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2,442 results for “Car”
Autologous CD22 CAR T Cells in Adults w/ Recurrent or Refractory B Cell Malignancies
ClinicalTrials.gov study NCT04088890. IPD Sharing: NO. Countries: 1. Publications: 2.
CD19 /22 CAR T Cells (AUTO3) for the Treatment of B Cell Acute Lymphoblastic Leukemia (ALL)
ClinicalTrials.gov study NCT03289455. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pembrolizumab in Patients Failing to Respond to or Relapsing After CAR T Cell Therapy for Relapsed or Refractory Lymphomas
ClinicalTrials.gov study NCT02650999. IPD Sharing: Not stated. Countries: 1. Publications: 1.
T Cells Expressing a Novel Fully-Human Anti-BCMA CAR for Treating Multiple Myeloma
ClinicalTrials.gov study NCT03602612. IPD Sharing: YES. Countries: 1. Publications: 0.
Single-cell expression and TCR data from CD19-specific CAR T cells in a phase I/II clinical trial
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Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade
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Reconstitution of the multiple myeloma microenvironment following lymphodepletion with BCMA CAR-T therapy
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Bone marrow Single cell sequencing data of CAR-T treated IL-2Ra -/- mice
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Detecting flying insects using car nets and DNA metabarcoding
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Microengineered transplantation of human solid tumors for in vitro studies of CAR T immunotherapy
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Bispecific antibodies and CAR T cells targeting a TP53 mutation-associated neoantigen show discordant affinity requirements
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Livorno, Urban driving, Automated car detects pothole
<p><strong>Scenario description</strong>:</p> <p>Test session for connected car detecting potholes using the combination of one or more of the following sensors: smartphone, 6LoWPAN vibration sensor, IMU.<br> The information is sent to the cloud and can be sent back to other connected vehicles for warning.<br> The information is also transmitted via V2V to AD cars that can automatically adapt the speed.</p> <p><strong>Session description</strong>:</p> <p>Test session with only a connected car with smartphone based pothole detector, lap of 1,9 km on the harbour's public road. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and connected car
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events:</p> <p>1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car, lap of 12,3 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, connected car system and data management
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events:</p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time, the CONTI cloud is subscribed to the oneM2M platform; it receives and shares with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane-change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>pre-test session with only connected cars, lap of 11,6 km on the highway. Goal is to check all the system and data management.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during the Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and two connected cars
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>A vehicle is driving in the first lane of a “smart highway” at 90 km/h with all the devices working correctly and connected to all services needed.</p> <p>Actions or events: </p> <p>1 The puddle monitoring system of the highway triggers a puddle hazard warning for a specific extended zone.</p> <p>2 The AD car receives the information by IoT based services and sets a speed limitation according to the area interested by hazard conditions: it smoothly decelerates in order to enter in the area at the proper speed.</p> <p>3 At the end of the dangerous area, as notified by the “smart road”, the vehicle will recover the legally allowed cruise speed.</p> <p>Relevant situations: How the AD function interacts with different IoT input: from oneM2M platform (advisory speed limit due to puddles); from I2V (DENM, puddle hazard warning); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and two connected cars, lap of 12,3 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Pilot session in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during Highway Pilot sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Pilot session in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Single-cell profiling identifies pre-existing CD19-negative subclones in a B-ALL patient with CD19-negative relapse after CAR-T therapy
<p>This repository contains necessary files for reproducing the analysis in Rabilloud, Potier et al. (2020). The instructions for reproducing the analysis are given in github (https://github.com/Delphine-Potier/B-ALL-CAR-T) and extra files are available in GEO/SRA (GSE153697 ; SRP269742).</p>
Data from: Voluntary home and car smoke-free rules in Japan: a cross-sectional study in 2015
Objectives: Recently, the Tokyo Metropolitan Assembly passed an ordinance prohibiting smoking in private homes and cars if children are present. However, no previous study has investigated existing, voluntary home and car smoke-free rules in Japan. Therefore, we examined prevalence and determinants of comprehensive home and car smoke-free rules. Design: A cross-sectional study. Setting: Internet survey data with adjustments using inverse probability weighting for 'being a respondent in an internet survey'. Participants: 5600 respondents aged 15-69 years in 2015 were analyzed to estimate weighted percentages and prevalence ratios (PRs) with 95% confidence intervals (CIs) of having comprehensive home and car smoke-free rules. Main outcome measures: Respondents who answered 'smoking is never allowed' in their home or car were defined as having home and car smoke-free rules. Results: Overall, 47.0% (95% CI = 45.8–48.3) of respondents implemented comprehensive home and car smoke-free rules. People who agreed with 'smoking relieves stress' were less likely to have comprehensive smoke-free rules (PR=0.76 [0.71–0.82]), especially among ever-users of electronic nicotine delivery systems (PR=0.49 [0.30–0.81]). Higher education was significantly associated with higher PR for comprehensive smoke-free rules (PR=1.30 [1.19–1.41]). Living with children was significantly associated with higher PR for smoke-free rules among current smokers than not living with children. Among never and former smokers, electronic device ever-users were less likely to have smoke-free rules than non-users (PR=2.91 [1.99–4.27]). Conclusions: In Japan, about 50% of respondents had voluntary smoke-free rules in the home and car. Information on current voluntary smoke-free rules will be useful as baseline information on home and car smoke-free status before enforcement of the 2018 Tokyo home and car smoke-free legislation.
50s style car with interior
Source: Objaverse 1.0 / Sketchfab
Dataset for Article: "Hitchhiking Mosquitoes: Direct Evidence of Adult Aedes albopictus Dispersal by Car"
<p>This is the dataset used for the analysis in the article Hitchhiking Mosquitoes: Direct Evidence of Adult Aedes albopictus Dispersal by Car, by Roger Eritja, John R.B. Palmer, David Roiz, Isis Sanpera-Calbet, and Frederic Bartumeus. </p>
A!ex autonomous car driving in manual mode dataset
<p>Dataset from autonomous car A!ex (https://www.aalto.fi/en/news/invisible-powers-are-driving-cars-and-cranes-but-what-goes-on-behind-the-scenes-to-get-these) driven manually around Otaniemi. </p>
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.