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61 results for “travel time”
The Role of Beta Oscillation in Mental Time Travel
ClinicalTrials.gov study NCT04582994. IPD Sharing: NO. Countries: 1. Publications: 5.
The Impact of Self-processing on Mental Time Travel
ClinicalTrials.gov study NCT06823193. IPD Sharing: NO. Countries: 1. Publications: 10.
Data from: Temporal population genetics of time travelling insects: a long term longitudinal study in a seed-specialized wasp
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Data from: Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media
In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen–Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.
Nitrate trend reversal in Dutch dual-permeability chalk springs, evaluated by tritium-based groundwater travel time distributions [Data set]
<p>Data set covering measured nitrate and sulfate data of 90 springs, the tritium and nitrate input data for the convolution models and the age distributions metrics for the base case model and the stringent denitrification model, which were described in the publication in Science of the Total Environment (2024); doi: <em>10.1016/j.scitotenv.2024.175250</em></p>
DATA_measure and predict the travel time reliability on the urban rail transit network
<p>(1)The automatic fare collection data recorded the time of entering and exiting stations for each trip.<br>(2)The automatic fare collection data format is CSV, and the columns of the table are in Chinese. The columns are ticket card number, ticket card type, date, time of entering the station, entering station name, time of exiting the station, exiting station name, and day of the week.</p>
Internal rotation and buoyancy travel time of 60 gamma Doradus stars from uninterrupted TESS light curves spanning 352 days
<p>Description:<br> Electronic versions of Table A.1 and A.2 from the Appendix of<br> Garcia et al. (2022b), as well as all analysed g-mode period-spacing<br> patterns from this work.</p> <p>Abstract:<br> Context. Gamma Doradus (hereafter gamma Dor) stars are gravity-mode<br> pulsators whose periods carry information about the internal structure of<br> the star. These periods are especially sensitive to the internal rotation<br> and chemical mixing, two processes that are currently not well constrained<br> in the theory of stellar evolution.<br> Aims. We aim to identify the pulsation modes and deduce the internal<br> rotation and buoyancy travel time for 106 gamma Dor stars observed<br> by the TESS mission in its southern continuous viewing zone (hereafter<br> S-CVZ). We rely on 140 previously detected period-spacing patterns, that is,<br> series of (near-)consecutive pulsation mode periods.<br> Methods. We used the asymptotic expression to compute gravity-mode<br> frequencies for ranges of the rotation rate and buoyancy travel time that<br> cover the physical range in γ Dor stars. Those frequencies were fitted to<br> the observed period-spacing patterns by minimizing a custom cost function.<br> The effects of rotation were evaluated using the traditional approximation<br> of rotation, using the stellar pulsation code GYRE.<br> Results. We obtained the pulsation mode identification, internal rotation<br> and buoyancy travel time for 60 TESS gamma Dor stars. For the remaining 46<br> targets, the detected patterns are either too short or contained too many<br> missing modes for unambiguous mode identification, and longer light curves<br> are required. For the successfully analysed stars, we found that<br> period-spacing patterns from 1-yr long TESS light curves can constrain the<br> internal rotation and buoyancy travel time to a precision of 0.03 d^{−1} and<br> 400s, respectively, which is about half as precise as literature results<br> based on 4-yr Kepler light curves of gamma Dor stars.</p>
Statistics of electric vehicle travel time in a certain city
<p>This dataset contains data on the charging time of electric vehicles for a typical month in a certain city. The types of electric vehicles include: buses, private passenger cars, ride hailing vehicles, logistics vehicles, and rental passenger cars. The activity area is divided into: office area, industrial area, residential area, and commercial area. The dataset takes one hour as the statistical cycle to calculate the charging frequency of electric vehicles of various types and regions during a certain period of time.</p>
Data from: Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media
Open the record for dataset details and reuse information.
Statistics of electric vehicle travel time in a certain city
Open the record for dataset details and reuse information.
Travel times of wide-angle seismic refraction profile OBS2020-1 at the northern continental margin of South China Sea
<p>The dataset contains travel times (including the primary and secondary phases) of the OBS2020-1 profile with 11 OBS stations at the northern continental margin of SCS.</p>
Memetic Time Travel: A Supporting Analysis of the GOD Framework
<p>This paper extends the Gravitational Omnipotent Dimensionality (GOD) framework by introducing the concept of Memetic Time Travel—a recursive process through which ideas transcend temporal constraints, influencing knowledge systems across eras. Grounded in the principles of Quantum Gravitational Cogenesis (QGC), Memetic Time Travel explains the persistence, resurfacing, and evolution of knowledge across time. This framework models idea propagation as a function of gravitational coherence and memetic wormholes, offering insights into cognition, temporal interaction, and knowledge refinement.</p>
Public transport travel time matrices for Great Britain (TTM 2023)
<h2><strong>Overview</strong></h2> <p>This dataset provides ready-to-use door-to-door public transport travel time estimates for each of the 2011 Census at the lower super output area (LSOA) and data zone (DZ) units (42,000 LSOA/DZ units in total) in Great Britain (GB) to every other reachable within 150 minutes during the morning peak for the year 2023 using. This information comprises an all-to-all travel time matrix (TTM) at the national level. The TTM are estimated for public transport, bicycle, and walking. Public transport estimates are estimated for two times of departure, specifically during the morning peak and at night. Altogether, these TTMs present a range of opportunities for researchers and practitioners, such as the development of accessibility measures, spatial connectivity, and the evaluation of public transport service changes throughout the day.</p> <p>A full data descriptor is available in 'technical_note.html' file as part of the records of this repository.</p> <h2>Data records</h2> <p>The TTM structure follows a row matrix format, where each row represents a unique origin-destination pair. The TTMs are offered in a set of sequentially named <code>.parquet</code> files (more information about Parquet format at: <a href="https://parquet.apache.org/">https://parquet.apache.org/</a>). The structure contains one directory for each mode, where ‘bike’, ‘pt’, and ‘walk’, correspond to bicycle, public transport, and walking, respectively.</p> <h3>Walking</h3> <p>The walking TTM contains 13.3 million rows and three columns. The table below offers a description of the columns.</p> <div> <span>Table 1: </span>Walking travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>Travel time walking in minutes</td> </tr> </tbody> </table> </div> <h3>Bicycle</h3> <p>The bicycle TTM includes 40 million rows and four columns which are described in the table below.</p> <div> <span>Table 2: </span>Bicycle travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>Travel time by bicycle in minutes</td> </tr> <tr> <td>travel_time_adj</td> <td>numeric</td> <td>Adjusted travel time by bicycle in minutes. This adds 5 minutes for locking to the unadjusted estimate.</td> </tr> </tbody> </table> </div> <h3>Public transport</h3> <p>The LSOA/DZ TTM consists of six columns and 265 million rows. The internal structure of the records is displayed in the table below:</p> <div> <span>Table 3: </span>Public transport LSOA/DZ travel time matrix codebook. <table><tbody><tr> <th>Variable</th> <th>Type</th> <th>Description</th> </tr> </tbody><tbody> <tr> <td>from_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of origin</td> </tr> <tr> <td>to_id</td> <td>nominal</td> <td>2011 LSOA/DZ geo-code of destination</td> </tr> <tr> <td>travel_time_p025</td> <td>numeric</td> <td>25 travel time percentile by public transport in minutes</td> </tr> <tr> <td>travel_time_p050</td> <td>numeric</td> <td>50 travel time percentile by public transport in minutes</td> </tr> <tr> <td>travel_time_p075</td> <td>numeric</td> <td>75 travel time percentile by public transport in minutes</td> </tr> <tr> <td>time_of_day</td> <td>nominal</td> <td>A discrete value indicating the time of departure used. Levels: ‘am’ = 7 a.m.; ‘pm’ = 9 p.m.</td> </tr> </tbody> </table> </div> <p> </p> <p> </p>
Time Travelers Taven 5
I this update for this scene i have added the hot tub time machine to the scene and some props for that and all of the UVs are now done so ive started textruing Source: Objaverse 1.0 / Sketchfab
Mental Time Travel and Identity in Bipolar Disorder Patients
ClinicalTrials.gov study NCT02793518. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data and code supporting the project "A global map of travel time to access veterinarians"
<p>The folder "data" includes 1) the dataset "vets_observed.rda", which reports the locations of veterinary practices, 2) the dataset "vets_national.csv", which reports the national estimates of veterinarians, 3) the dataset "vet_schools.csv", which reports the list of veterinary schools per country, along with the range of veterinary graduates per year, 4) the dataset "faostat_livestock_2019.csv", which reports the number and species of animals raised for food per country, 5) the dataset "sources.xlsx", which reports all the sources inspected to collect addresses of veterinary practices, and 6) the raster "obs_all_r.tif" which is the rasterized version of the "vets_observed.rda" dataset at the resolution of 10x10 km.</p> <p>The folder "scripts" includes the R and python code used for assembling the dataset of the locations of veterinarians and for performing all the analyses.</p>
SnowEx20 Grand Mesa IOP BSU Multi-polarization 1 GHz GPR CMP Travel-Times V001
This data set was collected during the SnowEx 2020 Intensive Observation Period (IOP) in Grand Mesa, Colorado. These data contain the radar two-way travel times from a Sensors & Software pulseEKKO PRO 1 GHz multi-polarization ground penetrating radar (GPR). Data were collected at three locations around Grand Mesa IOP snow pits 2N12 and 1S8 (see DOI: 10.5067/DUD2VZEVBJ7S for more details on Grand Mesa IOP snow pits). Data at snow pit 2N12 were acquired on the groomed snowmobile road (CMP1), in the fresh snow behind the snow pit wall (CMP2), and in the right rut of the SUSV track (CMP3). Data at snow pit 1S8 were acquired in the right rut of the SUSV track (CMP1), in the left rut of the SUSV track (CMP2), and in the fresh snow behind the snow pit wall (CMP3). The raw version of these data (DOI: 10.5067/CL5ZRBCEF8G3) are also archived at NSIDC.
Mathematical proof of a feasible warp drive and time travel mechanism
<p>If we keep on pulling an object suspended by a string exactly sidewards by another string the pull is subsequently directed downwards with resultant opposite reaction.Here in the image we see the horizontal component of the downward force due to two loads pulling on two strings sidewards ,the pull subsequently directed downwards as the spring is only compressed sidewards-the two weights also have acquired gravitational potential energy by descent downwards by height h. </p>
Data of resulting velocity and anisotropic models, and travel-times of the Tanlu fault zone
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CSRM Level 2 dataset: Pn travel time in continental China
<p>This dataset contains the manually-picked 95,878 Pn travel times collected from the regional P wave waveforms recorded by 3,446 seismic stations deployed in and around continental China for a total of 6,787 seismic events between 1992 and 2020. This dataset results from a project of constructing an uppermost mantle seismic Pn-velocity in continental China (<a href="https://doi.org/10.1029/2022JB025667">Ma et al., 2023, JGR: Solid Earth</a>) (<a href="https://doi.org/10.5281/zenodo.8112759">Model link</a>). The reviewers and associated editor can view the dataset by click the <a href="../records/8112291?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjQ3NjEyNzNkLWY0ZTAtNGE1Yi04ZGFjLThkMTI1OGI0NGNhZSIsImRhdGEiOnt9LCJyYW5kb20iOiIxYTQ2ODdlMjlkYzYxMzQ5NDIxNzg4YTI1NWNiNmJkMyJ9.FSrIlOHgile-Z6bEh5-tapPvsNeQQpj8gDwYq3vtAl7pmPB6C7Gn4t3_m2tI3zBticehT8QqP6CWX-QJ43saRA">link</a>.</p> <p>本数据库包含了人工挑选的95,878个Pn 波到时,这部分到时由中国大陆及周边地区的3,446个地震台记录的区域P波波形挑选而来。该数据库源于构建中国大陆区域上地幔顶部Pn波速度模型的工作 (<a href="https://doi.org/10.1029/2022JB025667">Ma et al., 2023, JGR: Solid Earth</a>) (<a href="https://doi.org/10.5281/zenodo.8112759">模型链接</a>)。</p> <p>If you face any problem or issue when using this dataset, please feel free to communicate with the corresponding author Jiayu Ma (<a href="mailto:majy18@mail.ustc.edu.cn">majy18@mail.ustc.edu.cn</a> or<a href="mailto: seisbird@gmail.com"> seisbird@gmail.com</a>).</p> <p> </p>
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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.