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13,117 results for “Maps”
Data from: 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, 2000, 2010, 2015, and 2017 to evaluate temporal and spatial trends of land use modification of terrestrial lands (excluding Antarctica). Our novel datasets are detailed (0.09 km2 resolution), temporally consistent (for 1990-2015), 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 for ~2017 with 14 stressors for an even more comprehensive dataset. Also provided is a land/water mask to support subsequent analyses.</p> <p>Please also be sure to check your spam folder if you do not receive an email with the link from Dryad, which is provided because of the large file size.</p>
Benchmark maps of 33 years of secondary forest age for Brazil
<p><strong>Title: </strong>Benchmark maps of 33 years of secondary forest age for Brazil</p> <p><strong>Contact:</strong> Celso H. L. Silva Junior (celsohlsj@gmail.com)</p> <p> </p> <p><strong>Data:</strong> Increment (Product 1), Extent (Product 2), Loss (Product 3), and Age (Product 4) of Brazilian Secondary Vegetation</p> <p><strong>Coverage:</strong> Brazil land</p> <p><strong>Period:</strong> 1986 to 2018 (1987 to 2018 for secondary forest loss)</p> <p><strong>Spatial resolution:</strong> 30-meters</p> <p><strong>Temporal resolution:</strong> Annual</p> <p><strong>Coordinate reference system:</strong> Geographic Coordinate System with Datum WGS84</p> <p><strong>File format:</strong> 131 zip files per year (being 33 for increment, 33 for extent, 33 for age and 32 for loss). Each zip file containing eight tiles in compressed TIFF format.</p> <p><strong>Code:</strong> <a href="https://github.com/celsohlsj/gee_brazil_sv">https://github.com/celsohlsj/gee_brazil_sv</a></p> <p><strong>Dataset usage</strong>: It is free to use, but if you use this dataset in your work, please make sure to cite the repository and our paper properly. We also welcome users to invite us for collaboration.</p> <p><strong>For the use of this dataset, please cite: </strong>Silva Junior, C.H.L., Heinrich, V.H.A., Freire, A.T.G., Broggio, I.S., Rosan, T.M., Doblas, J., Anderson, L.O., Rousseau, G.X., Shimabukuro, Y.E., Silva, C.A., House, J.I., Aragão, L.E.O.C. Benchmark maps of 33 years of secondary forest age for Brazil. Scientific Data (2020). <a href="https://doi.org/10.1038/s41597-020-00600-4">https://doi.org/10.1038/s41597-020-00600-4</a></p>
Hipathia version of the COVID-19 map
<p><strong>Hipathia version of the COVID-19 Disease Map provided by http://doi.org/10.17881/covid19-disease-map</strong></p>
Global soil saturated hydraulic conductivity map using random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution
<p>The global Ksat map at 1 km resolution was developed by harnessing the technological advances in machine learning and availability of remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. We merge concepts of predictive soil mapping with a large data set of Ksat measurements and local information (soil, vegetation, climate) into covariate-based “Geo Transfer Functions'' (CoGTFs) to generate global estimates of Ksat values (to highlight the impact of Geo-referenced covariates including various remote sensing maps, we use the term Geotransfer function GTF and not pedotransfer function PTF; in the latter case, typically only soil properties are used to estimate Ksat).</p> <p>The Ksat dataset is provided in GeoTIFF format. A total of 4 files that represent different soil depths (0, 30, 60, and 100 cm) are provided. The Ksat values are log-transformed (log10 Ksat) and cm/day was selected as a standardized unit.</p> <p>The Global Ksat training dataset used for this study is available here:<br> <a href="https://doi.org/10.5281/zenodo.3752721">https://doi.org/10.5281/zenodo.3752721</a></p> <p>The R code used for this study is available here:<br> <a href="https://github.com/ETHZ-repositories/Ksat_mapping_2020">https://github.com/ETHZ-repositories/Ksat_mapping_2020</a></p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Lehmann, P., Bonetti, S., Papritz, A., and Or, D., (2020): <strong>Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework</strong>. Journal of Advances in Modeling Earth Systems,<strong> </strong>13(4), e2020MS002242. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002242</li> </ul> <p>Other datasets related to this project:</p> <p>The Global vG training dataset is available here:</p> <p><a href="https://doi.org/10.5281/zenodo.5547338">10.5281/zenodo.5547338</a></p> <p>Examples of using this dataset to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>.</p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). We would like to thank Zhongwang Wei, Samuel Bickel and Simone Fatichi (ETH Zurich) for insightful discussions.</p> <p> </p> <p> </p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 2 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DLT40 instrument.
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 2 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DECam instrument.
Suitability Map of COVID-19 Virus Spread
<p><strong>This dataset is associated with the publication "G.Coro, (2020), A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate, Ecological Modelling, Volume 431, 109187, https://doi.org/10.1016/j.ecolmodel.2020.109187"</strong></p> <p> </p> <p>This image reports a Maximum Entropy model that estimates <em>suitable </em>locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.</p> <p>The model was trained just on locations in <em>Italy </em>that have reported a rate of new infections higher than the geometric mean of all Italian infection rates. The following environmental parameters were used, which are correlated to those used by other studies:</p> <ul> <li>Average Annual Surface Air Temperature in 2018 (NASA)</li> <li>Average Annual Precipitation in 2018 (NASA)</li> <li>CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)</li> <li>Elevation (NOAA ETOPO2)</li> <li>Population per 0.5° cell (NASA Gridded Population of the World)</li> </ul> <p>A higher resolution map, the model file (in ASC format) and all parameters used are also attached.</p> <p>The model indicates highest correlation with <em>infection rate</em> for CO2 around 0.03 gCm^−2day^−1, for Temperature around 11.8 °C, and for Precipitation around 0.3 kg m^-2 s^-1, whereas Elevation and Population density are poorly correlated with <em>infection rate</em>.</p> <p><strong>One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location</strong>, <strong>and Iran (around Teheran) as a suited location for virus' spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.</strong></p> <p><strong>Evaluation: </strong></p> <p>A <em>risk score</em> was calculated for each country/region reported by the JHU monitoring system (<a href="https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6">https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6</a>). This score is calculated as the summed normalised probability in the populated locations divided by their total surface. This score represents how much the zone would potentially foster the virus' spread.</p> <p>We assessed the reliability of this score, by selecting the country/regions that reported the <em>highest rates of infection</em>. These zones were selected as those with a rate higher than the upper confidence of a log-normal distribution of the rates.</p> <p>The agreement between the two maps (<a href="https://zenodo.org/api/files/23b09ea2-e5eb-415d-9f6c-fd3b5abfe6c9/covid_high_rate_vs_high_risk_24_03_2020.png">covid_high_rate_vs_high_risk.png</a>, where violet dots indicate <em>high infection rates </em>and countries' colours indicate estimated <em>high risk score</em>) is the following:</p> <p><strong>Accuracy </strong>(overall percentage of correctly predicted high-rate zones): <strong>77.25%</strong><br> <strong>Kappa </strong>(agreement between the two maps): <strong>0.46</strong> (Good, according to Fleiss' intepretation of the score) </p> <p><strong>This assessment demonstrates that our map can be used to estimate the risk of a certain country to have a high rate of infection, and indicates that the influence of environmental parameters on virus's spread should be further investigated.</strong></p> <p> </p>
Maps of reference evapotranspiration for the irrigation project in Brazil
<p>The maximum daily evapotranspiration data set for a project (ETproject) for Brazil. It has a spatial resolution of <strong>30 seconds (~ 1 km²)</strong>. The data set grid is in <strong>GeoTIFF format</strong> and corresponds perfectly to WorldClim. It uses the <strong>geographic coordinate</strong> reference system, with <strong>WGS84 projection (EPSG: 4326)</strong>.</p> <p>The objective study is to estimate and provide evapotranspiration values of monthly reference and the maximum of twelve months, for dimensioning irrigation systems throughout the Brazilian territory. With the meteorological data of two hundred and fifty-nine conventional INMET stations, the daily reference evapotranspiration (ETo) for 15 years was calculated. For each weather station, the data was grouped by month and the ETo for the irrigation project (ETproject) was determined to meet the eighty percent probability of occurrence, following the recommendations of FAO24. In parallel, monthly images of 15 years of ETo were acquired for Brazil, and the climatic variables of WorldClim. Using the ET values of the stations design, it was modeled for the rest of Brazil, using machine learning algorithms and the covariates. After modeling, the following performances were achieved: mean square error of 0.306 mm / d, mean bias error of -0.004 mm / d, mean absolute error of 0.227 mm / d, determination coefficient of 0.938 and efficiency of Nash-Sutcliffe 0.937. ETo values for irrigation projects were similar to several others reported in the literature when compared at a given point. With this research it was possible to determine the monthly and annual ETo for irrigation projects throughout the Brazilian territory.</p> <p>The article has been submitted for publication.</p>
Next-generation sequencing of newborn screening genes: The accuracy of short-read mapping
<p>We examine the effect of high homology genomic regions on the mapping of genes related to newborn screening while taking different read lengths and patient's ethnic background into consideration.</p>
Allen Coral Atlas maps of the benthic cover and geomorphic zones of coral reefs in western Micronesia
<p>This dataset includes shapefiles of classification of benthic cover and geomorphic zones of the shallow coral reefs in the area of western Micronesia. The boundary of the mapped area is included in the dataset. <br> <br> This is a subset of the Allen Coral Atlas global coral reef mapping dataset. Mapping methodology, other datasets and a full list of contributors are available at <a href="https://www.allencoralatlas.org/">https://www.allencoralatlas.org/</a>. Further guidance on licensing and citation can be found at <a href="https://github.com/CoralMapping/AllenCoralAtlas">https://github.com/CoralMapping/AllenCoralAtlas</a></p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event S190814bv
Attached in a .json file is the completed pointing information for 2293 observation(s) for the EM counterpart search associated with the gravitational wave event S190814bv. These observations were taken on the DECam instrument.
Cognitive map-based navigation in wild bats revealed by a new high-throughput tracking system
<p>Seven decades of research on the "cognitive map", the allocentric representation of space, have yielded key neurobiological insights, yet we still lack field evidence from free-ranging wild animals. Using a system capable of tracking dozens of animals simultaneously at high accuracy and resolution, we assembled a large dataset of 172 foraging Egyptian fruit bats comprising >18M localizations collected over 3,449 bat-nights across 4 years. Detailed track analysis, combined with translocation experiments, revealed that wild bats seldom exhibit random search but instead repeatedly forage in goal-directed, long and straight flights that include frequent shortcuts. Alternative non-map-based strategies were ruled out by simulations, time-lag embedding and other trajectory analyses. Our results are consistent with expectations from cognitive map-like navigation and support previous neurobiological evidence from captive bats. </p>
Microplastic and macroplastic yearly emission maps for Switzerland
<p>Dataset connected to the following publication:</p> <p><strong>Title: </strong><em>A proxy-based approach to predict spatially resolved emissions of macro- and microplastic to the environment</em><br> <strong>Authors:</strong> Delphine Kawecki; Bernd Nowack<br> <strong>Journal:</strong> Science of the Total Environment<br> <strong>Year: </strong>2020<br> <strong>DOI: </strong>10.1016/j.scitotenv.2020.141137</p> <p><strong>Contents:</strong><br> - Raster maps of emissions to soil and air<br> - Shapefiles of emissions to lakes and rivers</p> <p>Separate files are provided for:<br> - the seven different polymers included in the study: LDPE, HDPE, PP, PS, EPS, PVC, PET<br> - the two sizes of plastic modelled: macroplastic and microplastic (files for microplastic contain the string "MP")<br> - the maps created using the mean, the 5th percentile and the 95th percentile of the original emission flows<br> - the receiving environmental compartment: soil, air, rivers and lakes</p> <p>For a detailed description of the method, see the publication.</p>
SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections (Revision data)
<p>The submitted dataset, correspond to the RAW (*.czi format) and analysis files of the <strong>revisions</strong> of manuscript "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections", which has been processed for revision (2020-01-31) by PLOS Biology.</p> <p>The files are in *.zip format and the naming follows thenumbering of the manuscript figures, which will be found in bioRxiv.org (<strong>ID#: BIORXIV/2020/938571</strong>). Each *.zip files contains a document with the description of all the provided files.</p>
Map 2 in On the Aleochara subgenera Ceranota and Xenochara. IV. A revision of types, a new species, and additional records (Coleoptera: Staphylinidae: Aleocharinae)
Map 2: Distribution of Aleochara penicillata, based on revised records.
Map 4 in On the Aleochara subgenera Ceranota and Xenochara. IV. A revision of types, a new species, and additional records (Coleoptera: Staphylinidae: Aleocharinae)
Map 4: Distribution of Aleochara subtumida, based on revised records.
Chromonomer: a tool set for repairing and enhancing assembled genomes through integration of genetic maps and conserved synteny
<p class="BodyAA">The pace of the sequencing and computational assembly of novel reference genomes is accelerating. Though DNA sequencing technologies and assembly software tools continue to improve, biological features of genomes such as repetitive sequence as well as molecular artifacts that often accompany sequencing library preparation can lead to fragmented or chimeric assemblies. If left uncorrected, defects like these trammel progress on understanding genome structure and function, or worse, positively mislead this research. Fortunately, integration of additional, independent streams of information, such as a marker-dense genetic map and conserved orthologous gene order from related taxa, can be used to scaffold together unlinked, disordered fragments and to restructure a reference genome where it is incorrectly joined. We present a tool set for automating these processes, one that additionally tracks any changes to the assembly and to the genetic map, and which allows the user to scrutinize these changes with the help of web-based, graphical visualizations. Chromonomer takes a user-defined reference genome, a map of genetic markers, and, optionally, conserved synteny information to construct an improved reference genome of chromosome models: a "chromonome". We demonstrate Chromonomer's performance on genome assemblies and genetic maps that have disparate characteristics and levels of quality.</p>
Augmented emission maps: 1598 cc 85 kW Euro 6 diesel engine: update 1
<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 6 diesel engine that has been applied in 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 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1199 cc 55 kW Euro 4 petrol engine: update 1
<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 1199 cc 55 kW Euro 4 petrol engine that has been applied in the Opel, Agile and Astra.</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 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Data from: Visualizing mineralization processes and fossil anatomy using synchronous synchrotron X-ray fluorescence and X-ray diffraction mapping
<p>Fossils, including those that occasionally preserve decay-prone soft-tissues, are mostly made of minerals. Accessing their chemical composition provides unique insight into their past biology and/or the mechanisms by which they preserve, leading to a series of developments in chemical and elemental imaging. However, the mineral composition of fossils, particularly where soft-tissues are preserved, is often only inferred indirectly from elemental data, while X-ray diffraction that specifically provides phase identification received little attention. Here, we show the use of synchrotron radiation to generate not only X-ray fluorescence elemental maps of a fossil, but also mineralogical maps in transmission geometry using a two-dimensional area detector placed behind the fossil. This innovative approach was applied to millimetre-thick cross-sections prepared through three-dimensionally preserved fossils, as well as to compressed fossils. It identifies and maps mineral phases and their distribution at the microscale over centimetre-sized areas, benefitting from the elemental information collected synchronously, and further informs on texture (preferential orientation), crystallites size and local strain. Probing such crystallographic information is instrumental in defining mineralization sequences, reconstructing the fossilization environment and constraining preservation biases. Similarly, this approach could potentially provide new knowledge on other (bio)mineralization processes in environmental sciences. We also illustrate that mineralogical contrasts between fossil tissues and/or the encasing sedimentary matrix can be used to visualize hidden anatomies in fossils.</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.