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13,117 results for “Maps”
Singing Insects of North America: distribution maps (670) DwCA
Singing Insects of North America (SINA) is a unique resource providing extensive content covering the crickets and katydids (and, to a much lesser extent, cicadas) of North America north of Mexico. This content includes audio recordings, images, text, and identification keys. SINA was created and is still maintained by Dr. Thomas J. Walker of the University of Florida, a pioneer and leader in the study of North American crickets and katydids beginning in the late 1950s and continuing into the 21st century. <p></p>http://entnemdept.ifas.ufl.edu/walker/Buzz/
Smithsonian Tropical Research Instiitute: Distribution maps (358) DwCA
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Rapid mapping of flood inundation by deep learning-based image super-resolution
<div> <div># Rapid mapping of flood inundation by deep learning-based image super-resolution</div> <div># Developer: Wenke Song</div> <div># The University of Hong Kong</div> <div># Contact email: songwk@connect.hku.hk</div> <div># MIT License</div> <div># Copyright (c) 2024 songwk0924</div> <div> </div> <div>There are two folders in the compressed file: Trained_model and Test_cases:</div> <div>(1) Trained_model</div> <div> model_d_DenseUnet.pth, for predicting the maximum water depth;</div> <div> model_v_DenseUnet.pth, for predicting the maximum velocity.</div> <div> </div> <div>(2) Test_cases</div> <div> Test_d_r1.npy, Test_d_r2.npy, Test_d_r3.npy: Input features for predicting maximum water depth of rainfall events r1-r3;</div> <div> Test_v_r1.npy, Test_v_r2.npy, Test_v_r3.npy: Input features for predicting maximum velocity of rainfall events r1-r3; <div> </div> </div> <div> bathy_mat_5m_0p.csv: Elevation data to create mask layer;</div> <div> Fine_grid_flood_maps (2DSWEs):</div> <div> hmax_r1.asc, hmax_r2.asc, hmax_r3.asc, maximum water depth simulated by 2DSWEs of rainfall events r1-r3;</div> <div> velmax_r1.asc, velmax_r2.asc, velmax_r3.asc, maximum velocity simulated by 2DSWEs of rainfall events r1-r3;</div> <div> </div> <div><span>The aforementioned data will be used as input for model prediction (Prediction.py). </span></div> <div><a name="OLE_LINK902"></a><a name="OLE_LINK909"></a><a href="https://github.com/songwk0924/Flood-inundation-mapping"><span>https://github.com/songwk0924/Flood-inundation-mapping</span></a></div> </div>
FIGURE 20. Record map for S. boibumba spec. nov., S. cascalheira spec. nov., S. chicaque spec. nov., S. iguaque spec. nov., S. matupiri spec. nov., S. medina spec. nov. and S in An update on the genus Sparianthis Simon, 1880 (Sparassidae: Sparianthinae)
FIGURE 20. Record map for S. boibumba spec. nov., S. cascalheira spec. nov., S. chicaque spec. nov., S. iguaque spec. nov., S. matupiri spec. nov., S. medina spec. nov. and S. tuparro spec. nov.
FIGURE 21. Record map for S. caracarai Rheims, 2020, S in An update on the genus Sparianthis Simon, 1880 (Sparassidae: Sparianthinae)
FIGURE 21. Record map for S. caracarai Rheims, 2020, S. chickeringi (Gertsch, 1941), S. megalopalpa (Caporiacco, 1954) and S. ravida (Simon, 1898).
Data and code from: Improving accuracy and reproducibility of cartilage T2 mapping in the OAI dataset through extended phase graph modeling
<p>The repository contains the OAI data used to run the experiments reported in the study "Improving accuracy and reproducibility of cartilage T2 mapping in the OAI dataset through extended phase graph modeling", along with their segmentation and processed T2 maps with the different fitting algorithms.</p> <p>The <em><strong>code_repository</strong></em> folder contains a snapshot of the <a href="https://github.com/barma7/EPGfit_for_cartilage_T2_mapping">GitHub repository</a> at the time of the submission of the manuscript. Please visit the GitHub repository for the latest version. </p>
Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images
<p>Supplementary materials v2 for the article Unagaev A, Korotkova I and Efremova N. "Methodological approaches to identifying and mapping fields of specific crops on a basis of high-resolution satellite images using phenological, geographic and regional statistical information"<br> </p>
HyMes EAA 2024 Rome (presentation, maps, lists)
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Mapping the Indian Palaeolithic_ Sites from IARs
<p>A list of Palaeolithic sites from the open access Indian Archaeological Review (1953-1976) compiled by the author into a streamlined spreadsheet with key details. </p>
GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
<p>GDCLD : A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images</p> <p>Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., and Catani, F.: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-239, in review, 2024.</p> <p> </p> <p>Data description:</p> <p> </p> <p>The training dataset and the validation dataset are composed of UAV, PlanetScope, Gaofen-6 and Map World images of the 5 earthquake regions of Luding, Nippes, Hokkaido, Jiuzhaigou and Mainling. There is no overlapping area in each TIFF. The training dataset and the validation dataset are randomly divided at a ratio of approximately 0.75:0.25.</p> <p> </p> <p>train_dataset:</p> <p>train_data: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>train_label: The train dataset part of the GDCLD data set contains 11162 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Validation_dataset</p> <p>val_data: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 3) (TIFF).</p> <p>val_label: The validation dataset part of the GDCLD data set contains 4459 data matrices (TIFF), and the shape of the matrix is (1024, 1024, 1) (TIFF).</p> <p> </p> <p>Test_dataset (Lushan, Sumatra, Mesetas and Palu dataset)</p> <p>This package contains the original files of remote sensing images from three sources: UAV, Map World, and PlaneScope belonging to the Lushan, Sumatra, Mesetas and Palu earthquake regiones, which are used to display the test area.<br><br>Future work:<br>The future work includes additional landslide data that the authors will continue to upload. In this 2.0 version update, we have added UAV imagery and interpreted data for loess landslides triggered by the December 2023 M6.2 earthquake in Gansu, China, with a resolution of 0.1 m. Due to authorization constraints, we can only provide PNG files without geographic coordinates. Additionally, this update includes PlanetScope imagery of landslides induced by heavy rainfall in Guangdong, China, in 2024, as well as PlanetScope imagery and landslide labels for events triggered by the Hualien earthquake in Taiwan.<br><br>Please note that this landslide dataset is publicly available exclusively for scientific research purposes and must not be used for commercial purposes.</p>
FIGURE 5. Distribution maps. A. C in What's new in Chamaecrista (Fabaceae, Caesalpinioideae) from the Brazilian Cerrado?
FIGURE 5. Distribution maps. A. C. chaetostegia (□), C. obolaria (▲), and B. C. gymnothyrsa (), and C. sparsifolia (). The boundaries of the municipalities (thin line) are indicated.
FIGURE 1. A. Map showing study area. B in Morphological diversity of benthic Nostocales (Cyanoprokaryota/Cyanobacteria) from the tropical rocky shores of Huatulco region, Oaxaca, México
FIGURE 1. A. Map showing study area. B. Ophiotrix sp. growth at Cacaluta bay. D. Hassallia littoralis growth at El Arrocito bay. E. Petalonema sp. growth at San Agustín bay. F. Kyrtuthrix cf. maculans growth at Panteones beach. G, H. Brasilonema sp. growth at Tangolunda bay. B, C, D. Supratidal environments. E, F, G. Intertidal environments.
FIGURE 3. Vatica mizaniana. A. Geographic distribution map. B in Two new species of Vatica (Dipterocarpaceae) from Peninsular Malaysia
FIGURE 3. Vatica mizaniana. A. Geographic distribution map. B. Spatial distribution of trees in the population at Compartment 31, Jerangau Forest Reserve.
FIGURE 2. Distribution maps. A in A revision of Chuniophoenix (Arecaceae)
FIGURE 2. Distribution maps. A. Chuniophoenix hainanensis (red dots). C. suoitienensis (blue square). B. C. nana (red dots).
FIGURE 5. Map showing the known Mexican localities for Acianthera johnsonii, A. majakoluckae, A. obscura, A. pollardiana and A in A taxonomic synopsis of the Mexican species of Acianthera (Orchidaceae: Pleurothallidinae) including a new species
FIGURE 5. Map showing the known Mexican localities for Acianthera johnsonii, A. majakoluckae, A. obscura, A. pollardiana and A. pubescens.
FIGURE 3. Map showing the known Mexican localities for Acianthera angustifolia, A. angustisepala, A. breedlovei, A. chrysantha and A in A taxonomic synopsis of the Mexican species of Acianthera (Orchidaceae: Pleurothallidinae) including a new species
FIGURE 3. Map showing the known Mexican localities for Acianthera angustifolia, A. angustisepala, A. breedlovei, A. chrysantha and A. circumplexa.
FIGURE 4. Map showing the known Mexican localities for Acianthera eximia, A. greenwoodii, A. hartwegiifolia, A. herrerae and A in A taxonomic synopsis of the Mexican species of Acianthera (Orchidaceae: Pleurothallidinae) including a new species
FIGURE 4. Map showing the known Mexican localities for Acianthera eximia, A. greenwoodii, A. hartwegiifolia, A. herrerae and A. hondurensis.
FIGURE 6. Map showing the known Mexican localities for Acianthera sotoana, A. testifolia, A. tikalensis, A. unguicallosa and A in A taxonomic synopsis of the Mexican species of Acianthera (Orchidaceae: Pleurothallidinae) including a new species
FIGURE 6. Map showing the known Mexican localities for Acianthera sotoana, A. testifolia, A. tikalensis, A. unguicallosa and A. violacea.
Swift/BAT-GUANO upper limits map for S240825ar
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Swift/BAT-GUANO upper limits map for S240807h
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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.