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13,117 results for “Maps”
MAP 2 in The genus Rhabdomastix Skuse in Australia (Diptera: Tipuloidea: Limoniidae)
MAP 2. Distribution of the Australian species of Rhabdomastix (Rhabdomastix) tonnoirana group. R. collesiana sp. nov. R. rosae sp. nov. R. tonnoirana R. wilsoniana
MAP 1 in Revision of the genus Rhytidaspis Redtenbacher, 1891 including the description of a new genus Haudrhytidaspis gen. nov. (Orthoptera: Tettigoniidae: Conocephalinae)
MAP 1. Distribution records of Rhytidaspis species: 1, R. picta Redt., 1891; 2, R. arfak sp. n.; 3, R. camela sp. n.; 4, R. nigropunctata sp. n.; 5, R. variata meja ssp. n.; 6, R. variata variata ssp. n., 6?, R. variata ssp.; 7, R. variata brevis ssp. n.; 8, R. ornata sp. n.; 9, R. genyem sp. n.; 0, unidentified nymphs.
Murgia Alta: grassland change map (1990-2018).
<p>A binary change map for the land cover class "GRASSLAND" in "Murgia Alta" (Italy) PA, detected by the Cross-Correlation Analysis (CCA) [1,2] algorithm considering at time T1 the two layers "NATURAL GRASSLAND" (land cover) and "PASTURES" (land use) from CORINE LAND COVER dated 1990 and at time T2 a Sentinel-2A image dated 19 July 2018, at 10 meters spatial resolution, projected in WGS84/UTM33N. The map has binary values where value 1 indicates pixels changed from GRASSLAND to other whereas value 0 indicates No changed/Not considered pixels.</p> <p>[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).</p> <p>[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, Remote Sensing of Environment, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257</p> <p><br> </p>
Montado: grassland change map (2012-2016)
<p>A binary change map for the class "GRASSLAND" on Montado (Portugal) PA, detected by the Cross Correlation Analysis (CCA) [1,2] algorithm considering at time T1 the layer "GRASSLAND" from COPERNICUS Service layer dated 2012 and at time T2 a Sentinel-2A image dated 18 August 2016, at 20 meters spatial resolution, projected in WGS84/UTM29N. <br> The map has binary values where value 1 indicates pixels changed from GRASSLAND to other whereas value 0 indicates No changed/Not considered pixels.</p> <p>[1] Koeln, G., & Bissonnette, J. (2000). Cross-correlation analysis: Mapping landcover changes with a historic landcover database and a recent, single-date, multispectral image. Proc. 2000 ASPRS Annual Convention, Washington, D.C. (8 pp.).</p> <p>[2] C. Tarantino, M. Adamo, R. Lucas, P. Blonda. (2016). “Detection of changes in semi-natural grasslands by cross correlation analysis with Worldview-2 images and new Landsat 8 data”, <em>Remote Sensing of Environment</em>, Vol. 175C, pp. 65-72, doi: 10.1016/j.rse.2015.12.031, ISSN 0034-4257</p>
GIS data for the maps in publication Spatial perspectives enhance modeling of nanomaterial risks
<p>These files include the datasets utilized to perform geospatial modeling in the publication: Spatial perspectives enhance modeling of nanomaterial risks in the Journal of Industrial Ecology. </p> <p>The following data sources were used in this modeling effort:</p> <p><strong>National Hydrography Dataset (NHD): United States Geological Survey (USGS)</strong></p> <p>Upstate NY Lakes, ponds, streams, rivers, springs, and wells</p> <p><strong>Critical Environmental Areas in New York State: New York State Department of Environmental Conservation </strong></p> <p>Areas designated as critical under 6 NYCRR Part 617: “ecological, geological, or hydrological sensitivity that may be adversely affected by any change” (NY DEC)</p> <p><strong>National Land Cover Dataset (NLCD): United States Geological Survey (USGS)</strong></p> <p>National Land Cover Database classification schemes based primarily on Landsat data (2011)</p> <p><strong>Elevation Data: United States Geological Survey (USGS)</strong></p> <p>Digital Elevation Models (10-meter) for New York, elevation values were derived from USGS contour lines mapped at a scale of 1:24,000. </p> <p><strong>Interstate Highway: Federal Highway Administration’s National Transportation Atlas Database</strong></p> <p>Rural and urban highways for New York</p> <p> </p> <p><strong>Other references</strong></p> <p>Bureau, U.S. Census., American community survey 5-year estimates. 2017.</p> <p>EPA, Toxics Resource Inventory. 2019</p> <p> </p> <p>.</p>
CARTO17: Data and Scripts for Mapping 17th c. French Theatre
<p>All the data for our paper "A Theatre of PlacesMapping 17th c. French Theatre" (GIR'19, Lyon). It includes:</p> <ol> <li>A corpus of played with all place names tagged</li> <li>An R script to create a map with all relevant datasets</li> <li>Training data for three different NER recognizers</li> </ol>
Bio2RDF mappings
<p>Declarative mappings in RML created for 14 datasets integrated by the Bio2RDF Project.</p>
Digital versions of lunar geological maps
<p>As the link for this database is not working at USGS website (i.e., https://astrogeology.usgs.gov/search/map/Moon/Geology/Lunar_Geologic_GIS_Renovation_March2013), we upload this database.</p>
FIGURE 98. ITS2 gene tree and distribution map for both G. armatus and G in Crickets of the genus Gryllus in the United States (Orthoptera: Gryllidae: Gryllinae)
FIGURE 98. ITS2 gene tree and distribution map for both G. armatus and G. integer showing both geographic separation and zone of possible hybridization. Collection stop numbers for G. armatus: S04-121 (G358, G359); S05-110 (G511, G512, G513, G515); S07-26 (G1165); S07-33 (G1077); S07-79 (G1228); S10-62 (G1918); S10-63 (G1899); S11-90 (G2172); S12- 36 (G2264); S12-104 (G2411); S13-18 (G2473); S13-46 (G2566); S15-54 (G3096, G3077, G3081); S15-71 (G3101, G3074); S15-73 (G3072); Albuquerque, NM (2003-175); Cordes Junction, AZ (2006-241); Agua Fria, AZ (2006-244). Collection stop numbers for G. integer: S03-100 (G58, G59); S04-36 (G243); S04-40 (G210); S0455 (G245, G246); S04-60 (G218); S04-128 (G370); S05-23 (G477); S05-99 (G499); S06-77 (G629); S09-109 (G1439); S09-114 (G1441, G1445); S11-72 (G2113, G2148); S11-73 (G2143); S12-116 (G2431); S15-80 (G3195, G3316); S15-91 (G3178, G3265); S15-95 (G3292); S16-21 (G3416); S17-6 (G3502, G3525); S16-5 (G3374, G3381); S16-12 (G3366); Hwy 276 mile 30 Sinclair Gas, Garfield Co., UT (2003-038, 2003-039, 2003-041); Winslow, AZ (2003-312, 2012-029, 2012-030, 2012-031, 2012-032).
FIGURE 122. 16S gene tree and distribution map showing two 16S Clades for both G. vernalis and G in Crickets of the genus Gryllus in the United States (Orthoptera: Gryllidae: Gryllinae)
FIGURE 122. 16S gene tree and distribution map showing two 16S Clades for both G. vernalis and G. fultoni (the latter situation is discussed, below, under G. fultoni). G. vernalis samples: S03-56 (G26, G27, G1740); S03-57 (G28, G1700, G1701, G1738); S03-62 (G31, G33, G440); S14-35 (G2754, G2755). G. fultoni samples: S01-47 (G1704); S03-56 (G38, G1703); S03- 62 (G32, G34); S03-64 (G35, G1712); S07-22 (G1020, G1138).
Map. Distribution of Thrinoxethus verhoeffi (Kraus, 1956): 1 — San Alejandro; 2 — Alexander von Humboldt National Park; 3 — Selva Alta. Карта. Распространение Thrinoxethus verhoeffi (Kraus, 1956): 1 — San Alejandro; 2 — Alexander von Humboldt National Park; 3 — Selva Alta. in A new, montane record of the millipede, Thrinoxethus verhoeffi (Kraus, 1956) from the Andes of Peru (Diplopoda: Polydesmida: Aphelidesmidae)
Map. Distribution of Thrinoxethus verhoeffi (Kraus, 1956): 1 — San Alejandro; 2 — Alexander von Humboldt National Park; 3 — Selva Alta. Карта. Распространение Thrinoxethus verhoeffi (Kraus, 1956): 1 — San Alejandro; 2 — Alexander von Humboldt National Park; 3 — Selva Alta.
Fig. 1 Map showing major drainages and mahseer collection localities across India. 1 River Tawi, Jammu. 2 River Beas, Pong. 3 River Beas, Pathankot. 4 River Satluj, Nangal. 5 River Yamuna, Yamuna nagar. 6 River Ganga, Rishekesh. 7 River Bhagirathi. 8 River Kosi, Ramnagar. 9 River Sharda, Tanakpur. 10 River Gerua, Katarnia Ghat. 11 in Molecular evidence to reconcile taxonomic instability in mahseer species (Pisces: Cyprinidae) of India
Fig. 1 Map showing major drainages and mahseer collection localities across India. 1 River Tawi, Jammu. 2 River Beas, Pong. 3 River Beas, Pathankot. 4 River Satluj, Nangal. 5 River Yamuna, Yamuna nagar. 6 River Ganga, Rishekesh. 7 River Bhagirathi. 8 River Kosi, Ramnagar. 9 River Sharda, Tanakpur. 10 River Gerua, Katarnia Ghat. 11 River Tista, Mal/Udalabadi. 12 River Jaldhaka, Bindhu. 13 River Ziyabharali, Bhlukpong. 14 River Dikrong, Doimukh. 15 River Sank, Gwalior. 16 River Tons, Chackghat. 17 River Tawa, Itarsi. 18 River Mahanadi, Sambhalpur. 19 River Krishna. 20 River Chaliyaar, Nilambur. 21 River Chalakudy, Puzha
Hcropland30: A hybrid 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model
<p><strong>Hcropland30</strong><strong>:</strong><strong>A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model</strong></p> <p><strong>***Please note this dataset is undergoing peer review***</strong></p> <p><strong>Version</strong>: <strong>1.0</strong></p> <p><strong>Authors</strong>: Qiong Hu <sup>a, 1</sup>, Zhiwen Cai<sup> b, 1</sup>, Liangzhi You<sup> c, d</sup>, Steffen Fritz<sup> e</sup>, Xinyu Zhang<sup> c</sup>, He Yin<sup> f</sup>, Haodong Wei<sup>c</sup>, Jingya Yang<sup> g</sup>, Zexuan Li<sup> a</sup>, Qiangyi Yu<sup> g</sup>, Hao Wu<sup> a</sup>, Baodong Xu<sup> b *</sup>, Wenbin Wu<sup> g, *</sup></p> <p><em><sup>a</sup></em><em> Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China</em></p> <p><em><sup>b</sup></em><em> College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>c</sup></em><em> Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>d </sup></em><em>International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA</em></p> <p><em><sup>e </sup></em><em>Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</em></p> <p><em><sup>f </sup></em><em>Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA</em></p> <p><a name="_Hlk166672711"></a><em><sup>g </sup></em><em>State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China</em></p> <p><strong> </strong></p> <p><strong>Introduction</strong></p> <p>We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.</p> <p> </p> <p><strong>Dataset</strong></p> <p><strong><em><u>1. Hcropland30</u></em></strong><strong>:</strong> A hybrid 30-m global cropland map in 2020</p> <p>****<strong>Data format</strong>: GeoTiff</p> <p>****<strong>Spatial resolution</strong>: 30 m</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Values</strong>: 1 denotes cropland and 0 denotes non-cropland</p> <p>The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.</p> <p><strong><em><u>2. </u></em></strong><strong><em><u>1°×1° </u></em></strong><strong><em><u>Grids</u></em></strong><strong>:</strong> This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>Id:</strong> The grid’s ID number.</p> <p><strong>area:</strong> The area of the grid.</p> <p><strong>mode:</strong> Indicates the representative sample grid.</p> <p><strong>climate:</strong> The climate type the grid belongs to.</p> <p><strong>dem: </strong>Average DEM value of the grid.</p> <p><strong>ndvi_s1 to ndvi_s4:</strong> Average NDVI values for four seasons within the grid.</p> <p><strong>esa, esri, fcs30, fromglc, glad, globeland30:</strong> Proportion of cropland pixels of different publicly available cropland products.</p> <p><strong>inconsistent:</strong> Proportion of inconsistent pixels within the grid according to different public cropland products.</p> <p><strong>hcropland30:</strong> Proportion of cropland pixels of our Hcropland30 dataset.</p> <p><strong><em><u>3. Samples</u></em></strong>: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>type:</strong> 1 denotes cropland sample and 0 denotes non-cropland sample.</p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite the following paper:</p> <p>Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.</p> <p><strong>License</strong></p> <p>The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).</p> <p><strong>Disclaimer</strong></p> <p>This dataset is provided as-is, without any warranty, express or implied. The dataset author is not</p> <p>responsible for any errors or omissions in the data, or for any consequences arising from the use</p> <p>of the data.</p> <p><strong>Contact</strong></p> <p>If you have any questions or feedback regarding the dataset, please contact the dataset author</p> <p>Qiong Hu (huqiong@ccnu.edu.cn)</p>
FIGURE 1. Collection sites distribution map where fresh material was collected between 2010 and 2012 in Species of Entoloma (Entolomataceae) with cuboidal basidiospores from Brazil
FIGURE 1. Collection sites distribution map where fresh material was collected between 2010 and 2012.
Figs. 70–75. Distribution maps for Metaparia and Dispardentium species. 70 in A Comprehensive Revision of the Genus Metaparia Crotch, 1873 (Coleoptera: Chrysomelidae: Eumolpinae) and Description of a New Genus
Figs. 70–75. Distribution maps for Metaparia and Dispardentium species. 70) M. cephalotes (black circles), M. clytroides (black triangles), M. guatemalensis (gray triangles); 71) M. hybrida (gray triangles), M. lesueuri (black circles), M. mandibuloflexa, new species (black triangles); 72) M. opacicollis (black triangles); 73) M. prosopis, new species (black triangles); 74) M. viridimicans (black triangles); 75) D. laevicollis (black triangles).
Dynamics, synchronization and analog circuit implementation of a discrete neuron-like map with pulsating spiral dynamics
<p>These are experimental time series for an electronic model of neural dynamics. They are provided to support the replication of the results reported in the associated publication, as well as any further public-domain academic research in the field of neural dynamics, nonlinear electronic circuits, and related aspects, in compliance with the specified license terms and all applicable legal clauses.</p> <p>The following reference must be cited when using these data:</p> <div> <div> <div>Zhu W, Sun K, Wang H, Fu L, Minati L, Dynamics, synchronization and analog circuit implementation of a discrete neuron-like map with pulsating spiral dynamics, Chaos, Solitons and Fractals 186 (2024) 115281, DOI 10.1016/j.chaos.2024.115281</div> </div> </div> <p>This document is the results of the research project funded by the National Natural Science Foundation of China (Nos. 62071496, 62061008), and the Innovation Project of Graduate of Central South University (Nos. 2024ZZTS0241). L.M. gratefully acknowledges the support of the "Hundred Talents" program of the University of Electronic Science and Technology of China, of the "Outstanding Young Talents Program (Overseas)" program of the National Natural Science Foundation of China, and of the talent programs of the Sichuan province and Chengdu municipality.</p>
Map 1 in First records of Eulophidae from Cambodia (Hymenoptera: Chalcidoidea)
Map 1. Map of Cambodia showing both localities where of Eulophidae were collected during 2004 and 2006.
Fig. 8. Distribution maps. A in Two new species of Polydictya from Borneo and Siberut, and notes on P. chewi Nagai & Porion, 2004 and P. tanjiewhoei Bosuang, Audibert & Porion, 2015 (Hemiptera: Fulgoromorpha: Fulgoridae)
Fig. 8. Distribution maps. A, Polydictya jakli sp. nov. B, Polydictya barclayi sp. nov., P. chewi Nagai & Porion, 2004 and P. tanjiewhoei Bosuang, Audibert & Porion, 2015.
sample google map (EOL v3 test): raw eol google map
Open the record for dataset details and reuse information.
Singing Insects of North America: SINA maps curated
removed taxa w/o scientific name
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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.