Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13,117
datasets available to search
ShareScore release 0.9.0
Dataset results
13,117 results for “Maps”
FIGURE 2. Bittacus adult habitus images. a in The genus Bittacus Latreille (Insecta: Mecoptera) in Brazil: key to species, distribution maps, new synonym, and three new species
FIGURE 2. Bittacus adult habitus images. a) B. aripuanensis; b) B. blancheti; c) B. maculosus. Scale bars = 1 mm.
FIGURE 2. Locality map. A in A new brachymetopid trilobite from the Early Permian Shakhtau reef complex of the southwestern Urals, Bashkortostan, Russia
FIGURE 2. Locality map. A—the European part of Russia with administrative division; B—the Republic of Bashkortostan and adjacent regions; C—the area around the Shakhtau locality.
Land Use Map of Vietnam (2018) used in the making of Vietnam Biomass Atlas
<p>Albertine Potter van Loon. (2018). <em>Land Use Map of Vietnam used in the making of Vietnam Biomass Atlas </em>[Raster file]. Available from Energydata.info Website: <a href="https://energydata.info/dataset/vietnam-biomass-atlas-2017">https://energydata.info/dataset/vietnam-biomass-atlas-2017</a> #VIETSE</p>
Fig. 5. Maps. A in Populus primaveralepensis sp. nov. (Salicaceae, Malpighiales), a new species of white poplar from the Bosque La Primavera Biosphere Reserve in western Mexico
Fig. 5. Maps. A. Distribution of P. primaveralepensis A.Vázquez, Muñiz-Castro & Zuno sp. nov. and related species of Populus L. in western Mexico. B. Distribution of the species of Populus in Jalisco with a close up for P. luziarum A.Vázquez, Muñiz-Castro & Padilla-Lepe and P. primaveralepensis sp. nov.
DL-FRONT MERRA-2 weather front probability maps over North America, 1980-
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly spatial grids containing probability maps for each of five front-type categories—cold front, warm front, stationary front, occluded front, and no front.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>The DLNN was trained using MERRA-2 data and human-identified fronts from the NOAA National Weather Service (NWS) Weather Prediction Center (WPC) <a href="https://www.wpc.ncep.noaa.gov/html/sfc2.shtml">Coded Surface Bulletin</a> dataset. The training datasets covered the years 2003-2007.</p> <p>The dataset contains two sets of files. The first set contains the original front probability maps. The second set contains "one hot" versions of the front probability maps. In the one hot version the five front-type probabilities for a spatial grid cell for a given time step are replaced by the value 1 for the largest front-type probability, and by 0 for the others.</p> <p>The front probability files have names that follow the form merra2_merra2-1deg_fronts_<year>.nc. The one hot files have names that follow the form merra2_merra2-1deg_onehot_<year>.nc. Each file contains one year of hourly spatial data grids.</p>
Madrid light impact impact maps using ISS images
<p>Environmental impact maps of Madrid based on the ISS image ISS030-e-082052 from https://eol.jsc.nasa.gov.</p> <p>Includes:</p> <p>Estimations of the corrections to all Lucas et al. 2013 pigments using the ratios BG and GR. Also, Aubé et al 2013 index.</p> <p>These files have been produced calibrating procedure Sánchez de Miguel 2015 and updated procedures and Sánchez de Miguel et al 2019a and early stages of Sánchez de Miguel et. al 2019b.</p>
STORM Climate Indices Maps
<p>These data sets contains climate indices (for definitions please see README file) determined with the Climate Data Operator (cdo) software calculated based on EURO-CORDEX regional climate model projections using RCP4.5 and RCP8.5 for the period 2036-2065 as well as the time period 1971-2000 (baseline climate). The indices are based on an ensemble of EURO-CORDEX runs (see README file) after determining the indices based on each run individually, and are available for an area around the STORM pilot sites (located in Mellor, UK; Troia, Portugal; Rome, Italy; Rethymno, Greece; and Ephesus, Turkey). For more information and terms of use please consult the README file.</p>
FIGURE 1. Map showing the sampling site. A in Chelonocytherois omutai gen. et sp. nov. (Crustacea: Ostracoda) from the back of loggerhead sea turtles
FIGURE 1. Map showing the sampling site. A, Japan; B, Yakushima Island; C, Pacific coast of Northeast Japan. Solid circle indicates the place of a commercial set net at Funakoshi Bay, Yamada Town, Iwate Prefecture, Japan. Open circle indicates the type locality of Chelonocytherois omutai gen. et sp. nov.
Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale
<p>This package contains the gridded yield data for lignocellulosic bioenergy crops developed from a global yield observation dataset using a random forest algorithm based on climatic and soil conditions. The data are global yield maps of five important lignocellulosic bioenergy crops (eucalypt, Miscanthus, poplar, switchgrass and willow) under current technology, climate and atmospheric CO<sub>2</sub> conditions at a 0.5° × 0.5° spatial resolution. A combined “best bioenergy crop” yield map is also provided by selecting the one of the five crop types with the highest yield in each of the grid cell. The yield unit is “ton DM ha<sup>-1</sup> yr<sup>-1</sup>”. Please see more details in the associated paper on ESSD.</p>
Public and Private in the Roman House: Maps of Casa del Tramezzo di Legno, Herculaneum III,11
<p>Data pertaining to the research conducted by Public and Private in the Roman House Project in Herculaneum.</p> <p>Maps of Casa del Tramezzo di Legno (CdTdL), Herculaneum III,11.</p> <p>Drawn by architect, Dr. Juhana Heikonen</p>
Figure 5 in Vulnerability to snakebite envenoming: a global mapping of hotspots
Figure 5: Hotspots of vulnerable populations to medically important venomous snake species Hotspots are defined as people living in areas within the range of one or more medically important venomous snake species,and more than 3 h away from major urban centreswith Healthcare Access and Quality Index deciles of 1–3. (A) Pixel-level vulnerability surface (ie,vulnerability to all species of medically important snakes).(B) Aggregated second administrative level vulnerability to all species of medically important venomous snakes,as measured by the absolute number of people.(C) Aggregated second administrative level vulnerability to only those species for which no effective therapy is currently listed by WHO,as measured by the absolute number of people.
Figure 3 in Vulnerability to snakebite envenoming: a global mapping of hotspots
Figure 3: Average traveltime to nearest major city for populations living within snake ranges The light grey areas represent locations withoutthe presence of medically important venomous snake species.
Figure 4 in Vulnerability to snakebite envenoming: a global mapping of hotspots
Figure 4: Proportion of populations living within range of snake species by each HAQ Index decile (A) Populations living within the range of one or more medically important venomous snake species (either category one or two).(B) Populations living within the range of one or more medically important venomous snake species (either category one or two),for which no effective therapy is listed.HAQ=Healthcare Access and Quality.
Figure2 in Vulnerability to snakebite envenoming: a global mapping of hotspots
Figure2: Ranges of venomous snake species and number of medically important venomous snake species per 5 × 5 km location for which no effective therapy is currently listed byWHO (A)Counts range from low (n=1) to high (n=13).The light grey areas represent locations where no medically important venomous snake species are present.(B) Counts range from low (n=1) to high (n=7).The light grey areas represent locations where snake species present have effective therapies listed by WHO,and the dark grey areas represent locations where no medically important venomous snake species are present.
Figure 1 in Vulnerability to snakebite envenoming: a global mapping of hotspots
Figure 1: Conceptual overview of vulnerability to snakebite envenoming (A) Vulnerability can be considered as the intersection of populations who live within the range of venomous snakes that have no antivenoms available, cannot easily access health care,and have poor quality health care in delivery of antivenoms or ensuring necessary stocks.The intersection of all three defines the most vulnerable populations.(B)These factors vary in space.By overlaying these features,the most vulnerable populations can be identified spatially (represented here by the boxes outlined in black).
Human pancreatic afferent and efferent nerves: mapping and 3-D illustration of exocrine, endocrine, and adipose innervation
<p><em><strong>Supplemental Fig. S1 </strong>(related to Fig. 2)</em>. Mapping and detection of sensory neurons in human pancreatic duct lesion formation.</p> <p><strong><em>Supplemental Table S1.</em></strong> Summary of color codes presented in illustrations.</p> <p><em><strong>Supplemental Table S2. </strong></em>Summary of primary and secondary antibodies used in illustrations.</p> <p> </p> <p><strong>Supplemental Video Legends</strong></p> <p><em><strong>Supplemental Video S1</strong> (related to Fig. 2) </em></p> <p><strong>Human pancreatic substance P<sup>+</sup> sensory (afferent) nerves. </strong></p> <p><strong><em>Left</em> (still image): projection of pancreatic innervation at the acinar-islet boundary. </strong>Blue: glucagon (islet). Green: neuroendocrine marker PGP9.5 (nerve and islet). Red: substance P (SP, sensory nerve). Sensory nerves appear as yellow fibers (overlap of red SP and green PGP9.5 signals). Broken arrow: enlarged area (right panel).</p> <p><strong><em>Right</em>: in-depth recording of peri-ductal sensory nerves. </strong>Sensory nerves (yellow) are around the islet but not penetrating into the core. White: nuclei. Note that a portion of islet cells are SP dim. The still image and recording were derived from a normal human pancreas (female/age, 51 years/BMI, 20).</p> <p> </p> <p><em><strong>Supplemental Video S2</strong> (related to Fig. 2 and Supplemental Fig. S1)</em><em> </em></p> <p><strong>Mapping and detection of immunoreactive substance P<sup>+</sup> neuron in duct lesion formation. </strong></p> <p><strong><em>Left</em> (still image): tissue map of human pancreas distal to ductal adenocarcinoma. </strong>Acinar atrophy is apparent in this condition. Markers: nuclei, white; PGP9.5, green; substance P (SP), red. Broken arrow: intra-pancreatic ganglion (right panel).</p> <p><strong><em>Right</em>: in-depth recording of immunoreactive SP<sup>+</sup> neuron in ganglion. </strong>The nucleus (00:04) and the SP<sup>+</sup> varicosities are prominently seen. The latter contacts the surrounding SP<sup>-</sup> neurons. The still image and recording were derived from an area 5-cm distal to the pancreatic ductal adenocarcinoma (male/age, 77 years/staging, T1N0).</p> <p> </p> <p><em><strong>Supplemental Video S3</strong> (related to Fig. 3) </em></p> <p><strong>Mouse pancreatic substance P<sup>+</sup> sensory (afferent) nerves. </strong></p> <p><strong><em>Left</em> (still image): projection of pancreatic sensory innervation. </strong>In mice, the SP<sup>+</sup> sensory nerves follow the arteriole to the islet mantle and penetrating into the core (enlarged in right panel). Markers: blood vessels, blue; PGP9.5, green; substance P (SP), red.</p> <p><strong><em>Right</em>: in-depth recording of sensory innervation of islet. </strong>Unlike the human pancreatic sensory innervation, the mouse SP<sup>+</sup> sensory nerves (varicosities) are inside the islet and associate with the peri-islet ganglion. White: nuclear staining and tissue autofluorescence. The still image and recording were derived from a B6 mouse pancreas (male/age, 12 weeks).</p>
MAP. Distribution of Triphosa dubitata, T. silviae sp. n., T. lecerfi sp. n., T. sabaudiata, T. taochata and Hydria ravulata in the Middle East and Central Asia. in An integrative taxonomic revision of the genus Triphosa Stephens, 1829 (Geometridae: Larentiinae) in the Middle East and Central Asia, with description of two new species
MAP. Distribution of Triphosa dubitata, T. silviae sp. n., T. lecerfi sp. n., T. sabaudiata, T. taochata and Hydria ravulata in the Middle East and Central Asia.
MAP 1 in On the subgenus Eurysunius Reitter of Turkey VI. Two new species from Sivas province in central northern Anatolia, with a key to Turkish species (Coleoptera: Staphylinidae: Paederinae, Astenus)
MAP 1. Distribution of Astenus (Eurysunius) species in Turkey: A. bicoloratus Assing, 2002 (large filled circle), A. brachati Assing, 2011 (large filled triangle), A. goeki Anlaş, 2017 (large open triangle), A. gusarovi Anlaş, 2015 (large open square), A. honazicus Anlaş, 2015 (small filled triangles), A. ilgazi Anlaş, 2016 (small filled squares), A. kociani Assing, 2015 (large filled square), A. kumlutasi Anlaş, 2015 (small open circles), A. melendizicus Anlaş, 2018 (large open circle), A. occiduus Assing, 2007 (small hexagons), A. orgeli Anlaş, 2015 (small filled circles), A. paphlagonicus Assing, 2002 (small open squares), A. pe- linae sp. n. (open stars), A. rhodicus Assing, 2013 (filled cross), A. sandiklicus Anlaş, 2014 (small open triangles), A. sexsetosus Assing, 2002 (large open hexagon), A. sivasicus sp. n. (large filled hexagon), A. sultanicus Assing, 2010 (filled stars).
MAPS 1–6 in A taxonomic review of the Xanthonia species occurring in Texas (Coleoptera: Chrysomelidae, Eumolpinae)
MAPS 1–6. Distribution of Xanthonia species. 1) X. vagans; 2) X. furcata (■), X. marquai (♦), X. nitida (●); 3) X. hirsuta; 4) X. picturata; 5) X. angulata (♦), X. dentata (●), X. querci (■); 6) X. parva (■), X. villosula (●).
MAP 1 in The genus Rhabdomastix Skuse in Australia (Diptera: Tipuloidea: Limoniidae)
MAP 1. Distribution of the Australian species of Rhabdomastix (Rhabdomastix) ostensackeni group. R. borroloola sp. nov. R. dobrotworskyi sp. nov. R. dooragana sp. nov. R. hirsuta sp. nov. R. minima R. nivalis sp. nov. R. ostensackeni R. ponticulus sp. nov.
ScienceDex guides
Understand access before you commit
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.