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155 results for “Vehicle Data”
Vehicle CAN bus data (with GPS)
<p>The dataset contains 20Hz sampled CAN bus data from a passenger vehicle, e.g. WheelSpeed FL (speed of the front left wheel), SteerAngle (steering wheel angle), Role, Pitch, and accelerometer values per direction.</p> <p>In contrast to the dataset published at https://zenodo.org/record/2658168#.XMw2m6JS9PY we now have GPS data from the vehicle (see signals 'Latitude_Vehicle' and 'Longitude_Vehicle' in h5 group 'Math') and GPS data from the IMU device (see signals 'Latitude_IMU', 'Longitude_IMU' and 'Time_IMU' in h5 group 'Math') included. However, as it was exported with single_precision, therefore we lost some precision for those GPS values.</p> <p>We are currently looking for a solution and will update the records if possible.</p> <p>For data analysis we use R and R Studio (https://www.rstudio.com/) and the library h5.</p> <p>e.g. check file with R code:</p> <p>library(h5)</p> <p>f <- h5file("file path/20181113_Driver1_Trip1.hdf")</p> <p>summary(f["CAN/Yawrate1"][,])</p> <p>summary(f["Math/Latitude_IMU"][,])</p> <p>h5close(f)</p>
Open Data for SLR Artificial Intelligence and Electric Vehicles
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Hybrid Vehicle Research Data
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Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research
Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.
Data for project: A quantitative investigation into the impact of partially automated vehicles on vehicle miles travelled in California
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Data for project: Discontinuance among California’s electric vehicle buyers: Why are some consumers abandoning their electric vehicles?
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Data from: A view from above: A view from above: unmanned aerial vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies
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Data from: A few large roads or many small ones? How to accommodate growth in vehicle numbers to minimise impacts on wildlife
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Data from: Unmanned aerial vehicles for high-throughput phenotyping and agronomic research
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Data from: Interspecific analysis of vehicle avoidance behavior in birds
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STRIDE Project C - Performance Measurement and Management Using Connected and Automated Vehicle Data
<p>The main objective of this study is to develop a methodological framework to estimate system performance measurements using CV data. The study also provides a validation of the framework as a proof of concept by determining performance measurements from traditional and CV data. In doing so, the microscopic simulation software VISSIM with trajectory conversion algorithm (TCA) is used to generate CV data, particularly basic safety message (BSM) for a study corridor located in Birmingham, AL. The estimated performance measures can be used by a system operator, planner, or an automated system to support decisions associated with these processes. The measurements can be also used to derive information for dissemination to travelers, third-party data aggregators, traveler information service providers, and other agencies. The collected and archived data includes real-world data collected from different sources in addition to simulation model results.</p>
Raw data of the article:Proof-of-Concept Study on the Use of Tangerine-Derived Nanovesicles as siRNA Delivery Vehicles toward Colorectal Cancer Cell Line SW480
<p>In the last years, the field of nanomedicine and drug delivery has grown exponentially, providing new platforms to carry therapeutic agents into the target sites. Extracellular vesicles (EVs) are ready-to-use, biocompatible, and non-toxic nanoparticles that are revolutionizing the field of drug delivery. EVs are involved in cell-cell communication and mediate many physiological and pathological processes by transferring their bioactive cargo to target cells. Recently, nanovesicles from plants (PDNVs) are raising the interest of the scientific community due to their high yield and biocompatibility. This study aims to evaluate whether PDNVs may be used as drug delivery systems. We isolated and characterized nanovesicles from tangerine juice (TNVs) that were comparable to mammalian EVs in size and morphology. TNVs carry the traditional EV marker HSP70 and, as demonstrated by metabolomic analysis, contain flavonoids, organic acids, and limonoids. TNVs were loaded with DDHD1-siRNA through electroporation, obtaining a loading efficiency of 13%. We found that the DDHD1-siRNA complex TNVs were able to deliver DDHD1-siRNA to human colorectal cancer cells, inhibiting the target expression by about 60%. This study represents a proof of concept for the use of PDNVs as vehicles of RNA interference (RNAi) toward mammalian cells.</p>
Data from: Synchronous effects produce cycles in deer populations and deer-vehicle collisions
<p>Population cycles are fundamentally linked with spatial synchrony, the prevailing paradigm being that populations with cyclic dynamics are easily synchronized. That is, population cycles help give rise to spatial synchrony. Here we demonstrate this process can work in reverse, with synchrony causing population cycles. We show that timescale-specific environmental effects, by synchronizing local population dynamics on certain timescales only, cause major population cycles over large areas in white-tailed deer. An important aspect of the new mechanism is specificity of synchronizing effects to certain timescales, which causes local dynamics to sum across space to a substantial cycle on those timescales. We also demonstrate, to our knowledge for the first time, that synchrony can be transmitted not only from environmental drivers to populations (deer), but also from there to human systems (deer-vehicle collisions). Because synchrony of drivers may be altered by climate change, changes to population cycles may arise via our mechanism.</p>
Supporting data for "Health benefits of US light-duty vehicle electrification: roles of fleet dynamics, clean electricity, and policy timing"
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Data from: A new low-turbulence wind tunnel for animal and small vehicle flight experiments
Our understanding of animal flight benefits greatly from specialized wind tunnels designed for flying animals. Existing facilities can simulate laminar flow during straight, ascending and descending flight, as well as at different altitudes. However, the atmosphere in which animals fly is even more complex. Flow can be laminar and quiet at high altitudes but highly turbulent near the ground, and gusts can rapidly change wind speed. To study flight in both laminar and turbulent environments, a multi-purpose wind tunnel for studying animal and small vehicle flight was built at Stanford University. The tunnel is closed-circuit and can produce airspeeds up to 50 m s−1 in a rectangular test section that is 1.0 m wide, 0.82 m tall and 1.73 m long. Seamless honeycomb and screens in the airline together with a carefully designed contraction reduce centreline turbulence intensities to less than or equal to 0.030% at all operating speeds. A large diameter fan and specialized acoustic treatment allow the tunnel to operate at low noise levels of 76.4 dB at 20 m s−1. To simulate high turbulence, an active turbulence grid can increase turbulence intensities up to 45%. Finally, an open jet configuration enables stereo high-speed fluoroscopy for studying musculoskeletal control in turbulent flow.
Data from: Clap-and-fling mechanism in a hovering insect-like two-winged flapping-wing micro air vehicle
This study used numerical and experimental approaches to investigate the role played by the clap-and-fling mechanism in enhancing force generation in hovering insect-like two-winged flapping-wing micro air vehicle (FW-MAV). The flapping mechanism was designed to symmetrically flap wings at a high flapping amplitude of approximately 192°. The clap-and-fling mechanisms were thereby implemented at both dorsal and ventral stroke reversals. A computational fluid dynamic (CFD) model was constructed based on three-dimensional wing kinematics to estimate the force generation, which was validated by the measured forces using a 6-axis load cell. The computed forces proved that the CFD model provided reasonable estimation with differences less than 8%, when compared with the measured forces. The measurement indicated that the clap and flings at both the stroke reversals augmented the average vertical force by 16.2% when compared with the force without the clap-and-fling effect. In the CFD simulation, the clap and flings enhanced the vertical force by 11.5% and horizontal drag force by 18.4%. The observations indicated that both the fling and the clap contributed to the augmented vertical force by 62.6% and 37.4%, respectively, and to the augmented horizontal drag force by 71.7% and 28.3%, respectively. The flow structures suggested that a strong downwash was expelled from the opening gap between the trailing edges during the fling as well as the clap at each stroke reversal. In addition to the fling phases, the influx of air into the low-pressure region between the wings from the leading edges also significantly contributed to augmentation of the vertical force. The study conducted for high Reynolds numbers also confirmed that the effect of the clap and fling was insignificant when the minimum distance between the two wings exceeded 1.2c (c = wing chord). Thus, the clap and flings were successfully implemented in the FW-MAV, and there was a significant improvement in the vertical force.
Data set for reliability-based lift-to-power consumption optimization with an accelerated Kriging model for clapping-wing micro air vehicles
<p>Procedures of the reliability-based lift-to-power consumption optimization with an accelerated Kriging model</p> <p>Step 1: Run the file “LHS.m” to generate initial samples.</p> <p>Step 2: Modify the aerodynamic model according to initial samples (e.g. flapping1_Def.xml, flapping1.bat), and then run the “.bat file” to obtain the original force data.</p> <p>Step 3: Run the file “Kriging.m” to obtain the average lift using a filter.</p> <p>Step 4: Run the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result.</p> <p>Step 5: Find the new training sample and obtain the eigenvalue of the new training sample.</p> <p>Step 6: Rerun the file “FW_2.m”, “FW_3.m” to obtain sub-optimal-result by reloading the new “.mat” files (e.g. FW_2_41.mat, FW_2_P_20.mat).</p> <p>Step 7: Go to Step 4 until the convergence criteria are satisfied.</p> <p>Step 8: Obtain the optimal result. PS: Other files are function files.</p>
Total Cost of Ownership of Plug-in Electric Vehicles Calculations Data sheet
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Data from: Clap-and-fling mechanism in a hovering insect-like two-winged flapping-wing micro air vehicle
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Data from: Speed kills: ineffective avian escape responses to oncoming vehicles
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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)
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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.