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13,117 results for “Maps”
Image distortion data from "An open-source MRI compatible frame for multimodal presurgical mapping in macaque and capuchin monkeys"
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Maps 8–13 in Contribution to the knowledge of the fauna of rove beetles of the subfamily Omaliinae MacLeay, 1825 (Coleoptera: Staphylinidae) of Kunashir Island, Kurile Islands
Maps 8–13. Distribution of Omaliinae in Kunashir Island: 8 — Lesteva plagiata Sharp, 1889; 9 — Liophilydrodes puncticollis (Nakane et Sawada, 1956); 10 — Mannerheimia yasudai Watanabe, 1990; 11 — Archaeoboreaphilus miyamorii (Watanabe, 1988); 12 — Eudectus rufulus Weise, 1877; 13 — Acrolocha miyamorii Watanabe, 1990. Карты 8–13. Распространение Omaliinae на острове КунаШир: 8 — Lesteva plagiata Sharp, 1889; 9 — Liophilydrodes puncticollis (Nakane et Sawada, 1956); 10 — Mannerheimia yasudai Watanabe, 1990; 11 — Archaeoboreaphilus miyamorii (Watanabe, 1988); 12 — Eudectus rufulus Weise, 1877; 13 — Acrolocha miyamorii Watanabe, 1990.
Maps 2–7 in Contribution to the knowledge of the fauna of rove beetles of the subfamily Omaliinae MacLeay, 1825 (Coleoptera: Staphylinidae) of Kunashir Island, Kurile Islands
Maps 2–7. Distribution of Omaliinae in Kunashir Island: 2 —Acidota daisetsuzana Watanabe, 1990; 3 — Brathinus oculatus Lewis, 1886; 4 — Geodromicus iburinus (Watanabe, 1990); 5 — Geodromicus lestevoides (Sharp, 1889); 6 — Geodromicus nipponensis (Watanabe, 1990); 7 — Lesteva nipponica Watanabe, 1990. Карты 2–7. Распространение Omaliinae на острове КунаШир: 2 — Acidota daisetsuzana Watanabe, 1990; 3 — Brathinus oculatus Lewis, 1886; 4 — Geodromicus lestevoides (Sharp, 1889); 5 — Geodromicus nipponensis (Watanabe, 1990); 6 — Geodromicus iburinus (Watanabe, 1990); 7 — Lesteva nipponica Watanabe, 1990.
Map 1 in Contribution to the knowledge of the fauna of rove beetles of the subfamily Omaliinae MacLeay, 1825 (Coleoptera: Staphylinidae) of Kunashir Island, Kurile Islands
Map 1. Map of localities on Kunashir Island, 2008–2015. Карта 1. Места сбора на острове КунаШир в 2008–2015 г.г.
Maps 14–17 in Contribution to the knowledge of the fauna of rove beetles of the subfamily Omaliinae MacLeay, 1825 (Coleoptera: Staphylinidae) of Kunashir Island, Kurile Islands
Maps 14–17. Distribution of Omaliinae in Kunashir Island: 14 — Phloeostiba plana (Paykull, 1792); 15 — Phloeonomus orientalis Smetana, 1981; 16 — Phloeonomus pusillus (Gravenhorst, 1806); 17 — Eusphalerum sp.pr. hidakanum Watanabe, 1990. Карты 14–17. Распространение Omaliinae на острове КунаШир: 14 — Phloeostiba plana (Paykull, 1792); 15 — Phloeonomus orientalis Smetana, 1981; 16 — Phloeonomus pusillus (Gravenhorst, 1806); 17 — Eusphalerum sp.pr. hidakanum Watanabe, 1990.
Microstructure images, ilastik labels and probability maps.
<p>Microstructure images of several Al alloys reinforced with quasicrystals. Images were obtained using a Zeiss Axio Imager.A1m with AxioCam ERc 5s. Original images were enhanced using FIJI and analysed using ilastik. Included is the ilastik training set, labels used and probability maps.</p>
Map 1 in Description of a new Psalmopoeus Pocock, 1895 species (Araneae, Theraphosidae, Psalmopoeinae) from the Chocó region of Ecuador
Map 1. Distribution of Ecuadorian Psalmopoeus. Psalmopoeus pristirana n. sp. (turquoise star), Psalmopoeus satanas (yellow circle), Psalmopoeus chronoarachne (purple circle), Psalmopoeus ecclesiasticus Pocock, 1903 (red diamond) and uncertain Psalmopoeus (red diamond with question mark).
Swift/BAT-GUANO upper limits map for S240421ar
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A left-to-right bias in number-space mapping across ages and cultures
<p>Datasets and scripts related to the paper 'A universal left-to-right bias in number-space mapping across ages and cultures' </p> <p>Below is a description of the elements contained in each folder.</p> <p><strong>Code<br></strong>This folder contains all the scripts used to perform the analyses reported in the paper. It also includes the script used to run the numerosity comparison experiment (please notice it does not run as a stand-alone file, it is just for example purposes)</p> <p><strong>Data:<br></strong>This folder includes all the necessary to replicate the analyses cited in the manuscript. Both aggregated and individual raw files are available. Specifically within this folder, you'll find:</p> <ul> <li>subfolder with individual raw data for the numerosity comparison task for each population</li> <li>CSV file with aggregate percentage of card configuration shape (used for chi-square analysis for experiment 1)</li> <li>CSV file with the individual cards correlation score (used for testing distribution against chance level for experiment 1)</li> <li>CSV file with individual Inverse Efficiency Scores for all participants (used for Mixed Model Analysis)</li> <li>CSV file with the Congruency Effect already computed for each participant (used in the ANOVA analysis for experiment 2)</li> <li>CSV file with the combined datasets (used to test the correlation between experiment 1 and experiment 2 measures)</li> <li>CSV files with participants' anonymized information (.e.g. age, gender, literacy level)</li> </ul> <p><strong>JASP<br></strong>This folder contains all the output files from the JASP software used to compute Bayesian analysis supporting the null results.</p>
Spatial Mapping for Visually Impaired Individuals
<p>By understanding the strengths and limitations of different assistive devices, blind individuals can make informed choices to improve their independence and quality of life.</p>
SensRes dataset for downscaling soil maps
<p>This dataset contains soil information and sensor data from agricultural fields in Denmark, Lithuania, Northern Ireland, the Netherlands, and Turkey to downscale coarse-resolution maps to high resolution in the SensRes project (EJP SOIL).</p> <p> It includes 1455 soil samples with data on soil organic carbon and soil texture fractions (clay, silt, and sand). For each sampling site, the dataset also contains rasters of Sentinel-2 bare soil images, aerial images (RGB), and maps from Electromagnetic Induction and Gamma sensors. The soil data is provided in .txt file format, while the sensor data is available in .tif format. There are also shapefiles from each field in .shp format. </p> <p>The SensRes project developed a framework for downscaling soil maps, which was published as an R package (<a href="https://github.com/anbm-dk/soilscaler/tree/main">https://github.com/anbm-dk/soilscaler/tree/main</a>), and this dataset contains the required local inputs to apply the downscaling process. Part of the soil information present in this dataset has also been used in the STEROPES EJP SOIL project. </p>
Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
<p>This dataset consists of raw RGB and NIR images captured using the DJI Inspire 1 UAV equipped with interchangeable RGB and NDVI-modified cameras. Data were collected across 27 diverse study sites in Serbia, representing a variety of ecological and landscape features. The UAV flights followed pre-defined grid missions, capturing high-resolution imagery with a ground sampling distance (GSD) of 3–4 cm. The collected images were processed to produce georeferenced orthomosaics, which serve as the basis for detailed land cover classification and analysis.</p>
Backup for the Mapping DH instance on Wikibase Cloud – November 2024
<p>Backup for the Mapping DH instance on Wikibase Cloud – November 2024. </p>
Swift/BAT-GUANO upper limits map for S241116cq
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SHOWCASE Task 2.7 Biodiversity Indicators maps for the Hungarian case study area (Kiskunság)
<p>The data set <span>for the SHOWCASE Task 2.7 Hungarioan case study area of Kiskunság </span>encompasses 48 raster maps at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land.</p>
Dataset of Circumstances around the Extinction of Historical Indigenous Groups in the Brazilian Amazon, Based on Nimuendajú's Ethnohistorical Map
<p>Curt Nimuendajú's Ethnohistorical Map (Nimuendajú, 2017) is a rich source of information about Indigenous history in South America. In a recent paper (Barsanetti and Ferreira, 2024), we use the Ethnohistorical Map in a quantitative investigation on the influence of historical Indigenous extinctions on long-run occupation patterns in the Brazilian Amazon, especially deforestation. As a by-product of the analysis, we produced a dataset cataloging the extinction circumstances surrounding the extinctions of Indigenous groups in the ethnohistorical data.</p> <p>The classification is in the file List.xlsx. A dictionary explaining the classification of each group in detail is in Dictionary.pdf.</p> <p>This version: November, 2024.</p> <p>References:</p> <p>Barsanetti, Bruno and Alipio Ferreira (2024). “Historical Indigenous Extinctions and Modern Deforestation in the Amazon”. Working Paper.</p> <p>Nimuendajú, Curt (2017). Mapa Etno-Histórico do Brasil e Regiões Adjacentes. Brasília: Instituto do Patrimônio Histórico e Artístico Nacional, Instituto Brasileiro de Geografia e Estatística.</p> <p> </p>
SHOWCASE Task 2.7 Biodiversity Indicator maps for the Portuguese case study area (Evora, Portel, Cuba, Vidigueira and Beja)
<p>The data set <span>for the SHOWCASE Task 2.7 Portuguese<span> </span>case study area of the Municipalities of <span>Evora, Portel, Cuba, Vidigueira and Beja</span><span> </span></span>encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: permanent crops (olive orchards).</p>
SHOWCASE Task 2.7 Biodiversity Indicator maps for the Spanish case study area (Guadalquivida)
<p>The data set <span>for the SHOWCASE Task 2.7 Spanish case study area of Guadalquivida </span>encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: permane crops (stone fruit orchards)</p>
SHOWCASE Task 2.7 Biodiversity Indicator maps for the Swiss case study area (Solothurn)
<p>The data set for the SHOWCASE Task 2.7 Swiss case study area of Solothurn encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land.</p>
SHOWCASE Task 2.7 Biodiversity Indicator maps for the Dutch case study area (Zuid-Limburg)
<p>The data set for the SHOWCASE Task 2.7 Dutch case study area of Zuid Limburg encompasses 48 raster maps (GEOTIF format) at 10 m resolution (Reference system EPSG:3035) for the following 0-1 normalised biodiversity indicators:</p> <ol> <li>Wild bees abundance (WBA)</li> <li>Wild bees species richness (WBR)</li> <li>Spider abundance (SpA)</li> <li>Spider species richness (SpR)</li> <li>Vascular plants species richness (PlaR)</li> <li>Composite Biodiversity Indicator (BioDiv: 0-1 normalised sum of the indicators 1-5)</li> </ol> <p>Each indicator was predicted and mapped for two seasons (r1 and r2) in two years (2022 and 2023) under two management scenarios (control "treatmentiszero", and intervention "treatmentisone"). Target land use: arable land</p>
ScienceDex guides
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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.