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Dataset results
11 results for “vehicle technology”
Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland
<p>The probabilistic projections are part of the work: <br><em>Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, </em><a href="https://doi.org/10.1093/pnasnexus/pgad321"><em>https://doi.org/10.1093/pnasnexus/pgad321</em></a></p> <p>Please cite the article together with the Zenodo link when you use the data.</p> <p>The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:</p> <p>Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.<br>Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.<br>Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.</p> <p>The computations were performed at University of Geneva using Baobab HPC service.</p> <p>This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.</p>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
Dataset: First Trust S-Network Future Vehicles & Technology ETF (CARZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
UF & UAB's Phase I Demonstration Study: Older Driver Experiences with Autonomous Vehicle Technology (Project D2)
<p>Enclosed you will find the data collected during our STRIDE Phase I research project (D2) and a data dictionary.</p>
Analysis of intelligent vehicle technologies to improve vulnerable road users safety at signalized intersections
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EVALUATION OF ADVANCED VEHICLE AND COMMUNICATION TECHNOLOGIES THROUGH TRAFFIC MICROSIMULATION
<p>This folder contains products developed from STRIDE I-5 project.</p> <p>This project builds on a previously funded STRIDE project (D4) where a simulation extension was built using the micro simulator VISSIM to accurately represent vehicle autonomy and connectivity, and their operational and environmental effects.</p> <p>In this project, the research team expanded the functionality of the simulation extension to:</p> <p>i. Model human driven vehicles in the presence of AVs. An aggressive merging behavior model was implemented in VISSIM to study potential queue-jumping behavior at a freeway on-ramp. The research team also considered implementing this model in the open-source simulator SUMO.</p> <p>ii. Consider advanced vehicle dynamics and enable users to customize driver, vehicle, operating environment, and operating mode separately.</p> <p>iii. Incorporate a real-time optimization tool (RIO) previously developed with funding from the National Science Foundation (NSF). RIO jointly optimizes vehicle trajectories and signal control by taking advantage of CAV technologies.</p> <p> </p> <p>In this project, the research team conducted the following educational activities:</p> <p>i. Developed and conducted a nation-wide survey to understand the needs of the CAV education.</p> <p>ii. Developed six instructional modules for CAV education.</p> <p>iii. Developed a training module to model CAVs in microsimulation environment.</p> <p> </p> <p>The products developed from this project are:</p> <p>i. An enhanced simulation extension that can be used as a “plug and play” solution to model CAVs and human driven vehicles in presence of CAVs with options to consider advanced vehicle dynamics and incorporate signal/trajectory optimization.</p> <p>ii. Educational modules on how CAVs impact planning, design, modeling, and analysis of transportation facilities along with a training module to model CAVs using VISSIM</p>
CAVEMOVE: An acoustic database for the study of voice-enabled technologies inside moving vehicles
<p>We provide a collection of multichannel audio recordings obtained inside four different cars. The recording process involves (i) recordings of acoustic impulse responses, which are acquired at static conditions and provide the means for modeling the speech and car-audio components (ii) recordings of acoustic noise at a wide range of both static and in-motion conditions. Data is recorded with two different microphone configurations and particularly (i) a compact microphone array or (ii) a distributed microphone setup. A <a href="https://github.com/SPL-FORTH-ICS/CAVEMOVE">python API and a Matlab API</a>, that can be freely downloaded from CAVEMOVE github, is provided as the means to easily exploit the open access audio recordings to synthesize mixtures of speech and acoustic noise at different driving conditions. This way, the user can easily synthesize the microphone signals required for research on voice enabled technologies inside moving vehicles.</p> <p>All audio recordings and impulse responses are provided at 16 kHz sampling rate and are in the form of 8-channel .wav files.</p> <p>Some basic principles followed in CAVEMOVE APIs are the following. <br>- We provide functions for retrieving speech and noise components as separate entities (e.g. numpy arrays). Users must then add the speech and noise components to derive a mixture.<br>- Noise recordings are derived as a function of driving conditions, specifically the speed (in km/hour) and the window aperture (3 or 4 different windows conditions are considered in each vehicle)<br>-Apart from the basic noise components, we also provide means for adding ventilation/air-condition noise and also, interference from the built-in car audio system (e.g. radio, cd player etc)<br>-To produce the speech components, users must provide their own dry speech recordings <br>-To produce the car-audio components, users must provide their own audio signals.</p> <p>The Documentation .pdf file that we provide along with the audio recordings lists all the conditions that were recorded or measured inside the three cars (the same documantation can also be found in CAVEMOVE github). This information is important for correct use of the python API, since, asking for a condition that was not recorded can potentially produce an error. Note that a list of recorded driving conditions given the car name and the microphone configuration can also be retrieved from auxiliary functions included in the APIs.</p> <p>For any questions with respect to CAVEMOVE dataset or API, feel free to send an email to <br>Andreas Symiakakis at andrysmi@ics.forth.gr <br>or<br>Nikos Stefanakis at nstefana@ics.forth.gr</p> <p>CAVEMOVE project is funded by the Institute of Computer Science of the Foundation for Research and Technology-Hellas (FORTH).</p>
Driving Performance of People With Parkinson's Using Autonomous In-Vehicle Technologies
ClinicalTrials.gov study NCT04660500. IPD Sharing: NO. Countries: 1. Publications: 1.
Role of vehicle technology on use: Joint analysis of the choice of plug-in electric vehicle ownership and miles traveled
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Incorporating infrastructure and vehicle technology requirements, changes in demand, and Decarbonization policies’ considerations into freight planning
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Connected Emission Control Technologies for Freight Vehicles
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.