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13,117 results for “Maps”
83 Block--63k or 50k scale Maps of South Asia
<p>118 maps of the Survey of India covering block 83.</p> <p>In the time since this first batch of maps (see the list below) was posted additional maps of Block 83 have become available. These additional maps are posted on a separate page, and can be reached by clicking the link(s) below. Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 83.</p> <table> <tbody> <tr> <td><a href="../records/10460673">63k/50k Maps of South Asia--Block 83--additional 1--20240105</a></td> </tr> </tbody> </table>
78 Block--63k or 50k scale Maps of South Asia
<p>230 maps of the Survey of India covering block 78.</p> <p>In the time since this first batch of maps (see the list below) was posted additional maps of Block 78 have become available. These additional maps are posted on a separate page, and can be reached by clicking the link(s) below. Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 78.</p> <table> <tbody> <tr> <td><a href="../records/10460636">63k/50k Maps of South Asia--Block 78--additional 1--20240105</a></td> </tr> </tbody> </table>
Block 64--63k scale--Survey of India Maps
<p>448 maps of the Survey of India covering block 64.</p> <p>In the time since this first batch of maps (see the list below) was posted additional maps of Block 64 have become available. These additional maps are posted on a separate page, and can be reached by clicking the link(s) below. Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 64.</p> <table> <tbody> <tr> <td><a href="../records/10463330">63k/50k Maps of South Asia--Block 64--additional 1--20240105</a></td> </tr> <tr> <td><a href="../records/10463338">63k/50k Maps of South Asia--Block 64--additional 2--20240105</a></td> </tr> </tbody> </table>
Block 63--63k scale--Survey of India Maps
<p>416 maps of the Survey of India covering block 63.</p> <p>In the time since this first batch of maps (see the list below) was posted additional maps of Block 63 have become available. These additional maps are posted on a separate page, and can be reached by clicking the link(s) below. Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings in Block 63.</p> <table> <tbody> <tr> <td><a href="../records/10460507">63k/50k Maps of South Asia--Block 63--additional 1--20240105</a></td> </tr> <tr> <td><a href="10460513">63k/50k Maps of South Asia--Block 63--additional 2--20240105</a></td> </tr> </tbody> </table>
Mapping Catalyst–Solvent Interplay in Competing Carboamination/Cyclopropanation Reactions
<p>Selectivity and activity maps can be used to predict and rationalize reaction outcomes based on catalyst and solvent choice. Here, there utility is demonstrated by examining competing carboamination/cyclopropanation reactions catalyzed by Group 9 catalysts featuring cyclopentadienyl ligands, which reveals an interesting energetic interplay involving the role of the solvent and of the catalyst.</p>
Rainfall maps from commercial microwave link data for Ouagadougou, Burkina Faso
<p>Rainfall maps from commercial microwave link data for Ouagadougou, Burkina Faso, accompanying a submission to <em>Journal of Hydrometeorology </em>(<em>JHM</em>) are store in <em>CML_rainfall_maps_Ougadougou.nc</em> . The generation of these rainfall maps is described in the manuscript.</p> <p>The data are stored in a netCDF file with the dimensions <em>x</em>, <em>y</em> and <em>time</em>, <em>longitude</em> and <em>latitude</em> values defining the grid, and the variable <em>rain_rate </em>holding the rainfall maps.</p> <ul> <li>The dataset covers the area over Ouagadougou (longitude: -1.61 to -1.415 and latitude: 12.27 to 12.45) in a regular space grid of 72x79 with 0.55 km grid size.</li> <li>The temporal resolution is 5 minutes for the period from 2020-06-29 to 2020-10-10 <em> </em></li> </ul> <p>As supplementary material an animation (<em>Animation_2020_08_21.mp4</em>) showcases two rainfall events from 18:30 to 21:00 UTC on 2020-08-21.</p>
63k Maps of Burma
<p>A set of about 776 maps of Burma produced by the Survey of India before and during the Second World War at a scale of 1 inch per mile (63,360). Many of these maps are military reprints of earlier maps produced before the war.</p> <p> </p> <p>The file naming convention is to first give the <strong><em>number</em></strong> of the 4 degree x 4 degree block followed by the <strong><em>letter (A to P)</em></strong> of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by a "]" symbol to indicate a slash and then a <strong><em>number</em></strong> from 01 to 16 to indicate the number of the map in the 1 degree block. This <strong><em>Number Letter Number</em></strong> designation is followed by the name of one of the administrative districts encompassed by the map, followed by the <strong><em>date of publication</em></strong> in parenthesis.</p> <p>A Topographical Index produced by the Survey of India is provided to assist the viewer in selecting a particular map of interest.</p> <p>****************</p> <p>In the time since this first batch of 63k Burma maps (see the list below) were posted, additional maps of Burma have become available. These additional maps are posted on a separate page, and can be reached by clicking the link(s) below. Included in the final set of additional maps is an Index Map showing the total 63k/50k holdings for Burma.</p> <table> <tbody> <tr> <td><a href="../records/10463372">63k Maps of Burma--additional 1--20240105</a></td> </tr> </tbody> </table> <p> </p>
Data for detailed temporal mapping of global human modification from 1990 to 2017
<p>Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 1995, 2000, 2005, 2010, 2015, and 2017 to evaluate temporal changes and spatial patterns of land use modification of terrestrial lands (excluding Antarctica). These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km^2 resolution), temporally consistent (for 1990-2015, every 5 years), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset that represents ~2017 conditions and has 14 stressors for an even more comprehensive dataset, but the 2017 results should not be used to calculate change with the other datasets (1990-2015). <strong>Note that because of repo file size limits, the datasets for the for the HM overall for 1990 and 1995, as well as major stressors for all years, are located <a href="https://drive.google.com/drive/folders/1D1-S_IuPuPrduBSwCiRfY8b4kPjtho2f?usp=drive_link">this</a> Google Drive. </strong></p> <p>This version 1.5 provides the following updates:</p> <ol> <li> <p>Datasets are provided for each of the 6 stressor groups: built-up areas (BU), agricultural/timber harvest (AG), extractive energy and mining (EX), human intrusions (HI), natural system modifications (NS), and transportation & infrastructure (TI), available now at 300 m resolution for each of the time steps in the 1990-2015 time series.</p> </li> <li> <p>It provides the addition datasets for the years 1995 and 2005, calculated using linear interpolation when stressor data do not provide data at the specific year.</p> </li> <li> <p>The ESA 150 m water-mask dataset (<a href="https://www.mdpi.com/2072-4292/9/1/36">Lamarche et al. 2017</a>) was used to provide better and more consistent alignment of datasets at the ocean-land-inland water interfaces.</p> </li> <li> <p>The built-up stressor uses an updated version of the Global Human Settlement Layer (v2022A).</p> </li> <li> <p>Values provided are 32-bit floating point values, with human modification values ranging from 0.0 to 1.0.</p> </li> </ol> <p>For more details on the approach and methods, please see: Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020.</p> <p>Version 1.5 was completed in collaboration with the Center for Biodiversity and Global Change at Yale University and supported by the E.O. Wilson Biodiversity Foundation. </p>
Lanelet2 for nuScenes: Map Dataset
<p><a href="https://github.com/fzi-forschungszentrum-informatik/Lanelet2">Lanelet2</a> maps for the <a href="http://www.nuscenes.org/nuscenes">nuScenes</a> dataset, which enable the usage of diverse map-based anchor paths and spatial semantic information. For details see our <a href="https://openaccess.thecvf.com/content/CVPR2023W/E2EAD/html/Naumann_Lanelet2_for_nuScenes_Enabling_Spatial_Semantic_Relationships_and_Diverse_Map-Based_CVPRW_2023_paper.html">paper</a> and <a href="https://felixhertlein.github.io/lanelet4nuscenes/">project page</a>. We also provide a <a href="https://pypi.org/project/lanelet2anchors/">pip package</a> to facilitate the usage.</p> <p>The maps were generated automatically and subsequently manually refined.</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@InProceedings{naumannHertleinLanelet2NuScenes2023, author = {Naumann, Alexander and Hertlein, Felix and Grimm, Daniel and Zipfl, Maximilian and Thoma, Steffen and Rettinger, Achim and Halilaj, Lavdim and Luettin, Juergen and Schmid, Stefan and Caesar, Holger}, title = {Lanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-Based Anchor Paths}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {3247-3256} }</code></pre>
Cortical topographic motifs emerge in a self-organized map of object space
<p>The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflects a smooth mapping of a unified representational space.</p>
Data from: Maximum mutational robustness in genotype-phenotype maps follows a self-similar blancmange-like curve
<div class="section abstract"> <p>Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organised as bricklayer's graphs, so called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype (GP) maps for RNA secondary structure and the HP model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayer's graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.</p> </div>
High-resolution global map of closed-canopy coconut palm
<p>The file ‘GlobalCoconutLayer_2020_v1-2.zip’ contains 878 raster tiles of 100x100 km in geotiff format. The raster files are the result of a convolutional neural network that classified Sentinel-1 and Sentinel-2 annual composites into a coconut palm layer for the year 2020. The images have a spatial resolution of 20 meters and contain two classes: <br> [0] Other land covers that are not coconut palm.<br> [1] Coconut palm.</p> <p>The file ‘GlobalCoconutLayer_2020_densityMap_1km_v1-2.zip’ contains the 20-meter coconut palm classification aggregated to 1 km. The value of each pixel represents the coconut palm area (in squared meters) within the 1-km pixel. </p> <p>The file ‘Validation_points_GlobalCoconutLayer_2020_v1-2.shp’ includes the 10,200 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the class assigned by visual interpretation of sub-meter resolution images, and the attribute ‘predClass’, which reflects the predicted class by the convolutional neural network. The ‘predClass’ values are the same as the raster files:<br> [0] Other land covers that are not coconut palm.<br> [1] Coconut palm.<br> The attribute ‘Class’ contains the following values: <br> [0] Land cover could not be determined because sub-meter resolution data was not available.<br> [1] Other land covers that are not coconut palm.<br> [2] Sparse coconut palm. Low density of coconut palms; between 1 and 4 coconut palms within the 20-meter pixel.<br> [3] Dense open-canopy coconut palm; more than 4 coconut palms within the 20-meter pixel but coconut trees do not reach the full canopy closure. <br> [4] Closed -canopy coconut palm; more than 4 coconut palms within the 20-meter pixel and coconut palms fully cover the ground.<br> [5] Palm species that are not coconut palm.</p> <p> </p> <p>Changelog v1-2:</p> <p>- Pixels classified as class ‘coconut’ were reclassified to class ‘other’ in West Bengal.</p>
[HCB] BioImage Town (BIT) FAIR Data Metro Map
<p>Figures created collaboratively by the presenters of the Data Management and Analysis session of ELMI2023 (https://elmi2023.eu/) for their presentations. They represent an idealized metro through which data ("the passengers") travel between various solutions ("the stops") within bioimaging ("BioImage Town"), but also connecting to IT solutions, metadata, and other areas, though of course the real situation is much more complicated. Working together, we should be able to the improve the number of easy-to-use, performant, and complete solutions through BioImage Town for the benefit of the community.</p> <p>See previous version at https://zenodo.org/record/8019760</p> <p> </p>
Land Water Transition Zone Change Detection - Hydroperiod Maps - WQeMS raster products
<p>Within this dataset, Hydroperiod maps of the Polyphytos open surface water reservoir in Greece and Giaretta reservoir in Italy are available in GeoTIFF raster format. The maps provide information about the total number of days each pixel is inundated within a specific time period.They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project. Each raster file in the dataset is named according to the water body and period during which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps which were provided as input for the production of the hydroperiod maps.</p>
Land Water Transition Zone Change Detection - Transition Maps between two dates - WQeMS raster products
<p>Within this dataset, land water transition zone maps, which indicate the land-water transition between two instances in time, of the Polyphytos open surface water reservoir in Greece and Giaretta reservoir in Italy, are available in GeoTIFF raster format. The maps depict the transition zones by indicating the change of the pixel status from non-inundated to inundated and vice versa between two provided dates. They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project. Each raster file in the dataset is named according to the water body and the two dates during which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps on the two dates.</p>
Taxa mapping
<p>The dataset consists of the longitudinal and latitudinal coordinates of bird and mammal pollinator species of India.</p>
Weather Data Cutouts for the Wasserstoffatlas (Hydrogen Map)
<p>The cutouts are required to calculate the solar PV and wind onshore feed-in profiles of the <a href="http://wasserstoffatlas.de">Wasserstoffatlas</a>.</p> <p>The provided <strong>cutouts </strong>are spatiotemporal subsets of the European weather data from the <a href="https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation">ECMWF ERA5</a> reanalysis dataset and the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002">CMSAF SARAH-2</a> solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the <a href="https://github.com/PyPSA/atlite">atlite</a> tool (<a href="https://atlite.readthedocs.io/">https://atlite.readthedocs.io/</a>).</p> <p><strong>ECMWF ERA5</strong></p> <ul> <li><strong>Source: </strong><a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview</a></li> <li><strong>Terms of Use: </strong><a href="https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/20180314_Copernicus_License_V1.1.pdf</a></li> </ul> <p><strong>CMSAF SARAH-2</strong></p> <ul> <li>Pfeifroth, Uwe; Kothe, Steffen; Müller, Richard; Trentmann, Jörg; Hollmann, Rainer; Fuchs, Petra; Werscheck, Martin (2017): Surface Radiation Data Set - Heliosat (SARAH) - Edition 2, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/SARAH/V002, <a href="https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002">https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002</a>.</li> <li><strong>Terms of Use:</strong> <a href="https://www.eumetsat.int/cs/idcplg?IdcService=GET_FILE&dDocName=pdf_leg_data_policy&allowInterrupt=1&noSaveAs=1&RevisionSelectionMethod=LatestReleased">https://www.eumetsat.int/cs/idcplg?IdcService=GET_FILE&dDocName=pdf_leg_data_policy&allowInterrupt=1&noSaveAs=1&RevisionSelectionMethod=LatestReleased</a></li> </ul>
XRF mapping of painting reconstruction, inspired by Johannes Vermeer's "Girl with a Pearl Earring"
<p>This dataset contains the XRF mapping of painting reconstruction, inspired by Johannes Vermeer’s “Girl with a Pearl Earring” produced in the Microchemistry and Microscopy Art Diagnostic Laboratory (M2ADL) of the University of Bologna (2018). The painting was obtained by applying pigments mixed with linseed oil on a commercial canvas. Pigments were selected considering their elemental signals detectable by XRF spectroscopy. In a more detail, azurite (Cu<sub>3</sub>(CO<sub>3</sub>)<sub>2</sub>(OH)<sub>2</sub>) was used for the creation of the veil, lead white ((PbCO<sub>3</sub>)<sub>2</sub>·Pb(OH)<sub>2</sub>) for the flesh tone and vermilion (HgS) for the lips. The XRF map was collected with the micro-XRF portable spectrometer (<em>ELIO</em>) produced by BRUKER. The XRF system is equipped with a rhodium-target X-ray tube. The voltage range is between 10 and 50 kV, and the anode current range is between 5 and 200 μA. The X-ray fluorescence is revealed by a Peltier-cooled silicon drift detector (SDD) with CUBE technology, with an active area of 50 mm<sup>2</sup>. The typical energy resolution for Mn-Kα radiation is <140 eV. A motorized XY stage is mounted on a tripod for mapping analysis with a total travel of 100 mm x 100 mm. The collimator used for the analysis was of 0.5 mm.</p> <p>Please, cite as:</p> <p>R. Rocha de Oliveira, C. Malegori, G. Sciutto, P. Oliveri<br><strong>PoliBrush – A user-friendly software to aid multivariate image analysis dissemination</strong><br><em>Chemometrics and Intelligent Laboratory Systems</em>, 240 (2023) 104918<br><a href="https://doi.org/10.1016/j.chemolab.2023.104918">https://doi.org/10.1016/j.chemolab.2023.104918</a></p> <p> </p> <p>You may also be interested in:</p> <p> <strong>NIR-HSI Venus dataset</strong><br> <a href="https://www.doi.org/10.5281/zenodo.8143550">10.5281/zenodo.8143550</a></p> <p> <strong>PoliBrush</strong><br> <em>A freely distributed software for exploratory multivariate analysis in RGB and spectral imaging</em><br> <a title="PoliBrush" href="https://doi.org/10.5281/zenodo.8143341" target="_blank" rel="noopener">10.5281/zenodo.8143341</a></p> <p> </p>
Fig. 8 – Distribution maps. a in Five new species of the genus Griburius Haldeman from Central America (Coleoptera: Chrysomelidae, Cryptocephalinae)
Fig. 8 – Distribution maps. a, Griburius febriculosus; b, G. gracilis; c, G. mokaya; d, G. puncturatus; e, G. textus.
Maps and Validation result for LULC map of Pakyong sub-division, East Sikkim
<p>The repository contains the maps and images pertaining to the study area and the validation results.</p>
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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.