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4,763 results for “Mobility”
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
Rare earth-free motor designs for e-mobility
<p>First webinar of the ReFreeDrive's webinar series. This webinar gives an overview of the different motor technologies that will be developed during the project.</p> <p>The webinar is also available on Youtube. Link: <a href="https://www.youtube.com/watch?v=PziGDLfE5_w">https://www.youtube.com/watch?v=PziGDLfE5_w</a></p>
Data from: The impact of human mobility networks on the global spread of COVID-19
<p>This is empirical dataset from the paper "The impact of human mobility networks on the global spread of COVID-19". Specifically, the dataset includes several files: (a) the COVID-19 network - an origin/destination matrix (i.e., "covid_network.csv"); (b) the common language network - edgelist format (i.e. "edge_list_comlang.csv"); (c) the same continent network - edgelist format (i.e., "edge_list_continent.csv"; (d) the contiguity network (i.e., "edge_list_contig.csv"); (e) the migration network - edgelist format (i.e., "edge_list_migration_in.csv"; (f) the tourism network - edgelist format (i.e., edge_list_tourism_in.csv"); (g) the list of nodes (countries) corresponding to files (b)-(e) (i.e., "nodes.csv"). Additionally, we uploaded the Rcode used in the paper (i.e. "code"), as a .pdf file format, the data source for the figures included in the paper (i.e., "covid_network_matrix.csv", "matrix_migration_out.csv", "matrix_tourism.csv" - Figure 1; "Fig_2_a_matrix_comlang.csv", Fig_2_b_matrix_contig.csv", "Fig_2_c_matrix_continent.csv" - Figure 2; "Fig_3.graphmlz - Figure 3; Fig_4.graphmlz - Figure 4) and the "global network of COVID-19 onset" (an individual-level data) (i.e., "global_covid_network.csv"). </p> <p>For details, please, see the Methods section of the paper: The impact of human mobility networks on the global spread of COVID-19 (Hancean, M.-G., Slavinec, M., Perc, M). </p> <p> </p> <p> </p> <p> </p>
I-BiDaaS - TID - Synthetic Mobility Data
<p>This is a synthetic data stream based on real-time, cell network events. These events are picked up by the antennas that are closer to the mobile phone thus providing an approximate location of the device. Every transaction of a mobile phone generates one of those events. A transaction can be, for instance, placing or receiving a call, sending or receiving an SMS, asking for a specific URL in your mobile phone browser, or sending a text message or a data transaction from/to any mobile phone app. There are also some synchronization events like, for instance, turning your mobile phone on or off, or when switching between location area networks (relatively big geographical areas comprising several cell towers).</p>
Mobility analytic results
<p>The dataset contains information about the trips made by the participants with the MyCorridor app within the context of the second iteration phase in the MyCorridor project. The collected information is related to certain trip characteristics as trip length, trip distance, number of transfers and distribution of service clusters. Moreover, the data shows the number of users and trips.</p>
Microscopic vehicular mobility trace of Europarc roundabout, Creteil, France (vehicular-mobility-trace.github.io: v1.0)
<p>First release of the Europarc roundabout micro mobility dataset, Creteil, France.</p> <p>http://vehicular-mobility-trace.github.io/</p>
Social sensing of urban land use based on analysis of Twitter users' mobility patterns
<p>A companion dataset for the paper "Social sensing of urban land use based on analysis of Twitter users' mobility patterns". This dataset contains five files and one dictionary depicting the preferential return of Twitter users to their key locations and the urban land use types at these locations. More details can be found in the README file. </p>
Paper Survey Table - Smart Mobility - role of mobile games
<p>This table (CSV File) compiles the papers reviewed for the survey paper "Smart Mobility, the role of Mobile Games" presented at the 8th Serious Games Development & Applications (SGDA 2017) conference.<br> It compiles 140 documents read and described to understand the role of mobile games in the promotion of smart mobility, especially based on urban cycling. </p> <p>The images provide relevant insights of the survey and support the analysis of the survey paper they are briefly described as follows: </p> <p>Chart 1: Number of papers classified by publisher and type of publication reported.<br> Chart 2: Number of participants classified by type of publication reported.<br> Chart 3: Number of papers classified by the method reported and the number of citations.<br> Chart 4: Number of papers reporting cycling classified by type of motivation used for gamification strategies<br> Chart 5: Number of papers reporting cycling classified by device and location technology reported</p> <p>The following is a short explanation of each of the columns of the table and its contents, self-explanatory titles are omitted.</p> <ul> <li>Database: source or publisher of the paper</li> <li>Type: description of the kind of publication between conference paper, book chapter, or journal paper. </li> <li>Oldest Reference: year of publication of the oldest reference cited in the paper.</li> <li>Newest Reference: year of publication of the newest reference cited in the paper.</li> <li>Citation: Classification of the number of citations that the paper has. Values: none, 1-5, 5-10, +10.</li> <li>Participants: Classification of the number of participants that the paper reports. Values: none, 1-5, 5-10, +10. </li> <li>Method Description: Short description of the method reported by the paper.</li> <li>Reported Method: Classification of the reported method of the paper in three main categories. Design, Experiment / Test, Literature review, Survey.</li> <li>Gamification - Motivation: Classification of the sources of motivation reported. Values: Intrinsic Motivation, Extrinsic Motivation, Mixed.</li> <li>Gamification - Negative Issues: Filled when the paper is reporting the analysis of negative consequences of using gamification:</li> <li>Gamification: Classification of the kind of technique reported. Values: Gamified, not Gamified</li> <li>Device and Location Usage: Classification of the use of mobile devices, wearables and location technologies. Values: No device, Device enabled, Mobile and location enabled, Device and location enabled.</li> <li>App Name: The name of the application reported by the paper when it exists.</li> </ul>
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>
Synthetic mobile service traffic time series
<p>This dataset contains synthetic mobile service traffic time series used in the paper titled "kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices", presented at ACM MobiHoc 2023 in Washington, USA. It is composed of 20 time series representing the fluctuations of demands for diverse services categorized under 5G types, including enhanced Mobile Broadband (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC). The time series cover a period of XXX days, and were shown to yield similar properties as those observed in real-world traffic collected in a production mobile network.</p>
User Feedback Dataset from the Top 15 Downloaded Mobile Applications
<p>This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. <i><strong>The dataset is particularly suitable for Natural Language Processing (NLP) applications</strong></i>, such as text classification and topic modeling.</p><p><strong>List of Included Applications:</strong></p><ul><li>TikTok</li><li>Instagram</li><li>Facebook</li><li>WhatsApp</li><li>Telegram</li><li>Zoom</li><li>Snapchat</li><li>Facebook Messenger</li><li>Capcut</li><li>Spotify</li><li>YouTube</li><li>HBO Max</li><li>Cash App</li><li>Subway Surfers</li><li>Roblox</li><li>Data Columns and Descriptions:</li></ul><p><strong>Data Columns and Descriptions:</strong></p><ul><li>review_id: Unique identifiers for each user feedback/application review.</li><li>content: User-generated feedback/review in text format.</li><li>score: Rating or star given by the user.</li><li>TU_count: Number of likes/thumbs up (TU) received for the review.</li><li>app_id: Unique identifier for each application.</li><li>app_name: Name of the application.</li><li>RC_ver: Version of the app when the review was created (RC).</li></ul><p><strong>Terms of Use:</strong></p><p>This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:</p><p>M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: <a href="https://doi.org/10.1109/ACCESS.2023.3332644">10.1109/ACCESS.2023.3332644</a>.</p><blockquote><p>Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.</p></blockquote>
XR training and gameplay 6DoF mobility dataset
<p>User mobility in extended reality (XR) can have a major impact on millimeter-wave (mmWave) links and may require dedicated mitigation strategies to ensure reliable connections and avoid service outages. The available prior art has predominantly focused on XR applications with constrained user mobility and limited impact on mmWave channels.</p> <p>We have performed dedicated experiments to extend the characterisation of relevant future XR use cases featuring a high degree of user mobility. To this end, we have carried out a tailor-made XR mobility measurement campaign, capturing the movement of the head, hands, and body in 6DoF. </p> <p>For a usage example, see the provided Jupyter Notebook in cacerumd-usage-example.zip.</p> <p>A description of the measurement campaign and a characterisation of the recorded mobility can be found in the corresponding <a href="https://ieeexplore.ieee.org/abstract/document/10634047">IEEE Magazine paper</a> or a longer version, with more details about the experiment, on <a href="https://arxiv.org/abs/2407.02636">Arxiv</a>.</p>
(Im)moral Mobilities
<p>Based on an ethnographic case study in a Swiss valley on the border with France, this paper sheds light on the emergence of a regime of (im)moral mobilities. It investigates how and why the presence of a specific border in a peripheralized region - in this case a national border separating spheres of income inequality - informs and results in dynamics of morally contested mobilities. The analysis shows how some cross-border mobilities, while being legal (such as living in Switzerland and shopping in France or living in France and working in Switzerland), are negotiated by borderlanders, who perceive them as damaging to the economic and social well- being of the valley. It focuses on everyday practices and discourses to illuminate the informal and mundane (re)production of borders and boundaries. The deployment of the regime of (im)moral mobilities - and all the discourses and practices it comprises - produces immoralized individuals who are stigmatized, as well as moralized persons who feel they belong to a collective.</p>
Data for: Mobile impurities interacting with a few one-dimensional lattice bosons
<p>Dataset for <em>Mobile impurities interacting with a few one-dimensional lattice bosons</em> (<a href="https://iopscience.iop.org/article/10.1088/1361-6455/acb51b">10.1088/1361-6455/acb51b</a>). It corresponds to exact diagonalization results for energies and bipolaron sizes.</p> <p>The files correspond to the following figures in the preprint:</p> <p>Fig. 1a: Ep_UBB2.dat <br> Fig. 1b: Ep_UBB4.dat <br> Fig. 1c: Ep_UBB6.dat <br> Fig. 1d: Ep_UBB8.dat <br> Fig. 2: Ep_UBI50.dat<br> Fig. 5a: Ebp_UBB2.dat <br> Fig. 5b: Ebp_UBB4.dat <br> Fig. 5c: Ebp_UBB6.dat <br> Fig. 5d: Ebp_UBB8.dat <br> Fig. 6: Ebp_UBI50.dat <br> Fig. 7a: rbp_UBB2.dat <br> Fig. 7b: rbp_UBB4.dat <br> Fig. 7c: rbp_UBB6.dat <br> Fig. 7d: rbp_UBB8.dat <br> Fig. 8: rbp_UBI50.dat</p>
Investigating Types and Survivability of Performance Bugs in Mobile Apps
<p>Replication package of the paper entitled "Investigating Types and Survivability of Performance Bugs in Mobile Apps" published in The Empirical Software Engineering Journal</p>
Three-dimensional building and mobility infrastructure of the CONUS
<p>Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks.</p><p>This dataset features intermediate mapping results for estimating material stocks in the CONUS (see related identifiers) on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), Microsoft building footprints, NLCD Impervious data, and crowd-sourced geodata (OSM). These data may also be useful on their own.</p><p><strong>Provided layers @10m resolution</strong><br>- Building height<br>- Building type<br>- Building area<br>- Impervious fraction<br>- street, and rail area<br>- Building and street climate zones<br>- County zones<br>- State masks<br>- EQUI7 correction factors</p><p><strong>Spatial extent</strong><br>This dataset covers the whole CONUS. </p><p><strong>Temporal extent</strong><br>The maps are representative for ca. 2018.</p><p><strong>Data format</strong><br>The data are organized in 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p><p><strong>Further information</strong><br>For further information, please see the main publication.<br>A web-visualization of the resulting dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/us-stocks/">here</a>.<br>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 MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p><p><strong>Publication</strong><br>D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. <i>Nature Communications</i> <strong>14</strong>, 8014. <a href="https://doi.org/10.1038/s41467-023-43755-5">https://doi.org/10.1038/s41467-023-43755-5</a></p><p><strong>Funding</strong><br>This research was primarly 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><p><strong>Acknowledgments</strong><br>We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.</p>
A Systematic Review on Techniques and Approaches to Estimate Mobile Software Energy Consumption (SUSCOM Dataset)
<p>Dataset and replication data for the systematic review entitled "A Systematic Review on Techniques and Approaches \\to Estimate Mobile Software Energy Consumption".</p>
Role of environmental factors in the genetic structure of a highly mobile seabird
<p><strong>Aim:</strong> Environmental features can act as selection pressures and barriers to gene flow between populations. The genetic structuring of highly mobile but philopatric seabirds creates a paradox, and the role of oceanographic and geographic variables is still poorly understood. In this study, we investigate the influence of environmental and geographic variables in the genetic and phenotypic diversity of a pantropical seabird breeding in islands and archipelagos separated by different geographic distances, up to thousand kilometers, and which differ in environmental characteristics.</p> <p><strong>Location:</strong> Islands and archipelagos in the southwestern Atlantic Ocean.</p> <p><strong>Taxon:</strong> <em>Sula dactylatra</em>, Lesson, 1831 (masked booby)<em>.</em></p> <p><strong>Methods:</strong> The population structure of the species was accessed through mitochondrial and nuclear DNA. To test Isolation by Environment (IBE) <em>vs</em>. by Distance (IBD), sea surface temperature, primary productivity, and salinity, as well as isotopic niche based on carbon and nitrogen, and distances between colonies and from the continent, were used. We also tested the correlation between the genetic structure and the morphometry of individuals in each colony.</p> <p><strong>Results:</strong> We identified the presence of low genetic structure between populations. Nevertheless, differences were identified between inshore and offshore colonies, with the influence of landscape characteristics of these two types of environment. The morphometric and isotopic niche variations are consistent with this segregation.</p> <p><strong>Main conclusions:</strong> Environmental variables of coastal and oceanic environments seem to influence the genetic structure of masked boobies, even though it is low in the SW Atlantic Ocean, highlighting the role of environmental heterogeneity in shaping biodiversity.</p>
Dataset assoziated with the paper "Optimisation of mobility hub locations for a sustainable mobility system"
<p>This is supplementary data for the paper 'Optimisation of mobility hub locations for a sustainable mobility system'. The Excel file 'InputParameters' contains the parameters used as input for the bilevel optimization model. Note that it contains two sheets: one for the calibrated parameters in the utility function, and one for the mode-specific input parameters. The external cost data are based on the study by Bieler, C. & Sutter, D. (2019), whereas the cost parameters were derived from the websites of the local service providers.</p> <p>The result folder contains the result files of all the experiments discussed in the paper. Each subfolder corresponds to one test instance. The subfolders contain the information on the built mobility hubs (build_mobilityhubs.csv), the modal split information (wegcount.rating.csv for both absolute and proportional data), the number of transfers for each mode at each station (transfercount.csv), and also the full list of modes that each user group used in their travels (user_paths.csv). Note that the stations are given by ID, and the ID is taken from the GTFS data for Aachen.</p> <p>The additional experiments from Section 5.5 on the modal split for a higher number of bike- and car-sharing stations are contained in the "Further Maximization of Sharing Modes Test.zip." Each subfolder contains specific data for the test instances, while the Excel sheet modal_split_Percent.xlsx summarizes and visualizes the modal split data.</p> <p>Further result data can be provided upon request.</p> <p><a name="_CTVL00166b62df5ea8545b3990eea974b27cf8a"></a>Bieler, C., Sutter, D., 2019. Externe Kosten des Verkehrs in Deutschland: Straßen-, Schienen-, Luft- und Binnenschiffverkehr 2017.</p>
Dataset and additional figures for: decomposing geographical and universal aspects of human mobility
<p>Data and additional figures for: <em>Decomposing geographical and universal aspects of human mobility, </em><a href="https://arxiv.org/pdf/2405.08746">https://arxiv.org/pdf/2405.08746</a></p>
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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.