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1,308
datasets available to search
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Dataset results
1,308 results for “Vehicle”
Whole genome sequencing of vehicle control and hydroxyurea treated Bacillus subtilis cells
GEO Series GSE169591. Bacillus subtilis. 8 samples. Type: Other.
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.
Cre-recombinase expression cooperates with FLT3ITD/ITD to induce acute myeloid leukemia [RNASeq_SclCre_JQ1_vs_vehicle]
GEO Series GSE212220. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
RNA sequencing of norepinephrine- or vehicle-treated mouse colon cancer organoids
GEO Series GSE243326. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Next generation sequencing comparing effects of vehicle, PTH, and YKL-05-093 in Ocy454 cells
GEO Series GSE76932. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Analysis of gene expression levels between CCl4 induced mouse fibrotic liver tissues and vehicle treated mouse control liver tissues.
GEO Series GSE73985. Mus musculus. 5 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
HEK293T cells treated with a G9a small molecule inhibitor (UNC0638) or vehicle (DMSO), WIZ siRNA, G9a siRNA, or scrambled (control) siRNA
GEO Series GSE70914. Homo sapiens. 13 samples. Type: Expression profiling by array.
RNA sequencing of PTEN,Rb1 double knockout (DKO) mouse prostate organoids treated with vehicle or the small molecule mitochondrial pyruvate carrier (MPC) inhibitor UK5099
GEO Series GSE221021. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Long non coding RNA (lncRNA) profiling of the colon epithelium of chronicallly SIV infected Rhesus macaques(RMs) administered either Δ9 THC or Vehicle (VEH)
GEO Series GSE223482. Homo sapiens; Macaca mulatta. 17 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
microRNA expression in primary human muscle cells treated with 1,25(OH)2D3 or vehicle
GEO Series GSE70933. Homo sapiens. 16 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Mouse frozen liver: vehicle-control vs. CCl4+DEN treated
GEO Series GSE71849. Mus musculus. 36 samples. Type: Genome variation profiling by genome tiling array.
Genome-wide expression profile of human melanoma patient derived xenograft (PDX) tumors that were treated with ARN22089 at 0 (vehicle), 10 mg/kg and 25 mg/kg IV in NSG mice (NOD.Cg-Prkdcscid IL2rgtm1W
GEO Series GSE197068. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
FOXO-inhibited iPSC-derived cardiomyocytes compared against DMSO vehicle controls.
GEO Series GSE252627. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
On-road vehicle emission inventory and its spatio-temporal variations in North China Plain
<p>The estimated BC, CO, NH<sub>3</sub>, NMVOCs, NO<sub>x</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, and SO<sub>2</sub> emissions by each vehicle type, fuel type, and national emission standard in 53 cities in North China Plain. (Unit: tons)</p> <p> </p> <p><strong>To cite the data:</strong> Jiang, P., Zhong, X., Li, L., 2020. On-road vehicle emission inventory and its spatio-temporal variations in North China Plain. Environ. Pollut. 267, 115639. https://doi.org/10.1016/j.envpol.2020.115639.</p>
Real-Time Work Zone Traffic Management via Unmanned Air Vehicles
<p>Highway work zones are prone to traffic accidents when congestion and queues develop. Vehicle queues expand at a rate of 1 mile every 2 minutes. Back-of-queue, rear-end crashes are the most common work zone crash, endangering the safety of motorists, passengers, and construction workers. The dynamic nature of queuing in the proximity of highway work zones necessitates traffic management solutions that can monitor and intervene in real time. Fortunately, recent progress in sensor technology, embedded systems, and wireless communication coupled to lower costs are now enabling the development of real-time, automated, “intelligent” traffic management systems that address this problem. The goal of this project was to perform preliminary research and proof of concept development work for the use of UAS in real- time traffic monitoring of highway construction zones in order to create real-time alerts for motorists, construction workers, and first responders. The main tasks of the proposed system was to collect traffic data via the UAV camera, analyze that a UAV based highway construction zone monitoring systems would be capable of detecting congestion and back-of-queue information, and alerting motorists of stopped traffic conditions, delay times, and alternate route options. Experiments were conducted using UAS to monitor traffic and collect traffic videos for processing. Prototype software was created to analyze this data. The software was successful in detecting vehicle speed from zero mph to highway speeds. Review of available mobile traffic apps were conducted for future integration with advanced iterations of the UAV and software system that has been created by this research. This project has proven that UAS monitoring of highway construction zones and real-time alerts to motorists, construction crews, and first responders is possible in the near term and future research is needed to further development and implement the innovative UAS traffic monitoring system developed by this research.</p>
Replication Package: "When Uncertainty Leads to Unsafety: Empirical Insights into the Role of Uncertainty in Unmanned Aerial Vehicle Safety"
<p>Replication Package of the paper titled "When Uncertainty Leads to Unsafety: Empirical Insights into the Role of Uncertainty in Unmanned Aerial Vehicle Safety"</p>
A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles
<p>Datasets collected from the experiments described in the paper R. Milijas, L. Markovic, A. Ivanovic, F. Petric and S. Bogdan, "A Comparison of LiDAR-based SLAM Systems for Control of Unmanned Aerial Vehicles," <em>2021 International Conference on Unmanned Aircraft Systems (ICUAS)</em>, 2021, pp. 1148-1154, doi: 10.1109/ICUAS51884.2021.9476802.</p> <p>The datasets consist of ROS bags which hold the UAV and LiDAR data, and of zip files which hold only the lidar data in binary format for non-ROS users.</p>
Prediction of Yield and Nitrogen-Use Efficiency by Using Consumer-Grade Unmanned Aerial Vehicle Multispectral Images of Winter Wheat
<p>It contains supplementary materials(revised version)and supporting data of Tables of Prediction of Yield and Nitrogen-Use Efficiency by Using Consumer-Grade Unmanned Aerial Vehicle Multispectral Images of Winter Wheat.<em> </em>However, the artical has not published. Data is available upon request.</p> <p>If you need anything, please don't hesitate to contact me(liujk@ahstu.edu.cn).</p>
Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Dubois, Seychelles - 2022-10-23
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Dubois, Seychelles - 2022-10-23.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Arm06, Seychelles - 2022-10-22
<i>This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Arm06, Seychelles - 2022-10-22.</i> <br> <br><br>Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.<br><br> This dataset is part of larger collection referencing numerous underwater and aerial images <a href="https://doi.org/10.5281/zenodo.11125847" target="_blank">Seatizen Altas</a>. Methods, tools and scientific objectives are also described in a dedicated data paper.<br> <h2> Generic folder structure </h2> YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number <br> ├── DCIM : folder to store videos and photos depending on the media collected. <br> ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. <br> │ ├── BASE : files coming from rtk station or any static positioning instrument. <br> │ └── DEVICE : files coming from the device. <br> ├── METADATA : folder with general information files about the session. <br> ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. <br> │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. <br> │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. <br> │ ├── IA : destination folder for image recognition predictions. <br> │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. <br> └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). <br> <h2> Software </h2> All the raw data was processed using our <a href="https://github.com/SeatizenDOI/plancha-workflow/releases/tag/v1.0.3" target="_blank">worflow</a>. <br>All predictions were generated by our <a href="https://github.com/SeatizenDOI/plancha-inference/releases/tag/v1.0.0" target="_blank">inference pipeline</a>. <br>You can find all the necessary scripts to download this data in this <a href="https://github.com/SeatizenDOI/zenodo-tools" target="_blank">repository</a>. <br>Enjoy your data with <a href="https://github.com/SeatizenDOI" target="_blank">SeatizenDOI</a>! <br>
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.