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1,855 results for “Autonomous”
Datasets and Code for the paper "Toward Futuristic Autonomous Experimentation—A Surprise-Reacting Sequential Experiment Policy"
<p>This page has the data and code used in the paper "Toward Futuristic Autonomous Experimentation—A Surprise-Reacting Sequential Experiment Policy", to be published in IEEE Transactions on Automation Science and Engineering.</p>
SPARC Connectivity Knowledge base of the Autonomic Nervous System
<p>The SPARC Knowledge base of the Autonomic Nervous System (SCKAN) is an integrated graph database composed of three parts: the SPARC dataset metadata graph, ApiNATOMY and NPO models of connectivity, and the larger ontology used by SPARC which is a combination of the NIF-Ontology and community ontologies.</p> <p>The fastest way to get querying is to follow the instructions in the <a href="https://github.com/SciCrunch/sparc-curation/blob/master/docs/sckan/README.org#getting-started">SCKAN readme file</a>.</p> <p>For background information please see <a href="https://scicrunch.org/sawg/about/SCKAN">https://scicrunch.org/sawg/about/SCKAN</a> and <a href="https://sparc.science/resources/6eg3VpJbwQR4B84CjrvmyD">the SPARC portal resource page about SCKAN.</a></p> <p>This release contains the raw and compiled data for SCKAN. The release-*.zip contains raw data inputs along with the Blazegraph journal file, the sparc-sckan-graph-*.zip contains the SciGraph database, and sckan-data-*.tar.gz is a Docker image that contains the Blazegraph journal file and the SciGraph database along with the configuration files for running each of the servers. The image is intended to be used as a data volume with another Docker container that runs the SciGraph and Blazegraph server software.</p> <p>The Docker image containing this data is available live and is likely easier to use than the archived image included in this release. See the <a href="https://github.com/SciCrunch/sparc-curation/blob/master/docs/sckan/README.org#getting-started">SCKAN readme file</a> for the most up-to-date instructions.</p> <p>We would like to thank the members of the SAWG (SPARC Anatomy Working Group, RRID:SCR_018709) for their work on the various connectivity models included in this release.</p> <p>This work was funded by the NIH Common Fund under 3OT2OD030541-01S1.</p>
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>
Research data supporting: "Toward Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly"
<p>This repository contains the set of data shown in the paper "<em>Toward Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly</em>", published on <em>ACS Nano</em> (DOI: 10.1021/acsnano.1c0500).</p> <p>All the input data needed to run the simulations and get the results are organized in 5 different folders:</p> <ul> <li> <p><code>1-MinimlisticCGModel/</code></p> <p>in this folder there are the Gromacs and PLUMED input files for reproducing the minimalistic coarse-grained model shown in the article.</p> </li> <li> <p><code>2-AAandFineCGModels/</code></p> <p>the folder contains the atomistic (<code>monomerAA/</code>) and the coarse-grained Martini "wet" (<code>monomerGCWet/</code>) input files for modelling the Original monomer with one carboxyl group. The coarse-grained Martini "dry" (<code>monomerCGDry/</code>) model is reported for the Original monomer at the three different charged states.</p> </li> <li> <p><code>3-UnbiasedMD/</code></p> <p>this folder includes the Gromacs input files needed to reproduce the Unbiased simulations as shown in the article.</p> </li> <li> <p><code>4-MetaD/</code></p> <p>this folder incorporates the Gromacs and PLUMED input files necessary to reproduce both the Infrequent CG Metadynamics simulations (<code>InfrequentMetaD/</code>) and the Multiple-walker CG Metadynamics simulations (<code>MultipleWalkerMetaD/</code>)</p> </li> <li> <p><code>5-InSilico/</code></p> <p>in this folder all the files used to run the simulations about the exoliation experiments (<code>Original/</code>, <code>Type-1/</code>, <code>Type-2/</code>, <code>Type-3/</code>, <code>Type-4/</code>) are reported. Every subfolder contains all input files for the three different charged monomers (<code>Olig1-/</code>, <code>Olig2-/</code>, <code>Olig3-/</code>).</p> </li> </ul>
Barriers & Facilitators of People With Disabilities in Accepting & Adopting Autonomous Shared Mobility Services (Project A5)
<p>Enclosed you will find the data collected during our STRIDE Phase II Extension research project (A5) and a data dictionary.</p>
PAsCAL Pilot 2 Autonomous Driving Training
<p>The video documents and describes the activities of Pilot 2 "Autonomous Driving Training" of the PAsCAL vreal-world pilots. This pilot took place at the ACI-Sara Safe Driving Centre in Lainate, near Milan (Italy) on February 2022. It involved trainers, professional and experienced drivers to test the study on a new driving training methodology to encourage people to accept the transition from conventional cars with partial autonomous driving capabilities to fully autonomous cars. </p> <p> </p>
WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions
<p>This repository houses electronic structure data and metadata generated as part of a computational chemistry case study, enabling full analysis of the paper "WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions" by Rohan Yuri Sanspeur, Javier Heras-Domingo, John R. Kitchin and Zachary Ulissi.</p>
An archive of net community production estimates derived from autonomous profiler observations at Ocean Station Papa
<p>This dataset contains an archive of marine net community production estimates for Ocean Station Papa, derived using autonomous profilers, including BGC-Argo floats and sea gliders. The archive contains data from the following original publications:</p> <ol> <li>Haskell, W. Z., Fassbender, A. J., Long, J. S., and Plant, J. N.: Annual Net Community Production of Particulate and Dissolved Organic Carbon From a Decade of Biogeochemical Profiling Float Observations in the Northeast Pacific, Global Biogeochemical Cycles, 34, 1–22, <a href="https://doi.org/10.1029/2020GB006599">https://doi.org/10.1029/2020GB006599</a>, 2020.</li> <li>Huang, Y., Fassbender, A. J., Long, J. S., Johannessen, S., and Bernardi Bif, M.: Partitioning the Export of Distinct Biogenic Carbon Pools in the Northeast Pacific Ocean Using a Biogeochemical Profiling Float, Global Biogeochemical Cycles, 36, <a href="https://doi.org/10.1029/2021gb007178">https://doi.org/10.1029/2021gb007178</a>, 2022.</li> <li>Pelland, N. A., Eriksen, C. C., Emerson, S. R., and Cronin, M. F.: Seaglider Surveys at Ocean Station Papa: Oxygen Kinematics and Upper-Ocean Metabolism, Journal of Geophysical Research: Oceans, 123, 6408–6427, <a href="https://doi.org/10.1029/2018JC014091">https://doi.org/10.1029/2018JC014091</a>, 2018.</li> <li>Plant, J. N., Johnson, K. S., Sakamoto, C. M., Jannasch, H. W., Coletti, L. J., Riser, S. C., and Swift, D. D.: Net community production at Ocean Station Papa observed with nitrate and oxygen sensors on profiling floats, Global Biogeochemical Cycles, 30, 859–879, <a href="https://doi.org/10.1002/2015GB005349">https://doi.org/10.1002/2015GB005349</a>, 2016.</li> <li>Yang, B., Emerson, S. R., and Bushinsky, S. M.: Annual net community production in the subtropical Pacific Ocean from in-situ oxygen measurements on profiling floats, Global Biogeochemical Cycles, 31, 728–744, <a href="https://doi.org/10.1002/2016GB005545">https://doi.org/10.1002/2016GB005545</a>, 2017.</li> </ol> <p> </p> <p><strong>Dataset description:</strong></p> <p>Separate Matlab (.mat) and NetCDF (.nc) files are provided for each original publication dataset. At minimum, each contains time (recorded as absolute days since 01 Jan 2000, 00:00:00), and time-explicit depth-integrated NCP. Additional fields include depth-resolved NCP, and water column depth (Plant et al., 2016; Pelland et al., 2018).</p> <p>Note that the Haskell et al. (2020) dataset (haskell20.mat, haskell20.nc) contains climatological NCP estimates. Accordingly, the time field represents the Year-day only. </p> <p>All NCP units are recorded in units of mmol O2/m2/d (depth-integrated) or mmol O2/m3/d (depth-resolved).</p> <p>Additional information on each dataset is contained within the data files. Additional processing information is contained in Izett et al. (under review).</p> <p> </p> <p><strong>The data were compiled in: </strong></p> <p>Izett, R. W., Fennel, K., Stoer, A. C., and Nicholson, D. P. Reviews and syntheses: Expanding the global coverage of gross primary production and net community production measurements using BGC-Argo floats, <em>Submitted to Biogeosciences. 2023</em>.</p> <p> </p> <p><strong>Please cite as:</strong></p> <p>Izett, R., Haskell, W., Huang, Y., Pelland, N., Plant, J., and Yang, B. 2023. An archive of net community production estimates derived from autonomous profiler observations at Ocean Station Papa. Database. Zenodo. doi: 10.5281/zenodo.7667521. Accessed on <em>DATE</em>. </p>
Consensual videos of potentially re-identifiable individuals recorded at the Autonomous Driving Test Area Baden-Württemberg (raw images with location and IMU data).
<p>For the purpose of research on data intermediaries and data anonymisation, it is necessary to test these processes with realistic video data containing personal data. For this purpose, the <a href="http://treumoda.de">Treumoda</a> project, funded by the German Federal Ministry of Education and Research (BMBF), has created a dataset of different traffic scenes containing identifiable persons.</p> <p>This video data was collected at the <a href="https://taf-bw.de/">Autonomous Driving Test Area Baden-Württemberg</a>. On the one hand, it should be possible to recognise people in traffic, including their line of sight. On the other hand, it should be usable for the demonstration and evaluation of anonymisation techniques.</p> <p><strong>The legal basis for the publication of this data set the consent given by the participants as documented in the file Consent.pdf (all purposes) in accordance with Art. 6 1 (a) and Art. 9 2 (a) GDPR. Any further processing is subject to the GDPR.</strong></p> <p>We make this dataset available for non-commercial purposes such as teaching, research and scientific communication. Please note that this licence is limited by the provisions of the GDPR. Anyone downloading this data will become an independent controller of the data. This data has been collected with the consent of the identifiable individuals depicted.</p> <p>Any consensual use must take into account the purposes mentioned in the uploaded consent forms and in the privacy terms and conditions provided to the participants (see Consent.pdf). All participants consented to all three purposes, and no consent was withdrawn at the time of publication. KIT is unable to provide you with contact details for any of the participants, as we have removed all links to personal data other than that contained in the published images.</p>
Business process models for a ride fulfilment process in a ride-hailing company enabled by an autonomous driving system
<p>The repository contains the business process models of a ride fulfilment process in a ride-hailing company enabled by an autonomous driving system. The models are depicted using BPMN.2.0 language and are extended with annotations for usage in the <a href="https://dpotool.cs.ut.ee/">DPO tool</a>. The models, which contain "Pleak" as a part of the title, are created using PE-BPMN language and some of them are annotated for usage in the <a href="https://pleak.io/">Pleak</a> tool set. </p> <p>The repository also contains two .csv files which contain the results of the simple disclosure analysis ("<em>6_RideFulfillment_PK_SecSharing_combined-Simple-Disclosure-results.csv</em>") and the results of the leak-when analysis ("<em>7_Pleak_RideFulfillment_PK_SecrSharing_BPMN_Leak_When-results.csv</em>") in the ride fulfilment process. Both analyses have been conducted using the <a href="https://pleak.io/">Pleak</a> tool set and the business process models with respective names from this repository.</p>
Maneuverability Characterization of Autonomous Surface Vehicle (ASV): ITTC zig-zag test dataset
<p>The two files refer to the same dataset: the .csv file is the raw format that is acquired by the ASV robotic platform. The .nc file contains the same data but in a standard format and with global and variable metadata generated using a standardization workflow (based on FAIR Principles) developed at CNR INM which uses controlled and standard vocabularies (ACDD and standard CF).</p> <p>The data refer to the execution of zig-zag maneuvers of the ASV following the ITTC standards</p>
ADASIND: A Diverse Wide Angle Fisheye Camera Dataset for Autonomous Driving
<p>This dataset comprises images collected from a fisheye lens on Indian roads to capture a variety of driving scenarios in a real-world environment, with a focus on semi-urban roads that don't have dividers.. The dataset includes 10,000 RGB images and has well labeled annotations for 2d bounding box, moving object detection, calibration of the fisheye lens containing parameters, ground truth of the RGB images and previous image of the corresponding frames. With this dataset, we would like to encourage the community to adapt computer vision models for the fisheye camera instead of using naive rectification. </p> <p>The Adasind dataset was collected by recording a video of more than 10,000 seconds as a vehicle drove across the Silchar city, starting from the main gate of the National Institute of Technology Silchar during different time periods of the day considering traffic variations. To capture the video, we used a smartphone with a 64-megapixel camera, which was attached to a fisheye lens and a tripod stand. The smartphone was firmly held from the backside of the motor vehicle, ensuring that it was stable and wouldn't move during the recording process. The vehicle drove through the city at a steady pace, capturing video footage at approximately 30 frames per second (fps).</p> <p>The annotations for various tasks is available in their respective folder and described in details below:</p> <ol> <li> <p><strong>2d box annotations</strong> : This folder contains the object label, x-y coordinates, width and height of the various objects within the image in the .txt file. Here, the Yolov5 algorithm is used to perform object detection on a set of input images which uses a deep neural network to detect objects within an image. The files of this folder are in the same sequence as that of the original RGB image with the same file names. The use of 2D bounding box annotations has several benefits in computer vision, including object detection. 2D bounding boxes can be used to train object detection models, which are designed to identify the location and extent of objects within an image.</p> </li> <li> <p><strong>Motion instance annotations</strong> : This folder contains the annotations for the moving objects which have relative motion with respect to the motion of the vehicle. Those objects create bias in specific tasks like depth estimation and have to be masked before feeding into the network. The files in the folder are in the same sequence as the original image and can be masked pixel wise.</p> </li> <li> <p><strong>Calibration </strong>: The process of turning the 3D world into a 2D image is done by a camera and this process is said to be calibration which can be done using the camera parameters namely the intrinsic and extrinsic parameters. As of now only a single json file is dumped with the parametric values of the camera and users can make separate copies of the file for calibration tasks related to each original image. Together, the intrinsic and extrinsic parameters are used to model the relationship between the 3D world and the 2D image captured by the camera. This information is essential in many computer vision applications, such as 3D reconstruction, object tracking, and augmented reality.</p> </li> <li> <p><strong>Ground Truth</strong> : This is a subfolder inside the "Motion instance annotations" folder that contains the correct labels or annotations for each image or object in a dataset. These labels are in the form of images and the filenames are the same as those of the original images. Ground truth labels are used during evaluation to assess the accuracy and performance of the machine learning or deep learning models. The accuracy of a model is measured by comparing its predicted labels to the ground truth labels, using evaluation metrics.</p> </li> <li> <p><strong>RGB images</strong>: This folder contains the RGB images extracted from the video recording (30 fps) by considering one frame per second with 30 frame interval. A total of 10,000 RGB images are there in the folder indexed sequentially as appeared in the video recording. </p> </li> <li> <p><strong>Previous Image</strong> (not provided) : The folder for the previous images corresponding to each original image may be prepared as per the requirement using the original images present in the "RGB image" folder. The use of previous images is especially relevant in computer vision tasks that involve motion prediction or scene understanding, as it enables algorithms to better understand the dynamics of the scene and predict future states.<br> </p> </li> </ol> <p>The original images can be converted to 3d tensors with RGB channels or grayscale channels. The 2d box annotated values can be fed to the network for better understanding of the objects, the RGB image tensor values can be matrix multiplied with intrinsic parameters for converting from 3d to 2d and vice-versa, motion images can be multiplied with their respective original image so the moving objects is not taken into consideration, evaluation for the model with tensors from the ground truth can be done and at last previous image tensors can also be fetched along with the current image for upcoming predicted tasks.<br> <br> </p>
Discrete surface turbidity samples and underway sea surface temperature and sea surface salinity measured in Aarhus Bay during a demonstration of an experimental autonomous surface vehicle
<p>This dataset includes measurements obtained by an autonomous boat that was equipped with a surface water sampling system: the Naval Operating Research Drone Assessing Climate Change (NORDACC). </p> <p>This dataset includes two .csv files</p> <p><br> 2022-10-14_NORDACC_Turbidity.csv<br> This file contains the results of 8 discrete surface water samples that were analyzed for turbidity using a Hach turbidimeter. Surface water samples were acquired by NORDACC on the afternoon of 14 October 2022 in Aarhus Bay. The columns are separated by commas and correspond to: <br> Sample Number, Date (yyyy-mm-dd), UTC time (HH:MM:SS), Longitude (decimal degrees), Latitude (decimal degrees), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> 2022-10-14_NORDACC_UnderwayData.csv<br> This file contains 1 Hz data, delimited by commas, that were collected while NORDACC was in operation. The underway data columns correspond to:<br> Date & Time (ISO format yyyy-mm-ddTHH:MM:SS), Operation State (1=initializing, 2=sailing, 3=water sample), Longitude (decimal degrees), Latitude (Latitude), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> About NORDACC:</p> <p>The Naval Operating Research Drone Assessing Climate Change (NORDACC) was designed by Serbian Akbulut, Jeppe Fogh Rasmussen, Christian Søndergård Hestbech, and Marius Hjorth Andersen, a group of mechatronics students at Aarhus University. The project was supervised by Prof. Claus Melvad (AU) and received external guidance by Dr. Daniel Carlson (Helmholtz-Zentrum Hereon). The NORDACC project was partially supported by Helmholtz-Zentrum Hereon and the Klaus-Tschira Boost Fund that was administered by the German Scholars Organization.</p> <p>NORDACC designs, software, and BOM are open source and provided via Mendeley Data, doi:10.17632/rpzv35pccr.1 </p> <p>For more information about NORDACC see the accompanying paper in HardwareX. </p>
Data for "Symbiotic nutrient exchange enhances the long-term survival of cassiosomes, the autonomous stinging-cell structures of Cassiopea"
<p>Raw data linked to the publication "Symbiotic nutrient exchange enhances the long-term survival of cassiosomes, the autonomous stinging-cell structures of <em>Cassiopea".</em> NanoSIMS data and data from the survival experiments are available as individual tabs in the excel file.</p>
Paper data for DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles
<p>This repo contains the study and appendix data for "DeepManeuver: Adversarial Test Generation for Trajectory Manipulation of Autonomous Vehicles". DOI 10.1109/TSE.2023.3301443.</p>
Dataset - Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements
<p>With the trend towards multinary materials and the associated increase in measurement time, there is a clear need for increasing the efficiency of measurement procedures. In systems requiring long materials characterization times, the implementation of active learning can help decreasing the measurement duration significantly. This dataset is part of the publication in Digital Discovery under the same title and holds the algorithm as well as the data used to test its performance. The algorithm leverages an active learning approach with a Gaussian process model capable of selecting the next measurement area of a library of materials based on the highest uncertainty. Ten materials libraries were manufactured by magnetron sputtering, the composition was measured with EDX and the electrical resistance was measured using the described test stand. The code can also be found on <a href="https://gitlab.ruhr-uni-bochum.de/fthelen/auto-resist-meas">Gitlab</a>.</p>
Consensual videos of potentially re-identifiable individuals recorded at the Autonomous Driving Test Area Baden-Württemberg (raw images recorded daytime).
<p>For the purpose of research on data intermediaries and data anonymisation, it is necessary to test these processes with realistic video data containing personal data. For this purpose, the <a href="http://treumoda.de/">TreuMoDa</a> project, funded by the German Federal Ministry of Education and Research (BMBF), has created a dataset of different traffic scenes containing identifiable persons.</p><p>This video data was collected at the <a href="https://taf-bw.de/">Autonomous Driving Test Area Baden-Württemberg</a>. On the one hand, it should be possible to recognise people in traffic, including their line of sight. On the other hand, it should be usable for the demonstration and evaluation of anonymisation techniques.</p><p><strong>The legal basis for the publication of this data set the consent given by the participants as documented in the file Consent.pdf (all purposes) in accordance with Art. 6 1 (a) and Art. 9 2 (a) GDPR. Any further processing is subject to the GDPR.</strong></p><p>We make this dataset available for non-commercial purposes such as teaching, research and scientific communication. Please note that this licence is limited by the provisions of the GDPR. Anyone downloading this data will become an independent controller of the data. This data has been collected with the consent of the identifiable individuals depicted.</p><p>Any consensual use must take into account the purposes mentioned in the uploaded consent forms and in the privacy terms and conditions provided to the participants (see Consent.pdf). All participants consented to all three purposes, and no consent was withdrawn at the time of publication. KIT is unable to provide you with contact details for any of the participants, as we have removed all links to personal data other than that contained in the published images.</p>
Datasets for 'Estmating autonomous vehicle localization error using 2D Geographic Information'
<p>Datasets for 'Estmating autonomous vehicle localization error using 2D Geographic Information'</p>
Hypoglycemia and Autonomic Nervous System Function
ClinicalTrials.gov study NCT01816893. IPD Sharing: NO. Countries: 1. Publications: 4.
Data and code from: Deep learning-based autonomous retinal vein cannulation in ex vivo porcine eyes
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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.