a href="../api/records/11580414/draft/files/potential_agbd_Mgha_climate_envelope.pkl/content" target="_blank" rel="noopener noreferrer">inference_pipeline_potential_agbd_Mgha_climate.pkl</a> : Calibrated model for the prediction of potential AGBD given bioclimatic conditions. </p> <p><a href="../api/records/11580414/draft/files/predictors_data_global.zip/content" target="_blank" rel="noopener noreferrer">predictors_data_global.zip</a> : Global predictors data to apply the model on.</p>
Divergent geographic patterns and functional characteristics: Subtle mapping for ponds in the Yangtze River Delta Region
<p>Pond water surfaces (PWS) possess diverse functional types, such as aquaculture, agriculture-water supplement, and ecosystem regulation. However, existing research often treats PWS as a homogeneous aquatic ecosystem; the absence of a comprehensive PWS classification system hinders ours understanding of PWS background characteristics and is detrimental to watershed management. Here, a comprehensive classification system of PWS, including fish aquaculture ponds (FAP), shrimp and crab aquaculture ponds (SCAP), natural ponds (NP), and landscaping ponds (LP) was proposed from remote sensing perspectives. Additionally, interpretation rules were standardized from multi-features including spectrum, shape, topography, and surrounding geographical environments. Subsequently, refined spatiotemporal data product of PWS in the Yangtze River Delta from 2016 to 2022 was generated using Sentinel-2 images with 10 m spatial resolutions. The results indicate that: (1) The spatiotemporal changes exhibited three stages, i.e., “declining – stable – recovery.” The area of PWS decreased from 5186.52 km² to 4920.90 km²in 2016-2017, stabilized at approximately 4500 km² in 2019-2021, and then rebounded to 4834.12 km² in 2022. (2) Regarding different PWS functional types, significant differences were demonstrated in terms of area, surrounding environment, and spatiotemporal changes. Firstly, FAP dominated in terms of area, accounting for 47.81% of the total. Secondly, FAP was widely around rivers and lakes. At the same time, SCAP was concentrated around lakes or along the coast, LP was primarily found in urban areas, and NP was predominantly found in rural areas and mountainous regions; Thirdly, NP decreased as land remediation work progressed continuously, while LP increased due to policy support for urban renewal. Changes in aquaculture were more complex, experiencing a sharp decline from 2016 to 2020 due to reduced market demand but rebounded in 2021-2022 with supportive policies. In summary, the system and data products developed in this study reveal the diverse relationships of "functional type-geographical environment-driving factor" regarding PWS, implicating appropriate planning for aquatic ecosystem.</p>
PEATGRIDS: Mapping global peat thickness and carbon stock via digital soil mapping approach, dataset
<p>PEATGRIDS: a dataset containing the first peat thickness and carbon stock maps estimated over peatlands area across the globe at ~1 km x ~1 km resolution. Carbon stock was calculated across all depths of the predicted peat thickness, multiplied by peat bulk density (BD) and carbon content (CC) across five depths: 0-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Mapping effort was performed using quantile random forest regression based on remotely sensed data and environmental covariates, including topography, climate, soil properties, and land cover. The maps cover areas potentially as peatlands according to the UNEP's global peatland map obtained from the <a title="Global Peat Database" href="https://greifswaldmoor.de/global-peatland-database-en.html" target="_blank" rel="noopener">Global Peat Database</a>. We may update this dataset in the future, please consider using the latest version. </p> <p>Note: This version (2.0.1) clarifies the metric units for carbon stock per area in the previous version (2.0). </p>
Sensitivity maps of the Amundsen Sea Embayment to changes in external forcings using Automatic Differentiation
<p>Sensitivity maps of the final volume above flotation after 20 years to the basal friction coefficient, rheology factor, surface mass balance, and ocean-induced melting. These results were computed from STREAMICE and ISSM using automatic differentiation. See manuscript for complete description</p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
L'Alpe d'Huez: a dataset to benchmark topographic map generalisation
<p>This dataset derives from the one used in a past EuroSDR benchmark (http://dx.doi.org/10.1016/j.compenvurbsys.2009.06.002), and should be used as a benchmark for topographic map generalisation techniques. It contains several topographic layers as shapefiles (roads, buildings, rivers, forests, contour lines...), a style description to display the data at the 1:50k scale, and a table of the generalisation constraints that should be respected in this 1:50k scale map.</p> <p>The initial data can considered as detailed for maps at the 1:15k scale. The projection of the data is "Lambert II Etendu", EPSG:27572.</p> <p>The area is 11*11 km large.</p>
Front polylines extracted from DWD Maps
<p>Weather fronts extracted from surface analysis maps of the German Weather Service (Deutscher Wetterdienst, DWD) for the years 2015 to 2020</p> <p>Fronts are described by an identifier (warm, cold, occ) followed by several [lat, lon] coordinate pairs in degree [North, East]</p> <p>Stationary-Fronts are extracted as either cold or warm fronts</p> <p>e.g.</p> <p>warm [75.69211706913667, 29.558180185836815] [75.12348547753966, 26.89164117647018] ...</p> <p>No guarantee is given regarding correctness or completeness of extracted fronts</p>
Damage Absolute Probability map
<p>Damage Absolute Probability map is a layer in support to Area of Interest (AOI) definition for an earthquake event. It provides the spatial distribution, in the examined area, of the absolute values of probability of damage derived considering the most severe damage classes provided by loss assessment data</p>
Relative Potential Damage map
<p>Relative Potential Damage map is a layer in support to Area Of Interest (AOI) definition for an earthquake event. This index provides the indication of expected most affected areas</p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
[[Deprecated]] DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>A new version has been uploaded by Guillermo Schulz.</p>
KMA Mapping and alignment statistics : livestock fecal metagenomes against ResFinder and genomes
<p>Three zip archives are included used in the analysis of the European livestock resistome.</p> <p>Two of them contain 'mapstat' files produced by the KMA software using the 'extended features' flag.<br> Each mapstat file thus summarize the mapping and alignment statistics when using KMA on a metagenome against a database.</p> <p>The last archive contains the 'refdata' file used to annotate the genomic mapstat hits. It encodes the taxonomic affilication of sequences hit by one or more samples.<br> </p>
Raincheck: A new diachronic series of rainfall maps for Southwest Asia over the Holocene - Supplementary Material
<p>Supplementary Online Material for the publication</p> <p>Hewett, Z., de Gruchy, M., Hill, D., and Lawrence, D. (forthcoming) Raincheck: A new diachronic series of rainfall maps for Southwest Asia over the Holocene. <em>Levant</em>.</p> <p>Included are all the necessary data files and scripts (R) needed to create the rainfall maps that are the subject of the article.</p>
Predicted maps
<p>This dataset is the final product of research through the projects ANTARES (grant agreement No. 739570) and CYBELE (grant agreement No. 825355). The dataset consists of yield, protein content and selective harvesting soya maps at a resolution of 10 m. These maps were created by satellite images and soil properties data using machine learning algorithms. Maps are located in the Upper Austria region. Files with the name of "map yield" contain information about yield amount per pixel, while files with "map protein" denote parcels with predicted protein content. Also, the same protein map files contain an additional class column. Class 1 indicates pixels where soya have good quality (protein content > 41), while class 2 represents poorer quality.</p>
DIGITAL SOIL TEXTURE MAPS OF ARGENTINA
<p>Soil fractions of Argentina in g/100g, Clay, Silt and Sand, for 4 standard depth intervals (0–15, 15-30, 30–60, 60–100) at 1000 m resolution. Including textural classes for the four standard layers and error estimation using random forest.</p> <p>Global accuracy based on cross-validation</p> <table> <tbody> <tr> <td> <p><strong>sp</strong></p> </td> <td> <p><strong>RMSE</strong></p> </td> <td> <p><strong>Rsquared</strong></p> </td> <td> <p><strong>MAE</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 0-15 cm</strong></p> </td> <td> <p><strong>16.189</strong></p> </td> <td> <p><strong>0.640</strong></p> </td> <td> <p><strong>11.069</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 15-30 cm</strong></p> </td> <td> <p><strong>16.320</strong></p> </td> <td> <p><strong>0.629</strong></p> </td> <td> <p><strong>11.213</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 30-60 cm</strong></p> </td> <td> <p><strong>16.676</strong></p> </td> <td> <p><strong>0.618</strong></p> </td> <td> <p><strong>11.364</strong></p> </td> </tr> <tr> <td> <p><strong>Sand 60-100 cm</strong></p> </td> <td> <p><strong>16.762</strong></p> </td> <td> <p><strong>0.587</strong></p> </td> <td> <p><strong>11.472</strong></p> </td> </tr> <tr> <td> <p><strong>silt 0-15 cm</strong></p> </td> <td> <p><strong>12.011</strong></p> </td> <td> <p><strong>0.638</strong></p> </td> <td> <p><strong>8.352</strong></p> </td> </tr> <tr> <td> <p><strong>silt 15-30 cm</strong></p> </td> <td> <p><strong>11.807</strong></p> </td> <td> <p><strong>0.608</strong></p> </td> <td> <p><strong>8.388</strong></p> </td> </tr> <tr> <td> <p><strong>silt 30-60 cm</strong></p> </td> <td> <p><strong>11.504</strong></p> </td> <td> <p><strong>0.561</strong></p> </td> <td> <p><strong>8.168</strong></p> </td> </tr> <tr> <td> <p><strong>silt 60-100 cm</strong></p> </td> <td> <p><strong>11.728</strong></p> </td> <td> <p><strong>0.583</strong></p> </td> <td> <p><strong>8.263</strong></p> </td> </tr> <tr> <td> <p><strong>clay 0-15 cm</strong></p> </td> <td> <p><strong>8.766</strong></p> </td> <td> <p><strong>0.475</strong></p> </td> <td> <p><strong>5.721</strong></p> </td> </tr> <tr> <td> <p><strong>clay 15-30 cm</strong></p> </td> <td> <p><strong>10.723</strong></p> </td> <td> <p><strong>0.452</strong></p> </td> <td> <p><strong>7.432</strong></p> </td> </tr> <tr> <td> <p><strong>clay 30-60 cm</strong></p> </td> <td> <p><strong>11.211</strong></p> </td> <td> <p><strong>0.557</strong></p> </td> <td> <p><strong>7.842</strong></p> </td> </tr> <tr> <td> <p><strong>clay 60-100 cm</strong></p> </td> <td> <p><strong>11.005</strong></p> </td> <td> <p><strong>0.536</strong></p> </td> <td> <p><strong>7.734</strong></p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
ESCALATOR - Stakeholder map data workflow
<p>The stakeholder map project aims to collect and share data on Digital Humanities (DH), Computational Social Sciences (CSS) and related activities and initiatives in South Africa. This data includes information about South African researchers, projects, publications, tools, datasets, academic programmes, training events, learning materials, and more. The aim is to provide deeper insight into the breadth of activities in this area, facilitate enhanced networking and collaboration, and support the optimal use of resources. The stakeholder map will, for example, support researchers looking for collaborators, help potential students to identify undergraduate and postgraduate training programmes, and highlight gaps and opportunities to funders and institutions.</p> <p>The initial design of the data pipeline and workflow for data visualisation has been completed. The pipeline is primarily based on open-source software and platforms often used in the open science community. Development is currently under way. Data will be captured via Google Forms and manipulated using R scripts, available on GitHub and archived in Zenodo. Interactive visualisations will be published on the ESCALATOR website. These visualisations include a [Shiny app](https://shiny.rstudio.com/) that will allow the community to explore data through a web interface and a [Kumu network visualisation](https://kumu.io/). Research articles can be added to an [open collection in Zotero](https://www.zotero.org/groups/3866799/dhcssza) to facilitate easy access to publications from the South African community.</p> <p><br> This diagramme shows the high-level workflow. We anticipate the diagramme will be updated as design and development progresses to incorporate lessons learned and feedback from the community.</p>
Paddy Rice Mapping Learning Material(Sentinel-1 & labeling) in South Korea
<p>This dataset includes time series Sentinel-1 images and paddy rice labeling in South Korea for ML/DL model training. It consists of 7,762 training patches and 5,180 validation patches for each patch consists of 256 x 256 pixels. The dataset is saved in hdf5 format separated into training/valdation data, image/labeling, and part number which can be accessed by key: {tr/va}_{im/lb}_{0~4}.</p> <p>According to the phonological stage of paddy rice, the Sentinel-1 images were acquired through 8-time steps from May 10 to October 20 in 20 days’ interval. In order for the images to capture similar features of rice invariant to more or less difference of growth, minimum and maximum value composite were used at transplanting season and ripening season each. The acquisition year for each patch varies from 2017 to 2019 since it was matched to that of labeling source.</p> <p>The paddy rice labeling is a rasterized version of farm map produced by Korean Ministry of Agriculture, Food and Rural Affairs(MAFRA). The original source data was produced by visual interpreted by high-resolution satellite images and aerial photos referring the other national GIS data and it is accessible through the national open data platform (<a href="http://data.nsdi.go.kr/dataset/20210707ds00001">http://data.nsdi.go.kr/dataset/20210707ds00001</a>). As the data is distributed in a vector format, it was converted to 10 m x 10 m raster format which is compatible to the Sentinel-1, and used for labeling the images.</p>
Teaser Local Climate Zone maps extracted from the global map of Local Climate Zones.
<p>Local Climate Zone teaser maps for the 15 largest functional urban areas stratified by urban ecoregion. The data is extracted from the global 100 m spatial resolution LCZ map, and is used to plot Figures 5, 6 and 7 of Demuzere et al. (2022a). The full global LCZ map is available via Demuzere et al. (2022b).<br> <br> See readme.txt for more details.</p> <p><em>Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., Vliet, J. van, & Bechtel, B. (2022a). A global map of Local Climate Zones to support earth system modelling and urban scale environmental science. Earth Syst. Sci. Data Discuss.<br> Demuzere M, Kittner J, Martilli A, et al. Global map of Local Climate Zones. Zenodo. 2022b. doi:10.5281/zenodo.6364594</em></p>
Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'
<p>This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'.</p> <p>The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the <em>raster </em>package in <em>R.</em> Note that the unit is ppmv.</p> <p>Please note that both of these files must be in the same directory to open in <em>R</em> properly<em>.</em></p> <p>Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn</p>
Distribution of large carnivores in Europe 2012 - 2016: Distribution map for Golden Jackal (Canis aureus)
<p><strong>Abstract</strong></p> <p>Regular assessments of species’ status are an essential component of conservation planning and adaptive management. They allow the progress of past or ongoing conservation actions to be evaluated and can be used to redirect and prioritize future conservation actions. Most countries perform periodic assessments for their own national adaptive management procedures or national red lists. Furthermore, the countries of the European Union have to report on the status of all species listed on the directives of the Habitats Directive every 6 years as part of their obligations under Article 17. However, these national level assessments are often made using non-standardized procedures and do not always adequately reflect the biological units (i.e., the populations) which are needed for ecologically meaningful assessments.</p> <p>Since the early 2000’s the Large Carnivore Initiative for Europe (a Specialist Group of the IUCN’s Species Survival Commission) has been coordinating periodic surveys of the status of large carnivores across Europe (e.g., von Arx et al. 2004; Salvatori & Linnell 2005, Kaczensky et al. 2013). These have covered the Eurasian lynx (<em>Lynx lynx</em>), the wolf (<em>Canis lupus</em>), the brown bear (<em>Ursus arctos</em>) and the wolverine (<em>Gulo gulo</em>). The golden jackal (<em>Canis aureus</em>) has been added to the LCIE prerogatives in 2014. The species is rapidly expanding in Europe (Trouwborst <em>et al.</em> 2015; Männil & Ranc 2022), a large-scale phenomenon that resembles that of the other large carnivores. Golden jackals are thriving in human-dominated landscapes (Ćirović <em>et al.</em> 2016; Lanszki <em>et al.</em> 2018; Fenton <em>et al.</em> 2021), where they are often functioning as the top predators, despite having smaller body size that is typical for large carnivores. The expansion of the species triggers many questions among scientists, stakeholders, and policy makers (Trouwborst <em>et al.</em> 2015; Hatlauf <em>et al.</em> 2021), that are closely connected to those raised by the other large carnivores (e.g., potential conflicts with livestock or hunting). In this context, monitoring the species’ expansion, delineating populations, assessing the species' legal and protection status, and addressing the concerns raised by this rapidly expanding carnivore requires a high level of coordination among regional experts.</p> <p>These surveys involve the contributions of the best available experts and sources of information. While the underlying data quality and field methodology varies widely across Europe, these coordinated assessments do their best to integrate the diverse data in a comparable manner and make the differences transparent. They also endeavor to conduct the assessments on the most important scales. This includes the continental scale (all countries except for Russia, Belarus, Moldova and the parts of Ukraine outside the Carpathian Mountain range), the scale of the EU 28 (where the Habitats Directive operates) and of the biological populations which reflect the scale at which ecological processes occur (Linnell et al. 2008). In this way, the independent LCIE assessments provide a valuable complement to the ongoing national processes.</p> <p>Our last assessments covered the period 2006-2011 (Kaczensky et al. 2013; Chapron et al. 2014) but, at the time, did not include golden jackals. The current assessment is based on the period 2012-2016 and broadly follows the same methodology. Explicit distinctions are made between classification based on empirical data and expert opinion. The population definitions used in this report follow those proposed in (Ranc <em>et al.</em> 2018); areas whose presence category was defined by expert opinion were not assigned to a specific population, though. </p> <p> </p> <p><strong>Methods</strong></p> <p>The mapping approach follows the methods described in Chapron et al. (2014) and Kaczensky et al. (2013). It updates the published Species Online Layers (SPOIS) to the period 2012-2016.</p> <p>In short, large carnivore presence was mapped at a 10x10 km ETRS89-LAEA Europe grid scale. This grid is widely used for the Flora-Fauna-Habitat reporting by the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2</p> <p>The map encompasses the EU countries plus the non-EU Balkan states, Switzerland, Norway, and the Carpathian region of Ukraine. Presence in a grid cell was ideally mapped based on carnivore presence and frequency in a cell resulting in:</p> <p>1 = Permanent (presence confirmed in >= 3 years in the last 5 years OR in >50% of the time OR reproduction confirmed within the last 3 years)</p> <p>3 = Sporadic (highly fluctuating presence) (presence confirmed in <3 years in the last 5 years OR in <50% of the time)</p> <p>5 = Expert-based presence (high confidence) (expert-based opinion; very suitable habitat near permanent presence areas)</p> <p>6 = Expert-based presence (low confidence or unconfirmed records) (expert-based opinion; suitable habitat near presence areas or unconfirmed C3 records of jackal presence)</p> <p>7 = Expert-based absence (high confidence) (jackal presence according to coarse-resolution hunting bag data but experts think, with high confidence, the species is not present)</p> <p>8 = Expert-based absence (low confidence) (jackal presence according to coarse-resolution hunting bag data but experts think the species is not present)</p> <p>Where grid cells were assigned different values between neighboring countries; the “disputed” cells were given the “higher” presence values e.g., a cell categorized as “sporadic” by one country and “permanent” by another was categorized as “permanent”. Data-based categories (1,3) were given priority over expert-based categories (5 through 8).</p> <p>To assess the quality of carnivore signs we used the SCALP criteria developed for the standardized monitoring of Eurasian lynx (<em>Lynx lynx</em>) in the Alps (Molinari-Jobin et al. 2012):</p> <p>Category 1 (C1): “Hard facts”, verified and unchallenged large carnivore presence signs (e.g., dead animals, DNA, verified camera trap images);</p> <p>Category 2 (C2): Large carnivore presence signs controlled and confirmed by a large carnivore expert (e.g., trained member of the network), which requires documentation of large carnivore signs; and</p> <p>Category 3 (C3): Unconfirmed category 2 large carnivore presence signs and all presence signs such as sightings and calls which, if not additionally documented, cannot be verified.</p> <p>See Hatlauf and Böcker (2022) for best practices regarding golden jackal records.</p> <p> </p> <p><strong>Usage Notes</strong></p> <p>The data available consists of a shapefile at a 10 x 10 km resolution compiled for the period 2012-2016 for the Large Carnivore Initiative of Europe IUCN Specialist Group and for the IUCN Red List Assessment.</p> <p> </p> <p><strong>References</strong></p> <p>Boitani, L., F. Alvarez, O. Anders, H. Andren, E. Avanzinelli, V. Balys, J. C. Blanco, U. Breitenmoser, G. Chapron, P. Ciucci, A. Dutsov, C. Groff, D. Huber, O. Ionescu, F. Knauer, I. Kojola, J. Kubala, M. Kutal, J. Linnell, A. Majic, P. Mannil, R. Manz, F. Marucco, D. Melovski, A. Molinari, H. Norberg, S. Nowak, J. Ozolins, S. Palazon, H. Potocnik, P.-Y. Quenette, I. Reinhardt, R. Rigg, N. Selva, A. Sergiel, M. Shkvyria, J. Swenson, A. Trajce, M. Von Arx, M. Wolfl, U. Wotschikowsky and D. Zlatanova. 2015. Key actions for Large Carnivore populations in Europe. Institute of Applied Ecology (Rome, Italy). Report to DG Environment, European Commission, Bruxelles. Contract no. 07.0307/2013/654446/SER/B3</p> <p>Ćirović, D., A. Penezić and M. Krofel. 2016. Jackals as cleaners: Ecosystem services provided by a mesocarnivore in human-dominated landscapes. <em>Biological Conservation</em>, 199: 51–55.</p> <p>Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Iliopoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., Männil, P., Manz, R., Marboutin, E., Marucco, F., Melovski, D., Mersini, K., Mertzanis, Y., Mysłajek, R.W., Nowak, S., Odden, J., Ozolins, J., Palomero, G., Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., Skrbinšek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska[1]Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., Zlatanova, D. and Boitani, L. 2014. Recovery of large carnivores in Europe’s modern human-dominated landscapes. <em>Science</em> 346: 1517-1519.</p> <p>Fenton, S., Moorcroft, P.R., Ćirović, D., Lanszki, J., Heltai, M., Cagnacci, F., Breck, S., Bogdanović, N., Pantelić, I., Ács, K. and Ranc, N. 2021. Movement, space-use and resource preferences of European golden jackals in human-dominated landscapes: insights from a telemetry study. <em>Mammalian Biology</em>, 101: 619–630.</p> <p>Hatlauf, J. and Böcker, F. 2022. Recommendations for the documentation and assessment of golden jackal (<em>Canis aureus</em>) records in Europe. BOKU reports on wildlife research and willdife management 27. Ed: Institute of Wildlife Biology and Game Management (IWJ), University of Natural Resources and Life Sciences, Vienna. ISBN: 978-3-900932-94-7</p> <p>Hatlauf, J., Bayer, K., Trouwborst, A. and Hackländer, K. 2021. New rules or old concepts? The golden jackal (<em>Canis aureus</em>) and its legal status in Central Europe. <em>European Journal of Wildlife Research</em>, 67, 25.</p> <p>Kaczensky, P., Chapron, G., Von Arx, M., Huber, D., Andrén, H. and Linnell, J. 2013. Status, management and distribution of large carnivores - bear, lynx, wolf and wolverine - in Europe. Istituto di Ecologia Applicata, Rome, Italy.</p> <p>Lanszki, J., Schally, G., Heltai, M. and Ranc, N. 2018. Golden jackal expansion in Europe: first telemetry evidence of a natal dispersal. <em>Mammalian Biology</em>, 88: 81–84.</p> <p>Linnell, J.D.C., Salvatori, V. and Boitani, L. 2008. Guidelines for population level management plans for large carnivores in Europe. A Large Carnivore Initiative for Europe report prepared for the European Commission (contract 070501/2005/424162/MAR/B2).</p> <p>Männil, P. and Ranc, N. 2022. Golden jackal (<em>Canis aureus</em>) in Estonia: development of a thriving population in the boreal ecoregion. <em>Mammalian Research,</em> 67: 245-250.</p> <p>Molinari-Jobin, A., Kéry, M., Marboutin, E., Molinari, P., Koren, I., Fuxjäger, C., Breitenmoser-Würsten, C., Wölfl, S., Fasel, M., Kos, I., Wölfl, M. and Breitenmoser, U. 2012. Monitoring in the presence of species misidentification: the case of the Eurasian lynx in the Alps. <em>Animal Conservation </em>15: 266-273.</p> <p>Ranc, N., Krofel, M. and Cirovic, D. 2018. IUCN Red List Mapping for the regional assessment of the Golden Jackal (<em>Canis aureus</em>) in Europe. IUCN Red List Threatened Species, 13.</p> <p>Salvatori, V. and Linnell, J.D.C. 2005. Report on the conservation status and threats for wolf (Canis lupus) in Europe. Council of Europe Report T-PVS/Inf (2005) 16.</p> <p>Trouwborst, A., Krofel, M. and Linnell, J.D.C. 2015. Legal implications of range expansions in a terrestrial carnivore: the case of the golden jackal (<em>Canis aureus</em>) in Europe. <em>Biodiversity Conservation</em>, 24: 2593–2610.</p> <p>von Arx, M., Breitenmoser-Würsten, C., Zimmermann, F. and Breitenmoser, U. 2004. Status and conservation of the Eurasian lynx (<em>Lynx lynx</em>) in Europe in 2001. KORA Report 19e: 1-330.</p> <p> </p> <p><strong>Contact information</strong></p> <p>Nathan Ranc, nathan.ranc@inrae.fr</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.