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13,117 results for “Maps”
FIGURE 5 in Amblytropidia paranaensis n. sp., a new species of grasshopper (Orthoptera: Acrididae: Gomphocerinae) from Paraná State, Brazil, including new records, a distribution map and a key to the Brazilian's species
FIGURE 5. (A) Areas where Amblytropidia paranaensis n. sp. was collected. (A) Parque Estadual Cabeça do Cachorro, municipality of S"o Pedro do Iguaçu, PR; (B) Parque Estadual Rio Guarani, municipality of Três Barras do Paraná, PR; (C) Parque Nacional do Iguaçu, municipality of Céu Azul, PR.
FIGURE 3 in Amblytropidia paranaensis n. sp., a new species of grasshopper (Orthoptera: Acrididae: Gomphocerinae) from Paraná State, Brazil, including new records, a distribution map and a key to the Brazilian's species
FIGURE 3. (A) Phallic complex of Amblytropidia paranaensis n. sp. holotype, male lateral view; (B) Ectophallic plates, cingulum and aedeagal valves with sheath, dorsal view; (C) Epiphallus, dorsal view.
FIGURE 6 in Amblytropidia paranaensis n. sp., a new species of grasshopper (Orthoptera: Acrididae: Gomphocerinae) from Paraná State, Brazil, including new records, a distribution map and a key to the Brazilian's species
FIGURE 6. Map of geographical distribution of Amblytropidia species by geographical coordinates precision (circle = high accuracy; square = average accuracy; triangle = low precision).
FIGURE 1 in Amblytropidia paranaensis n. sp., a new species of grasshopper (Orthoptera: Acrididae: Gomphocerinae) from Paraná State, Brazil, including new records, a distribution map and a key to the Brazilian's species
FIGURE 1. (A) Amblytropidia paranaensis n. sp., holotype, male, lateral view; (B) Head, male, frontal view; (C) Head and pronotum of Amblytropidia paranaensis n. sp., holotype male, dorsal view; (D) Head and pronotum, male, lateral view; (E) End of male abdomen, holotype, dorsal view; (F) End of male abdomen, holotype, dorsal view.
FIGURE 2 in Amblytropidia paranaensis n. sp., a new species of grasshopper (Orthoptera: Acrididae: Gomphocerinae) from Paraná State, Brazil, including new records, a distribution map and a key to the Brazilian's species
FIGURE 2. (A) Head and pronotum of Amblytropidia paranaensis n. sp., paratype, female, dorsal view; (B) Head and pronotum, paratype, female, lateral, view; (C) Ovipositor valves, female, dorsal view; (D) Ovipositor valves, female, ventral view; (E) Ovipositor valves, female, lateral view.
FIGURE 4 in Amblytropidia paranaensis n. sp., a new species of grasshopper (Orthoptera: Acrididae: Gomphocerinae) from Paraná State, Brazil, including new records, a distribution map and a key to the Brazilian's species
FIGURE 4. (A) In-life photo of Amblytropidia paranaensis n. sp. male, dorsal-lateral view: (B) Amblytropidia paranaensis n. sp., female, dorsal-lateral view. Photos by Neucir Szinwelski.
Swift/BAT-GUANO upper limits fits map for S231118an
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Swift/BAT-GUANO upper limits fits map for S231118d
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Swift/BAT-GUANO upper limits fits map for S231114n
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Swift/BAT-GUANO upper limits fits map for S231113bb
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Full_variant_map for GWAS munging
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Mapping Enzyme Activity in Living Systems by Real-Time Mid-Infrared Photothermal Imaging of Nitrile Chameleons
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Landslide susceptibility maps using ensemble machine learning models on basin and regional level in Lombardy, Italy
<p>A selection of landslide susceptibility maps computed through ensemble machine learning models for the basins of Val Tartano, Upper Valtellina and Valchiavenna, and on a regional level for the Lombardy region in Italy.</p> <p>A list of the used base machine learning methods:</p> <ul> <li>Random Forest,</li> <li>AdaBoost,</li> <li>Neural Networks.</li> </ul> <p>A list of the used ensemble models:</p> <ul> <li>Stacking,</li> <li>Blending,</li> <li>Soft Voting.</li> </ul> <p>A full list of the model combinations can be found in the "Case Studies" document.</p> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632).</p> <p>The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:</p> <p><em>Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case</em><br><em>study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263</em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.</p> <p> </p>
FIGURE 70. Distribution map. 1 in New data on the Harpacteinae of Turkey (Araneae, Dysderidae)
FIGURE 70. Distribution map. 1 Dasumia antalyaensis sp. nov. 2 Dasumia capacii sp. nov. 3 Dasumia yagmuri sp. nov. 4 Harpactea bilecenoglui sp. nov. 5 Harpactea elvericii sp. nov. 6 Harpactea bilgenur sp. nov. 7 Harpactea kankilicorum sp. nov. 8 Harpactea yanardagi sp. nov. Red square Harpactea alanyana record of Dimitrov (2018) Red star Type locality of Harpactea alanyana.
TREECHANGE: Global tree species' range maps generated via alpha-hull methods
<p>Global tree species' range maps generated via alpha-hull methods, as described in Guo, W.-Y., Serra-Diaz, J. M., Schrodt, … Svenning, J.-C. (2022). High exposure of global tree diversity to human pressure. PNAS, doi:10.1073/pnas.2026733119</p>
Swift/BAT-GUANO upper limits fits map for S231129ac
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Mapping firescapes for wild and prescribed fire management: a landscape classification approach
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Leveraging the Power of Unsupervised Learning for Flood Mapping
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Bodily Maps of Spontaneous Thought
<h2><strong>Data and scripts for Shin, Lux, Kim, & Woo (2023)</strong></h2><p>Bodily Maps of Spontaneous Thought<br>Hyemin Shin*, Byeol Kim Lux*, Hong Ji Kim, Choong-Wan Woo§ <br>*co-first authors<br>§corresponding author: Choong-Wan Woo (choongwan.woo@gmail.com)</p><p>Preprint: <a href="https://www.biorxiv.org/content/10.1101/2023.07.06.547921v1">biorxiv</a></p><p>All scripts are written in Matlab and has been tested on Matlab version 2020b.</p><h2><strong>Dependencies</strong></h2><ul><li><a href="https://github.com/canlab/canlabcore">CanlabCore</a></li><li><a href="https://github.com/cocoanlab/cocoanCORE">CocoanCORE</a></li></ul><h2><strong>Time</strong></h2><ul><li>Expected install time is approximately 5 minutes.</li><li>Expected run time is around an hour in total.</li></ul><h2><strong>Main scripts</strong></h2><p>The following are the major scripts that allow you to regenerate the results and figures of the manuscript. The order follows the results as they appeared in the manuscript. Additional external toolboxes and data shared from previous works have been included in the 'external' subfolder. The data shared from previous works were cited in the manuscript.</p><ul><li>bodymap_1_across_model.m</li><li>bodymap_2_within_model.m</li><li>bodymap_3_nummenmaa.m</li><li>bodymap_4_heartcluster_1.m</li><li>bodymap_4_heartcluster_2.m</li><li>bodymap_5_somatotopy.m</li></ul><p>To use these scripts, download and unzip the provided zipfile. Open the code in Matlab and set the current folder to the unzipped folder. Add the path to the current folder and its subfolders. The necessary data and functions to run the scripts has been saved in the folders. You can also find some interim results that are already included.</p><h2><strong>Abstract</strong></h2><p>The intricate relationship between the body and the mind has long been recognized, but the specific bodily representations of spontaneous thought remain elusive. Here, we developed and validated predictive models of spontaneous thought based on body maps using the emBODY and Free-Association Semantic tasks. Our valence and self-relevance models demonstrated robust prediction performances across three test datasets, with the valence model accurately decoding the bodily topography of emotions and feelings. Model weight patterns revealed the significance of peripheral limbs and heart area in predicting valence, while the head area played a crucial role in predicting self-relevance. Furthermore, we investigated the neurobiological underpinnings of body map representations using fMRI and ECG data and found evidence for the reflection of body map responses in central and autonomic nervous system activities. Overall, this study provides insights into the bodily representations of spontaneous thought, highlighting the interconnected relationships between the body and the mind.</p>
Swift/BAT-GUANO upper limits fits map for S231206cc
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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.