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13,117 results for “Maps”
1989 General Landcover Map of the Eastern Shore of Virginia
This generalized landcover classification of the Eastern Shore of Virginia focuses on major landforms and habitats of the Virginia Coast Reserve LTER. The classification is based on a Landsat 5 Thematic Mapper (TM) satellite image taken on July 25, 1989 at 15:14 GMT (11:14 EDT), which is roughly concurrent with mid-tide water levels as predicted for Wachapreague, VA (L=8:25, H=14:52 EDT). The classification divides the landscape into three components: water (code 1); salt marsh (code 2); and upland/mainland areas (code 3). The primary purpose of this dataset is to provide a background map layer over which VCRLTER researchers and students can plot individual research site locations for figures intended for publications and presentations.
Combined three-dimensional Milky Way extinction map (2019)
<p>This file contains an updated version of the combined Milky-Way extinction map from Bovy, Rix, Green et al. (2015) (see the Appendix) in HDF5 format. The component of the original dust map coming from Pan-STARRS data was updated to the Pan-STARRS dust map from Green et al. (2019). The format is similar to that described here: http://argonaut.skymaps.info/usage, except that no samples are included (so only the /best_fit attribute and other supporting ones are included). This file is downloaded and read by the https://github.com/jobovy/mwdust code.</p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event S191205ah
<p>Attached in a .json file is the completed pointing information for 12 observation(s) for the EM counterpart search associated with the gravitational wave event S191205ah.</p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event S191216ap
Attached in a .json file is the completed pointing information for 30 observation(s) for the EM counterpart search associated with the gravitational wave event S191216ap. These observations were taken on the Sinistro, and Spectral instruments.
Mapping of nanoparticles diffusion in cells
<p>We mapped the diffusion of particles in the cytoplasm. To do so, we electroporated QDs-SB particles in cells as shown in Debayle et al 2019. Here we were using RPE1 cells. We present the recording of three cells and the scripts that we used to generate some maps of diffusion in the cell.</p> <p>The scripts were made to use the output tracks from Slimfast 1.103e but can be adapted by changeing the beginning of the script.</p> <p>Some functions were found in Matlab Central and their corresponding license is in their respective folders.</p>
GIS Map of project 'Skopje 2014'
<p>This is the GIS file, data, metadata and a first series of maps that were produced through research on project 'Skopje 2014' (REPLICIAS MSCA grant). The information was collected through personal survey, newspaper sources, Prizma's research (BIRN), the Association of Architects of North Macedonia, and online forums. </p> <p>Researchers are welcome to further enrich this and return new versions. Attribution is necessary. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.</p>
Regional Land Use mapping code and dataset for Early and Middle Neolithic Ireland (3750-3600 cal BC and 3600-3400 cal BC, respectively)
<p>Dataset and mapping code associated with an example of regional land use mapping for prehistoric Ireland, produced for Fig 5 of paper by S. Harrison et al. '<em>Development and testing of scenarios for implementing Holocene LULC in Earth System Model Experiments'</em> submitted for publication to Geoscientific Model Development. Data points derive from <sup>14</sup>C dated archaeological sites and distributions of settlements and monuments that have been assigned to each archaeological period following the dataset published in McLaughlin et al. (2016).</p>
Paper Repository and References for "Early software defect prediction: A systematic map and review"
<p>Context: Software defect prediction is a trending research topic, and a wide variety of the published papers focus on coding phase or after. A limited number of papers, however, includes the prior (early) phases of the software development lifecycle (SDLC).<br> Objective: The goal of this study is to obtain a general view of the characteristics and usefulness of Early Software Defect Prediction (ESDP) models reported in scientific literature. <br> Method: A systematic mapping and systematic literature review study has been conducted. We searched for the studies reported between 2000 and 2016. We reviewed 52 studies and analyzed the trend and demographics, maturity of state-of-research, in-depth characteristics, success and benefits of ESDP models. <br> Results: We found that categorical models that rely on requirement and design phase metrics, and few continuous models including metrics from requirements phase are very successful. We also found that most studies reported qualitative benefits of using ESDP models.<br> Conclusion: We have highlighted the most preferred prediction methods, metrics, datasets and performance evaluation methods, as well as the addressed SDLC phases. We expect the results will be useful for software teams by guiding them to use early predictors effectively in practice, and for researchers in directing their future efforts.</p>
Dar Es Salaam Very-High-Resolution Land Cover Map
<p>This is a very-high-resolution land cover map of Dar es Salaam derived from satellite imagery (Pleiades, 0.5m resolution). The majority of the area is classified from a 2016 (July) image while a small part of it from two images collected in January and March 2018, respectively.</p> <p> The pixel values related to the following legend:</p> <p>5=tree<br> 8=shadow<br> 3=artificial ground surface<br> 4=low vegetation<br> 2=water<br> 7=bare ground<br> 1=building<br> 113=high elevated buildings<br> 112=medium elevated buildings<br> 111=low elevated buildings</p> <p>The Out of Bag error of the product is 6,38% with the following class errors:</p> <p>Building = 0.035826</p> <p>Water = 0.049934</p> <p>Artificial Ground Surface = 0.077108</p> <p>Low Vegetation = 0.108709</p> <p>Tall Vegetation = 0.062278</p> <p>Bare Ground = 0.13803</p> <p>Shadow = 0.019872</p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p>This dataset was produced in the frame of 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>
Map 1 in On the Staphylinidae of the Greek island Samothraki (Insecta Coleoptera)
Map 1: Geographic position of Samothraki in the Aegean Sea, Greece.
Lyman-alpha tomographic map of the large-scale matter distribution using the eBOSS - Stripe 82 data
<p><strong>Lyman-alpha tomography map using DR16 eBOSS data</strong></p> <p>This data release contains products associated to the Lyman-alpha large-scale tomographic map realized with the 16<sup>th</sup> data release of SDSS-eBOSS. The densest Lyman-alpha forest set of eBOSS in the Stripe 82 field is used : 220 deg<sup>2</sup> wide field with a 37 deg<sup>-2</sup> density. A Lyman-alpha flux contrast map over a volume of 0.94 h<sup>-3</sup>Gpc<sup>3</sup> is obtained. Voids and protoclusters are detected in this portion of the sky.</p> <p>For more details : see https://arxiv.org/abs/2004.01448</p> <p>All the data detailed below can be opened by using the short python script read_data_release.py.</p> <p><strong>Pixels and map data</strong></p> <p>The pixel file pixels_lya_tomography_stripe82.bin was used to create the Lya tomographic map map_lya_tomography_stripe82.bin by using the dachshund algorithm (https://github.com/caseywstark/dachshund).</p> <p>Pixel and map file are in the binary numpy format. Pixels are created from 8999 Lya forests positioned from -43° to +45° in the RA(J2000) direction, from -1.25° to +1.25° in the DEC(J2000) direction and from z=2.1 to z=3.2. The (RA,DEC,z) coordinates are converted to (X,Y,Z) coordinates in Mpc.h<sup>-1</sup>. The pixel file shares the same origin than the map. Each pixel is defined by a set of five floats corresponding to (X, Y, Z, σ, 𝛿) where 𝛿 is the Lyman-alpha flux contrast and σ its associated error based on pipeline noise.</p> <p>The map is also a binary numpy file. It contains a (6354,181,834) Mpc.h<sup>-1</sup> cube of Lyman-alpha flux contrast with a pixel shape (2928,90,417). The origin of the map (0,0,0) corresponds to the coordinates RA=-43°, DEC=-1.25° and z=2.1 and in the cube coordinate to (X,Y,Z)=(0,0,0) Mpc.h<sup>-1</sup>.</p> <p>Note that to obtain this map, the pixel file was first separated to parallelize the tomographic procedure. In comparison to the map detailed in the linked article, this map is rebinned to be less voluminous. Furthermore, a mask is applied to the map where the distance to the nearest line-of-sight of the pixel file is below 20 Mpc.h<sup>-1</sup>. At these locations, map flux contrast is put to 0.</p> <p> </p> <p><strong>Derived catalog data</strong></p> <p>Voids and proto-cluster searches are applied to the tomographic map. The results of this procedure, along with additional catalog cut is given in this data release. Catalogs of voids (catalog_voids_lya_stripe82_*.fits) and proto-clusters (catalog_protoclusters_lya_stripe82_*.fits) are given in the (X,Y,Z) and (RA,DEC,z) coordinates of the map, sharing the same origin. The catalogs are in the FITS format which can be opened with the fitsio python library.</p>
2013 mapping survey of South African laboratories
<p>Mapping survey of laboratory facilities in South Africa conducted in 2013 as part of the ASSAf project: The State of Biosafety and Biosecurity in South Africa (see <a href="https://www.assaf.org.za/files/reports/evidence_based/Prelims.pdf">https://www.assaf.org.za/files/reports/evidence_based/Prelims.pdf</a>)</p>
soilmap_simple: a simplified and standardized derivative of the digital soil map of the Flemish Region
<p>The data source <code>soilmap_simple</code> is a simplified and standardized derived form of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a>' (the shapefile of which we named <code>soilmap</code>, for analytical workflows in R) published by 'Databank Ondergrond Vlaanderen’ (DOV). It is a GeoPackage that contains a <strong>spatial polygon layer</strong> ‘<code>soilmap_simple</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>), plus a <strong>non-spatial table</strong> ‘<code>explanations</code>’ with the meaning of category codes that occur in the spatial layer. Further documentation about the digital soil map of the Flemish Region is available in Van Ranst & Sys (2000) and Dudal et al. (2005).</p> <p>This version of <code>soilmap_simple</code> was derived from version '<code>soilmap_2017-06-20</code>' (<a href="https://doi.org/10.5281/zenodo.3387008">Zenodo DOI</a>) as follows:</p> <ul> <li>all attribute variables received English names (purpose of standardization), starting with prefix <code>bsm_</code> (referring to the 'Belgian soil map');</li> <li>attribute variables were reordered;</li> <li>the values of the morphogenetic substrate, texture and drainage variables (<code>bsm_mo_substr</code>, <code>bsm_mo_tex</code> and <code>bsm_mo_drain</code> + their <code>_explan</code> counterparts) were filled for most features in the 'coastal plain' area. <ul> <li>To derive morphogenetic texture and drainage levels from the geomorphological soil types, a conversion table by Bruno De Vos & Carole Ampe was applied (for earlier work on this, see Ampe 2013).</li> <li>Substrate classes were copied over from <code>bsm_ge_substr</code> into <code>bsm_mo_substr</code> (<code>bsm_ge_substr</code> already followed the categories of <code>bsm_mo_substr</code>).</li> </ul> These steps coincide with the approach that had been taken to construct the <code>Unitype</code> variable in the <code>soilmap</code> data source;</li> <li>only a minimal number of variables were selected: those that are most useful for analytical work.</li> </ul> <p>See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/b3c6696/src/generate_soilmap_simple">'n2khab-preprocessing' at commit b3c6696</a> for the creation from the <code>soilmap</code> data source.</p> <p>A reading function to return <code>soilmap_simple</code> (this data source) or <code>soilmap</code> in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>.</p> <p><strong>The attributes</strong> of the spatial polygon layer <code>soilmap_simple</code> can have <code>mo_</code> in their name to refer to the <em>Belgian Morphogenetic System</em>:</p> <ul> <li><code>bsm_poly_id</code>: unique polygon ID (numeric)</li> <li><code>bsm_region</code>: name of the region</li> <li><code>bsm_converted</code>: boolean. Were morphogenetic texture and drainage variables (<code>bsm_mo_tex</code> and <code>bsm_mo_drain</code>) derived from a conversion table (see above)? Value <code>TRUE</code> is largely confined to the 'coastal plain' areas.</li> <li><code>bsm_mo_soilunitype</code>: code of the soil type (applying morphogenetic codes within the coastal plain areas when possible, just as for the following three variables)</li> <li><code>bsm_mo_substr</code>: code of the soil substrate</li> <li><code>bsm_mo_tex</code>: code of the soil texture category</li> <li><code>bsm_mo_drain</code>: code of the soil drainage category</li> <li><code>bsm_mo_prof</code>: code of the soil profile category</li> <li><code>bsm_mo_parentmat</code>: code of a variant regarding the parent material</li> <li><code>bsm_mo_profvar</code>: code of a variant regarding the soil profile</li> </ul> <p>The <strong>non-spatial table</strong> <code>explanations</code> has following variables:</p> <ul> <li><code>subject</code>: attribute name of the spatial layer: either <code>bsm_mo_substr</code>, <code>bsm_mo_tex</code>, <code>bsm_mo_drain</code>, <code>bsm_mo_prof</code>, <code>bsm_mo_parentmat</code> or <code>bsm_mo_profvar</code></li> <li><code>code</code>: category code that occurs as value for the corresponding attribute in the spatial layer</li> <li><code>name</code>: explanation of the value of <code>code</code></li> </ul>
Dataset for Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals
<p>Dataset of paper "Radio-based Sensing and Indoor Mapping with Millimeter-Wave 5G NR Signals" presented in International Conference on Localization and GNSS (ICL-GNSS) 2020.</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted at an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing.</p> <p>The file "indoorMapping_processing.m" shows how to process and plot the shared data.</p> <p> </p> <p> </p>
Showcase video of hourly RADOLAN and CML rainfall maps
<p>Supplementary material to Graf et al. (2020): Video of a 48 hour showcase of RADOLAN-RW and CML derived rainfall maps from 12.05.2018 - 14.05.2018 with an hourly resolution. RADOLAN-RW is a rainfall product made available by the German Weather Service and CML data is derived with an automated data acquisition from Ericsson, Germany (Chwala et al. 2016). </p> <p>Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020.</p> <p>Chwala, C., Keis, F., and Kunstmann, H.: Real-time data acquisition of commercial microwave link networks for hydrometeorological applications, Atmos. Meas. Tech., 9, 991–999, https://doi.org/10.5194/amt-9-991-2016, 2016.</p>
Solotvyno hazard & risk maps_ImProDiReT-783232_EN
<p>As a result of the analysis of geological natural environment of Solotvyno, several natural and anthropogenic processes those are potentially dangerous for the population have been identified: karst and suffosion (subsidence, sinkholes, collapses), seasonal and flash floods, flooding, slope erosion, landslides.</p> <p>A set of hazards / risk maps has been elaborated based on expert complex assessment of the natural and anthropogenic hazardous processes manifestations:</p> <p>1. Inventory map of hazardous technogenic-geological and engineering-geological processes manifestations and phenomena for Solotvyno</p> <p>2. Zonation of the hazardous technogenic-geological and engineering-geological processes manifestations;</p> <p>3. Specific land use for Solotvyno;</p> <p>4. Category of land for Solotvyno (according to StateGeoCadastre data);</p> <p>5. Risk Map of Natural and Natural-Antropogenic Hazards for Solotvyno;</p> <p>6. Risk Map of Natural and Natural-Antropogenic Hazards for Solotvyno (with critical infrastructure objects).</p> <p> </p>
Conformational maps of human 20S proteasomes reveal PA28- and immuno-dependant inter-ring crosstalks
<p>Data and R scripts associated to the HDX-MS analysis of the 20S core particule and its PA28 regulators:</p> <p>Here, we used for the first time Hydrogen-Deuterium eXchange coupled to Mass Spectrometry (HDX-MS) to investigate conformational differences between the human standard 20S (std20S) and immuno 20S (i20s) proteasomes alone or in complex with PA28αβ or PA28γ activators. Their solvent accessibility (in the form of deuteration kinetics) were analysed through a dedicated bioinformatic pipeline including stringent statistical analysis and 3D visualisation. These data confirmed the existence of allosteric differences between the std20S and i20S at the surface of the α-ring triggered from inside the catalytic β-ring. Additionally, binding of the PA28 regulators to the 20S proteasomes modified solvent accessibility due to conformational changes of the β-rings. This work is not only a proof of concept that HDX-MS can be used to get structural insights on large multi-protein complexes in solution, it also demonstrates that the binding of the std20S or i20S subtype to any of its PA28 activator triggers allosteric changes that are specific to this 20S/PA28 pair, in terms of the regions affected. </p>
Figure 2 in Mapping the terrestrial reptile distributions in Oman and the United Arab Emirates
Figure 2. The Persian Wonder Gecko Teratoscincus keyserlingii photographed near Jebel Ali, Dubai.
Map 1 in First record of Caliscelis (Caliscelis) bonellii (L , 1807) (Hemiptera: Fulgoromorpha: Caliscelidae) from Turkey, with redescription of the species
Map 1: Distribution of Caliscelis bonellii in Turkey.
Map 1 in An enigmatic new genus and species of Anthophagini from Georgia (Coleoptera: Staphylinidae: Omaliinae)
Map 1: Distribution of Caucanthobium mirandum.
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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.