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82 results for “driving simulation”
MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI
<p>The dataset provided resulted from a driving simulator study conducted by Chemnitz University of Technology (TUC) within work package 3 of the MEDIATOR project. The study focused on the user evaluation of the Mediator system and its functionalities, including the innovative Human Machine interface (HMI). The user evaluation centred on acceptance, trust, usability, comfort and the experience of Transitions of Control (TOCs). The core idea of the Mediator system is to mediate between the human driver and the automated system. Thereby, the Mediator system aims at establishing both as a team that is aware of each other’s strengths, limitations as well as current states in order to achieve safe TOCs, which are actively proposed by the HMI. The main focus of the driving simulator study was on comfort TOCs from manual to automated driving, simulated automation degradation and related TOCs by the human driver, comfort critical situations (i.e., close approach to the rear-end of a traffic jam) as well as the influence of driver characteristics. The provided dataset contains the questionnaire data of 74 German-speaking participants.</p> <p> </p> <p>This document contains information about the study methodology and coding of the variables. For a detailed description, please consult Mediator deliverable D3.3 ‘Results of the MEDIATOR driving simulator evaluation studies’ (Part II – Driving simulator study Germany). Please note that selected passages of this deliverable were adopted (partly in a slightly modified manner) in this document.</p> <p> </p> <p>This dataset is licensed under a <a href="https://spdx.org/licenses/CC-BY-4.0.html">Creative Commons Attribution 4.0 International</a> License.</p> <p> </p> <p>The research leading to this dataset received funding from the European Commission Horizon 2020 programme under the project MEDIATOR (<a href="https://mediatorproject.eu/">https://mediatorproject.eu/</a>), grant agreement number 814735.</p> <p> </p> <p>If you use the dataset, please cite it as: MEDIATOR (2023). MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI. <a href="https://doi.org/10.5281/zenodo.7638299">https://doi.org/10.5281/zenodo.7638299</a></p> <p> </p> <p>For further information, please contact: <a href="mailto:cornelia.hollander@psychologie.tu-chemnitz.de">cornelia.hollander@psychologie.tu-chemnitz.de</a>.</p>
Simulation code and simulated data for: Transient polymorphisms in parental care strategies drive divergence of sex roles
<p>This repository contains C++ code, simulated datasets, an R-script for data analysis and a Mathematica notebook for mathetical analysis.</p><p>Datasets are organised into ZIP files named after the corresponding figure in the publication. All of the figures based on simulation data in the manuscript and supplementary materials can be created with the R-script. For further information see the article published in <i>Nature Communications (</i>doi:<i> </i>https://doi.org/10.1038/s41467-023-42607-6).</p><p> </p><p> </p><p> </p>
Supplementary Simulation Data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation"
<p>Additional simulation data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation", which has been submitted as a preprint to ChemRxiv.</p>
Simulation data for: "Unique Amphipathic a-helix Drives Membrane Insertion and Enzymatic Activity of ATG3"
<p>Simulation data from Nishimura et al. (2023), "Unique Amphipathic a-helix Drives Membrane Insertion and Enzymatic Activity of ATG3".</p> <p>The dataset contains the MD simulations executed for the Atg3/LC3/lipid membrane system, both in the WT and 5W-mutated variants.</p> <p>More information can be found in the README file and in Table 1 of the cited paper.</p> <p> </p>
Simulation of ripple oscillations in a large interneuron network under different levels of constant external drive
<p>Example simulation to be used with code on GitHub repository: https://github.com/NatalieSchieferstein/interneuron_ripples_with_ifa.git .</p><p>Simulation data was generated using pypet (pypet.readthedocs.io/) and Brian2.</p><p>Code and simulation data are Supplement to publication: 10.1101/2023.01.30.526209 .</p><p> </p>
Simulation models from: Can CRISPR-mediated gene drive work in pest and beneficial haplodiploid species?
<p>Gene drives based on CRISPR/Cas9 have the potential to reduce the enormous harm inflicted by crop pests and insect vectors of human disease, as well as to bolster valued species. In contrast with extensive empirical and theoretical studies in diploid organisms, little is known about CRISPR gene drive in haplodiploids, despite their immense global impacts as pollinators, pests, natural enemies of pests, and invasive species in native habitats. Here we analyze mathematical models demonstrating that, in principle, CRISPR homing gene drive can work in haplodiploids, as well as at sex-linked loci in diploids. However, relative to diploids, conditions favoring the spread of alleles deleterious to haplodiploid pests by CRISPR gene drive are narrower, the spread is slower, and resistance to the drive evolves faster. By contrast, the spread of alleles that impose little fitness cost or boost fitness was not greatly hindered in haplodiploids relative to diploids. Therefore, altering traits to minimize damage caused by harmful haplodiploids, such as interfering with transmission of plant pathogens, may be more likely to succeed than control efforts based on introducing traits that reduce pest fitness. Enhancing fitness of beneficial haplodiploids with CRISPR gene drive is also promising.</p>
Dataset for Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor
<p><strong>SDC-Scissor tool for Cost-effective Simulation-based Test Selection in Self-driving Cars Software</strong></p> <p>This dataset provides test cases for self-driving cars with the BeamNG simulator. Check out the repository and demo video to get started.</p> <p><strong>GitHub:</strong> <a href="https://github.com/ChristianBirchler/sdc-scissor">github.com/ChristianBirchler/sdc-scissor</a></p> <p>This project extends the tool competition platform from the <a href="https://github.com/se2p/tool-competition-av">Cyber-Phisical Systems Testing Competition</a> which was part of the <a href="https://sbst21.github.io/">SBST Workshop in 2021</a>.</p> <p><strong>Usage</strong></p> <p><strong>Demo</strong></p> <p> <a href="https://youtu.be/Cn8p648KnfQ">YouTube Link</a></p> <p><strong>Installation</strong></p> <p>The tool can either be run with <a href="https://docs.docker.com/get-docker/">Docker</a> or locally using <a href="https://python-poetry.org/docs/">Poetry</a>.</p> <p>When running the simulations a working installation of <a href="https://beamng.gmbh/research/">BeamNG.research</a> is required. Additionally, this simulation cannot be run in a Docker container but must run locally.</p> <p>To install the application use one of the following approaches:</p> <ul> <li>Docker: <code>docker build --tag sdc-scissor .</code></li> <li>Poetry: <code>poetry install</code></li> </ul> <p><strong>Using the Tool</strong></p> <p>The tool can be used with the following two commands:</p> <ul> <li>Docker: <code>docker run --volume "$(pwd)/results:/out" --rm sdc-scissor [COMMAND] [OPTIONS]</code> (this will write all files written to <code>/out</code> to the local folder <code>results</code>)</li> <li>Poetry: <code>poetry run python sdc-scissor.py [COMMAND] [OPTIONS]</code></li> </ul> <p>There are multiple commands to use. For simplifying the documentation only the command and their options are described.</p> <ul> <li>Generation of tests: <ul> <li><code>generate-tests --out-path /path/to/store/tests</code></li> </ul> </li> <li>Automated labeling of Tests: <ul> <li><code>label-tests --road-scenarios /path/to/tests --result-folder /path/to/store/labeled/tests</code></li> <li><em>Note:</em> This only works locally with BeamNG.research installed</li> </ul> </li> <li>Model evaluation: <ul> <li><code>evaluate-models --dataset /path/to/train/set --save</code></li> </ul> </li> <li>Split train and test data: <ul> <li><code>split-train-test-data --scenarios /path/to/scenarios --train-dir /path/for/train/data --test-dir /path/for/test/data --train-ratio 0.8</code></li> </ul> </li> <li>Test outcome prediction: <ul> <li><code>predict-tests --scenarios /path/to/scenarios --classifier /path/to/model.joblib</code></li> </ul> </li> <li>Evaluation based on random strategy: <ul> <li><code>evaluate --scenarios /path/to/test/scenarios --classifier /path/to/model.joblib</code></li> </ul> </li> </ul> <p>The possible parameters are always documented with <code>--help</code>.</p> <p><strong>Linting</strong></p> <p>The tool is verified the linters <a href="https://flake8.pycqa.org/en/latest/">flake8</a> and <a href="https://pylint.org/">pylint</a>. These are automatically enabled in <a href="https://code.visualstudio.com/">Visual Studio Code</a> and can be run manually with the following commands:</p> <pre>poetry run flake8 . poetry run pylint **/*.py</pre> <p><strong>License</strong></p> <p>The software we developed is distributed under GNU GPL license. See the <a href="https://github.com/ChristianBirchler/sdc-scissor/blob/main/LICENSE.md">LICENSE.md</a> file.</p> <p><strong>Contacts</strong></p> <p>Christian Birchler - Zurich University of Applied Science (ZHAW), Switzerland - <a href="mailto:birc@zhaw.ch">birc@zhaw.ch</a></p> <p>Nicolas Ganz - Zurich University of Applied Science (ZHAW), Switzerland - <a href="mailto:gann@zhaw.ch">gann@zhaw.ch</a></p> <p>Sajad Khatiri - Zurich University of Applied Science (ZHAW), Switzerland - <a href="mailto:mazr@zhaw.ch">mazr@zhaw.ch</a></p> <p>Dr. Alessio Gambi - Passau University, Germany - <a href="mailto:alessio.gambi@uni-passau.de">alessio.gambi@uni-passau.de</a></p> <p>Dr. Sebastiano Panichella - Zurich University of Applied Science (ZHAW), Switzerland - <a href="mailto:panc@zhaw.ch">panc@zhaw.ch</a></p> <p><strong>References</strong></p> <ul> <li>Christian Birchler, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, and Sebastiano Panichella. 2022. Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor. In 2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE.</li> </ul> <p><strong>If you use this tool in your research, please cite the following papers:</strong></p> <pre><code>@INPROCEEDINGS{Birchler2022, author={Birchler, Christian and Ganz, Nicolas and Khatiri, Sajad and Gambi, Alessio, and Panichella, Sebastiano}, booktitle={2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), title={Cost-effective Simulationbased Test Selection in Self-driving Cars Software with SDC-Scissor}, year={2022}, }</code></pre>
Driving Forces of Extreme Updrafts Associated with Convective Bursts in the Eyewall of a Simulated Tropical Cyclone
<p>The model-simulated data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (qnn_nancy@yahoo.com).</p>
User Driving Recordings in Simulated Environment for Manoeuvre Anticipation
<p>This is a dataset acquired with the Euro Track Simulator 2 for the evaluation of driver intention based on face tracking and vehicular data. For each subject we collected the environment video and the face looking video plus the telemetry expressed in JSON.</p> <p>For each manoeuvre we provide the 5 seconds before. </p> <p>This dataset has been used for evaluating a technique of Domain Adversarial Recurrent Neural Network in the paper "Adaptive Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks"</p>
Ungulate herbivores promote beta diversity and drive stochastic plant community assembly by selective defoliation and trampling: From a four-year simulation experiment
<p>Ungulate herbivores shape grassland plant communities at multiple scales, ultimately affecting ecosystem function. However, ungulates have complex effects on grasslands, including defoliation, trampling, excreta return, and their interactions. Moreover, the effects of ungulate density on grasslands are regulated by these three mechanisms. Nevertheless, how these three mechanisms affect biodiversity at multiple scales and community assembly remains poorly understood.</p> <p>Here, we conducted a 4-year novel field experiment to disentangle the effects of defoliation, trampling, and excreta return by ungulates on plant community assembly in a temperate grassland in Inner Mongolia, China. This experiment set two different scenarios: moderate ungulate density (Moderate, characterised by selective defoliation and moderate trampling) and high ungulate density (Intense, characterised by non-selective defoliation and heavy trampling), including different combinations of defoliation, trampling, and excreta return in each scenario.</p> <p>We found that defoliation and trampling increased stochasticity in community assembly and promoted alpha and beta diversity under both scenarios. Specifically, defoliation promoted the coexistence of species with multiple resource acquisition strategies (higher functional trait diversity) by reducing interspecific competition; trampling tended to facilitate random species colonisation. Conversely, excreta return favoured grasses, promoting deterministic assembly and impacting species coexistence. Notably, selective defoliation in the moderate scenario led to a dominance of stochastic processes during community assembly, whereas non-selective defoliation still did not change the dominance of deterministic processes. Further, communities subject to selective defoliation were insensitive to changes in soil properties caused by trampling and excreta return, maintaining a high-level beta diversity and the stochastic of community assembly.</p> <p><em>Synthesis:</em></p> <p>Our study provides important insights into the mechanisms by which ungulate herbivores influence plant community assembly, suggesting that defoliation and trampling have the potential to drive stochastic processes, while excreta return plays the opposite role. Our study also suggests that selective foraging by ungulates acts as stronger stochastic forces during community assembly compared to non-selective defoliation. These results imply that considering ungulate feeding preferences and foraging behaviour in grassland management will help prevent biodiversity loss and biotic homogenisation.</p>
Sedative-Anxiolytic Effects on Simulated Driving Performance
ClinicalTrials.gov study NCT03297944. IPD Sharing: NO. Countries: 1. Publications: 1.
Driving cell response through deep learning, a study in simulated 3D cell cultures
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Simulation models from: Can CRISPR-mediated gene drive work in pest and beneficial haplodiploid species?
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Ungulate herbivores promote beta diversity and drive stochastic plant community assembly by selective defoliation and trampling: From a four-year simulation experiment
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Water, not carbon, drives drought-constraints on stem terpene defense against simulated bark beetle attack in Pinus edulis
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Molecular dynamics simulation files for: a molecular machine efficiently drives glycosaminoglycan assembly and secretion for osteoarthritis therapy
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Ionospheric Electron Densities, Neutral Temperature, Winds and Post-processed Output from TIEGCM Simulations in Support of Publication "Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill"
This dataset is associated with the publication "Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill". In particular, the NCAR- Community model: high-resolution thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) has been used to investigate the response of ionospheric F2-region electron density (Ne) at Millstone Hill (42.610N, 71.480W, maximum obscuration: 63%) to the Great American Solar Eclipse on 21 August 2017. Two sets of model runs were done, one with the eclipse and the other without. Model outputs of winds, temperatures and electron densities, as well as diagnostic variables of key chemical and physical processes that determined the ionosphere responses to the eclipse, were analyzed and used in producing a paper: Physical Processes Driving the Response of the F2-region Ionosphere to the 21 August 2017 Solar Eclipse at Millstone Hill.
Molecular Dynamics Simulation Dataset for "Hydrophobic Mismatch Drives Self-Organization of Designer Proteins into Synthetic Membranes"
<p>This repository contains molecular dynamics (MD) simulation data from the study on the self-organization of designer proteins in synthetic membranes. The data includes simulations for different single lipid compositions (DOPC, DPPC, DYPC) denoted as [lipid]-PL* where PL stands for the different TMD constructs. Multi component simulation are named accordingly. The repository provides initial (eqi.gro) and final (prod.gro) coordinates for each simulation. The 'cmd' file in each directory outlines the assembly process of each simulation, and the 'mdp' folder contains all input files for the simulations. </p>
Path Planning for Autonomous Bus Driving in Urban Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Urban Environments".</pre>
Path Planning for Autonomous Bus Driving in Highly Constrained Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Highly Constrained Environments".</pre>
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.