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13,117 results for “Maps”
Map 9 in The American Goblin Spiders Of The New Genus Escaphiella (Araneae, Oonopidae)
Map 9. Records of Escaphiella itys.
Map 12 in The American Goblin Spiders Of The New Genus Escaphiella (Araneae, Oonopidae)
Map 12. Records of Escaphiella gigantea
Map 13 in The American Goblin Spiders Of The New Genus Escaphiella (Araneae, Oonopidae)
Map 13. Records of Escaphiella cidades (circles) and E. hesperoides (stars).
Map 6 in The American Goblin Spiders Of The New Genus Escaphiella (Araneae, Oonopidae)
Map 6. Records of Escaphiella nayarit (circles) and E. magna (stars).
Map 5 in The American Goblin Spiders Of The New Genus Escaphiella (Araneae, Oonopidae)
Map 5. Records of Escaphiella tonila (star) and E. chiapa (circles).
Map 1 in The American Goblin Spiders Of The New Genus Escaphiella (Araneae, Oonopidae)
Map 1. Records of Escaphiella hespera.
Map 3 in The American Goblin Spiders Of The New Genus Escaphiella (Araneae, Oonopidae)
Map 3. Records of Escaphiella iguala (circles) and E. acapulco (stars).
Indicative distribution map for Ecosystem Functional Group T1.3 Tropical/Subtropical montane rainforests
<p>This archive contains indicative distribution maps and profiles for <strong>T1.3 Tropical/Subtropical montane rainforests</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Diurnal variability of Black Carbon in Modena: Modeled concentration maps for winter 2020 and 2021
<p>This video presents concentration maps of Black Carbon over the city of Modena during the winters of 2020 and 2021. The concentrations are categorized into Fossil Fuel, Biomass Burning, and their combined totals (Fossil Fuel + Biomass Burning). This supplementary material supports the paper titled "Measurement report: Source attribution and estimation of black carbon levels in an urban hotspot of the central Po Valley: An integrated approach combining high-resolution dispersion modelling and micro-aethalometers," published in EGUsphere (https://doi.org/10.5194/egusphere-2023-2641).</p>
wikidata mapping to taxonomic ids from 11 other databases
Open the record for dataset details and reuse information.
Material stock map of the United Kingdom and the Republic of Ireland
<p>Understanding the size and spatial distribution of material stocks is crucial for sustainable resource management and climate change mitigation. This study presents high-resolution maps of buildings and mobility infrastructure stocks for the United Kingdom (UK) and the Republic of Ireland (IRL) at 10 m, combining satellite-based Earth observations, OpenStreetMaps, and material intensities research. Stocks in the UK and IRL amount to 19.8 Gigatons or 279 tons/cap, predominantly aggregate, concrete and bricks, as well as various metals and timber. Building stocks per capita are surprisingly similar across medium to high population density, with only the lowest population densities having substantially larger per capita stocks. Infrastructure stocks per capita decrease with higher population density. Interestingly, for a given building stock within an area, infrastructure stocks are substantially larger in IRL than in the UK. These maps can provide useful insights for sustainable urban planning and advancing a circular economy.</p> <p>This dataset features a detailed map of material stocks in the United Kingdom and the Republic of Ireland on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.</p> <p><strong>Spatial extent</strong><br>This dataset covers the whole British Isles. Due to processing reasons, the dataset is internally structured into the Island of Ireland, and the Island of Great Britain.</p> <p><strong>Temporal extent</strong><br>The map is representative for ca. 2018.</p> <p><strong>Data format</strong><br>The data are organized by nations. Within each nation, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p> <p>Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).</p> <p>Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.</p> <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> <p>For each nation, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. </p> <p>Additionally, the grand total mass per nation is tabulated for each island in <em>mass_grand_total_t_10m2.tif.csv</em>. County code and the ID in this table can be related via zones_name_pop<em>.csv</em>.</p> <p><strong>Material layers</strong><br>Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). </p> <p><strong>Further information</strong><br>For further information, please see the publication.<br>Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Publication</strong></p> <p>D. Wiedenhofer, F. Schug, H. Gauch, M. Lanau, M. Drewniok, A. Baumgart, D. Virág, H. Watt, A. Cabrera Serrenho, D. Densley Tingley, H. Haberl, D. Frantz (2024): Mapping material stocks of buildings and mobility infrastructure in the United Kingdom and the Republic of Ireland. <em>Resources, Conservation and Recycling</em> 206, 107630. <a href="https://doi.org/10.1016/j.resconrec.2024.107630">https://doi.org/10.1016/j.resconrec.2024.107630</a></p> <p><strong>Funding</strong><br>This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p> <p><strong>Acknowledgments</strong><br>We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.</p>
SDG Mapping Results for 1,000 Publications from Large Language Models: GPT-4o, Mixtral, Llama 2, Llama 3, Gemma 2, Qwen 2 and GPT-4o-mini
<p>Randomly selected 1,000 publications from the Swinburne University of Technology research bank were used for SDG mapping tasks with the large language model GPT-4o and the open-source models Mixtral, Llama 2, Llama 3, Gemma 2, Qwen 2 and GPT-4o-mini.</p> <p>The input to each model consisted of the publication’s title and abstract.</p> <p>The designed prompt is as follows: </p> <p>PROMPT = '''Analyze the publication and determine the SDGs it aligns with. Evaluate against all the 17 SDGs provide the reason for alignment. In the end summarise the confidence levels(%) for each assigned SDG in JSON format. For example: {example}. Title: {title} Description: {description}'''</p> <p>example = '''{ 'Goal 6': 0.67, 'Goal 11': 0.50, 'Goal 3': 0.25}'''</p>
Map 2 in A revision of Palaearctic Lobrathium MULSANT & REY. III. New species, new synonyms, and additional records (Coleoptera: Staphylinidae: Paederinae)
Map 2: Distribution of Lobrathium rugipenne, based on examined records.
Map 1 in A revision of Palaearctic Lobrathium MULSANT & REY. III. New species, new synonyms, and additional records (Coleoptera: Staphylinidae: Paederinae)
Map 1: Distribution of Lobrathium anale, based on examined records.
Map 3 in A revision of Palaearctic Lobrathium MULSANT & REY. III. New species, new synonyms, and additional records (Coleoptera: Staphylinidae: Paederinae)
Map 3: Distribution of Lobrathium farsicum in Iran.
Fig. 1. Map denoting 3 in Marine algal flora of Oho-ri, Gosung-gun, Gangwon-do, Korea
Fig. 1. Map denoting 3 islets and its diving stations where the survey was conducted.
MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors
<p>Supplementary Material for Poster Presentation</p> <p>MOH-AMM-SC-2024 ePoster ID 44: Mapping The Landscape of Precision Public Health: A Bibliometric Analysis of Influential Works and Key Contributors</p> <p>Conference: 15th MOH-AMM Scientific Meeting 2024 in conjunction with 25th NIH Scientific Conference</p>
EARice10: A 10 m Resolution Annual Rice Distribution Map of East Asia for 2023
<p>This dataset provides a 10 m <span>Resolution </span><span>Annual Rice </span><span>Distribution </span><span>Map of East Asia </span><span>for </span><span>2023 </span>(<strong>EARice10</strong>). The accuracy of EARice10 <span>is </span>evaluated using more than 90,000 validation samples and achieves an overall accuracy of 90.48%, with both user’<span>s</span> and producer’<span>s</span> <span>accuracies</span> exceeding 90%. The reliability of the dataset is verified by an R<sup>2</sup> <span>values ranging</span> between 0.94 and 0.98 with respect to official statistics<span>,</span> and between 0.79 and 0.98 with respect to previous rice mapping datasets.</p> <p><strong>File format:</strong> tif</p> <p><strong>Spatial extent:</strong> East Asia</p> <p><strong>Pixel size:</strong> 10 m</p> <p><strong>Projection information:</strong> EPSG: 4326 (WGS84)</p> <p><strong>Crop type: </strong>Paddy rice</p> <p><strong>Year:</strong> 2023</p>
Map 2 in A revision of Amarochara THOMSON of the Holarctic region V. A new species from China, a new combination, the male of A. caeca ASSING, and additional records (Coleoptera: Staphylinidae: Aleocharinae: Aleocharini)
Map 2: Distribution of Amarochara megalops in the East Palaearctic region.
MAP 21 in Atlas of the Reptiles of Libya
MAP 21. Distribution of Tarentola annularis in Libya.
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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)
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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.