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13,117 results for “Maps”
FIg. 1. Map of the central western coastline of Western Australia with known distribution of Urodacus butleri, n. sp. (solid circles). Inset A illustrates the continent of Australia and the area containing the known distribu- tion. Inset B illustrates an enlargement of Barrow Island with the distribution plotted.
FIg. 1. Map of the central western coastline of Western Australia with known distribution of Urodacus butleri, n. sp. (solid circles). Inset A illustrates the continent of Australia and the area containing the known distribu- tion. Inset B illustrates an enlargement of Barrow Island with the distribution plotted.
Fig. 1. Geographic distribution of bogidiellid amphipods. Large map: type localities of: 1, Bogidiella veneris n. sp.; 2, Bogidomma australis; 3, Xystriogidiella capricornea; and 4, Xystriogidiella juliani on the Australian continent (with kind permission of demis.nl). Insert map: global distribution of Bogidiellidae and several, possibly closely related taxa (modified after Koenemann and Holsinger, 1999).
Fig. 1. Geographic distribution of bogidiellid amphipods. Large map: type localities of: 1, Bogidiella veneris n. sp.; 2, Bogidomma australis; 3, Xystriogidiella capricornea; and 4, Xystriogidiella juliani on the Australian continent (with kind permission of demis.nl). Insert map: global distribution of Bogidiellidae and several, possibly closely related taxa (modified after Koenemann and Holsinger, 1999).
Spherical convergence maps dataset
<p>This dataset is composed of 60 spherical convergence maps (30 per class), separated into a training set of 40 maps and a testing set of 20 maps.</p> <p>As a preprocessing, we recommend to remove the mean of each map and to smooth them with a Gaussian symmetric beam of 3 arcmins (sphtfunc.smoothing in Healpy).</p> <p>This dataset was created by R. Sgier and T. Kacprzak based on the work in paper by Sgier R. et al. 2018, "Fast Generation of Covariance Matrices for Weak Lensing", arxiv.org/abs/1801.05745.</p> <p>This dataset is used in "DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications" arxiv.org/abs/1810.12186.</p>
Map of Sri Lanka, showing inscription sites
<p>Map of Sri Lanka, showing inscription sites.</p>
Simulation dataset for "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters"
<p>Simulated Illumina reads for SNP distance method evaluation and comparison used in the article "Computational pan-genome mapping and pairwise SNP-distance improve detection of Mycobacterium tuberculosis transmission clusters".</p> <p>Details for simulation can be found at https://gitlab.com/rki_bioinformatics/panpasco/tree/master/simulation_dataset.</p>
High resolution annual average air pollution concentration maps for the Netherlands
<p>Raster-based air pollution concentration maps for the Netherlands. The dataset consists of air pollution concentration maps for six pollutants (NO2, NO2background, NOx, PM2.5, PM2.5absorbance, PM10), covering the land mass of the Netherlands at 5m spatial resolution. The maps were calculated using the Land Use Regression models from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Several Python scripts used for data preparation and the model scripts creating the datasets are included. Use the free 7-Zip to uncompress. Uncompressed size: 78 GiB.</p> <p>A description of concepts, datasets and scripts is given in the manuscript "High resolution annual average air pollution concentration maps for the Netherlands" by Oliver Schmitz, Rob Beelen, Maciej Strak, Gerard Hoek, Ivan Soenario, Bert Brunekreef, Ilonca Vaartjes, Martin J. Dijst, Diederick E. Grobbee, and Derek Karssenberg. <em>Scientific Data</em> 6:190035 (2019). <a href="https://doi.org/10.1038/sdata.2019.35">https://doi.org/10.1038/sdata.2019.35</a></p> <p>The datasets are licensed under a Creative Commons license (CC-BY 4.0). The Python scripts are licensed under the MIT License.</p> <p>Contact: o.schmitz@uu.nl</p>
Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa
<p>The dataset contains input and output files for the following paper:</p> <p>Yann Forget, Michal Shimoni, Marius Gilbert and Catherine Linard. <em>"Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa"</em>. In Press. 2018.</p> <p>The source code used to produce the output files is available on <a href="https://github.com/yannforget/landsat-sentinel-fusion">Github.</a></p>
Permafrost and Ground Ice Map of Switzerland
<p>Save the layer file (.lyr) together with the other files (Shapefiles) in a common folder and load the layer file in a GIS or geoviewer application to see the map.</p> <p>If your GIS-Application does not support .lyr files you can define the visualization of the map by yourself using the field “indicator” in the shapefiles attribute table. This field contain the numbers -3; -2; -1; 0; 1; 5 and 9 which have the following meaning:</p> <p>-3 = permafrost < -3°C</p> <p>-2 = permafrost -2 to -3°C</p> <p>-1 = permafrost < -1 to -2°C</p> <p>0 = permafrost < 0 to -1°C</p> <p>1 = Ground temperatures 0 to +1°C (possible patchy permafrost)</p> <p>5 = potential ice-rich permafrost</p> <p>9= glacier</p> <p> </p> <p>The PGIM and legends can also be accessed online at www.slf.ch\pgim</p> <p>The shapefile contain a countrywide permafrost distribution map of Switzerland, indicating ground temperatures and ice content. A new representation of ground temperatures is achieved by distinguishing ice-poor and ice-rich permafrost in the modelling process. There is a very strong correlation of ground temperatures with elevation and potential incoming solar radiation in ice-poor and ice-free ground. The distribution of ice-rich permafrost was defined by modelling mass wasting processes and the integration of snow and ice into the ground caused by them. This approach yields a relatively accurate and largely unambiguous map. Permafrost occurrence is represented by two clearly defined classes: Zone 1 representing modelled ground temperatures and zone 2 indicating excess ground ice outside of zone 1. 58% of 92 validation sites could be definitively classified as having permafrost or no permafrost. If only ice-poor or –free ground is considered, this value reaches 90%.</p>
Association mapping identified novel candidate loci affecting wood formation in Norway spruce
<p>Genotypic data set for the association mapping in Norway spruce for wood formation and tracheid traits.</p>
Dakar population estimates at 100x100m spatial resolution - grid layer - Dasymetric mapping
<p>This dataset contains the a raster layer with the population estimates obtained using a dasymetric mapping procedure (top-down approach). For a detailed description of the methodology, please refer to the following paper:</p> <p>Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” <em>Data</em> 4 (1): 13. <a href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/data4010013</a>.</p> <p>Funding and aknowledgement: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be/">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p> <p>The authors gratefully thanks the \href{http://assess-sn.org/}{ASSESS project}, funded by the \href{https://www.ares-ac.be}{ARES-CDD}, that provided the access to the census data.</p>
UNICITY: A depth maps database for people detection in security airlocks
<p><strong>UNICITY: A depth maps database for people detection in security airlocks.</strong></p> <p>UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries, like for instance tailgating (when a person walks very close to another to get into a restricted area). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</p> <p><strong>Main Features:</strong></p> <ul> <li>UNICITY consists of 58k images using two depth sensors.</li> <li>65 recorded sequences with one or two people performing different behaviors such as attacks and tailgating.</li> <li>UNICITY also provides code for evaluation and visualization, and full annotation of people such as the location of head and shoulders.</li> <li>This new dataset is perfectly suited for training and adapting machine learning algorithms for video surveillance applications.</li> </ul> <p><strong>Citation</strong>:</p> <p>Please cite the following paper if you use the UNICITY dataset in your work (papers, articles, reports, books, software, etc):</p> <ul> <li>UNICITY: A depth maps database for people detection in security airlocks. J. Dumoulin, O. Canevet, M. Villamizar, H. Nunes, O.A. Khaled, E. Mugellini, F. Moscheni, and J.M Odobez. International Conference on Advanced Video and Signal-based Surveillance Workshop (AVSSW). November 2018.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>Joël Dumoulin, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Olivier Canévet, Idiap Research Institute, Martigny, Switzerland.</li> <li>Michael Villamizar, Idiap Research Institute, Martigny, Switzerland.</li> <li>Hugo Nunes, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Omar Abou Khaled, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Elena Mugellini, HumanTech Institute, HES-SO Fribourg, Switzerland.</li> <li>Fabrice Moscheni, Fastcom Technology SA, Lausanne, Switzerland.</li> <li>Jean-Marc Odobez, Idiap Research Institute, Martigny, Switzerland.</li> </ul> <p><strong>Acknowledgement:</strong></p> <p>The work was supported by Innosuisse, the Swiss innovation agency, through the UNICITY (3D scene understanding through machine learning to secure entrance zones) project.</p> <p><strong>Links:</strong></p> <p>Next links contain additional information about the dataset:</p> <ul> <li>Innosuisse UNICITY project: <a href="https://www.idiap.ch/en/scientific-research/projects/UNICITY">[link]</a></li> <li>Paper describing the dataset: <a href="http://publications.idiap.ch/index.php/publications/show/3939">[link]</a></li> <li>Video presenting the dataset: <a href="https://www.youtube.com/watch?time_continue=2&v=pGrnI12OhmA">[link]</a></li> <li>Paper using the dataset for counting people and detecting intrusions: <a href="http://michael-villamizar.com/avss18.html">[link]</a> <ul> <li>WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems.<br> M. Villamizar, A. Martinez-Gonzalez, O. Canevet and J-M. Odobez.<br> International Conference on Advanced Video and Signal-based Surveillance (AVSS) - 2018.</li> </ul> </li> </ul> <p><strong>Contact</strong>:</p> <p>For any questions, please contact:</p> <ul> <li>Michael Villamizar, Idiap Research Institute, Martigny -Switzerland</li> </ul>
Excel mapping tools for 2018 zoonoses data reporting
<p>The main objective of the mapping tool is to provide a simple and useable platform for MSs to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the DCF.</p> <p>The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.</p> <p>You can choose between the dynamic or the manual version of the tool.</p>
Systematic spatio-temporal mapping reveals divergent cell death pathways in three mouse models of hereditary retinal degeneration
<p>Values for immuohistochemical analysis or enzymatic analysis of various markers theorised to have roles in retinal dystrophies in three models of mouse retinal degeneration with a control. Analysis has been broken down by marker, mouseline, age and region of the retina recorded from</p>
India, political map 1857
<p>India, political map 1857</p>
Mapped reads for the 24h labeling experiment of Herzog et al., Nature Methods 2017
<p>This are the mapped reads for samples GSM2666816,GSM2666817,GSM2666818,GSM2731767,GSM2731768,GSM2731769 against the Mouse genome (mm10, gtf from Ensembl 90). Reads were mapped (after rRNA removel) using STAR:</p> <p>STAR --runMode alignReads --runThreadN 8 --genomeDir ens90.STAR-index --readFilesIn X.fastq --outSAMmode NoQS --outSAMattributes nM MD </p> <p>These are the test data for GRAND-SLAM (https://github.com/erhard-lab/gedi/wiki/GRAND-SLAM)</p>
Multi-sensor mapping and localization datasets
<p>This is a dataset for testing outdoors mapping and localization algorithms. It contains two Rosbag files that provide different measurements from an 3D Velodyne LIDAR, a visual sensor, an altimeter, an IMU, and Ultra-Wide-Band Time-of-Flight sensors, all mounted in an aerial vehicle. The ground-truth localization of the robot is given by a RTK GPS with accuracy of < 2cm. The transformations between sensor frames are also given in the bags. The flight experiments were conducted in an outdoors scenario near Seville, during September 2018.</p>
Barcelona light impact impact maps using ISS images
<p>Environmental impact maps of Barcelona based on the ISS image ISS035-e-023385 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>
Bodily maps of emotions are culturally universal
<p>Data from the manuscript "Bodily maps of emotions are culturally universal".</p> <p>Pheno.csv contains data for each subject as well as the amount of time in ms to paint each emotion and the amount of pixels painted.</p> <p>The folder "bodymaps" contains the 3954 bodymaps in matlab format.</p>
Mapping of the Faas Platforms
<p>To validate the FaaS reference architecture and gain insight into real-world FaaS platforms, we conducted a year-long process of identifying, mapping, and analyzing FaaS-like platforms, that is, platforms for resource, function, and workflow management.</p>
Gazetteer and map of place names in Eustorgio y Clorilene (1629)
<p>Output data for the project "Beyond the canon: the Byzantine genre in the Spanish baroque literature".</p> <p>The dataset contains a gazetteer of places (CSV) and a self-contained HTML file with a map of the places (frequencies and proximity) of the novel <em>Eustorgio y Clorilene</em> (1629).</p> <p>The dataset has been produced during a research visit at the Université de Genève funded by the Swiss National Science Foundation (SNSF).</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.