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23 results for “Mapping Mobility”
Mapping Mobility Motivation Survey
<p>The Mapping Mobility motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc.<br> Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement.<br> The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting air pollution in the Mapping Mobility pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/mapping-mobility/">https://actionproject.eu/citizen-science-pilots/mapping-mobility/</a>.</p> <p>The Mapping Mobility motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed and administered using the <a href="https://coney.cefriel.com/">Coney</a> toolkit.</p> <p>The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are:</p> <ul> <li><em>*-procedure.ttl</em> contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-results.ttl </em>contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-survey.tll </em>contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-results.csv </em>contains the CSV of the answers collected</li> </ul>
COVID19 Flow-Maps Mobility-Associated-Risk
<p><strong>The Mobility Associated Risk</strong></p> <p>The Mobility Associated Risk is a risk score combines mobility and COVID-19 incidence to estimate how many cases could theoretically be exported/imported between different origin-destination pairs of regions.</p> <p>For more information about how the MAR is calculated visit: <a href="https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk">https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk</a></p> <p>Dashboard The Mobility Associated Risk combines mobility and COVID-19 incidence to estimate how many cases could theoretically be exported/imported between different origin-destination pairs of regions.</p> <p>For more information about how the MAR is calculated visit: <a href="https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk">https://flowmaps.life.bsc.es/flowboard/board_what_is_risk#what_is_risk</a></p> <p>Dashboard <a href="https://flowmaps.life.bsc.es/flowboard/">https://flowmaps.life.bsc.es/flowboard/</a></p>
Viabundus map of premodern European transport and mobility
<p>Viabundus.eu is a freely accessible online street map of late medieval and early modern northern Europe (1350-1650). Originally conceived as the digitisation of Friedrich Bruns and Hugo Weczerka's <em>Hansische Handelsstraßen</em> (1962) atlas of land roads in the Hanseatic area, the Viabundus map moves beyond that. It includes among others: a database with information about settlements, towns, tolls, staple markets and other information relevant for the pre-modern traveller; a route calculator; a calendar of fairs; and additional land routes as well as water ways.</p> <p>Viabundus is a work in progress. Version 2, released on 25 April 2025, contains a rough digitisation of the land routes from <em>Hansische Handelsstraßen</em>, as well as a thoroughly researched road network for the current-day Netherlands, Denmark, Finland, the German states of Lower Saxony, Schleswig-Holstein, Thuringia, Saxony-Anhalt, Brandenburg, Mecklenburg-Vorpommern, Hesse and North Rhine-Westfalia, and parts of Poland (Pomerania, Royal Prussia, Greater Poland). The inclusion of other regions is currently being planned. Additions to the dataset will be released as new versions in the future.</p> <p>The project's homepage viabundus.eu contains a web map application to explore the data. To allow for more advanced spatial and historical analyses, the underlying dataset is available for download under the CC-BY-SA license.</p> <p>The dataset is designed as a network model and therefore consists of two main elements: 1) a relational database of nodes, i.e. geographical places, with historical information about settlements, towns, tolls, staple markets, fairs, bridges, ferries, harbours and shipping locks; 2) a database with edges, i.e. the geospatial representations of the land and water routes that connected these nodes. The entire database is available in CSV format (with geospatial geometry as WKT); the edges and the outlines of towns in the 16th century are also separately available as geojson and GML files. For more information about the structure of the dataset, theoretical considerations and sources, please consult the enclosed documentation file.</p>
Dataset for Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation
<p>Dataset of paper "Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation".</p> <p>The measurement data contains indoor mapping results using millimeter-wave 5G NR signals at 28 GHz. The measurement campaign was conducted in an indoor office environment in Hervanta Campus of Tampere University. Six different sets of measurements contain the range profiles after the proposed radar processing. The shared data contains the IQ data of both transmit and receive signals used during the measurement campaign.</p> <p>The file "main.m" shows how to process and plot the shared data.</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 9.The educational blog on Google+ Time Maps page–the weaving techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D reconstruction), slightly different from those available on the Time Maps web site, but containing the same information. The children had to make a little effort to relate this information with the one presented on the site, to make a connection between the questions, the fragments from videos at which the answers referred to and the information from the site. The set of questionnaires lead the school children through the majority of data offered by the web site regarding to the two historical periods (Figures 9, 10).</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 14. The survey as a public posting on Google+ Time Maps page
<p>During this experimentation phase a symposium was organized at Vădastra School with the purpose to present our learning experiment to a group of 30 teachers from the Olt County. An open history lesson on the Time Maps web site was held by a history teacher, and a school girl described the Facebook page of the Vădastra School (Figure13), maintained by both teachers and children. The invited teachers gave a feedback on the effectiveness and utility of the Time Maps learning system by responding to a questionnaire-based survey, which was posted on the Google+ page (Figure 14). </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 10. The educational blog on Google+ Time Maps page – the glass manufacturing techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D<br> reconstruction), slightly different from those available on the Time Maps web site, but containing<br> the same information. The children had to make a little effort to relate this information with the one<br> presented on the site, to make a connection between the questions, the fragments from videos at<br> which the answers referred to and the information from the site.<br> The set of questionnaires lead the school children through the majority of data offered by the<br> web site regarding to the two historical periods (Figures 9, 10).</p>
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Visualizing phage infection
<p>We staged infections at four multiplicities of infection (MOI 0, 0.01, 0.1, and 1), and took snapshots every ten minutes over a 40-minute period. We designed FISH probes targeting the non-coding strand of the <em>gp34</em> gene, which encodes a tail fiber protein and quantified cells with 5 or more MGE spots, less than 5 spots, and no spots</p>
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
Spatial Mapping of Mobile Genetic Elements and their Cognate Hosts in Complex Microbiomes - Identifying the host taxon of a previously undescribed plasmid
<p>We investigated the taxonomic association of an unknown plasmid within a plaque biofilm of a patient diagnosed with stage 3 periodontitis. We combined long- and short- read sequencing to identify a complete plasmid with minimal homology to any sequence in the RefSeq database. The plasmid carried several predicted genes for mobilization and toxin-antitoxin systems. We designed MGE-FISH probes for the plasmid and combined this MGE-FISH stain with an 18-genera HiPR-FISH panel.</p> <p>Images are labeled by collection time such that the laser order for a given field of view (fov) is: 488nm Lambda, 514nm Lambda, 561nm Lambda, 633nm Airyscan, 405nm Lambda. We used Flye (https://github.com/fenderglass/Flye) to assemble the plasmid using long read Nanopore sequencing only and we used OPERA-MS (https://github.com/CSB5/OPERA-MS) to do hybrid assembly with Illumina short reads and Nanopore long reads. The assemblies are in the fasta files and the reads that map to the assemblies are in the fastq files. </p>
Spatial Mapping of Mobile Genetic Elements and their Cognate Hosts in Complex Microbiomes - Combined MGE and taxonomic mapping
<p>We used rRNA-FISH to stain five common oral genera, <em>Veillonella, Streptococcus, Corynebacterium, Lautropia, </em>and <em>Neisseria, </em>each with a different fluorophore, and we used MGE-FISH to stain the <em>termL</em> gene of the active prophage with a sixth fluorophore.</p> <p>We assembled contigs using combined long- and short-read sequencing and identified a highly abundant plasmid. Alignment of this contig to the plasmid database (PLSDB) showed that the plasmid had previously been observed in <em>Prevotella nigrescens</em> (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_018127865.1/). We selected two genes from the contig with metallo-β-lactamase (MBL) domains as targets for MGE-FISH (https://www.uniprot.org/uniprotkb/V8CNR4/entry, https://www.uniprot.org/uniprotkb/V8CNR9/entry). We stained both putative MBL genes (<em>pMBL</em>) with the same color using MGE-FISH. For taxonomic mapping, we broadened our target panel by employing HIPR-FISH. We selected a target panel of 18 genera that are highly abundant and prevalent in human plaque. We designed a HiPR-FISH spectral encoding using a 5-fluorophore combinatorial barcoding scheme, whereby each fluorophore represents a binary bit, providing 31 possible barcodes (2^5 - 1 = 31). The fluorophore for MGE-FISH was spectrally distinct from those of HiPR-FISH, enabling simultaneous implementation of both methods.</p> <p>Images are labeled by collection time such that the laser order for a given field of view (fov) is: 488nm Lambda, 514nm Lambda, 561nm Lambda, 633nm Airyscan, 405nm Lambda. We used OPERA-MS (https://github.com/CSB5/OPERA-MS) to do hybrid assembly with Illumina short reads and Nanopore long reads. The assemblies are in the fasta files and the reads that map to the assemblies are in the fastq files. </p>
A collection of map-based applications created to communicate sustainable mobility topics
<h2>Source of the sample map-based applications</h2> <div> <table> <tbody> <tr> <td>ID</td> <td>Published year</td> <td>Link</td> </tr> <tr> <td>R1</td> <td>2021</td> <td>https://futuretransport-news.com/voi-launches-impact-dashboard-for-moresustainable-transport-choices/ </td> </tr> <tr> <td>R2</td> <td>2022</td> <td>https://trafficinfratech.com/ritm3-effective-solution-for-sustainable-traffic-management</td> </tr> <tr> <td>R3</td> <td>2022</td> <td>https://staex.io/smart-city-staex-regioit </td> </tr> <tr> <td>R4</td> <td>2022</td> <td>https://www.paircity.com/home </td> </tr> <tr> <td>R5</td> <td>2019</td> <td>https://www.geodan.com/knowledge-and-innovation/managing-urban-processesintelligently-with-the-amsterdam-smart-city-dashboard/</td> </tr> <tr> <td>R6</td> <td>2018</td> <td>https://www.greenappsandweb.com/en/android-en/rewarding-sustainable-mobilitysolutions/</td> </tr> <tr> <td>R7</td> <td>2021</td> <td>https://unhabitat.org/sites/default/files/2021/08/sump-guidelineseng.pdf </td> </tr> <tr> <td>R8</td> <td>2020</td> <td>https://kidsgogreen.eu/en/ </td> </tr> <tr> <td>R9</td> <td>2023</td> <td>https://datatopics.worldbank.org/sdgatlas/goal-9-industryinnovation-and-infrastructure/?lang=en</td> </tr> <tr> <td>R10</td> <td>2020</td> <td>https://www.edinburgh.gov.uk/downloads/file/29320/city-mobility-plan-2021-2030 </td> </tr> <tr> <td>R11</td> <td>2021</td> <td>https://plan4better.de/en/tutorials/isochrone/ </td> </tr> <tr> <td>R12</td> <td>2022</td> <td>https://www.eiturbanmobility.eu/wpcontent/uploads/2022/11/EIT-UrbanMobilityNext915−min−City144dpi.pdf </td> </tr> <tr> <td>R13</td> <td>2023</td> <td>https://www.nature.com/articles/s41598-023-32326-9 </td> </tr> <tr> <td>R14</td> <td>2022</td> <td>https://mobilitylab.hel.fi/app/uploads/2022/09/Digital-Twin-for-Mobilty.-Working-paper-version-9-September-2022.pdf </td> </tr> <tr> <td>R15</td> <td>2020</td> <td>https://xyzt.ai/2020/12/01/become-a-traffic-data-rockstar/ </td> </tr> <tr> <td>R16</td> <td>2022</td> <td>https://www.argaleo.com/en/oplossingen/fietsbeleid/ </td> </tr> </tbody> </table> </div> <div> </div>
COVID19 Flow-Maps Daily-Mobility for Spain
<p>This data-set contains daily aggregations of the hourly data provided by MITMA, aggregated at different levels of spatial resolution.</p> <p><strong>Maestra 1: Origin-Destination matrix for the mobility layer, with hourly resolution</strong> Each entry has a date and time period (the range between two consecutive hours), the origin and destination zones and the number of trips from origin to destination. Origin and destination zones correspond to geometries from the MITMA mobility layer and internal trips (same layer of origin and destination) are also reported.</p> <p><strong>Maestra 2: Trips per person matrix on each mobility area on a daily basis.</strong> This indicator reports population-based daily mobility behavior. For each date and zone from the MITMA mobility layer, the indicator reports how many persons have performed 0, 1, 2 or more than 2 trips. While the indicator does not provide the destination of the trips, it accounts for the fractions of people performing at least one trip or none, as well as the estimated total population in that zone for the given date (considering as population those persons who stay overnight in the zone on that date).</p> <p>Original data records come from a study conducted by the MITMA, which analyses the mobility and distribution of the population in Spain from February 14th 2020 to May 9th 2021. The study is based on a sample of more than 13 million anonymised mobile phone lines provided by a single mobile operator whose subscribers are evenly distributed.</p> <p>For more information visit: <a href="https://flowmaps.life.bsc.es/flowboard/data">https://flowmaps.life.bsc.es/flowboard/data</a> and <a href="https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data">https://www.mitma.gob.es/ministerio/covid-19/evolucion-movilidad-big-data</a>.</p>
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Mapping MGEs in oral plaque biofilms at high specificity
<p>We stained for the GFP gene in samples that contained mixtures of plaque and GFP-transformed E. coli. We mapped mefE, an AMR gene located on a plasmid and encoding an antibiotic efflux pump, in the plaque metagenomic data of volunteer A but not volunteer B. To test the efficacy of gel embedding and clearing, we used orthogonal FISH probes, designed to not target any sequence in the plaque. We identified a T7-like prophage via metagenomic analysis and developed probes targeting its capsB gene, which encodes the minor capsid protein. We identified a highly prevalent prophage of the class Caudoviricetes with a large terminase gene, termL, and were able to design a large set of FISH probes to stain in three different colors simultaneously. We identified three non-plasmid AMR genes within metagenome assembled genomes: patA, patB, and adeF.</p>
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Optimization of single molecule MGE FISH
<p>We used <em>Escherichia coli </em>transformed with pJKR-H-tetR plasmids encoding an inducible <em>GFP</em> gene as a model system to assess and optimize MGE-FISH on a confocal microscope. We designed FISH probes for the non-coding strand of the <em>GFP</em> gene, used non-transformed <em>E. coli </em>as a negative control, and tested six different FISH protocols.<strong> </strong></p>
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Combined taxonomic mapping and MGE mapping
<p>We used rRNA FISH to stain five common oral genera, <em>Veillonella, Streptococcus, Corynebacterium, Lautropia, </em>and <em>Neisseria</em>, each with a different fluorophore, and we used MGE-FISH to stain the <em>termL</em> gene of an active prophage with a sixth fluorophore. </p> <p>We chose a target panel of 18 genera that are highly abundant and prevalent in human plaque and designed a HiPR-FISH probe panel using a 5-fluorophore combinatorial barcoding scheme. Using MGE-FISH, we stained a plasmid carrying mefE, subunit of a major-facilitator-superfamily antibiotic efflux pump. </p>
NPM3D dataset with instance label. Dataset used in paper "A Review of Panoptic Segmentation for Mobile Mapping Point Clouds"
<p>NPM3D is a public benchmark for point cloud semantic segmentation, with 10 classes including: ground, building, pole (road sign and traffic light), bollard, trash can, barrier, pedestrian, car, natural (vegetation) and unclassified. Results are evaluated only w.r.t. 9 classes, disregarding the "unclassified" label. The data has been captured with a mapping-grade mobile laser scanning system in different cities in France. There are 4 regions designated for training, all captured in Paris and Lille; and 3 regions for testing, captured in Dijon and Ajaccio. The standard 10-class version described above has actually been derived from a more fine-grained version of the dataset by keeping only the most frequent labels. The original annotations feature 50 different semantic classes (most of which are very rare), and also individual object instance labels for the training regions. For panoptic segmentation, a new version has been generated that still uses the 10 semantic category labels listed above, but also includes instance labels. The classes ground, building and barrier are considered "stuff" and are not separated into instances. As no instance labels are available for the 3 test regions, our version for panoptic (or pure instance) segmentation only contains 4 different regions from Paris and Lille. Instead of a fixed training/test split all experiments therefore use 4-fold cross-validation.</p>
Datasets for figure files in "A map of high mobility molecular semiconductors", Nature Materials (2017)
<p>The data contained here correspond to the figure files appearing in the work "A map of high mobility molecular semiconductors", published in Nature Materials (2017)</p>
Data for: Mapping aids using output-directed programming increase novices' performance in programming mobile robotic systems
Open the record for dataset details and reuse information.
Map of the Mobility of the Western- and Central-European Nobility, 1350-1550.
<p>Map visualizing the mobility (from place of birth to place of death) of the rulers and consorts of Western- and Central-Europe between 1350-1550. Lines indicate an individual's movement from place of birth to place of death. Green lines refer to men. Pink lines refer to women.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.