Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
17
datasets available to search
ShareScore release 0.9.0
Dataset results
17 results for “mobility flows”
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>
Debris Flow Carbon Mobilization Table
This file contains the Tongass National Forest Landslide Inventory ID, planimetric area, aboveground carbon amount, and soil carbon amount mobilized by each landslide. Carbon amounts were determined using two aboveground carbon models and a soil carbon model, available at (https://databasin.org/people/brianbuma/). These values were used to estimate the amount of carbon mobilized by debris flow type landslides in SE Alaska, within the Tongass National Forest.
Higher-order Mobility Flow Data
<p>This dataset is a collection of higher-order mobility datasets, primarily aimed at trajectory data mining applications. These datasets have been created using the Point2Hex tool, allowing us to transform traditional GPS-based geolocations and check-in data into sequences of higher-order geometric elements, particularly hexagons. This transformation has various advantages, including reduced sparsity, analysis at different levels of granularity, improved compatibility with common machine learning architectures, enhanced generalization and overfitting reduction, and efficient visualization.</p> <p>Seven popular mobility datasets, typically utilized in various trajectory-related tasks and technical problems, were subjected to this transformation process. These include applications like trajectory prediction, classification, clustering, imputation, and anomaly detection, among others.</p> <p>To foster the culture of reusability and reproducibility, we are providing not only the transformed higher-order mobility flow datasets but also the source code for the Point2Hex tool and comprehensive documentation. This offering aims to streamline the generation process, ensuring that users have clear guidance on how to reproduce curated or customized versions of these datasets. The material is stored in publicly accessible repositories, ensuring its widespread accessibility.</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>
The codes and datasets for the paper titled "Don't Confuse! Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users"
<h3>Project Title:</h3> <p>Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users</p> <h3>Description:</h3> <p>This project enhances GUI navigation accessibility for visually impaired users by analyzing GUI structures, identifying issues, and optimizing navigation flow.</p> <h3>Contents:</h3> <ol> <li><strong>Risk Warnings</strong></li> <li><strong>Variable Explanations</strong></li> <li><strong>Function Descriptions</strong></li> <li><strong>Usage Instructions</strong></li> <li><strong>Contact Information</strong></li> </ol> <h3>1. Risk Warnings:</h3> <ul> <li>Navigation analysis focuses on visible nodes only.</li> <li>Code maintenance issue in loop C.</li> <li>Prior reading of the "Info" button warning is essential.</li> <li>Potential information loss in the reordering algorithm.</li> </ul> <h3>2. Variable Explanations:</h3> <ul> <li>Constants: parameter1, parameter2, outputSign.</li> <li>Global Variables: nodeString, nodeList, intToRect, intToDir, intToInfo, infoToInt, intToSubRect, intToSubKind.</li> <li>Local Variables: sortedSon, sign, acceptable.</li> </ul> <h3>3. Function Descriptions:</h3> <ul> <li><strong>isNodeVisibleOnScreen</strong>: Checks if a node is visible on the screen.</li> <li><strong>Gestalt</strong>: Conducts a depth-first search traversal of all nodes and records the Gestalt_inspired order.</li> <li><strong>checkNodeNecessity</strong>: Further checks if a node is necessary for navigation.</li> <li><strong>DFS</strong>: Used for the reordering algorithm.</li> </ul> <h3>4. Usage Instructions:</h3> <ul> <li>Ensure thorough understanding of risk warnings.</li> <li>Modify and maintain the code as necessary.</li> <li>Read the warning prompt before using the "Info" button.</li> <li>Exercise caution with potential information loss in reordering.</li> </ul> <h3>5. Contact Information:</h3> <ul> <li>Developer: Mengxi Zhang</li> <li>Email: <a target="_new">zmxalakay@126.com</a></li> </ul> <p><strong>Note</strong>: This README provides a brief overview. Refer to the User_Guidelines documentation for detailed information.</p>
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>
Rheology of mobile sediment beds in laminar shear flow: effects of creep and polydispersity
<p>Simulation data to accompany the journal article "Rheology of mobile sediment beds in laminar shear flow: effects of creep and polydispersity" by Rettinger et al. (2022) in Journal of Fluid Mechanics.</p> <p>The data set features the four simulation setups with varying degrees of polydispersity, denoted as mono, poly10, poly50, and poly100.</p> <p>Each setup has two subfolders: 'particle' and 'fluid'.</p> <p>The 'particle' folder contains files with particle-related quantities. The name of the file indicates the simulation time step. Each file contains seven columns of data: the x-, y-, and z-coordinate of the particle's center of mass, followed by its radius and the three translational velocity components <strong>u</strong><sub>p</sub> = (u<sub>x</sub>, u<sub>y</sub>, u<sub>z</sub>). Each row thus contains the instantaneous properties of a single particle. Additionally, the row-ordering between time steps remains the same, i.e., the x-th row always denotes the same particle.</p> <p>The 'fluid' folder contains files with fluid-related quantities. Again, the name denotes the simulation time step. Each file contains three columns of data: the three velocity components <strong>u</strong><sub>f</sub> = (u<sub>x</sub>,u<sub>y</sub>,u<sub>z</sub>) of the horizontally averaged flow field. Each row corresponds to a particular vertical (z-) coordinate, denoting the height above the bottom plane. This coordinate is given in the additional file 'height.txt'.</p> <p>All data is given in simulation units where the lattice Boltzmann unit system is used.</p> <p> </p> <p>Updated version 1:</p> <ul> <li>Added a jupyter notebook (in python) that exemplifies how vertical profiles, e.g., of solid volume fraction and fluid velocity, can be extracted from the provided data.</li> <li>Fix in fluid height profiles (height.txt).</li> </ul> <p>Updated version 2:</p> <ul> <li>Added missing last column (z-velocity) to the particle data.</li> <li>Maintained consistent ordering of entries in particle data throughout all time steps.</li> </ul>
LES-DPM simulation dataset for oscillatory flow over a mobile, rippled bed
Open the record for dataset details and reuse information.
Movies, temperature and pressure measurements associated with the study "Small amounts of dissolved salt increases the mobility of mud flows on Mars and other extra-terrestrial bodies"
<p>Movies, temperature and pressure measurements logs associated with the study "<em>How salts affect mud mobility at low pressures: implications for Mars and other extraterrestrial bodies</em>" that are showing behavior of muds with various content of different types of salts.</p>
Effects of Atorvastatin 10 mg Versus 40 mg in Eight Months Follow-up Coronary Flow Reserve and Bone Marrow Stem Cell Mobilization in Patients With Acute Myocardial Infarction
ClinicalTrials.gov study NCT00536887. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Population divergence and gene flow in an endangered and highly mobile seabird
Open the record for dataset details and reuse information.
Beyond the landscape: resistance modelling infers physical and behavioural gene flow barriers to a mobile carnivore across a metropolitan area
Open the record for dataset details and reuse information.
Grains-energy release governs debris flow high mobility
<p>These files include the data to generate Figs. 1-2 and 4 and Figs. S7-S13 in supporting information</p>
NOVAFOODIES Mobile App mobile software for Google Android and Apple iOS user flow
Open the record for dataset details and reuse information.
Fast Modelling of Vegetated Flow and Sediment Transport over Mobile Beds Using Shallow Water Equations with Anisotropic Porosity
<p>The data are the numerical solutions and errors in different cases by the present model.</p> <p> </p>
Blood Flow Restriction on Functional Mobility in Pre-frail Older Adults
ClinicalTrials.gov study NCT05209685. IPD Sharing: NO. Countries: 1. Publications: 0.
Instrument Assisted Soft Tissue Mobilization Effect on Blood Flow
ClinicalTrials.gov study NCT04785079. IPD Sharing: NO. Countries: 1. Publications: 0.
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.