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13,117 results for “Maps”
Text-fig. 1. Map of the early Eocene Okanagan Highlands British Columbia, Canada and Washington, United States of America. Modified from Archibald et al. 2014: fig. 2, courtesy of Bruce Archibald. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 1. Map of the early Eocene Okanagan Highlands British Columbia, Canada and Washington, United States of America. Modified from Archibald et al. 2014: fig. 2, courtesy of Bruce Archibald.
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site.
Repository: Rayleigh-wave attenuation and phase velocity maps of the greater Alpine region from ambient noise
<p><br>Repository organized by Henrique Berger Roisenberg for the paper Roisenberg et al. (2024). The files are organized as follows:</p> <p><strong>Folders:</strong></p> <p><strong>-dispersion_curves:</strong><br>inside this folder there is a .zip file that contains all the dispersion curves calculated;</p> <p><strong>-attenuation:</strong><br>comprising three files with the results of attenuation calculations, i.e., the attenuation values, the grid, and the periods;</p> <p><strong>-c:</strong><br>comprising three files with the results of phase velocity calculations, i.e., the phase velocity values, the grid, and the periods;</p> <p><strong>-scripts: </strong><br>contains two python scripts, one called 'figures' to plot the figure 1, 4, and 6 of the paper, and another called 'alparray_computations' to perform the computations with the original alparray data, using seislib, resulting on the figures 2, 3, and 5 of the paper.</p> <p>Inside the folder '<strong>inputs</strong>' there are three folders that serve as input for the figures of the paper, to be used in the scripts. These are:</p> <p><strong>-raster: </strong><br>contains the topography raster used to plot the map of the study area;</p> <p><strong>-shapefiles:</strong><br>contains the shapefiles used in the regionalization analysis;</p> <p><strong>-station locations: </strong><br>contains the latitudes and longitudes of the stations used in this study.</p> <p> </p>
Mapping of swMATH ids and Software Heritage ids
<p>We present the mapping from swMATH identifiers and software heritage identifiers for selected git repositories, with the following fields:</p> <p><strong>swmathid</strong>: Unique identifier from swMATH. Prefix with https://swmath.org/software/ to visit resolve. For example, 4503 is associated with https://swmath.org/software/4503 <br><strong>cvs</strong>: The link to the git repository<br><strong>swhid</strong>: Unique software heritage identifer. Prefix with <code>https://archive.softwareheritage.org/</code> to resolve. For example, swh:1:snp:e9e31cc35f3677801b896fc4d84f1c26fd3df3d2 is assocated with <code>https://archive.softwareheritage.org/</code>swh:1:snp:e9e31cc35f3677801b896fc4d84f1c26fd3df3d2 </p> <p><span>The following software versions were used</span></p> <ol> <li><span><a href="https://archive.softwareheritage.org/swh:1:cnt:3c525f7ef4feb933692f2d33d8186f69481d248b;origin=https:/github.com/MaRDI4NFDI/swMATH4EOSC;visit=swh:1:snp:ef1fa467e1a15ad613cf328b4b9bfe6db5be7f9d;anchor=swh:1:rev:8d7031d00664ca66e9b50ea4631cf297d1f16dc6;path=/swMATH_analysis/swMATH_no_empty_source_code.csv"><span>swh:1:cnt:3c525f7ef4feb933692f2d33d8186f69481d248b</span></a> to get the swmath and git repositories</span></li> <li><span><a href="https://archive.softwareheritage.org/swh:1:cnt:3aea0d0f97d9276282ee1de5ebc5945cc66e77b5;origin=https:/phabricator.wikimedia.org/diffusion/EMAS/extension-mathsearch.git;visit=swh:1:snp:2ba33a0d4adb14c00a63ab4cfb595412c3e47cbc;anchor=swh:1:rev:4f2f4b12f4763a817e8533e2997ece9407cd0b04;path=/maintenance/AddSwhids.php" target="_blank" rel="noopener"><span>swh:1:cnt:3aea0d0f97d9276282ee1de5ebc5945cc66e77b5</span></a> to get the corresponding software heritage identifiers</span></li> </ol> <p> </p> <p>Then the following Query was used to retrieve the uploaded file.</p> <p>SPARQL Query (<a href="https://query.portal.mardi4nfdi.de/#PREFIX%20wdt%3A%20%3Chttps%3A%2F%2Fportal.mardi4nfdi.de%2Fprop%2Fdirect%2F%3E%0A%0ASELECT%20%3Fswmathid%20%20%28str%28%3Frepo%29%20as%20%3Fcvs%29%20%3Fswhid%0AWHERE%20%7B%0A%20%20%3Fitem%20wdt%3AP13%20%3Fswmathid.%0A%20%20%3Fitem%20wdt%3AP339%20%3Frepo.%0A%20%20%3Fitem%20wdt%3AP1454%20%3Fswhid%0A%7D" target="_blank" rel="noopener">try it out</a>)</p> <pre><code>PREFIX wdt: <https://portal.mardi4nfdi.de/prop/direct/> SELECT ?swmathid (str(?repo) as ?cvs) ?swhid WHERE { ?item wdt:P13 ?swmathid. ?item wdt:P339 ?repo. ?item wdt:P1454 ?swhid }<br><br></code>For more details see our upcoming paper.</pre>
Mapping of OrphaCodes to SNOMED CT with the help of Usagi
<p>The CSV file contains the mapping of OrphaCodes to SNOMED CT carried out with Usagi.<br>This file has not yet been quality assured, but will be made available for validation.</p>
Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston
<p>Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and quiet locations such as parks. This project demonstrates that changes in human activity can reduce noise pollution and that simple smartphone technology can be used to make highly detailed maps of noise pollution that identify sources of high sound levels potentially harmful to humans in urban environments.</p>
Vegetation Maps of the Early Pleistocene Guadix-Baza Basin
<p>Following a methodology based on fossil material, paleogeographic data and paleoclimate calculations allows generating maps of the Early Pleistocene vegetation units of Guadix-Baza Basin for both glacial and interglacial scenarios.</p> <p>The resulting vegetation maps represent a great diversity of vegetation types in the Guadix-Baza Basin, with seven different units which change their distribution according to climatic changes, i.e., dry (glacial) and humid (interglacial) periods. During dry periods the dominant vegetation type is the steppe, with Mediterranean woodlands and deciduous and conifer forests largely reduced and restricted to valleys or mountainous areas. During humid periods, the steppes are replaced by open Mediterranean woodlands, while deciduous and conifer forests occupy larger areas in the mountain ranges.</p> <p>For additional information check the publication: Altolaguirre, Y., Schulz, M., Gibert, L., Bruch, A.A., 2021. Mapping Early Pleistocene environments and the availability of plant food as a potential driver of early <em>Homo</em> presence in the Guadix-Baza Basin (Spain). Journal of Human Evolution, ----.</p>
Mapping sugarcane globally at 10 m resolution using GEDI and Sentinel-2
<p><strong>Dataset Abstract:</strong><br>Sugarcane is an important source of food, biofuel, and farmer income in many countries. At the same time, sugarcane is implicated in many social and environmental challenges, including water scarcity and nutrient pollution. Currently, few of the top sugar-producing countries generate reliable maps of where sugarcane is cultivated. To fill this gap, we introduce a dataset of detailed sugarcane maps for the top 13 producing countries in the world, comprising nearly 90% of global production. Maps were generated for the 2019-2022 period by combining data from the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2 (S2). GEDI data were used to provide training data on where tall and short crops were growing each month, while S2 features were used to map tall crops for all cropland pixels each month. Sugarcane was then identified by leveraging the fact that sugar is typically the only tall crop growing for a substantial fraction of time during the study period. Comparisons with field data, pre-existing maps, and official government statistics all indicated high precision and recall of our maps. Agreement with field data at the pixel level exceeded 80% in most countries, and sub-national sugarcane areas from our maps were consistent with government statistics. Exceptions appeared mainly due to problems in underlying cropland masks, or to under-reporting of sugarcane area by governments. <br>The final maps should be useful in studying the various impacts of sugarcane cultivation and producing maps of related outcomes such as sugarcane yields.</p> <p><strong>USAGE: Users must mask the provided sugarcane map with the most appropriate crop mask from the ones provided. If none of the provided crop masks are suitable, users can use an external crop mask instead.</strong></p> <p>Validation results for the sugarcane maps are detailed in Section 4.3 of the paper. For Indonesia and Guatemala, no field-level data or raster datasets were available for validation of our sugarcane maps.</p> <p><br><strong>Dataset:</strong> <br>5 bands<br>b1: Number of tall months<br>b2: Sugarcane Map: 0 = non-sugarcane, 1 = sugarcane<br>b3: ESA crop mask: 0 = non-cropland, 1 = cropland<br>b4: ESRI crop mask: 0 = non-cropland, 1 = cropland<br>b5: GLAD crop mask: 0 = non-cropland, 1 = cropland</p> <p> </p> <p>The dataset can be accessed on Google Earth Engine (GEE) at <br><strong><a href="https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/?asset=projects/lobell-lab/gedi_sugarcane/maps/imgColl_10m_ESAESRIGLAD</a><br></strong><br>Example GEE script for visualizing and masking the sugarcane maps by country available at:<br><strong><a href="https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2Flobell-lab%2Fgedi_sugarcane%2Fmaps%2FimgColl_10m_ESAESRIGLAD">https://code.earthengine.google.com/545a87ce9bc29f2b5ad180955d974f8c?asset=projects%2fl Bell-lab%2Fgedi_sugarcane%2 Maps%2FimgColl_10m_ESAESRIGLAD</a></strong></p>
Data for 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'
<div>This dataset contains avalanche deposit outlines (as shapefiles) derived for the study 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'</div> <div> </div> <div>They were outlined at three different sites (Mt Blanc, Everest and Hispar regions) for the periods 11/2016-10/2021 (Mt Blanc) and 11/2017-10/2022 (Everest and Hispar). The time period is indicated in the file name.</div> <div> </div> <div>For each dataset we give the raw outlines (Automated_outlines_dates), the manually updated (Automated_outlines_dates_ManualUpd) and the manually updated after accounting for surface elevation change (Automated_outlines_dates_ManualUpd_shifted). </div> <div> </div> <div>In order to know which scenes were used for the mapping (if no avalanche was detected, we did not provide a shapefile, but this doesn't been that there is a gap in the Sentinel-1 time series), we provide a Sentinel1_date file that shows all the Sentinel-1 RGB pairs that we used to detect the avalanches.</div> <div> </div> <div>We also provide as geotiffs the temporally aggregated outlines (Automated_outlines_dates_ManualUpd_shifted_aggregated; over one specific year yn - from 01/11/yn-1 to 01/11/yn - or the full study period):</div> <div>- as heatmaps (where the value of each pixel corresponds to the number of avalanches that occured) </div> <div>- as binary maps of deposits (where 1 is when an avalanche occured over the time period and 0 is where none were detected).</div> <div> </div> <div> </div> <div>Finally we provide a csv file for each region with metrics per glacier:</div> <div> </div> <div>RGI ID</div> <div>Glacier size (in m^2)</div> <div>Catchment size (in m^2)</div> <div>Area of slopes steeper than 30° (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the ascending obits (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the descending obits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the ascending orbits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the descending orbits (in m^2)</div> <div>The area of the glacier visible in the ascending orbits (in m^2)</div> <div>The area of the glacier visible in the descending orbits (in m^2)</div> <div> </div> <div> </div> <div>The main Google Earth Engine and Matlab scripts used to pre-process the Sentinel-1 GRD images and to map the avalanches are available on GitHub: https://github.com/MarinKneib/S1_avalanches</div> <div> </div>
Estimated Annual Agricultural Pesticide Use for USA48 2000 to 2019 (gridded maps at 250-m resolution)
<p>Estimated low and high pescticide uses based on census data for USA48, provided for the cropland mask at 250-m spatial resolution. The original <a href="https://water.usgs.gov/nawqa/pnsp/usage/maps/">USGS maps</a> are only available as PNGs, so we have prepared here a code to rasterize the values and produce GeoTIFFs. For cropland mask we use the <a href="https://www.usgs.gov/centers/eros/science/national-land-cover-database">National Land Cover Database (NLCD)</a>. All processing is fully documented in: <a href="https://github.com/Envirometrix/pesticide-use-USA48">https://github.com/Envirometrix/pesticide-use-USA48</a>. All maps are projected in the <a href="https://epsg.io/5070-1252">EPSG:5070</a>. Description of the layers:</p> <ul> <li><code>croplands_20**_250m.tif</code> = cropland mask (0–100%) estimated based on NLCD.</li> <li><code>GLYPHOSATE_EPEST.HIGH.KG.KM2_20**_250m.tif</code> = estimated pesticide use per county (high estimate) expressed in kg/km-square.</li> <li><code>diff.GLYPHOSATE_EPEST.HIGH.KG.KM2_250m.tif</code> = annual difference in pesticide use from 2000 to 2019;</li> </ul> <p><strong>Disclaimer</strong>: this code is under construction and USGS makes is clearly available that there are some limitations to this data:</p> <ul> <li>These estimates are made by using projected county crop acres from the previous Census of Agriculture and are expected to be revised upon availability of updated crop acreages in the following Census of Agriculture.</li> <li>The files do not include pesticide use estimates for California. Data for California are obtained from the online Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) database and are typically not available at the time the preliminary pesticide use estimates are generated for the rest of the U.S.</li> </ul> <p>Pease have in mind these limitations when using these maps for further modeling.</p>
MHONGOOSE single-track moment maps
<h2>MHONGOOSE single-track moment maps</h2> <p>This repository contains all MHONGOOSE single-track moment maps as described in de Blok et al. (2024).</p> <p>For each galaxy, two resolutions are provided, derived with robust parameter 0.5 and 1.5, respectively. The<br>former highlights small-scale structures at the cost of a reduced column density sensitivity, the<br>latter emphasises low column density structures.</p> <p>For each galaxy and resolution we provide:</p> <ul> <li>zeroth moment map (mom0) - integrated HI intensity map</li> <li>primary-beam-corrected zeroth moment map (mom0_pb)</li> <li>first moment map - intensity-weighted mean velocity field</li> <li>second moment map (mom2) - velocity spread map</li> <li>number of channels (nchan) contributing to each pixel</li> </ul> <p>Average properties of the observations are given below. Beam sizes for the individual galaxies areprovided in the headers.</p> <table> <tbody> <tr> <td>robust value</td> <td>pixel size (arcsec)</td> <td>beam (arcsec)</td> <td>noise (mJy/beam)</td> <td>log(NHI) (cm-2) [1sigma; 1 chan]</td> <td>log(NHI) (cm-2) [3sigma; 16 km/s]</td> </tr> <tr> <td>1.5</td> <td>7</td> <td>30</td> <td>0.49</td> <td>17.94</td> <td>18.94</td> </tr> <tr> <td>0.5</td> <td>3</td> <td>12</td> <td>0.54</td> <td>18.79</td> <td>19.79</td> </tr> </tbody> </table> <p>For a full description of the derivation of the moment maps see de Blok et al (2024).</p>
The Adult Adansonia digitata L. (baobab tree) distribution map derived from very high resolution satelite imagery for 2010s across the Sahel at 1km resolution
<p>The baobab tree (<em>Adansonia digitata</em> <em>L.</em>) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify nearly 3 million baobab trees in the Sahel, a dryland region of 1.5 million km<sup>2</sup>. This achievement is considered an essential step towards improving valuable woody species' management and monitoring system. The map's overall underestimate bias is 0.27. To prevent mismanagement of this specific tree species, we aggregated every single adult baobab tree map to 1 × 1 km grids. We also classified the baobab trees using the tree crown diameters( small: 3-9m; medium 9m-13m; large: >13m). The baobab tree count map is also available for these three different size classes. </p>
SSSOM-like mapping of PanglaoDB to Wikidata
<p>Mappings from PanglaoDB to Wikidata. <br>See details at https://jvfe.github.io/paper_wdt_panglao/. </p>
Anchoring pan-scalar map Agile Dataset
<p>This dataset contains data used for the design of anchored pan-scalar maps in Madison and Chambery. Anchoring pan-scalar maps means improving the saliency and memorability of some cartographic elements, i.e. the pan-scalar anchors, to enhance the navigational cues that can be used for self-localization during or after a zoom in a map.</p> <ul> <li>Madison :</li> </ul> <ol> <li>VECTORSQUELETON : This folder contains the digitized data used to create map skeletons at different zoom levels in Madison.(12,13,14,15,16)</li> <li>ANCHOR TRAINLINE : This folder contains the map anchor generalized train line zoom 12,13,14,15,16<br>(12,13,14,15,16) pou madison</li> <li>ANCHOR PENINSULA :This folder contains the peninsula map anchor generalized to zoom 12,13,14,15,16 for Madison.</li> <li>...</li> </ol> <ul> <li>QGIS Project : Qgis project to make the squeleton map</li> <li>Illustration : </li> <li>Web : Anchoring map from madison</li> <li>Scalemaster : Scaleline of madison for anchoring</li> <li>Globalscaline : Scaleline on the global aspect of pan-scalar map</li> </ul>
FIGURE 1. Geological and biostratigraphical maps. A in Geometric morphometric assessment of Guanshan trilobites (Yunnan Province, China) reveals a limited diversity of palaeolenid taxa
FIGURE 1. Geological and biostratigraphical maps. A. The Shitangshan Section showing approximate stratigraphic distribution of Redlichia mansuyi and Redlichia mai (modified from Hu et al. 2010). B–D. Studied sections showing their approximate stratigraphic levels respectively in Wulongqing Formation. B. Huanglongqing Section. C. Longbaoshan Section. D. Xinglongcun Section. E. Map of Kunming showing the localities of studied sections. The red lines with arrows showing approximate sampling interbeds in each section.
FIGURE 1. Location map showing the locality where the AMU-CURS 184 in A new Megatheriinae skull (Xenarthra, Tardigrada) from the Pliocene of Northern Venezuela - implications for a giant sloth dispersal to Central and North America
FIGURE 1. Location map showing the locality where the AMU-CURS 184 specimen was recovered from San Gregorio Fm. outcrops.
FIGURE 1. Map locality, 1 in A new group of late Oligocene mysticetes from México
FIGURE 1. Map locality, 1) state of Baja California Sur, Mexico; 2) Oligocene outcrops of San Juan Member, El Cien Formation; 3) El Saladito and mesa El Tosoro fossilferous localities, north to San Juan de la Costa village.
F I G U R E 1 Map displaying the 14 in Migration patterns and navigation cues of Atlantic salmon post-smolts migrating from 12 rivers through the coastal zones around the Irish Sea
F I G U R E 1 Map displaying the 14 capture sites in 12 rivers where Atlantic salmon smolts (n = 1008) were captured for tagging in England, Scotland, Northern Ireland and the Republic of Ireland for this study. In addition, 60 hatchery origin smolts were tagged and released in the River Burrishoole. The coastal region each river belongs to is referenced in brackets next to the river name. Where Region one (1) refers to the Solway Firth (Rivers Derwent, Nith, Bladnoch); Region two (2) refers to the Clyde Estuary (Rivers Endrick and Gryffe); Region three (3) refers to the Bush Coastal region (rivers Bann, Bush, Carey and Glendun); Region four (4), refers to Lough Foyle (rivers Roe and Faughan); Region five (5), refers to Clew Bay (River Burrishoole). Tagged fish release sites are represented by stars, and acoustic receivers (n = 183) are represented by gray dots. Marine monitoring lines (A and B) in the Irish Sea are labeled in alphabetical order from south to north. Twenty-two acoustic receivers were initially deployed at monitoring line A. One hundred and eight acoustic receivers were deployed at monitoring line B and are labeled in numerical order from the furthest west receiver (R1) on the monitoring line to the furthest east (R108). Refer to Figure S2 for the locations of acoustic receivers that were not retrieved from marine monitoring line A (n = 2) and B (n = 9).
Geographic range maps for Mammal Diversity Database v1.3 taxonomy
<p>Update of mammal maps based on the taxonomy of the Mammal diversity database. These maps are different from the original, have been downscaled and are distributed under the R package mdd (github.com/alrobles/mdd).</p>
Time-frequency maps of gravitational waves embeded in noise
<p>This data set constains the time-frequency maps of gravitational waves embeded in noise. </p>
ScienceDex guides
Understand access before you commit
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.