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13,117 results for “Maps”
Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy
<p>Quantifying neuron morphology and distribution at the whole brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single cell labeling and whole brain imaging to study human brains. We propose Adaptive Cell Tomography (ACTomography), a low-cost, high-throughput, high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and 1 donated fresh postmortem brain. We collected 3-D images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor-field of neuron morphology to characterize anatomical modularity of a human brain.</p>
GAN, PCA, and Statistical Shape Models for the Creation of Synthetic Craniosynostosis Distance Maps
<p>This dataset is part of the publication "Classification of Craniosynostosis Trained Only On Synthetic Data Using GANs, PCA, and Statistical Shape Models".</p> <p><strong>dataset28.zip</strong> includes 2D distance maps constructed of surface scans of craniosynostosis patients: sagittal suture fusion (scaphocephaly), metopic suture fusion (trigonocephaly), coronal suture fusion (brachycephaly and anterior plagiocephaly), and a control model (normocephaly and positional plagiocephaly).<br> <br> <strong>synthetic_1000.zip</strong> contains are random 1000 samples per class created from each individual synthetic data source (GAN, PCA, statistical shape model).<br> <br> This repository contains only the images. To synthesize your own data, please use the github repository.</p>
The quantification of Open Scholarship - A mapping review
<p>We present the data set for the mapping review "The quantification of open scholarship"</p>
FIGURE 13. Distribution maps for the Griburius scutellaris species-group. a in Revision of the Griburius scutellaris (Fabricius, 1801) species group (Coleoptera: Chrysomelidae: Cryptocephalinae)
FIGURE 13. Distribution maps for the Griburius scutellaris species-group. a: G. lecontii, Mexican range; b: G. lecontii, U. S. range; c: G. montezuma, Mexican and Central American range; d: G. montezuma, U. S. range; e: G. rileyi; f: G. scutellaris. Black markings: data from specimens analyzed during the study. Red markings: data from the public database Global Biodiversity Information Facility (GBIF.com) or literature data. Black stars: the presence in the state is reported in the consulted literature, but without more precise data on the location.
FIGURE 12. Distribution maps for the Griburius scutellaris species-group. a in Revision of the Griburius scutellaris (Fabricius, 1801) species group (Coleoptera: Chrysomelidae: Cryptocephalinae)
FIGURE 12. Distribution maps for the Griburius scutellaris species-group. a: G. albilabris; b: G. biverrucatus; c: G. boucardi; d: G. decoratus; e: G. errans; f: G. larvatus. Black markings: data from specimens analyzed during the study. Red markings: data from the public database Global Biodiversity Information Facility (GBIF.com).
FIGURE 2. Map showing the previous records for H in The identity of Thraulodes limbatus Navás, 1936 (Ephemeroptera: Leptophlebiidae)
FIGURE 2. Map showing the previous records for H. maculipennis in Pernambuco (PE), Paraná (PR) and Santa Catarina (SC) (in red). Detail of Santa Catarina State showing type localities of T. limbatus in Corupá and H. maculipennis in Ibirama. Scale km.
Map of Spitzbergen showing collection sites. Ba = Bayelva; Bl = Blomstrandhalvöya; E = Eskjeret; F = Fardalen; J = Juttahl; Kj = Kjarsvika; Kv = Kvadehuk; L = Longyearbyen; M = Magdalenafjord; N = Ny Âlesund; S = Stuphallet; V = Virgohamna; Z = Zeppelinfjellet. in FREELIVING AND PLANT PARASITIC NEMATODES FROM SPITZBERGEN, COLLECTED BY MR. H. VAN ROSSEN
Map of Spitzbergen showing collection sites. Ba = Bayelva; Bl = Blomstrandhalvöya; E = Eskjeret; F = Fardalen; J = Juttahl; Kj = Kjarsvika; Kv = Kvadehuk; L = Longyearbyen; M = Magdalenafjord; N = Ny Âlesund; S = Stuphallet; V = Virgohamna; Z = Zeppelinfjellet.
ACA CO(J=2-1) Mapping of the Nearest Spiral Galaxy M33. I. Initial Results and Identification of Molecular Clouds
<p>(1) M33_ACA7m+IRAM_12CO21_cube_pbcor_kelvin.fits.tar.gz (661 MB): <sup>12</sup>CO(<em>J</em>=2-1) data cube obtained with the ACA 7m array combined with the IRAM 30m telescope, 7".31 x 6".50 beam, 0.7 km/s resolution<br> (2) M33_ACA7m-only_12CO21_cube_pbcor_kelvin.fits.tar.gz (660.6 MB): <sup>12</sup>CO(<em>J</em>=2-1) data cube obtained with the ACA stand-alone mode (7m array only), 7".31 x 6".50 beam, 0.7 km/s resolution<br> (3) M33_ACA7m-only_13CO21_cube_pbcor_kelvin.fits.tar.gz (331.5 MB): <sup>13</sup>CO(<em>J</em>=2-1) data cube obtained with the ACA stand-alone mode (7m array only), 7".72 x 6".86 beam, 1.4 km/s resolution<br> (4) M33_ACA7m-only_C18O21_cube_pbcor_kelvin.fits.tar.gz (289.9 MB): C<sup>18</sup>O(<em>J</em>=2-1) data cube obtained with the ACA stand-alone mode (7m array only), 7".82 x 6".96 beam, 1.6 km/s resolution</p>
Self-Potential dataset for mapping groundwater flow patterns in the Chaîne des Puys (Auvergne, France)
<table> <tbody> <tr> <td>This dataset was created from self potential data acquired over a period of time between 1989 and 2022. The data acquired on the Volvic watershed were first used in a DIREN report (Labazuy, 2000) and in the thesis of Joux (2002). This database has been largely completed in the framework of the CAPRICE project (Aumar, 2022) on the Come and Veyre watersheds.</td> </tr> </tbody> </table>
My map
Map with cities
PRISMA 2020 flow diagram of the literature mapping
<p>PRISMA 2020 flow diagram of the literature mapping. Part of the study "What do we mean by GenAI?"</p>
Western Tigray in 165 historical and 33 ethno-linguistic maps (1475-2014)
<p>There is a wide array of historical maps and records which jointly reveal that the territorial organisation of northern Ethiopia has tremendously changed over the last four centuries.</p> <p>Historical maps representing the Western Zone of the Tigray Region (Welkait and adjacent districts, Fig. 1) have been retrieved from well-established repositories. Only maps prepared in the same period (co-eval maps) were used. Each map was screened for representation of internal borders, indicating sway or territorial control. Out of 165 maps, spanning the period 1475-1967, 159 were precise enough to be analysed, of which 93 display territorial control. Descriptive statistics of the dataset as a whole are presented.</p> <p>Starting from the late 16<sup>th</sup> C., territorial control is frequently and clearly shown, with 57 maps (between 1570 and 1941) displaying a boundary that is located well south of the Tekeze River, or even south of the Simien mountains (Fig. 2). Welkait is explicitly included within a larger Tigray confederation (periods 1707-1794, 1829-1898, 1908-1909, and 1935-1941) (Fig. 3); it is briefly mapped as part of Amhara in 1891-1896 and part of Gondar from 1944-1990. At other periods it appears independent or part of a larger Mezaga (“dark earth”) lowland region. The Amhara/Gondar – Tigray border is mapped on the Tekeze River at short intervals in 1844-1847, 1860-1872, and 1891-1896 and then more permanently between 1944 and 1990. The meta-analyis of the historical maps shows that for the larger part of the last 300 years, Western Tigray has been under Tigray jurisdiction (Fig. 5). However, at times of upheaval, the territory has been briefly reorganized under either the Amhara polities or was autonomous.</p> <p>These maps demonstrate that the argument that Welkait was “<em>always</em> a part of Begemdir”, or else Amhara, is not based on evidence, apart from a territorial reorganisation in the mid-twentieth century. In contrast, contemporary Tigray is a valid territory, whose legitimacy stems from modern federalism’s faculty to create a region away from concepts of the Ethiopian empire-state. In 1991-4, when the boundaries of the Tigray Region were established as part of federal Ethiopia, local self-determination was way more important than historical maps. Remarkably, the consensus of the 20<sup>th</sup> C. ethno-linguistic maps of the Ethiopian state sustains the current extent of the Tigray region.</p> <p>In other words: the Amhara nationalist narrative that there was continuous ancestral ownership of Western Tigray is not confirmed by a meta-analysis of the historical maps. We conclude with the suggestion to especially consider the recent and subrecent actual world, including language maps.</p>
A first high-quality vector data of buildings in East Asian countries based on a comprehensive large-scale mapping framework
<p>We generate a high-quality and complete vector data of buildings in East Asian regions, including China, Japan, North Korea, South Korea and Mongolia, filling the gaps in the existing public buildings data.</p>
A dataset for the threat degree map of human expansion to biodiversity in Asia over 2000-2020
<p>A dataset for the threat degree map of human expansion to biodiversity in Asia over 2000-2020</p> <p>The dataset includes three inputs (density maps of patch number, patch area, and number of threatened species involved in the patches of human expansions), and one output (i.e. final result, a threat degree map). The threat degree map was calculated as the geometric mean of three indicators(inputs) (see corresponding publication).</p> <p>The threat degree map (output file) is compressed GeoTiff files (*.tif). The spatial resolution is 1km×1km. The threat degree map represents the threat degree of human expansion (referring to cropland and artificial surface expansion patches over 2000-2020) to biodiversity. The value of threat degree map varied from 0% to 100%, with a larger value indicating a higher threat of human expansion to biodiversity.</p> <p>Cite as<br>Chao Yang, et al. (2023). Human Expansion-Induced Biodiversity Crisis over Asia from 2000 to 2020. Research, 6, 0226. DOI:10.34133/research.0226</p>
MAP 2 in Review of the American genus Bolostromus Ausserer, 1875 with the description of fourteen new species (Araneae, Cyrtaucheniidae)
MAP 2. Distribution map of Bolostromus in Ecuador. Bolostromus devriesi n. sp. (green triangle), B. riveti Simon, 1903 (turquoise star), B. hubeni n. sp. (yellow circle), B. urku n. sp. (green half circle), B. epiphyticus n. sp. (red square), B. pristirana n. sp. (pink losange), B. nischki n. sp. (blue inverted triangle), B. fonsecai n. sp. (black cross), B. primus n. sp. (black rectangle), B. losrios n. sp. (red pentagone), B. stridulator n. sp. (blakc circle), B. valdivia n. sp. (yellow square), B. italoi n. sp. (orange circle), B. gaujoni (Simon, 1889) (yellow star).
MAP 1 in Review of the American genus Bolostromus Ausserer, 1875 with the description of fourteen new species (Araneae, Cyrtaucheniidae)
MAP 1. Distribution map of Bolostromus in Central America and Northern South America. Bolostromus laheredia n. sp. (turquoise circle), B. panamanus (Petrunkevitch, 1925) (blue star), B. holguinensis Rudloff, 1996 (green star), B. busu n. sp. (red circle), B. insularis (Simon, 1892) (pink star), B. pulchripes (Simon, 1889) (red star), B. fauna (Simon, 1889) (black star), Bolostromus venustus Ausserer, 1875 (orange star).
ALMA cubes and integrated maps of the d203-506 protoplanetary disk
<p>These are the ALMA reduced data of the d203-506 protoplanetary disk. The data was obtained as part of the project 2017.1.01478.S (PI J.~Champion). The data was reduced at the ALMA regional node in Grenoble thanks to the help of Edwige Chapillon. </p>
Swift/BAT-GUANO upper limits map for S230731an
<p>Swift/BAT-GUANO upper limits map for S230731an, the solid and dashed lines indicate the 90% and 50% GW contour levels, respectively.</p>
Deep and fast label-free Dynamic Organellar Mapping - Imaging data
<p>Imaging data from the article "Deep and fast label-free Dynamic Organellar Mapping", published in Nature Communications by Schessner et al., from Fig. 6, 7 and Supp. Fig. 6c.</p> <p>Widefield images were captured on a Leica DMi8 inverted microscope equipped with an iTK LMT200 motorised stage, a 63x/1.47 oil objective (HC PL APO 63x/1.47 OIL) and a Leica DFC9000 GTC Camera, and controlled with LAS X (Leica Application Software X).</p> <p>Fig6_GLG1_TGOLN2_GALNT2_SuppFig6c_LC3B: Widefield imaging of wild-type HeLa cells cultured for 1h in either: 1) full growth medium (Control); 2) EBSS to starve the cells (Starve); 3) full growth medium plus 100 nM BafA (Control + BafA); or 4) EBSS plus 100 nM BafA (Starve + BafA). Cells were labelled with anti-GALNT2 (Alexa Fluor 488), in combination with either anti-GLG1 (Alexa Fluor 568) or anti-TGOLN2 (Alexa Fluor 680), as shown in Fig. 6, or were labelled with anti-LC3B (Alexa Fluor 488), as shown in Supp. Fig. 6c. In all images, cells were stained with DAPI to label nuclei.</p> <p>Fig7_GALNT2_GLG1_TM9SF2_TGOLN2_GOLIM4_SDF4: Widefield imaging of HeLa cells left untreated in full growth medium (0h) or cultured in the presence of 100 nM BafA for 0.5, 1, 2, 4, 6 or 8 hours, before fixation. Cells were labelled with anti-GALNT2 (Alexa Fluor 647) in combination with either anti-GLG1, anti-TM9SF2, anti-TGOLN2, anti-GOLIM4 or anti-SDF4 (Alexa Fluor 555). In all images, cells were stained with DAPI and phalloidin-488 to label nuclei and cytoplasm, respectively.</p>
Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"
<p>The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (<a href="https://doi.org/10.1029/2022EF003211">https://doi.org/10.1029/2022EF003211</a>). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: </p> <p>- data pre-processing scripts</p> <p>- code to run the self-organizing map analysis</p> <p>- code to calculate the SOM and ETC statistics</p> <p>- composites of 500-hPa geopotential height for each dataset as organized by the SOM</p> <p>- ETC tracking script and tracking output for each dataset</p> <p>- SOM output for each dataset </p>
ScienceDex guides
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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.