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13,117 results for “Maps”
Images from Newspaper Navigator predicted as maps, with human corrected labels
<p>The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/). </p> <blockquote> <p>[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in <em>Chronicling America</em>. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the <a href="https://labs.loc.gov/work/experiments/beyond-words/">Beyond Words</a> crowdsourcing project.</p> <p>source:<a href="https://news-navigator.labs.loc.gov/"> https://news-navigator.labs.loc.gov/</a></p> </blockquote> <p>One of these categories is 'maps'. In the original training data for Newspaper Navigator, there were relatively few labelled examples of maps. The predictions for maps have an <a href="https://github.com/LibraryOfCongress/newspaper-navigator">Average Precision of 69.5%, and 34 images in the validation data</a>.</p> <p>This dataset contains a sample of these images which have been predicted as 'maps'. It also includes additional labels which indicate whether the predicted map image is a 'map' or 'not a map'. </p> <p>The data is organised as follows:</p> <ul> <li>The images themselves can be found in 'newspaper_maps.zip' </li> <li>`2020_30_10_13_19_228_sample.json` contains metadata about each image drawn from the Newspaper Navigator Dataset.</li> <li>map_labels.csv contains the labels for the images as a CSV file </li> </ul>
Supplementary Dataset for Detecting structured sources in noisy images via Minkowski maps
<p>Supplementary Dataset of the publication "<a href="https://dx.doi.org/10.1209/0295-5075/128/60001">Detecting structured sources in noisy images via Minkowski maps</a>" by the same authors:</p> <p>M. A. Klatt, K. Mecke. <em>EPL (Europhysics Letters)</em> <strong>128</strong>:60001 (2019)<br> <a href="https://dx.doi.org/10.1209/0295-5075/128/60001">https://dx.doi.org/10.1209/0295-5075/128/60001</a></p> <p>The dataset contains all simulated data and parameters of the figures and table in the main text and supplementary material.</p> <p>The corresponding code is available at the GitHub repository:<br> <a href="https://github.com/michael-klatt/minkmaps">https://github.com/michael-klatt/minkmaps</a></p>
T2 mapping of the sacroiliac joints in patients with axial spondyloarthritis
<p><strong>Purpose: </strong>To test whether T2 mapping of the sacro-iliac joints (SIJs) might help identifying patients with spondyloarthritis.</p> <p><strong>Method: </strong>This study included 20 biologic-naive patients with axial spondyloarthritis (10 females; mean age: 38 ± 9years; range, 19-47) and 27 controls (16 males; mean age = 39 ± 13years; range = 28-71) who prospectively underwent SIJs MRI at 1.5 T, including a multislice multiecho spin-echo sequence. Standard MRIs were reviewed to assess the SIJs according to the Assessment of SpondyloArthritis International Society (ASAS) criteria and SPondyloArthritis Research Consortium of Canada (SPARCC) MRI index. T2 maps obtained from multiecho sequences were used to draw regions of interests in the cartilaginous part of the SIJs. Disease activity was assessed using BASDAI questionnaire. Bland-Altman method, ROC curve analysis, Chi square, Mann-Whitney U, Pearson's and Spearman's correlation coefficient were used for data analysis.</p> <p><strong>Results: </strong>According to ASAS criteria, MRI was positive for sacroiliitis in 5/20 patients (25 %). Inter-observer reproducibility of T2 values was 87 % (coefficient of repeatability = 7.0; bias = 0.49; p < .001). Mean T2 values of patients (58.5 ± 4.4 ms, range: 52.6-68.2 ms) were significantly higher (p < .001) than those of controls (44.1 ± 6.6 ms, range: 33.6-67.2 ms). A T2 value of 52.51 ms yielded 100 % sensitivity and 91.7 % specificity to differentiate patients from controls. No statistically significant association/correlation was found between T2 values and BASDAI (r=-.026, p = .827), disease duration (r = .024, p = .871), SPARCC (r=-.004, p = .981), ASAS criteria (p = .476), HLA-B27-positivity (p = .139), age (r=-.2.53, p = .891), and gender (p = .404).</p> <p><strong>Conclusions: </strong>T2 relaxation times of the SIJs were significantly higher in patients than in healthy controls, making this tool potentially helpful to early identify patients with spondyloarthritis.</p>
Augmented emission maps: 1461 cc 81 kW Euro 6 diesel engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1461 cc 81 kW Euro 6 diesel engine that has been applied in the several Renault, Nissan and Mercedes-Benz models (Kadjar, Megane, Scenic, Talisman, Captur, Clio, Kangoo, Qashqai, Juke, Pulsar, NV200, Citan).</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 999 cc 45 kW Euro 3 petrol engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 999 cc 45 kW Euro 3 petrol engine that has been applied in the Hyundai.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1598 cc 85 kW Euro 6dT diesel engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1598 cc 85 kW Euro 6dT diesel engine that has been applied in the several Volkswagen group models (Volkswagen Golf, Golf Sportsvan, Touran and T-roc, Audi A1, A1 Sportback, A3, A3 Limousine, A3 Sportback and Q2, Seat Ibiza, Arona, Leon, Ateca and Toledo, Skoda Rapid, Octavia, Karoq). Note that the Hyundai/Kia engine with the same specifications (but a different alliance code) is not covered.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1995 cc 120 kW Euro 6b diesel engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1995 cc 120 kW Euro 6b diesel engine that has been applied in the BMW.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 2198 cc 74 kW Euro 5a diesel engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 2198 cc 74 kW Euro 5a diesel engine that has been applied in the Ford Transit and Tourneo.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)
<p>Dataset accompanying the paper "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)".</p>
Data from: Mapping molar shapes on signaling pathways
<p><span>A major challenge in evolutionary developmental biology is to understand how genetic mutations underlie phenotypic changes. In principle, selective pressures on the phenotype screen the gene pool of the population. Teeth are an excellent model for understanding evolutionary changes in the genotype-phenotype relationship since they exist throughout vertebrates. Genetically modified mice (mutants) with abnormalities in teeth have been used to explore tooth development. The relationship between signaling pathways and molar shape, however, remains elusive due to the high intrinsic complexity of tooth crowns. This hampers our understanding of the extent to which developmental factors explored in mutants explain developmental and phenotypic variation in natural species that represent the consequence of natural selection. Here we combine a novel morphometric method with two kinds of data mining techniques to extract data sets from the three-dimensional surface models of lower first molars: i) machine learning to maximize classification accuracy of 22 mutants, and ii) phylogenetic signal for 31 Murinae species. Major shape variation among mutants is explained by the number of cusps and cusp distribution on a tooth crown. The distribution of mutant mice in morphospace suggests a nonlinear relationship between the signaling pathways and molar shape variation. Comparative analysis of mutants and wild murines reveals that mutant variation overlaps naturally occurring diversity, including more ancestral and derived morphologies. However, taxa with transverse lophs are not fully covered by mutant variation, suggesting experimentally unexplored developmental factors in the evolutionary radiation of Murines.</span></p>
Mapping Africa's biodiversity: more of the same is just not good enough
Species distribution data are fundamental to the understanding of biodiversity patterns and processes. Yet, such data are strongly affected by sampling biases, mostly related to site accessibility. The understanding of these biases is therefore crucial in systematics, biogeography and conservation. Here we present a novel approach for quantifying sampling effort and its impact on biodiversity knowledge, focusing on Africa. In contrast to previous studies assessing sampling completeness (percentage of species recorded in relation to predicted), we investigate whether the lack of knowledge of a site attracts scientists to visit these areas and collect samples of species. We then estimate the time required to sample 90% of the continent under a constant sampling rate and the number of sampling events required to record ≥50% of the species. Using linear and spatial regression models, we show that previous sampling has been strongly influencing the resampling of areas, attracting repeated visits. This bias has existed for over two centuries, has increased in recent decades, and is most pronounced among mammals. It may take between 172 and 274 years, depending on the group, to achieve at least one sampling event per grid cell in the entire continent. Just one visit will however not be enough: in order to record ≥50% of the current diversity, it will require at least 12 sampling events for amphibians, 13 for mammals and 27 for birds. Our results demonstrate the importance of sampling areas that lack primary biodiversity data and the urgency with which this needs to be done. Current practice is insufficient to adequately classify and map African biodiversity; it can lead to incorrect conclusions being drawn from biogeographic analyses, and can result in misleading and self-reinforcing conservation priorities.
Data for "TrueEBSD: correcting spatial distortions in electron backscatter diffraction maps"
<p>Data for "TrueEBSD: correcting spatial distortions in electron backscatter diffraction maps" published in Ultramicroscopy.</p> <p>Journal DOI: <a href="https://doi.org/10.1016/j.ultramic.2020.113130">https://doi.org/10.1016/j.ultramic.2020.113130</a>;<br> Preprint DOI: <a href="https://arxiv.org/abs/1909.00347">https://arxiv.org/abs/1909.00347</a>.</p> <p>The zipped folder contains:</p> <ol> <li>Readme (text file)</li> <li>'Ti-64' data subfolder: data for one of the maps in the Ti-64 map stitching example</li> <li>'ZrH' data subfolder: data for the hydride-containing Zircaloy-4 example</li> <li>'CP-Zr' data subfolder: data for the in-situ deformed Zr example</li> <li> 'MATLAB scripts' subfolder: TrueEBSD source code.</li> </ol> <p> </p> <p>Each data subfolder contains:</p> <ul> <li>Input image files</li> <li>EBSD orientation files in Bruker CTF format</li> <li>'Outputs' subfolder containing output figures as image files.</li> </ul> <p>The 'MATLAB scripts' subfolder contains TrueEBSD source code:</p> <ul> <li>The primary user interface is 'input_deck.m'. Most user settings can be changed here. <ul> <li>The input deck entries here have been pre-filled for the Ti-64 dataset.</li> </ul> </li> <li>To use TrueEBSD, run 'input_deck.m' in MATLAB.</li> <li>The method is outlined in 'main.m', which calls functions in 'MATLAB scripts\code\'.</li> </ul>
SCYM Maize Yield Maps (Low Resolution) from Deines et al. 2021, Lobell et al. 2020
<p>Annual maize yield maps for the US Corn Belt for 1999-2018, aggregated to 10 km resolution. Yield maps are produced with the SCYM methodology at 30 m resolution using Landsat satellite data. The original high resolution yield maps are aggregated to a 10 km grid resolution to preserve privacy.</p> <p>Dataset creation is described in <a href="https://doi.org/10.1016/j.rse.2020.112174">Deines et al., 2020</a> in Remote Sensing of Environment (open access). The 2008-2018 map dataset described therein was extended back to 1999 for <a href="https://doi.org/10.1038/s43016-020-00165-w">Lobell et al. 2020</a> in Nature Food (<a href="https://rdcu.be/b9cXQ">read-only accessible version</a>). Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by <a href="https://www.nature.com/articles/s41597-020-00646-4">Wang et al. 2000</a> (data + manuscript are open access).</p> <p><strong>Metadata</strong></p> <p>Yield values are provided in metric tons/hectare. </p> <p>No Data value = -999 (grid cells outside of the study area + cells with <1% coverage of SCYM 30 m yield maps)</p> <p>Map projection: EPSG:5070, CONUS Albers Equal Area</p> <p> </p> <p> </p> <p>Preferred citation(s):</p> <p>Deines, J.M., R. Patel, S. Lang, W. Dado, & D.B. Lobell. 2020. A million kernels of truth: Insights in to scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt. <em>Remote Sensing of Environment. </em><a href="https://doi.org/10.1016/j.rse.2020.112174">https://doi.org/10.1016/j.rse.2020.112174</a></p> <p>D.B. Lobell, J.M. Deines, & S. Di Tomasso. 2020. Changes in the drought sensitivity of U.S. maize yields. <em>Nature F</em><em>o</em><em>o</em><em>d</em> 1:729-735. <a href="https://doi.org/10.1038/s43016-020-00165-w">https://doi.org/10.1038/s43016-020-00165-w</a></p> <p> </p>
FIGURES 143–144. Distribution maps for Sparianthis spp. 143 S in Revision of the spider genus Sparianthis Simon, 1880 (Araneae, Sparassidae Sparianthinae)
FIGURES 143–144. Distribution maps for Sparianthis spp. 143 S. beebei sp nov., S. caracarai sp. nov., S. granadensis (Keyserling), S. juruti sp. nov., S. magalopalpa (Caporiacco) and S. picta (Simon); 144 S. boraris sp. nov., S. chickeringi (Gertsch), S. crulsi (Mello-Leitão), S. humaita sp. nov., S. juazeiro sp. nov. and S. ravida (Simon).
Biodiversity Hotspots Map (English labels)
<p>This map displays the global <a href="https://zenodo.org/record/3261807#.X8_HgNhKg2x">Biodiversity Hotspots 2016.1 dataset</a>. The colors assigned to the hotspots are only used to distinguish adjacent hotspots and have no other meaning. The biodiversity hotspots represent terrestrial biodiversity only. The offshore lines are a cartographic device to group and highlight islands that are part of the same hotspots (e.g. Polynesia-Micronesia, Indo-Burma). The background image is from Natural Earth. This map is labelled with hotspot names in English; a version without text is also available: 10.5281/zenodo.4311831. </p> <p>There are currently <a href="https://www.cepf.net/node/1996">36 recognized biodiversity hotspots</a>. These are Earth’s most biologically rich—yet threatened—terrestrial regions.</p> <p>To qualify as a biodiversity hotspot, an area must meet two strict criteria:</p> <ul> <li>Contain at least 1,500 species of vascular plants found nowhere else on Earth (known as "endemic" species).</li> <li>Have lost at least 70 percent of its primary native vegetation.</li> </ul> <p>Many of the biodiversity hotspots exceed the two criteria. For example, both the Sundaland Hotspot in Southeast Asia and the Tropical Andes Hotspot in South America have about <strong>15,000</strong> endemic plant species. The loss of vegetation in some hotspots has reached a startling <strong>95</strong> percent.</p>
MAP 1 in Systematic revision of the North American velvet ant genus Invreiella Suárez (Hymenoptera: Mutillidae) with description of eleven new species
MAP 1. Geographic distribution of Invreiella species. MAP 2. Biogeographic distribution of Invreiella species (shapefiles from Morrone et al. 2017).
Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union in 2019 - monthly maps
<p>Here you can find monthly maps referred in publication 'Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union in 2019' by EFSA.</p> <p>Figure legend for all figures:<br> Monthly observations and samples from wild birds on the EFSA list of target species for 2019 by NUTS3 region. The green colour scale represents the number of wild bird observations from the target species, as per data provided by the EuroBirdPortal project. The black dots represent the number of wild bird samples from target species tested within the countries' AI passive surveillance programmes. Wild bird samples reported at NUTS2 level are not shown on these maps.</p>
FIGURE 1. Distribution map. B in One less monotypic genus in Leptophlebiidae (Ephemeroptera): A new species of Bessierus Thomas & Orth and new record from Brazil
FIGURE 1. Distribution map. B. doloris: Black circle, record from Brazil; Grey circles, records from French Guiana; Red circle, new record from Brazil. Bessierus riobranco sp. n.: Yellow star, type locality.
Data from: A method for mapping morphological convergence on three-dimensional digital models: the case of the mammalian saber-tooth
<p>Morphological convergence can be assessed through a variety of statistical methods. None of the methods proposed to date enable the visualization of convergence. All are based on the assumption that the phenotypes either converge, or do not. However, between species, morphologically similar regions of a larger structure may behave differently. Previous approaches do not identify these regions within the larger structures or quantify the degree to which they may contribute to overall convergence. Here we introduce a new method to chart patterns of convergence on three-dimensional models, deployed with the R function conv.map. The convergence between pairs of models is mapped onto them to visualize and quantify the morphological convergence. We applied conv.map to a well-known case study, the saber-tooth morphotype which has evolved independently among distinct mammalian clades, from placentals to metatherians. Although previous authors have concluded that saber-tooths kill using a stabbing 'bite' to the neck, others have presented different interpretations for specific taxa, including the iconic Smilodon and Thylacosmilus. Our objective was to identify any shared morphological features among the saber-tooths that may underpin similar killing behaviours. From a sample of 49 placental and metatherian carnivores, we found stronger convergence among saber-tooths than for any other taxa. We found that the morphological convergence is most apparent in the rostral and posterior parts of the cranium. The extent of this convergence implies similarity in function among these phylogenetically distant species. In our view this function is most likely the killing of relatively large prey by a stabbing bite.</p>
FIGURE 27. Maps. a in On the enigmatic genus Oectropsis Blanchard (Coleoptera, Cerambycidae Lamiinae): tribal allocation, synonymy, and new species
FIGURE 27. Maps. a) South America with bioregionalizations by Morrone (2015); b) Distribution of species of Oectropsis Blanchard, 1851.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.