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155
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Dataset results
155 results for “Vehicle Data”
Data from: A new low-turbulence wind tunnel for animal and small vehicle flight experiments
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Data from: Nature, extent and ecological implications of night-time light from road vehicles
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Data from: Synchronous effects produce cycles in deer populations and deer-vehicle collisions
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Data from: extended use and end of life in the Global South: a transportation justice perspective of US-Mexico second-hand vehicle trade
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LoRaWAN gateway performance and vehicle tracking data in AERPAW testbed
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Attack data from electronic logging device for medium and heavy duty vehicles
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Vehicle trajectory data in Eco-friendly Cooperative Traffic Optimization (EcoTOp) system at signalized intersections
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Expression data from triple negative breast cancer-MDA-MB-231 cells treated with vehicle and salvia chinensia benth
GEO Series GSE189547. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Microarray transcriptomic data from LNCaP cells treated with vehicle, 10 nM bufalin or 20 nM bufalin for 24 h
GEO Series GSE118261. Homo sapiens. 3 samples. Type: Expression profiling by array.
RNAseq data for COV362 and PEO1 cell lines treated with DMSO (vehicle) or Prexasertib 20 nM
GEO Series GSE215340. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
DNA methylation data from renal cancer patient-derived xenograft tumor treated with temsirolimus or vehicle
GEO Series GSE133444. Homo sapiens. 2 samples. Type: Methylation profiling by array.
Expression data from retinoic acid injections into the third ventricle of F344 rats compared to vehicle injected rats
GEO Series GSE65300. Rattus norvegicus. 8 samples. Type: Expression profiling by array.
Supporting Data for Integrating Human Factors Expertise into Development of Automated Vehicles
<p>This dataset provides the questionnaires utilized during various stages of data collection for our manuscript titled "Integrating Human Factors Expertise into Development of Automated Vehicles".</p> <p>In addition to the questionnaires, we offer scripts and survey data essential for replicating and evaluating the methodology outlined in our manuscript</p> <p>The dataset includes the following files:</p> <ol> <li>Questionnaire used during Stage 1 of the data collection process.</li> <li>Questionnaire employed in Stage 2 of the data collection.</li> <li>Survey questionnaire used in Stage 3.</li> <li>Zip file providing the dataset and scripts used for quantitative analysis.</li> </ol> <p>For any inquiries or further clarification, please contact <a target="_new" rel="noreferrer">amnap@chalmers.se. </a></p>
Data for Near-Surface Characterization Using Classified Vehicle-Induced Surface Waves from DAS
<p>In this study, we characterize surface waves generated by vehicles of varying sizes and speeds to provide insights into accurate and efficient near-surface imaging using vehicle-induced DAS data. We employ a specialized Kalman filter algorithm to track vehicle locations and classify them into lightweight, midweight, and heavyweight based on the maximum amplitudes of quasi-static DAS records. Vehicles are also classified by their traveling speed (slow, medium, and fast) using their arrival times at DAS channels. Virtual shot gathers for the same class of vehicles are constructed from their surface wave windows selected using the tracked vehicle trajectories. We analyze the dispersion of the phase velocity to investigate the influence of vehicle characteristics on the induced surface waves.</p> <p><code>Put these pickle files into '/das_diff_veh/data/sw_data/' and run the scripts at https://github.com/jingxiaoliu/das_diff_veh</code></p> <p>If you found this useful, please cite our papers:</p> <blockquote> <p>[1] Liu, J., Li, H., Yuan, S., Noh, H. Y., & Biondi, B. (2024). Characterizing Vehicle-Induced Distributed Acoustic Sensing Signals for Accurate Urban Near-Surface Imaging. <em>arXiv preprint arXiv:2408.14320</em>.</p> <p>[2] Yuan, S., Liu, J., Noh, H. Y., Clapp, R., & Biondi, B. (2024). Using vehicle‐induced DAS signals for near‐surface characterization with high spatiotemporal resolution. <em>Journal of Geophysical Research: Solid Earth</em>, <em>129</em>(4), e2023JB028033.</p> <p>[3] Liu, J., Yuan, S., Dong, Y., Biondi, B., & Noh, H. Y. (2023). TelecomTM: A fine-grained and ubiquitous traffic monitoring system using pre-existing telecommunication fiber-optic cables as sensors. <em>Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies</em>, <em>7</em>(2), 1-24.</p> </blockquote>
Expression data from C3H10T1/2 cells treated with 3.3 nM BMP7 vs vehicle
GEO Series GSE71101. Mus musculus. 8 samples. Type: Expression profiling by array.
Data from: Fostering the use of zero and near zero emission vehicles in freight operations
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Sociodemographic data for battery electric vehicle owning households in California (From NCST Project "Understanding the Early Adopters of Fuel Cell Vehicles")
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RNAseq data of frontal cortex of 4 months high fat diet fed WT, AKO, 4 months high fat diet fed conditional human amylin expressing mice followed by vehicle and Tamoxifen injection, and 2 months high
GEO Series GSE309843. Mus musculus. 49 samples. Type: Expression profiling by high throughput sequencing.
mRNA data from C8+ T cells exposed to DCMU or control vehicle (DMSO)
GEO Series GSE189440. Homo sapiens. 48 samples. Type: Expression profiling by high throughput sequencing.
Expression data from vehicle and adrenomedullin treated human lymphatic endothelial cells
GEO Series GSE59938. Homo sapiens. 20 samples. Type: Expression profiling by array.
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.