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56 results for “human mobility”
Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean
<p>Koptekin et al. (2022) "<strong><em>Spatial and temporal heterogeneity in human mobility patterns in Holocene Southwest Asia and the East Mediterranean</em></strong>", Current Biology <a href="https://doi.org/10.1016/j.cub.2022.11.034">https://doi.org/10.1016/j.cub.2022.11.034</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>
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>
Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome
<p><strong>The dataset from the article </strong><strong>Putative mobilized colistin resistance (mcr) genes co-occurring with other antibiotic resistance genes are widespread in the human gut microbiome</strong></p>
Transcriptional determinants of lipid mobilization in human adipocytes
<p>Defects in adipocyte lipolysis drive multiple aspects of cardiometabolic disease but the transcriptional framework controlling this process has not been established. To address this, we performed a targeted perturbation screen in primary human adipocytes. Our analyses identified 37 transcriptional regulators of lipid mobilization, which we classified as: i) transcription factors, ii) histone chaperones, and iii) mRNA processing proteins. Based on its strong relationship with multiple readouts of lipolysis in patient samples, we performed mechanistic studies on one hit, ZNF189, which encodes the Zinc Finger Protein 189. Using mass-spectrometry and chromatin profiling techniques, we show that ZNF189 interacts with the tripartite motif family member TRIM28 and represses the transcription of an adipocyte-specific isoform of Phosphodiesterase 1B (PDE1B2). The regulation of lipid mobilization by ZNF189 requires PDE1B2 and overexpression of PDE1B2 is sufficient to attenuate hormone-stimulated lipolysis. Thus, our work identifies the ZNF189-PDE1B2 axis as a determinant of human adipocyte lipolysis and highlights a link between chromatin architecture and lipid mobilization.</p>
YJMob100K: City-Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories
<p>The YJMob100K human mobility datasets (YJMob100K_dataset1.csv.gz and YJMob100K_dataset1.csv.gz) contain the movement of a total of 100,000 individuals across a 75 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of 80,000 individuals across a 75-day business-as-usual period, while the second dataset contains the movement of 20,000 individuals across a 75-day period (including the last 15 days during an emergency) with unusual behavior. </p> <p>While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (cell_POIcat.csv.gz). The list of 85 POI categories can be found in POI_datacategories.csv. </p> <p>For details of the dataset, see Data Descriptor: </p> <ul> <li>Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. <em>Scientific Data</em>, <em>11</em>(1), 397. <a href="https://www.nature.com/articles/s41597-024-03237-9" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03237-9</a> </li> </ul> <p> </p> <p> </p> <p><strong>--- Details about the Human Mobility Prediction Challenge 2023 (ended November 13, 2023) --- </strong></p> <p>The challenge takes place in a mid-sized and highly populated metropolitan area, somewhere in Japan. The area is divided into 500 meters x 500 meters grid cells, resulting in a 200 x 200 grid cell space.</p> <p>The human mobility datasets (task1_dataset.csv.gz and task2_dataset.csv.gz) contain the movement of a total of 100,000 individuals across a 90 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of a 75 day business-as-usual period, while the second dataset contains the movement of a 75 day period during an emergency with unusual behavior.</p> <p>There are 2 tasks in the Human Mobility Prediction Challenge.</p> <p>In task 1, participants are provided with the full time series data (75 days) for 80,000 individuals, and partial (only 60 days) time series movement data for the remaining 20,000 individuals (task1_dataset.csv.gz). Given the provided data, Task 1 of the challenge is to predict the movement patterns of the individuals in the 20,000 individuals during days 60-74. Task 2 is similar task but uses a smaller dataset of 25,000 individuals in total, 2,500 of which have the locations during days 60-74 masked and need to be predicted (task2_dataset.csv.gz).</p> <p>While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (which is optional for use in the challenge) (cell_POIcat.csv.gz).</p> <p>For more details, see https://connection.mit.edu/humob-challenge-2023</p>
Explaining human mobility predictions through a pattern matching algorithm
<p>The name of the file indicate information:<br> {type of sequence}_{type of measure}_{sequence properites}_{additional information}.csv</p> <p>{type of sequence} - 'synth' for synthetic or 'london' for real mobility data from London, UK.<br> {type of measure} - 'r2' for R-squared measure or 'corr' for Spearman's correlation<br> {sequence properties} - for synthetic data there are three types of sequences, described in the research article (random, markovian, nonstationary). For real mobility data this part includes information about data processing parameters: (...)_london_{type of mobility sequence}_{DBSCAN epsilon value}_{DBSCAN min_pts value}. {type of mobility sequence} is 'seq' for next-place sequences and '30min' or '1H' for the next time-bin sequences and indicate the size of the time-bin.<br> Files with 'predictability' at the end of the file contain R-squared and Spearman's correlation of measures calculated in relation to the predictability measure.</p> <p>R2 files include values of R-squared for all types of modelled regression functions.<br> 'line' indicates {y = ax + b} for single variable and {y = ax + by + c} for two variables.<br> 'expo' indicates {y = a*x^b + c} for single variable and {y = a*x^b + c*y^d + e} for two variables<br> 'log' indicates {y = a*log(x*b) + c} for single variable and {y = a * x + c * log(y) + e + d*x * log(y)} for two variables.<br> 'logf' indicates {y = a*log(x) + c * log(y) + e + b*log(x) * log(y)} for two variables</p>
Data for: Mobility of the human foot's medial arch helps enables upright bipedal locomotion
<p class="MsoNormal"><span>Developing the ability to habitually walk and run upright on two feet is one of the most significant transformations to have occurred in human evolution. Many musculoskeletal adaptations enabled bipedal locomotion, including dramatic structural changes to the foot and, in particular, the evolution of an elevated medial arch. The foot's arched structure has previously been assumed to play a central role in directly propelling the center of mass forward and upward through leverage about the toes and a spring-like energy recoil. However, it is unclear whether or how the plantarflexion mobility and height of the medial arch support its propulsive lever function. Here we show, using high-speed biplanar x-ray, that regardless of intraspecific differences in medial arch height, arch recoil enables a longer contact time and favorable propulsive conditions at the ankle for walking upright on an extended leg. This mechanism may have helped drive the evolution of the longitudinal arch after our last common ancestor with chimpanzees, who lack this plantarflexion mobility during push-off. We discovered that the generally overlooked navicular-medial cuneiform joint is primarily responsible for arch recoil in human arches, suggesting that future morphological investigations of this joint will provide new interpretations of the fossil record. Our work further suggests that enabling longitudinal arch recoil in footwear and surgical interventions may be critical for maintaining the ankle's natural propulsive ability.</span></p>
Data for: Mobility of the human foot's medial arch helps enables upright bipedal locomotion
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Transcriptional determinants of lipid mobilization in human adipocytes
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Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms
<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> <p><strong>15 participants</strong> took part in the experiment, with the dataset capturing <strong>5 minutes of HRSI motion for each participant</strong>.</p> <h3>Experiment Description</h3> <p>The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m. <br>Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants.</p> <p>The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions.</p> <h3>Directory Structure</h3> <p>Dataset<br>|<br>|____Map: folder containing the map of the environment extracted from the TIAGo robot<br>|<br>|____RosBags: forder containing the rosbag for each partipant<br>| |____A1.bag<br>| |____A2.bag<br>| |____A3.bag<br>| |____A4.bag<br>| |____A5.bag<br>| |____A6.bag<br>| |____A7.bag<br>| |____A8.bag<br>| |____A9.bag<br>| |____A10.bag<br>| |____A11.bag<br>| |____A12.bag<br>| |____A13.bag<br>| |____A14.bag<br>| |____A15.bag<br>|<br>|____Trajectories: postprocessed trajectories extracted for the rosbag files <br> |____A1_traj.csv<br> |____A2_traj.csv<br> |____A3_traj.csv<br> |____A4_traj.csv<br> |____A5_traj.csv<br> |____A6_traj.csv<br> |____A7_traj.csv<br> |____A8_traj.csv<br> |____A9_traj.csv<br> |____A10_traj.csv<br> |____A11_traj.csv<br> |____A12_traj.csv<br> |____A13_traj.csv<br> |____A14_traj.csv<br> |____A15_traj.csv</p>
Processed data for the analysis of human mobility changes from COVID-19 lockdown on bird occupancy in North Carolina, USA
<p>The COVID-19 pandemic lockdown worldwide provided a unique research opportunity for ecologists to investigate the human-wildlife relationship under abrupt changes in human mobility, also known as Anthropause. Here we chose 15 common non-migratory bird species with different levels of synanthrope and we aimed to compare how human mobility changes could influence the occupancy of fully synanthropic species such as House Sparrow (<em>Passer domesticus</em>) versus casual to tangential synanthropic species such as White-breasted Nuthatch (<em>Sitta carolinensis</em>). We extracted data from the eBird citizen science project during three study periods in the spring and summer of 2020 when human mobility changed unevenly across different counties in North Carolina. We used the COVID-19 Community Mobility reports from Google to examine how community mobility changes towards workplaces, an indicator of overall human movements at the county level, could influence bird occupancy.</p>
LYMob-4Cities: Multi-City Human Mobility Dataset
<p>This multi-city human mobility dataset contains data from 4 metropolitan areas (cities A, B, C, D), somewhere in Japan. Each city is divided into 500 meters x 500 meters cells, which span a 200 x 200 grid. The human mobility datasets contain the movement of individuals across a 75-day period, discretized into 30-minute intervals and 500-meter grid cells. Each city contains the movement data of 100,000, 25,000, 20,000, and 6,000 individuals, respectively. </p> <p>While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) for the four cities as supplementary information (e.g., POIdata_cityA). The list of 85 POI categories can be found in POI_datacategories.csv. </p> <p>This dataset was used for the HuMob Data Challenge 2024 competition. For more details, see https://wp.nyu.edu/humobchallenge2024/ </p> <p><em><strong>Researchers may use this dataset for publications and reports, as long as: 1) Users shall not carry out activities that involve unethical usage of the data, including attempts at re-identifying data subjects, harming individuals, or damaging companies, and 2) The Data Descriptor paper of an earlier version of the dataset (citation below) needs to be cited when using the data for research and/or commercial purposes. </strong></em>Downloading this dataset implies agreement with the above two conditions. </p> <ul> <li>Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. <em>Scientific Data</em>, <em>11</em>(1), 397. <a href="https://www.nature.com/articles/s41597-024-03237-9" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03237-9</a> </li> </ul> <p>This data contains movement information generated from user location data obtained from LY Corporation smartphone applications. It does not reveal the actual timestamp, latitude, longitude, etc., and does not identify individuals. This data can only be used for the purpose of participating in the Humob Challenge 2024. </p> <p> </p>
Dataset of "Tracking Urban Human Activity from Mobile Phone Calling Patterns" PLOS Computational Biology paper
<p>This are the dataset file for "Tracking Urban Human Activity from Mobile Phone Calling Patterns", to be published in PLOS Computational Biology.</p> <p>The files contain probability distributions of finding a first, last, or any call as a function of time, derived from anonymized call detail records for a 12 months period in the year 2007 from a mobile phone service provider in a European country. The first data file contains the data obtained fom 30 different cities. the second for the six most populated cities, splitting the data into different age and gender groups.</p> <p>Details in README files.</p>
Dataset | Human mobility messages on Telegram
<p>This dataset comprises six sets of IDs of messages about human mobility shared on thematic Telegram public groups and channels. The themes of the groups and channels from where the messages were extracted: African continent, Climate action, Conspiracism, Iranian, Nationalism, Slovakian.</p> <p>Messages were queried using TeleCatch, an Open Scource tool that enables visualizing, filtering, and extracting Telegram messages data and image files.</p> <p>The keywords used to query Telegram grouops and channels and generate the datasets were: displacement, migrant, migration, diaspora, immigrant, emigration, “brain drain”, remittance, xenophobia, multicultural, border control, asylum, refugee, deport, “human traffic”, resettle, IDP, “border agency”, IOM, UNHCR.</p> <p>The message ID provided by Telegram API is unique only for the group or channel where it was posted. For this reason, the dataset provides the message ID and the group and/or username (@). </p> <p><strong>Technical info </strong></p> <p>The Python script includes a functionality to identify the exclusion or blockage of Telegram groups and/or channels. In addition to generating the messages output file, the script produces a log file. This log file records the status of each Telegram group, providing insights into their current operational state.</p> <p><strong>Other info</strong></p> <p>This dataset was created during the 2024 Digital Methods Initiative Summer School for the <strong>Uncovering visual narratives about human mobility on Telegram </strong>project.<br><br>2024 Digital Methods Initiative Summer School URL: https://wiki.digitalmethods.net/Dmi/SummerSchool2024</p>
Sample data for "Urban Dynamics Through the Lens of Human Mobility"
<p>Sample data in Boston for paper "Urban Dynamics Through the Lens of Human Mobility".</p> <p> </p>
Processed data for the analysis of human mobility changes from COVID-19 lockdown on bird occupancy in North Carolina, USA
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A case for human mobility data applications in wildlife management
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Data from: Quantifying human mobility perturbation and resilience in Hurricane Sandy
Human mobility is influenced by environmental change and natural disasters. Researchers have used trip distance distribution, radius of gyration of movements, and individuals' visited locations to understand and capture human mobility patterns and trajectories. However, our knowledge of human movements during natural disasters is limited owing to both a lack of empirical data and the low precision of available data. Here, we studied human mobility using high-resolution movement data from individuals in New York City during and for several days after Hurricane Sandy in 2012. We found the human movements followed truncated power-law distributions during and after Hurricane Sandy, although the β value was noticeably larger during the first 24 hours after the storm struck. Also, we examined two parameters: the center of mass and the radius of gyration of each individual's movements. We found that their values during perturbation states and steady states are highly correlated, suggesting human mobility data obtained in steady states can possibly predict the perturbation state. Our results demonstrate that human movement trajectories experienced significant perturbations during hurricanes, but also exhibited high resilience. We expect the study will stimulate future research on the perturbation and inherent resilience of human mobility under the influence of hurricanes. For example, mobility patterns in coastal urban areas could be examined as hurricanes approach, gain or dissipate in strength, and as the path of the storm changes. Understanding nuances of human mobility under the influence of such disasters will enable more effective evacuation, emergency response planning and development of strategies and policies to reduce fatality, injury, and economic loss.
Data from: Active and reactive behaviour in human mobility: the influence of attraction points on pedestrians
Human mobility is becoming an accessible field of study thanks to the progress and availability of tracking technologies as a common feature of smart phones. We describe an example of a scalable experiment exploiting these circumstances at a public, outdoor fair in Barcelona (Spain). Participants were tracked while wandering through an open space with activity stands attracting their attention. We develop a general modeling framework based on Langevin Dynamics, which allows us to test the influence of two distinct types of ingredients on mobility: reactive or context-dependent factors, modelled by means of a force field generated by attraction points in a given spatial configuration, and active or inherent factors, modelled from intrinsic movement patterns of the subjects. The additive and constructive framework model accounts for some observed features. Starting with the simplest model (purely random walkers) as a reference, we progressively introduce different ingredients such as persistence, memory, and perceptual landscape, aiming to untangle active and reactive contributions and quantify their respective relevance. The proposed approach may help in anticipating the spatial distribution of citizens in alternative scenarios and in improving the design of public events based on a facts-based approach.
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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.