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13,117 results for “Maps”
BLE RSS dataset for fingerprinting radio map calibration
<p>The dataset contains Bluetooth Low Energy signal strengths measured in a fully furnished flat. The dataset was originally used in the study concerning RSS-fingerprinting based indoor positioning systems. The data were gathered using a hybrid BLE-UWB localization system, which was installed in the apartment and a mobile robotic platform equipped for a LiDAR. The dataset comprises power measurement results and LiDAR scans performed in 4104 points. The scans used for initial environment mapping and power levels registered in two test scenarios are also attached.</p> <p>The set contains both raw and preprocessed measurement data. The Python code for raw data loading is supplied.</p> <p>The detailed dataset description can be found in the <em>dataset_description.pdf</em> file.</p> <p>When using the dataset, please consider citing the original paper, in which the data were used:</p> <p>M. Kolakowski,<strong> “Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning”</strong>, <em>Sensors</em> , vol. <em>21</em>, 6270, Sep. 2021 <a href="https://doi.org/10.3390/s21186270">https://doi.org/10.3390/s21186270</a></p> <p> </p>
Map from Ashrafyan K. E. settlement of chiefs (caciques) in Florida in the middle of the XVI century (16th century) according to the memoirs of Fontaneda in 1575) Карта от Ашрафьяна К.Э. расселения вождей и поселений во Флориде в середине XVi века (16 century) по воспоминаниям Фонтанеда 1575 года)
<p>Карта расположения вождей и поселений во Флориде в середине 16 века, выполненная Ашрафьяном К. Э. (аспирант МГОУ) на основе переводов оригинальных документов испанца Фонтанеда от 1575 года, прожившего среди индейцев племени Калуса с 1551 по 1566 год (с 13 лет до 30 лет) и написавшего 5 разных текстов, найденных и собранных профессором Д. Ворсом (J. Worth) и предоставленных Генеральным Архивом Индий в Севилье (Испания).</p> <p>Map of the location of chiefs and settlements in Florida in the mid-16th century, made by Ashrafyan K. E. (graduate student of Moscow State University) based on translations of the original documents of the Spaniard Fontaneda from 1575, who lived among the Calusa Indians from 1551 to 1566 (from 13 to 30 years) and wrote 5 different texts found and collected by Professor J. Worth and provided by the General Archive of the Indies in Seville (AGI) (Spain).</p>
Historical City Maps Semantic Segmentation Dataset
<p>This dataset includes a total of 635 annotated image patches from historical city maps. It is designed for the semantic segmentation of the maps into 5 semantic classes (building blocks, non-built, water, road network, background frame). 330 patches are taken from maps of the city of Paris, while the 305 others are taken from a balanced corpus of city maps from 90 countries all around the world.</p> <p>Please read the detailed informations about data collection methodology, associated metadata and annotation ontology in README.md hereunder :</p>
Data for "Microstructural mapping of A. islandica shells reveals environmental and physiological controls on biomineral size"
<p>This data contains the image segmentation workflow, data processing procedure and all data generated for the publication "Microstructural mapping of A. islandica shells reveals environmental and physiological controls on biomineral size" currently under review.</p>
Rainbow colour maps remain widely used in the geosciences
<p>This dataset is the result of a systematic survey of scientific publication to investigate the extent to which rainbow colour maps are used in geoscience publications. Papers were surveyed from five journals - Earth System Dynamics (ESD), Geophysical Research Letters (GRL), Ocean Science (OS), Solid Earth (SE) and The Cryosphere (TC) - for the years 2005, 2010, 2015 and 2020.</p> <p>The final data set includes the pre-existing Stoelzle and Stein (2021) survey data for HESS, which is independently available at https://doi.org/10.5281/zenodo.5145746 (Stoelzle, 2021).</p> <p>All papers (n=2638) were classified according to the type of colour encoding used.</p> <p>year = year of publication (YYYY)<br> title = full paper title<br> authors = list of authors seperated by semi-colon<br> n_authors = number of authors<br> col_code = color-issue classification (see below)<br> volume = Journal volume<br> start_page = first page of paper (where available)<br> end_page = last page of paper (where available)<br> base_url = url to access the paper on the journal website <br> filename = file name of the PDF version of the paper</p> <p>Color classification is stored in the col_code variable with:</p> <p>0 = Colour visualisations with no colour issues<br> 1 = At least one visualisation with red-green issues<br> 2 = At least one visualisation with a rainbow colour map<br> bw = Black and white paper</p>
Dataset: Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan
<p><strong>Description:</strong><br>This version of the repository holds an updated dataset of the annual forest disturbance agents/stable land cover maps across Japan using Landsat time series data. This version covers the period for 1985-2023 (1985-2019 in the original dataset). Harvest, Conversion, Thinning, and Other disturbances are mapped as different disturbance agents. The maps cover the entire country of Japan, excluding several isolated islands. The maps are available in compressed GeoTIFF format in Albers Equal Area Conic projection. The maps can be browsed in <a href="https://dulvrq3317.users.earthengine.app/view/japan-harvest-year-1985-2023">Google Earth Engine Apps</a>.</p> <p><strong>Citation:</strong><br>Shimizu, K. and Saito, H. (2021) Country-wide mapping of harvest areas and post-harvest forest recovery using Landsat time series data in Japan. <em>International Journal of Applied Earth Observation and Geoinformation</em> 104: 102555. <a href="https://doi.org/10.1016/j.jag.2021.102555">https://doi.org/10.1016/j.jag.2021.102555</a></p> <p><strong>Period:</strong><br>1985 to 2023 annually</p> <p><strong>Spatial resolution:</strong><br>30m</p> <p><strong>Projection:</strong><br>Albers Equal Area Conic Projection (Two standard parallels: 33N and 44N, Central Meridian: 135E)</p>
Co-citations Map: Literature review on Learning Ecologies, 1991-2018
<p>The co-citation map was adopted as type of analysis over 85 papers sampled from five scientific databases (pls. cfr: <a href="https://zenodo.org/record/1503775#.W_rUn-hKg2w">Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysi</a>s". This was adopted as method to further understand the relationships and advancement of work relating the concept of LE.</p> <p>The software tool to carry out this phase of the study was <a href="http://www.citnetexplorer.nl/ ">Citenet Explorer</a> for the analysis and visualization of co-citations along the period 1999-2018.</p> <p>The total number of citations and the relationships between most cited authors and all authors were extracted from the corpus analyzed (<a href="https://zenodo.org/record/1503775#.W_rUn-hKg2w">Systematic Review on the Research Topic "Learning Ecologies" - Dataset and Analysi</a>s"; a specific dataset was created and the outputs were processed by the specialized software that delivers the bibliometric maps as output.</p> <p>All the instructions to use the two files that compose the dataset requested by the software, as well as the software specifications can be found at CiteNet Explore page: http://www.citnetexplorer.nl/ </p> <p>Finally, the Fig. 7 with the analysis we performed shows the co-citations map, where at a first sight it is possible to see two main groups of nodes or authors cited (X axis) across a timespan (Y axis), and sparse elements at the center and at the beginning of the period (1991). The main and more compact group (also in terms of clusterisation of nodes, which are showed in green) related the publications that cite the seminal work of Barron (2004). These publications are mostly classified as using socio-constructivist theories and are placed in the area of social sciences, while for other categories (such as the methodological approach, research applications and the alignment are more fragmented). There are four seminal works (Abd-El-Khalick & Akerson, 2004; Barron, 2004; T ; Okamoto, Kayama, Inoue, & Cristea, 2002; Toshio Okamoto & Kayama, 2004) to which other papers can be connected. Beyond the mentioned work of Barron, the other three works can be placed in the area of technology (development of eLearning environments) and STEM education, supporting the idea of disciplinary fragmentation.</p>
MIDAS MeSH to Ontology mappings
<p>These mapping files contain mappings from the RDF version of MeSH to the Geographical Entity Ontology and the NCBI Taxonomy for use in the Ontology Based Catalogue for Infection Disease Epidemiology.</p>
N3C-Formatted OMOP2OBO Mappings
<p><strong>OMOP2OBO Mappings - N3C OMOP to OBO Working group</strong></p> <p>This repository stores OMOP2OBO mappings which have been processed for use within the National COVID Cohort Collaborative (N3C) Enclave. The version of the mappings stored in this repository have been specifically formatted for use within the N3C Enclave.</p> <p><strong>N3C OMOP to OBO Working Group: </strong>https://covid.cd2h.org/ontology</p> <p> </p> <p><em><strong>Accessing the N3C-Formatted Mappings </strong></em></p> <p>You can access the three OMOP2OBO HPO mapping files in the Enclave from the Knowledge store using the following link: <a href="https://unite.nih.gov/workspace/compass/view/ri.compass.main.folder.1719efcf-9a87-484f-9a67-be6a29598567">https://unite.nih.gov/workspace/compass/view/ri.compass.main.folder.1719efcf-9a87-484f-9a67-be6a29598567</a>.</p> <p>The mapping set includes three files, but you only need to merge the following two files with existing data in the Enclave in order to be able to create the concept sets:</p> <ul> <li><em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</em></li> <li><em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</em></li> </ul> <p> </p> <p>The first file <em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</em>, contains columns for the OMOP concept ids and codes as well as specifies information like whether or not the OMOP concept’s descendants should be included when deriving the concept sets (defaults to FALSE). The other file <em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</em>, contains details on the mapping’s label (i.e., the HPO curie and label in the concept_set_id field) and its provenance/evidence (the specific column to access for this information is called intention).</p> <p> </p> <p><em><strong>Creating Concept Sets</strong></em></p> <p>Merge these files together on the column named <em>codeset_id</em> and then join them with existing Enclave tables like concept and <em>condition_occurrence</em> to populate the actual concept sets. The name of the concept set can be obtained from the <em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</em> file and is stored as a string in the column called <em>concept_set_id</em>. Although not ideal (but is the best way to approach this currently given what fields are available in the Enclave), to get the HPO CURIE and label will require applying a regex to this column.</p> <p>An example mapping is shown below (highlighting some of the most useful columns):</p> <pre><code>codeset_id: 900000000 concept_set_id: [OMOP2OBO] hp_0002031-abnormal_esophagus_morphology concept: 23868 code: 69771008 codeSystem: SNOMED includeDescendants: False intention: Mixed - This mapping was created using the OMOP2OBO mapping algorithm (https://github.com/callahantiff/OMOP2OBO). The Mapping Category and Evidence supporting the mappings are provided below, by OMOP concept: 23868 ******* Mapping Category: Automatic Exact - Concept ------------------------------------------------ Mapping Provenance ------------------ OBO_DbXref-OMOP_ANCESTOR_SOURCE_CODE:snomed_69771008 | OBO_DbXref-OMOP_CONCEPT_SOURCE_CODE:snomed_69771008 | CONCEPT_SIMILARITY:HP_0002031_0.713</code></pre> <p> </p> <p><strong>Release Notes - v2.0.0</strong></p> <p><em>Preparation</em></p> <p>In order to import data into the Enclave, the following items are needed:</p> <ul> <li>Obtain API Token, which will be included in the authorization header (stored as GitHub Secret)</li> <li>Obtain username hash from the Enclave</li> <li><a href="https://zenodo.org/record/7250177">OMOP2OBO Mappings (v1.5.0)</a></li> </ul> <p><em>Data</em></p> <ul> <li>Concept Set Container (<em>concept_set_container</em>): <em>CreateNewConceptSet</em></li> <li>Concept Set Version (<em>code_sets</em>): C<em>reateNewDraftOMOPConceptSetVersion</em></li> <li>Concept Set Expression Items (<em>concept_set_version_item</em>): <em>addCodeAsVersionExpression</em></li> </ul> <p><em>Script</em></p> <ul> <li><a href="https://github.com/callahantiff/OMOP2OBO/blob/master/applications/N3C/n3c_mapping_conversion.py">n3c_mapping_conversion.py</a></li> </ul> <p><em>Generated Output</em></p> <p>Need to have the <em>codeset_id</em> filled from self-generation (ideally, from a conserved range) prior to beginning any of the API steps. The current list of assigned identifiers is stored in the file named <em>omop2obo_enclave_codeset_id_dict_v2.0.0.json</em>. Note that in order to accommodate the 1:Many mappings the codeset ids were re-generated and rather than being ampped to HPO concepts, they are mapped to SNOMED-CT concepts. This creates a cleaner mapping and will easily scale to future mapping builds.</p> <p> </p> <p>To be consistent with OMOP tools, specifically Atlas, we have also created Atlas-formatted json files for each mapping, which are stored in the zipped directory named <em>atlas_json_files_v2.0.0.zip</em>. Note that as mentioned above, to enable the representation of 1:Many mappings the filenames are no longer named after HPO concepts they are now named with the OMOP concept_id and label and additional fields have been added within the JSON files that includes the HPO ids, labels, mapping category, mapping logic, and mapping evidence.</p> <p> </p> <p><strong>File 1: concept_set_container</strong></p> <ul> <li><strong>Generated Data:</strong> <em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_container.csv</em></li> <li>Columns: <ul> <li>concept_set_id</li> <li>concept_set_name</li> <li>intention</li> <li>assigned_informatician</li> <li>assigned_sme</li> <li>project_id</li> <li>status</li> <li>stage</li> <li>n3c_reviewer</li> <li>alias</li> <li>archived</li> <li>created_by</li> <li>created_at</li> </ul> </li> </ul> <p> </p> <p><strong>File 2: concept_set_expression_items</strong></p> <ul> <li><strong>Generated Data: </strong><em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</em> </li> <li>Columns: <ul> <li>codeset_id</li> <li>concept_id</li> <li>code</li> <li>codeSystem</li> <li>ontology_id</li> <li>ontology_label</li> <li>mapping_category</li> <li>mapping_logic</li> <li>mapping_evidence</li> <li>isExcluded</li> <li>includeDescendants</li> <li>includeMapped</li> <li>item_id</li> <li>annotation</li> <li>created_by</li> <li>created_at</li> </ul> </li> </ul> <p> </p> <p><strong>File 3: concept_set_version</strong></p> <ul> <li><strong>Generated Data: </strong><em>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</em> </li> <li>Columns: <ul> <li>codeset_id</li> <li>concept_set_id</li> <li>concept_set_version_title</li> <li>project</li> <li>source_application</li> <li>source_application_version</li> <li>created_at</li> <li>atlas_json</li> <li>most_recent_version</li> <li>comments</li> <li>intention</li> <li>limitations</li> <li>issues</li> <li>update_message</li> <li>status</li> <li>has_review</li> <li>reviewed_by</li> <li>created_by</li> <li>provenance</li> <li>atlas_json_resource_url</li> <li>parent_version_id</li> <li>is_draft</li> </ul> </li> </ul> <p> </p> <p><strong><em>Generated Output:</em></strong></p> <ul> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_container.csv</li> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</li> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</li> <li>atlas_json_files_v2.0.0.zip</li> <li>omop2obo_enclave_codeset_id_dict_v2.0.0.json</li> </ul>
Annual land cover maps of Germany based on Sentinel-2 MSI Level 3A (WASP) data
<p>Overview:<br> This annual land cover product is available for the years 2016, 2019, 2020, 2021 for the whole of Germany. It was generated based on Sentinel-2 MSI L3A WASP Data provided by DLR (https://geoservice.dlr.de/data-assets/4hcq6dgkj648.html). For a complete description of the classification procedure please refer to<br> Riembauer, G.; Weinmann, A.; Xu, S.; Eichfuss, S.; Eberz, C.; Neteler, M.: Germany-wide Sentinel-2 based land cover classification and change detection for settlement and infrastructure monitoring. In: Proceedings of the 2021 conference on Big Data from Space (doi:10.2760/125905), 2021.</p> <p>Source data:</p> <ul> <li>Satellite data <ul> <li>German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648</li> </ul> </li> <li>Auxiliary data <ul> <li>European Union, Copernicus Land Monitoring Service, European Environment Agency (EEA), <strong>Copernicus High Resolution Layer: Imperviousness Status Map, 2018 </strong>(https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness/status-maps/imperviousness-density-2018)</li> <li><strong>OpenStreetMap</strong> Planet dump retrieved from https://planet.osm.org, https://www.openstreetmap.org</li> <li><strong>S2GLC Map of Europe</strong> (R. Malinowski, S. Lewiński, M. Rybicki, E. Gromny, M. Jenerowicz, M. Krupiński, A. Nowakowski, C. Wojtkowski, M. Krupiński, E. Krätzschmar, and P. Schauer, "Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery," Remote Sensing, vol. 12, no. 21, p. 3523, 2020.)</li> </ul> </li> </ul> <p>File naming:<br> classification_map_germany_[year].tif example: classification_map_germany_2020.tif</p> <p>Projection + EPSG code:<br> WGS 84 / UTM zone 32N (EPSG: 32632)</p> <p>Spatial extent:<br> north: 55:03:38.646483N<br> south: 47:08:24.738401N<br> west: 5:33:47.816647E<br> east: 15:34:24.108516E</p> <p>Spatial resolution:<br> 10 m</p> <p>Format: COG (Cloud-Optimized GeoTIFF)</p> <p>Pixel values:<br> 10: forest<br> 20: low vegetation<br> 30: water<br> 40: built-up<br> 50: bare soil<br> 60: agriculture</p> <p>Temporal coverage:<br> Years 2016, 2019, 2020, 2021</p> <p>Software used:<br> GRASS 7.8, actinia</p> <p>Original dataset license:<br> The Sentinel-2 level 3A data produced and distributed by DLR are based on Copernicus Sentinel-2 level 1C data, which are subject to the following license: https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf One of the following citations is mandatory for using the provided MAJA/WASP L3A product: German Aerospace Center (DLR): Sentinel-2 MSI - Level 3A (MAJA/WASP Tiles) - Germany, DOI: 10.15489/4hcq6dgkj648 or Contains modified Copernicus Sentinel data, processed by DLR, licensed under CC-BY 4.0</p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p>
MODIS Snow-Cover Frequency Maps
<p>The 365 global snow cover frequency maps were derived from MODIS MOD10C1 data. There is one map for each day of the year (leap year days are excluded). Each map displays the frequency of snow cover for the 20-year time series, Hydrological Years 2000 - 2020, on a per-grid cell basis. Each grid cell is approximately 5 X 5 km. The dataset is described in detail in Riggs et al. (in press).</p>
High resolution cropland agreement map (30 m) circa 2020
<p>Accurate and precise measurements of global cropland extent are needed for monitoring the sustainability of agriculture at all scales. Recent advancement in remote sensing and land cover mapping methods have greatly increased the ability to estimate cropland area distribution and trends. Here the FAO presents a map of cropland agreement produced by consolidating information at pixel level from six high-resolutions maps for <em>circa </em>2020. The following six high resolution layers were used: ESRI 10 meter LU/LC, FROM-GLC, GLAD, GLC-FCS30, Globeland30 and Worldcover.</p> <p>Two bands are included in the dataset:</p> <ol> <li>Simple agreement (values between 1 and 6)</li> <li>Detailed agreement (values between 1 and 63)</li> </ol> <p>The map, developed in the Google Earth Engine platform, combines the 6 land cover/cropland layers to show their cropland agreement on pixel level at a spatial resolution of 30 meters. The simple agreement has pixel values that range from 1 (only 1 dataset classifies as cropland) to 6 (all datasets agree on presence of cropland). Pixels with a value of 0 indicate pixels where all datasets agree on absence of cropland. The second band includes a detailed agreement, showing which combination of the 6 datasets classify a pixel as cropland. The overview table (<em>DetailedAgreement_LookupTable.xlsx</em>) shows what the pixel values of this detailed agreement (from 1 to 63) correspond to.</p> <p>The dataset has been uploaded in 16 tiles, in the preview below and in the file "A<em>CroplandAgreement_30m_Tiles.png</em>" the extent of each tile can be found.</p> <p>For more information on FAO statistics on land cover and land use:</p> <p>FAO. 2022. <em>Land use statistics and indicators. Global, regional and country trends, 2000–2020</em>. FAOSTAT Analytical Brief, no. 48. Rome. <a href="https://doi.org/10.4060/cc0963en">https://doi.org/10.4060/cc0963en</a></p> <p>FAO. 2021. <em>Land cover statistics. Global, regional and country trends, 2000–2019</em>. FAOSTAT Analytical Brief Series No. 37. Rome. </p>
X-ray Fluorescence Mapping dataset for use in Heritage Science
<p>The following data sets were collected to support the potential uses of opensource data in the context of digital humanities and heritage sciences. </p> <p>This proposed experiment is conducted by the UCL Institute for Sustainable Heritage in collaboration with the Centre for Digital Humanities. Imaging methods including Photography, Multispectral Imaging, Hyperspectral Imaging and Xray Fluorescence Mapping have been collected along with the complete readout metadata of the instrumentation. </p> <p>We hope that you find the data helpful, and we welcome you to use the data in any way you wish, for all and any analysis development purposes. For us to build upon this research, we ask that in return you would be willing to share in some regard your experiences in using open-source data, using our data, successes and issues. </p> <p>If you would be willing to engage with us in this endeavor, please feel free to contact us so that we may be able to follow up with you. </p> <p>Other Data sets available <a href="https://zenodo.org/record/7319696#.Y3NuOXbP2Uk">Here</a></p> <p>E: <a href="mailto:molly.fort.21@ucl.ac.uk">molly.fort.21@ucl.ac.uk</a> </p> <p>Object Paradata; </p> <ul> <li><strong>Postcard – c. Early 1900's </strong></li> <li><strong>Language – Eng. </strong></li> <li><strong>Materials – colour print on card, metallic leafing. </strong></li> <li><strong>Front transcription - </strong></li> <li><strong> ‘Greetings’ </strong></li> <li><strong> ‘May your Birthday bring you Peace & perfect Happiness, Golden hopes & Love of Friends, And every Happiness this world can send.’ </strong></li> <li><strong>Object Dimensions – 138mm X 88mm</strong></li> </ul> <p>The postcard is an item of ephemera donated to the UCLDH Digitisation Suite by Prof Melissa Terras, for teaching and training purposes in 2015.</p> <p>This folder contains:</p> <p>X-ray fluorescence imaging map collected from a <a href="https://www.bruker.com/en/products-and-solutions/elemental-analyzers/micro-xrf-spectrometers/m4-tornado-plus.html">Bruker M4+ Tornado Micro-XRF System</a></p> <ul> <li>Postcardmap1.bcf - Bruker composite file containing full fluorescence spectral and mapping data with some other information. Can be read by Bruker software or using freely dowloadable <a href="https://hyperspy.org/">Hyperspy</a>.</li> <li>EDX.hdf5 - full fluorescence spectral and mapping data in open format, created and readable via. Hyperspy.</li> <li>Postcard_data.txt - metadata saved in ASCII format by Bruker system.</li> <li>postcardmap1_*.png - individual png images of mapped elements, listed in filename. </li> </ul> <p> </p>
Supplementary Materials for "Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms"
<p>This dataset was created as suplementary material for research article: <strong>Influence of Measured Radio Environment Map Interpolation on Indoor Positioning Algorithms</strong></p> <p>This package contains packet capture files of 802.11 probe requests captured at Geotec office at University Jaume I, Spain by 5 ESP32 microcontrollers. The packet capture files are in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p> <p>The data are split between radio map data captured at all accessible reference positions in our office spread in 1m grid and evaluation data gathered alligned to 0.5m grid, as well as in hard to access locations. The location the data were collected are available in the office.</p> <p>The dataset has 4 parts, and all subsets of the dataset can be generated from the captured pcap files:</p> <p><strong>Data</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations representing the whole radio environment map. The folder name stands for each of the 5 ESP32 sniffer stations and the name of the file points to a reference location the data were captured in. Example of the coordinates matching the reference location grid names are in following table:</p> <table> <caption>Data Point Coordinates</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"> </th> <th scope="col">X</th> <th scope="col">Y</th> <th scope="col"><strong>...</strong></th> </tr> </thead> <tbody> <tr> <th scope="row">A1</th> <td>0.85</td> <td>0.1</td> <td><strong>B1</strong></td> <td>1.85</td> <td>0.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A2</th> <td>0.85</td> <td>1.1</td> <td><strong>B2</strong></td> <td>1.85</td> <td>1.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A3</th> <td>0.85</td> <td>2.1</td> <td><strong>B3</strong></td> <td>1.85</td> <td>2.1</td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">...</th> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> <td><strong>...</strong></td> </tr> <tr> <th scope="row">A11</th> <td>0.85</td> <td>10.1</td> <td><strong>B11</strong></td> <td>1.85</td> <td>10.1</td> <td><strong>...</strong></td> </tr> </tbody> </table> <p><strong>Data_Eval</strong></p> <p>This folder contains pcap files from all 5 ESP32 stations with data captured at 31 locations not found in the original reference location grid. The naming corresponds to the X and Y location in which the data were collected.</p> <p><strong>Processed_Data</strong></p> <p>Additionally, there are 3 folders with processed CSV files. One folder that combines all radio map values, second folder contains combined evaluation values and third is with linearly interpolated radio map values.</p> <p>The CSV files are in a format:</p> <blockquote> <p><code>X, Y, RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</code></p> </blockquote> <p><strong>Data_Scenarios</strong></p> <p>This folder for the ease of use, contains data for exact reproducibility of our results in the paper. There 14 scenarios described in the following table:</p> <table> <caption>Scenario Descriptions</caption> <thead> <tr> <th scope="col"> <p>Data Name</p> </th> <th scope="col"> <p>Scenario Description</p> </th> </tr> </thead> <tbody> <tr> <td>GPR00</td> <td>Only measured data, 50 samples per reference position</td> </tr> <tr> <td>GPR01</td> <td>Measured data with empty spots filled using Linear interpolation, 50 samples per reference position</td> </tr> <tr> <td>GPR02</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR03</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR04</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR05</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR06</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR07</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 50 samples per reference position</td> </tr> <tr> <td>GPR08</td> <td>Gaussian Regression trained only on measured data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR09</td> <td>Gaussian Regression trained only on measured data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR10</td> <td>Gaussian Regression trained on linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR11</td> <td>Gaussian Regression trained on linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR12</td> <td>Gaussian Regression trained selection of linearly interpolated data - 1m output grid, 1 sample per reference position</td> </tr> <tr> <td>GPR13</td> <td>Gaussian Regression trained selection of linearly interpolated data - 0.5m output grid, 1 sample per reference position</td> </tr> </tbody> </table> <p>The folder contains 4 files for each scenario. The Beginning of the filename corresponds to the data name, with suffix describing what data are in the file. The descriptions of used suffixes are in the following table:</p> <table> <caption>File Suffix Descriptions</caption> <tbody> <tr> <td> <p><strong>Suffix</strong></p> </td> <td> <p><strong>Suffix Description</strong></p> </td> </tr> <tr> <td>_trncrd</td> <td>Training Labels</td> </tr> <tr> <td>_trnrss</td> <td>Training RSSI Values</td> </tr> <tr> <td>_tstcrd</td> <td>Evaluation Labels</td> </tr> <tr> <td>_tstrss</td> <td>Evaluation RSSI Values</td> </tr> </tbody> </table> <p>These data are in format compatible with systems that apart from X and Y coordinates also detect, building, floor etc.</p> <p>The RSSI data are in format:</p> <blockquote> <p>RSSI_1, RSSI_2, RSSI_3, RSSI_4, RSSI_5</p> </blockquote> <p>The Labels are in format: (Since we only use positioning in 1 office, apart X and Y coordinates are set to 0)</p> <blockquote> <p>X, Y, 0, 0, 0</p> </blockquote>
A map of global peatland extent created using machine learning (Peat-ML)
<p>Map of global peatland extent estimated by machine learning. The download includes both a netcdf file version and a GeoTIFF (as a zip archive)</p> <p>Abstract from associated paper:</p> <p>Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and<br> hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed<br> by Earth System Models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here,<br> we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning<br> techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of<br> peatland formation and include spatially distributed climate, geomorphological and soil data, along with remotely-sensed<br> vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with<br> mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualititative comparisons to other maps<br> in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out<br> cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r<sup>2</sup><br> of 0.73 with a root mean squared error and mean bias error of 9.11% and -0.36%, respectively. Our second error estimate<br> was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited<br> Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping<br> products generated through more traditional approaches, at least for boreal peatlands.</p>
Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning
<p>Data for: Assets in Periphery, Agents in the Core: Mapping the Micro Structures of International Tax Planning</p> <p>See paper at: https://osf.io/preprints/socarxiv/hyc4p</p> <p>In the last two decades, tax avoidance has risen to the top of the agenda of policy makers and international organizations. The majority of political action and academic research has focused on the macro-level of states, pointing towards the responsibility of ‘tax havens’ or ‘offshore financial centers’. Research on the micro-level has demonstrated the importance of non-state actors who facilitate tax planning, but tax advisors have never been studied systematically with global data. In this paper, we connect the micro and macro levels. We map tax advisors geographically using a novel empirical approach based on LinkedIn. We show that tax advisors generally locate in large cities in the EU and OECD, rather than in places targeted as ‘tax havens’. We further consider what determines the locations of tax advisors. Using multiple regression analysis, we find that locations of tax advisors does not correlate with the location of corporate profits, financial secrecy, or economic activity. Rather, it correlates with the managerial and financial activity. Our results underscore the core-periphery structure in offshore finance. Effective regulation of tax avoidance should focus on tax advisors, not only on the destination of money flows, since the active facilitation does not occur in those places.</p>
The Quest for the Missing Dust: New Herschel Maps of Local Group Galaixes (LMC, SMC, M31, M33) that Restore Previously-Missed Extended Emission, Along With SED-Fitting Results, Hydrogen Gas Maps, and Swift UV Observations
<p>Here we provide the data products from publications:</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: I – Restoring Large Scale Emission in Herschel Maps of Local Group Galaxies</em>, ApJ 921 35</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: II – Two Orders of Magnitude of Evolution in the Dust-to-Gas Ratio Resolved Within Local Group Galaxies</em>, ApJ 946 42</p> <p>This data concerns four Local Group galaxies: the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, and M33.</p> <p> </p> <p>For each galaxy, we provide our new Herschel maps, as described in the above publications, which were combined in Fourier space ('feathered') with Planck, IRAS, and COBE data, in order to restore extended emission that was removed from previous Herschel reductions for these galaxies.</p> <p>For each galaxy, we provide this new Herschel data for 5 Hershcel bands: the PACS 100 and 160 <span>\(\mu\)</span>m bands, and the SPIRE 250, 350, and 500 <span>\(\mu\)</span>m bands. This data is provided in FITS format, with one FITS file for each band for each galaxy. Each of these files contains 4 extensions. Extension 1 (IMAGE) provides the standard feathered map. Extension 2 (UNC) provides the uncertainty map. Extension 3 (MASK) provides a binary mask map indicating the portion of the data where reliable, fully-feathered high-resolution coverage is available. Extension 4 provides the foreground-subtracted version of the feathered map (FGND_SUB), the header of which also describes the uncertainty on that subtraction. All maps are in units of MJy/sr (except the MASK extension, which is boolean).</p> <p> </p> <p>We also provide the outputs of our Spectral Energy Distribution (SED) fitting to this data, as described in the publications. For each galaxy, we provide FITS files giving the median value of each parameter in each pixel, and maps of the uncertainties on those medians (being the 68.3% quantile around the median). The parameters are dust mass surface density (SED_Sigma_Mass.fits), dust temperature (SED_Temp.fits), beta 1 (SED_Beta1.fits), beta 2 (SED_Beta2.fits), break wavelength (SED_Break.fits), and 500 <span>\(\mu\)</span>m excess (SED_Excess500.fits). Each of these files contain 2 extensions. Extension 1 (median) provides the map of pixel parameter median values. Extension 2 (uncert) provides the map of uncertainties on those medians.</p> <p>Additionally, we provide the full posterior probability distribution for all SED parameters, consisting of 1000 posterior samples, for all pixels, in the form of a FITS file containing a 4-dimensional hypercube, with axes corresponding to right ascension, declination, parameters (in order: dust mass surface density, dust temperature, beta 1, beta 2, break wavelength, and 500 <span>\(\mu\)</span>m excess), and samples. This is provided as a gzip compressed FITS file for each galaxy.</p> <p>Furthermore, provide the Swift-UVOT maps used in Paper II. This data is provided for Swift-UVOT bands W1, W2, and M2. For each band, we provide a FITS file containing 3 extensions. Extension 1 (SURF_BRI) provides the map of surface brightness in MJy/sr (converted using the Swift-UVOT zero points given in Breeveld et al., 2011). Extension 2 (RATE) provides the map of count rate (in photons/sec). Extension 3 (EXP) provides the map of exposure time (in sec). The maps for the LMC and SMC are those presented in Hagen et al. (2017). The maps for M31 and M33 are were reduced following the same process as those in Hagen et al. (2017), and will be fully presented in Decleir et al. (in prep.), but are provided here for the purposes of reproducibility.</p> <p>Lastly, for each galaxy, we provide our maps of the hydrogen surface density (Sigma_H.fits), and dust-to-gas ratio (DtG.fits). None of the maps presented have had deprojection corrections applied</p> <p> </p>
Geophysical and physical oceanography data and acoustic facies mapping results of the Central Basin in the northwestern Ross Sea margin, Antarctica
<p>Multi-channel seismic (MCS), sub-bottom profiler (SBP), multi-beam echosounder (MBES) and expandable conductivity-temperature-depth (XCTD) data and acoustic facies mapping results of the Central Basin in the northwestern Ross Sea margin, Antarctica. The Coordinate Reference System (CRS) for the MCS, SBP, MBES data and acoustic mapping results is WGS 84 / Antarctic Polar Stereographic (EPSG:3031). ). The geophysical data (MCS, SBP, MBES) and oceanographic measurements (XCTD) collected by the RV <em>Araon</em> are provided by the Korea Polar Data Center (<a href="https://kpdc.kopri.re.kr">https://kpdc.kopri.re.kr</a>).</p>
Atrophy Pattern Maps of Alzheimer's Disease, Mild Cognitive Impairment, Parkinson's Disease, and Frontotemporal Dementia
<p>The files contain voxel-wise t-statistics maps contrasting deformation based morphometry (DBM) measurements of Alzheimer's disease (AD), Parkinson's disease (PD), mild cognitive impairment (MCI), and fronto-temporal dementia (FTD) patients against matched normal controls.</p> <p>AD and MCI maps are based on ADNI data, available at:</p> <p>PD map is based on PPMI data, available at:</p> <p>FTD map is based on NIFD data, available at:</p> <p>For more information regarding the participants and method details, see:</p> <p>Dadar, Mahsa, et al. "White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia." <em>Neurobiology of aging</em> 111 (2022): 54-63.</p>
Occupations on the map: Using a super learner algorithm to downscale labor statistics, data
<p>This repository contains all the input and output data (including maps) related to <a href="https://doi.org/10.1371/journal.pone.0278120">Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics</a>. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm.</p> <p>In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.</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.