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230 results for “interactive map”
Interactive maps for the visualization of ESRIUM automated driving tests with various EGNSS localization solutions
<p>In order to make the test results available to a broader audience in an easy manner, we have generated interactive maps. These maps are attached to this report and can be viewed in a web-browser. </p><p>Due to the large number of datasets, we have color-coded them on the map and in the menu. An arbitrary number of datasets can be selected at a time.</p><p>Due to the high accuracy of the EGNSS receivers, one can clearly identify the lane on which the vehicle was driving, and where the vehicle was performing a lane-change. However, the satellite/areal-images are not perfectly geo-referenced, thus one can notice a slight offset between satellite/areal-images and real-world lanes.</p><p> </p><p><strong>How to use the map?</strong></p><ul><li>The map can be used in a similar manner than other map-applications, such as google maps. By using the mouse, you can set the focus on the area of your interest. By using the +/- buttons (top left), you can zoom in/out.</li><li>By hovering over the layer-symbol (top right), a popup emerges. Here, you can select different background-tiles (such as satellite/areal-images). In addition, you can select different datasets which should be visualized on the map.</li></ul><p><strong>Background-tiles:</strong></p><ul><li>Basemap – Sat - Satellite/Areal images (from Basemap) -Symbolic map with high resolution (from Basemap)</li><li>Basemap – HighDPI Symbolic map with high resolution (from Basemap)</li><li>OpenStreetMap - Symbolic map (from OpenStreetMap)</li><li>OpenTopoMap - Symbolic map including topology information (from OpenTopoMap)</li></ul><p><strong>Datasets:</strong></p><ul><li>GNSS (Vehicle) - Position of vehicle, according to on-board GPS receiver</li><li>EGNSS (AsteRx SB3 Pro+) - Position of vehicle, according to AsteRx SB3 Pro+ receiver</li><li>EGNSS (mosaic-X5) - Position of vehicle, according to mosaic-X5 receiver</li><li>EGNSS (mosaic-H) - Position of vehicle, according to mosaic-H receiver</li><li>PVT Mode: EGNSS (AsteRx SB3 Pro+) - PVT Mode of AsteRx SB3 Pro+ receiver</li><li>PVT Mode: EGNSS (mosaic-X5) - PVT Mode of mosaic-X5 receiver</li><li>PVT Mode: EGNSS (mosaic-H) - PVT Mode of mosaic-H receiver</li><li>in-lane Offset Change-Request - Position, at which an in-lane offset change (relative to middle of the current lane) was requested via C-ITS</li><li>Lane Change to left - Position, at which a lane-change towards left was performed </li><li>Lane Change to right - Position, at which a lane-change towards right was performed</li></ul><p>Interactive maps are attached are two precision levels one with 4 and the other in 7 digits. The list files and the corresponding test conditions are listed below. </p><p>Test velocities [km/h]: 90, 110, 130 </p><p>interactive map files: </p><p>speed: 90 km/h</p><ul><li>Testrun_01.html</li><li>Testrun_03.html</li><li>Testrun_04.html</li></ul><p>speed: 110 km/h</p><ul><li>Testrun_05.html</li><li>Testrun_06.html</li><li>Testrun_07.html</li></ul><p>speed: 130 km/h </p><ul><li>Testrun_08.html</li><li>Testrun_09.html</li><li>Testrun_10.html</li></ul>
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
A complete map of specificity encoding for a partially fuzzy protein interaction
<p>All data required to run analyses for "A complete map of specificity encoding for a partially fuzzy protein interaction". Please see <a href="https://github.com/lehner-lab/fuzzy_specificity">https://github.com/lehner-lab/fuzzy_specificity</a> for instructions. </p>
Interactive map of distribution of gene fragments indicative of cyanotoxin biosynthesis and cyanotoxins in the European Alps
<p><span>Distribution of cyanotoxins and cyanotoxin biosynthesis genes in Alpine region determined by LC-MS/MS and (q)PCR. Cyanotoxins and cyanotoxin genes are mapped on separate layers, and two basemaps are available (simple and relief). Results can be filtered by location, sample type, water body type, cyanotoxins and cyanotoxin genes. Note that cyanotoxin analyses were not performed on all sampling points.</span></p>
IVMOOC 2017 - GloBI Data for Interactive Tableau Map of Spatial and Temporal Distribution of Interactions
<p>Global Biotic Interactions (GloBI, www.globalbioticinteractions.org) provides an infrastructure and data service that aggregates and archives known biotic interaction databases to provide easy access to species interaction data. This project explores the coverage of GloBI data against known taxonomic catalogues in order to <em>identify ‘gaps’ in knowledge of species interactions</em>. We examine the richness of GloBI’s datasets using itself as a frame of reference for comparison and explore interaction networks according to geographic regions over time. The resulting analysis and visualizations intend to provide insights that may help to enhance GloBI as a resource for research and education.</p> <p>Spatial and temporal biotic interactions data were used in the construction of an interactive Tableau map. The raw data (IVMOOC 2017 GloBI <em>Kingdom</em> Data Extracted 2017 04 17.csv) was extracted from the project-specific SQL database server. The raw data was clean and preprocessed (IVMOOC 2017 GloBI Cleaned Tableau Data.csv) for use in the Tableau map. Data cleaning and preprocessing steps are detailed in the companion paper.</p> <p>The <strong>interactive Tableau map</strong> can be found here: https://public.tableau.com/profile/publish/IVMOOC2017-GloBISpatialDistributionofInteractions/InteractionsMapTimeSeries#!/publish-confirm</p> <p>The<strong> companion paper</strong> can be found here: doi.org/10.5281/zenodo.814979</p> <p><strong>Complementary high resolution visualizations </strong>can be found here: doi.org/10.5281/zenodo.814922</p> <p><strong>Project-specific data </strong>can be found here: doi.org/10.5281/zenodo.804103 (SQL server database)</p>
Interactive map of Heat Stress Compensability Classification (HSCC) application in 96 United States cities.
<p>This repository includes the interactive map in format .html of the very first application of the <strong>Heat Stress Compensability Classification (HSCC) in 96 cities in the United States </strong>showing the proportion of days with compensable and uncompensable heat stress from the top 10th percentile of hottest days from 2005-2020 in each place.</p> <p>This map offers the detailed results of the very first application of the classification system as in the journal article: <strong>The Development of an Adaptive Heat Stress Compensability Classification Applied to the United States</strong>, published in the 4th SNP special issue in the International Journal of Biometeorology. The results of this visualization were obtained from open-source data and coding packages such as Folium, and the model results were obtained by applying the Python Human Heat Balance (PyHHB) on weather dataset freely available.</p> <p>The interactive map offers a detailed visualization of the results from each of the cities, allowing you to see 3 tabs when the icon of the pie chart from each location is clicked.</p> <p><strong>Tab statistics:</strong> Detail per city of Figure 4b of related paper.</p> <p><strong>Tab Histogram 2D: </strong>Details per city of Fig 6 of related paper</p> <p><strong>Tab How to read: </strong>Figure 2 in related paper.</p> <p>Please for questions related to this dataset/code contact Gisel Guzman-Echavarria (gguzma20@asu.edu).</p> <p>Guzman-Echavarria, G., & Vanos, J. (2023). PyHHB: Physiological-based estimations of human survivability and liveability to heat in a changing climate (Nature Communications (1.0.0)). Zenodo. https://doi.org/10.5281/zenodo.10020137</p> <p> </p> <p> </p>
Figure 5. A front view of the "Mary and John Gray Library"-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 5 represents the avatar standing outside and in front of the Mary and John Gray Library after selecting the option “Library”. The library’s main purpose is to facilitate students with a variety of scholarly information within the overall composition of the University’s stated mission. Figure 5 shows the path generated by A* algorithms with a red color.</p>
Figure 3. The 'Welcome Screen' of our implementation-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 3 shows an avatar ready to start the game. The user has the option to click the button called ‘Go Cardinals! Start’ Button. Once that happens, the avatar gets to choose going to one of the buildings of interest mentioned above.</p>
Figure 1. The Statechart for the Player movement and the Navigation System-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>The next section describes the Unified Modelling Language (UML) diagrams designed for the project, which are a state diagrams (also known as statecharts) for the Player movement, the Navigation system (Figure 1). In addition, we used a class diagram for the Player and Camera movement (Figure 2). When the avatar-based game starts, the state of the Player is Idle, i.e., Player_IDLE. When the user selects the building, it enables the navigation path towards the destination. If the user selects any arrow keys (Right, Left & Up) the state of the player will change to running (i.e., Player_Running). Also, the path will diminish along with the player movement; hence, the state of navigation path will change to Changing_Path.</p>
Figure 2. The class diagram for the Player and Camera movements-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>The next section describes the Unified Modelling Language (UML) diagrams designed for the project, which are a state diagrams (also known as statecharts) for the Player movement, the Navigation system (Figure 1). In addition, we used a class diagram for the Player and Camera movement (Figure 2). When the avatar-based game starts, the state of the Player is Idle, i.e., Player_IDLE. When the user selects the building, it enables the navigation path towards the destination. If the user selects any arrow keys (Right, Left & Up) the state of the player will change to running (i.e., Player_Running). Also, the path will diminish along with the player movement; hence, the state of navigation path will change to Changing_Path.</p>
Figure 4. An inside view of the "Maes building along with the Navigation Path" -Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 4 represents the navigation path to the Department of Computer Science inside the Maes building after selecting the option “D.C.S.”, which stands for Department of Computer Science.</p>
Figure 6. An inside view of Mary and John Gray Library-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 6 exhibits the ambience of the study environment that allows students to have group discussions, and when to access Internet, and more. The photographs have been digitized in a very realistic way.</p>
Data for "Interactive maps in the Jupyter notebook"
<p>Dataset used for the lesson "<a href="https://annefou.github.io/jupyter_maps/index.html">Interactive maps in the Jupyter notebook</a>" </p> <p> </p> <p>Taught at CarpentryConnect, Manchester 2019. </p>
Data for the "Systematic mapping of protein-metabolite interactions in central metabolism of Escherichia coli"
<p>This dataset contains raw and processed NMR data used in the publication.</p>
Interactive web map of local heritage sites in Inhambane province, Mozambique
<p>Interactive web map containing locally protected forest patches, ceremonial places, archaeological sites and other landscape features making up cultural heritage of Inhambane province, Mozambique. Using geonarratives and local cartography, information about the location of locally protected forest patches, ceremonial places and other landscape features (e.g., lakes, caves and wells) making up cultural heritage was provided by local leaders and communities stewarding local heritage sites in Inhambane province. Data about archaeological sites is derived from the study done by Adamowicz and Nhatule (2011). Data about protected areas was derived from National Cartography and Remote Sensing Centre in Mozambique. The interactive web map was produced using the qgis2web plugin built-in the QGIS. </p> <p>Adamowicz, L., & Nhatule, E. (2011). <em>Environment and social impact assessment for the proposed exploration in EPC area “A”.</em> Maputo: Archaeological Circle of Cultural Heritage.</p>
Prototyping Modes of Interactive Mapping
<p>3<sup>rd</sup> Project Presentation</p>
GridKit extract of ENTSO-E interactive map
<p>This dataset was generated based on a map extract from May 11, 2016. This is an <em>unofficial</em> extract of the ENTSO-E interactive map of the European power system (including to a limited extent North Africa and the Middle East). The dataset has been processed by GridKit to form complete topological connections. This dataset is neither approved nor endorsed by ENTSO-E.</p> <p>This dataset may be inaccurate in several ways, notably:</p> <ul> <li>Geographical coordinates are transfered from the ENTSO-E map, which is known to choose topological clarity over geographical accuracy. Hence coordinates will not correspond exactly to reality.</li> <li>Voltage levels are typically provided as ranges by ENTSO-E, of which the lower bound has been reported in this dataset. Not all lines - especially DC lines - contain voltage information.</li> <li>Line structure conflicts are resolved by picking the first structure in the set</li> <li>Transformers are <em>not present</em> in the original ENTSO-E dataset, there presence has been derived from the different voltages from connected lines.</li> <li>The connection between generators and busses is derived as the geographically nearest station at the lowest voltage level. This information is again not present in the ENTSO-E dataset.</li> </ul> <p>All users are advised to exercise caution in the use of this dataset. No liability is taken for inaccuracies.</p>
Ultra-high scale cytometry-based cellular interaction mapping - Data repository
<p>This is the repository for datasets used in Vonficht, Jopp-Saile, Yousefian, Flore <em>et al. </em>Ultra-high scale cytometry-based cellular interaction mapping, <em>Nature Methods </em>(2025) <a href="https://doi.org/10.1038/s41592-025-02744-w" rel="nofollow">https://doi.org/10.1038/s41592-025-02744-w</a>. Associated analysis code can be found at <a href="https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping">https://github.com/agSHaas/ultra-high-scale-cytometry-based-cellular-interaction-mapping</a>, and the repository for the accompanying R package is hosted at <a href="https://github.com/agSHaas/PICtR">https://github.com/agSHaas/PICtR</a>. </p>
Mapping the interaction surface between CaVβ and actin and its role in calcium channel clearance
<p><strong>HADDOCK protein-protein docking data for the Ca<sub>V</sub>β/F-actin complex models reported in "Mapping the interaction surface between Ca<sub>V</sub>β and actin and its role in calcium channel clearance".</strong></p> <p> </p> <p>The dataset is divided in four different folders:</p> <ol> <li><strong>Cavbeta2:</strong> data for the docking between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) using XL-MS-derived distance restraints </li> <li><strong>Cavbeta4:</strong> data for the docking between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>4</sub> (PDB 1VYV) using XL-MS-derived distance restraints </li> <li><strong>Monomer:</strong> data for the control docking between <em>monomeric</em> actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) using XL-MS-derived distance restraints </li> <li><strong>Ab_initio:</strong> data for the control dockings between dimeric actin (PDB 5OOE) and Ca<sub>V</sub>β<sub>2</sub> (PDB 5V2P) <em>without</em> XL-MS-derived distance restraints and using the <em>ab initio</em> options available in HADDOCK 2.4</li> </ol> <p> </p> <p>Each of the <strong>Cavbeta</strong> folders (1-2) and the <strong>Monomer</strong> folder (3) contain:</p> <p>- Inputs: </p> <ul> <li>HADDOCK run parameter file (job_params.json)</li> <li>XL/MS-derived unambiguous distance restraints (unambig.tbl)</li> <li>Bioinformatics-derived (CPORT and NACCESS) ambiguous distance restraints (ambig.tbl)</li> </ul> <p>- Outputs:</p> <ul> <li>Top 4 models of the selected HADDOCK cluster (cluster1_1.pdb, ..., cluster1_4.pdb)</li> <li>Source data for the docking analyses presented in the Supplementary Information (Supplementary Figures 4, 6, 9, 13 and 14 and Supplementary Table 4)</li> </ul> <p> </p> <p>The <strong>Ab_initio</strong> folder (4) contains:</p> <p>- Input: </p> <ul> <li>HADDOCK run parameter files for each of the six control simulations presented in Supplementary Table 6 (job_params.json)</li> </ul> <p>- Outputs:</p> <ul> <li>Source data for the docking analyses presented in Supplementary Table 6</li> <li>Model from control simulation 1 shown in Supplementary Figure 5</li> </ul> <p> </p>
The ScaleMaster: The decompostion of Pan-Scalar, Interactive Map (OSM,Google Maps,IGN scan)
<p>The ScaleMaster diagram of Brewer and Buttenfield, "where the scaleLine replaces the timeLine", is a formal tool (Excel sheets) designed to formalize the rules for manual map design and "emphasize changes to the map display" . Inspired by Brewer and Buttenfield, we use ScaleMaster to standardize and formalize changes while zooming and exploring each of pan-scalar map (OSM,Google Maps,Scan IGN). In our methodology, however, we go a step further. The timeline of exploration is also examined in addition to the scaleline of zooming. We focus on map design practices that account for pan-scalar map exploration. For example, we account for generalization changes between scales based on empirically or theoretically justifiable reasons.</p> <p>we use ScaleMaster to analyze particular and common geographic entities in the maps (including rivers, urban areas, bus stations, and administrative borders) representing but a fraction of all map ontologies (e.g., water, roads, transportation networks, relief, points-of-interest, vegetation, administrative districts). We constructed a ScaleMaster for each of the three pan-scalar maps (OSM, Google Map, Scan IGN). </p> <p>Our hope is that this first analysis, and the resulting categories below, will lead to critique, comment, and iterative improvement in the future. In other words, our initial findings are just that – outcomes that further exploration on pan-scalar maps can add to, revise, and improve upon. </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.