Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,855
datasets available to search
ShareScore release 0.9.0
Dataset results
1,855 results for “Autonomous”
Data from: The brassinosteroid receptor gene BRI1 safeguards cell-autonomous brassinosteroid signaling across tissues
Open the record for dataset details and reuse information.
McMurdo Dry Valleys Lake Bonney Autonomous Lake Profiler and Samplers (ALPS): Particulate Organic Carbon and Nitrogen Concentrations
Knowledge of the McMurdo Dry Valley (MDV) lakes is limited by winter access, a period which is most relevant in understanding the habitability of other icy worlds and critical to understanding the overall function of these lakes. Owing to the lack of winter access, data that normally require human presence are incomplete. Our goal was to conduct the first year-round investigation of the biogeophysics of these unique lakes. An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This data set addresses this core area of research and quantifies the particulate carbon and nitrogen found at specific depths in McMurdo Dry Valley lakes.
McMurdo Dry Valleys Lake Bonney Autonomous Lake Profiler and Samplers (ALPS): Conductivity, Temperature, and Pressure
Knowledge of the McMurdo Dry Valley (MDV) lakes is limited by winter access, a period which is most relevant in understanding the habitability of other icy worlds and critical to understanding the overall function of these lakes. Owing to the lack of winter access, data that normally require human presence are incomplete. Our goal was to conduct the first year-round investigation of the biogeophysics of these unique lakes. An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This data set addresses this core area of research and quantifies the vertical profile of conductivity, temperature, and pressure in Lake Bonney.
McMurdo Dry Valleys Lake Bonney Autonomous Lake Profiler and Samplers (ALPS): Dissolved CO2 Concentrations
Knowledge of the McMurdo Dry Valley (MDV) lakes is limited by winter access, a period which is most relevant in understanding the habitability of other icy worlds and critical to understanding the overall function of these lakes. Owing to the lack of winter access, data that normally require human presence are incomplete. Our goal was to conduct the first year-round investigation of the biogeophysics of these unique lakes. An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This data set addresses this core area of research and quantifies the dissolved CO 2 concentrations found at specific depths in McMurdo Dry Valley lakes.
McMurdo Dry Valleys Lake Bonney Autonomous Lake Profiler and Samplers (ALPS): Photosynthetically Active Radiation
Knowledge of the McMurdo Dry Valley (MDV) lakes is limited by winter access, a period which is most relevant in understanding the habitability of other icy worlds and critical to understanding the overall function of these lakes. Owing to the lack of winter access, data that normally require human presence are incomplete. Our goal was to conduct the first year-round investigation of the biogeophysics of these unique lakes. An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This data set addresses this core area of research and quantifies the intensity of photosynthetically active radiation (PAR) at specific depths in McMurdo Dry Valley lakes.
McMurdo Dry Valleys Lake Bonney Autonomous Lake Profiler and Samplers (ALPS): BBE moldanke Fluoroprobe data
Knowledge of the McMurdo Dry Valley (MDV) lakes is limited by winter access, a period which is most relevant in understanding the habitability of other icy worlds and critical to understanding the overall function of these lakes. Owing to the lack of winter access, data that normally require human presence are incomplete. Our goal was to conduct the first year-round investigation of the biogeophysics of these unique lakes. An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This data set addresses this core area of research and quantifies the fluorometric characteristics of phytoplankton species year-round
Dreams4Cars Experimental data from Autonomous Test Vehicle
<p>The Horizon 2020 project Dreams4Cars (<a href="http://www.dreams4cars.eu">www.dreams4cars.eu</a>) has developed dream-like (offline) learning methods to be used for the development of Autonomous Driving and –more in general– as mechanisms to increase the Cognition abilities and Autonomy of robots. The purpose of dreamlike learning in Dreams4Cars is to deal with (possibly rare) dangerous events <em>synthetizing</em> correct behaviour and control without needing to experience the events, and more efficiently than via straightforward trial and errors. That is, to discover potential threats before they actually happen and prepare appropriate action strategies in advance.</p> <p>During the 3-years development process the project has collected and processed a wealth of experimental data from autonomous test vehicles. Parts of these data and advice how to use these data are made available to the public.</p> <p>The datasets and how they can be accessed is described in the attached report (project deliverable D5.5 Section 2), the datasets are provided in the ZIP-file.</p> <p><strong>Purpose of the Dataset</strong></p> <p>The data provided here have the purpose of demonstrating learning of forward models (the first building block of mental imagery and dreams). There are two sets of data: one for the lateral dynamics and another for the longitudinal dynamics. Each dataset has its own example of training of the corresponding forward model). Then following paper provides additional theoretical aspects: M. Da Lio, D. Bortoluzzi, e G. P. Rosati Papini, «Modelling longitudinal vehicle dynamics with neural networks», Vehicle System Dynamics, pagg. 1–19, lug. 2019, doi: <a href="http://10.1080/00423114.2019.1638947">10.1080/00423114.2019.1638947</a></p> <p><strong>Contacts:</strong></p> <p>Mauro Da Lio, University of Trento, <a href="mailto:mauro.dalio@unitn.it">mauro.dalio@unitn.it</a></p> <p>Elmar Berghoefer, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Elmar.Berghoefer@dfki.de</p> <p>Mehmed Yueksel, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Mehmed.Yueksel@dfki.de</p>
Project O2 - A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic
<p>The dataset includes Python code and VISSIM file for the research paper "A Cooperative Bypassing Algorithm for Connected and Autonomous Vehicles in Mixed Traffic".</p> <p> </p> <p> </p>
Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system
<p>This dataset comprises of the IDL code referenced in the 'Open Research' section of the Kaye and Pittman (2020) study 'Fluorescence‐based detection of field targets using an autonomous unmanned aerial vehicle system' published in <em>Methods in Ecology and Evolution</em> (<a href="https://doi.org/10.1111/2041-210X.13402">https://doi.org/10.1111/2041-210X.13402</a>).</p> <p>This study describes a proof‐of‐concept autonomous unmanned aerial vehicle (UAV) system that utilizes the fluorescence characteristics unique to different materials to scan and acquire targets in the field e.g. fossils, rocks and minerals, organisms and archaeological artefacts. This is possible because these targets are often highly fluorescent against lower fluorescence backgrounds and may exhibit different colours. Fluorescence is stimulated by a near‐UV laser that is projected across the ground as a horizontal line directly below the UAV. The IDL code is for laser line and colour extractions in the laser scan strip. The raw .jpeg data for the IDL code is not provided here as this depends on what target is being scanned. All image data are made available in the paper. Additional contextual information is provided in the '2 MATERIALS AND METHODS' section of the paper, especially in Figure 3.</p>
Development, Education, and Implementation of a Low-Cost Audio Sensor-based Autonomous Surveillance System for Smart and Connected Transportation Infrastructure Construction and Maintenance
<p>Each DOT has to govern and oversee an enormous number of transportation construction and maintenance projects. However, since a transportation construction project entails several miles of a job site including numerous work tasks and equipment operations, it has been increasingly challenging for each DOT to consistently monitor progress of all projects in each State as well as efficiently evaluate work performance. In particular, with limited human resources and time, DOTs in Region 6 States have managed large-scale transportation construction and maintenance projects by a human inspection and recovered direct and indirect damages of transportation infrastructure systems caused from the recent natural disasters. In this demanding situation, DOT practitioners and project managers have long recognized the importance of automated monitoring and surveillance of transportation construction and maintenance processes that helps consistently track work progress and take immediate remedial action. As one promising supplement for site monitoring and human inspection, this project proposes a new approach for low-cost audio sensor-based autonomous site and safety surveillance of transportation construction and maintenance, which allows for faster, more convenient, and more accurate work zone monitoring. The proposed innovation using the sound-based site and safety monitoring framework possesses several competitive advantages over traditional site management and existing vision-based work monitoring methods, which not only sounds can be easily recognized and instantly analyzed by diverse sound sensors. In addition, this sound-based monitoring approach supports an unlimited range of monitoring angles and illumination levels with lightweight data processing and comparatively quick analytics. To achieve these goals, this study developed a low-cost wearable audio-sensor for automated work zone monitoring and real-time activity log generation. This new intelligent site and safety surveillance system is expected to support real-time monitoring of construction progress, evaluation of task performance, and rapid identification of safety issues in transportation construction and maintenance projects.</p>
Data: Autonomous Drones for Search and Rescue in Forests
<p>Supplementary Dataset for the article Autonomous Drones for Search and Rescue in Forests.</p> <p><strong>Abstract:</strong></p> <p>Drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a first prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found 38 out of 42 hidden persons; average precision was 86% for predefined flight paths, while adaptive path planning (where potential findings are double-checked) increased confidence by 15%. Image processing, classification, and dynamic flight-path adaptation are computed on-board in real time and while flying. Our finding that deep-learning-based person classification is unaffected by sparse and error-prone sampling within one-dimensional synthetic apertures allows flights to be shortened and reduces recording requirements to one tenth of the number of images needed for sampling using two-dimensional synthetic apertures. The goal of our adaptive path planning is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, as it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.</p>
UIUC Autonomous Vehicles High Bay Lab Data
<p>ROS Bag files with various published ROS topics including LIDAR scans for SLAM.</p> <p>Data collected as part of course, CS598: Building Autonomous Vehicles in UIUC taught by Prof. David Forsyth.</p> <p>For details and usage, have a look at the GitHub repo: https://github.com/jatinarora2702/autonomous-vehicles</p>
Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
Data from: Pollen limitation and autonomous selfing ability interact to shape variation in outcrossing rate across a species range
Premise of the study: Hermaphroditic plants commonly reproduce through a mixture of selfing and outcrossing. The degree to which outcrossing rates reflect the availability of outcross pollen, genetic differentiation in the ability to autonomously self-fertilize, or both is often unclear. Despite the potential for autonomy and the pollination environment to jointly influence outcrossing, this interaction is rarely studied. Methods: We reviewed literature testing whether the pollination environment or floral traits causing autonomous selfing predict outcrossing rate variation among populations. We also measured outcrossing rates in 23 populations of Campanula americana and examined associations with the pollination environment, autonomy, and their interaction. Key Results: Our review revealed that traits facilitating selfing were often negatively associated with outcrossing rates while most aspects of the pollination environment poorly predicted outcrossing. Populations of C. americana varied from mixed mating to highly outcrossing but variation was unrelated to population size, density, pollen limitation, or autonomous selfing ability. Outcrossing rate was significantly influenced by an interaction between autonomous selfing ability and pollen limitation. In highly autonomous populations, elevated pollen limitation was associated with reduced outcrossing, while there was no relationship for less autonomous populations. Conclusions: Both the ability to self autonomously and pollen limitation interact to shape outcrossing rates in C. americana. This work suggests autonomy affords mating system flexibility, though it is not ubiquitous in all populations across the species range. Interactions between traits influencing autonomy and pollen limitation are likely to explain variation in outcrossing rates among populations of flowering plants.
Autonomous Mobile Robots: Past, Present and Future of SLAM 2013
<p>“Autonomous Mobile Robots: Past, Present and Future of SLAM” 2013. In:Workshop<br /> at the First RSI/ISM International Conference on Robotics and Mechatronics by<br /> Sharif University of Technology. Presenter: Prof. Hamid D. Taghirad, 2013.</p>
Dataset for article: Luque-Casado, A., Perakakis, P., Ciria, L.F., Sanabria, D. (2016). Transient autonomic responses during sustained attention in high and low fit young adults. Sci. Rep. 6, 27556
<p>Dataset for article:</p> <p>Luque-Casado, A., Perakakis, P., Ciria, L.F., Sanabria, D. (2016). Transient autonomic responses during sustained attention in high and low fit young adults. Sci. Rep. 6, 27556</p>
Relationship between the autonomic nervous system and cerebral autoregulation during controlled breathing
<h2>Version 3 of the database</h2> <p>The updated version of the database includes autonomic nervous system data (HRV metrics) estimated using the ECG signal.</p> <p>The previous versions of the database included data estimated from non-invasive, photoplethysmography-based ABP signals.</p> <h1>Funding</h1> <p>SONATA 18 UMO-2022/47/D/ST7/00229 National Science Centre, Poland (database 2)</p> <p>SONATA-BIS UMO-2013/10/E/ST7/00117 National Science Centre, Poland (database1)</p> <h1>General information</h1> <p>Two datasets were used in this study.</p> <p>The dataset 1 includes <strong>49 healthy volunteer</strong>s (28 females, 21 males, median age: 23 years, range: 18-31 years) who were measured at the Neuroengineering Laboratory at Wroclaw University of Science and Technology (WUST) between October 2014 and June 2015 (Biomedical Committee Agreement number: KB-170/2014).</p> <p>The dataset 2 includes <strong>12 healthy volunteers</strong> (8 females, 4 males, median age: 25 years, range: 20-26 years) who were prospectively measured at WUST between October 2023 and January 2024 (Biomedical Committee Agreement number: KB-179/2023/N).</p> <h1>Signal recordings description</h1> <ul> <li>ABP was measured non-invasively by a servo-controlled plethysmograph (Finometer MIDI, FMS Medical Systems, Amsterdam, The Netherlands in dataset 1 and Finapres Nova, FMS Medical Systems in dataset 2). The cuff was placed on the middle finger of the left hand and held at the level of the heart.</li> <li>A three-lead surface electrocardiogram (ECG) was used to record the heart's electrical activity</li> <li>CBv was measured in the MCA using transcranial Doppler ultrasonography (Doppler BoxX, DWL, Compumedics Germany GmbH, Singen, Germany in database 1; EMS-9PB, Delica, Shenzhen, China in database 2).</li> <li>Expired end-tidal CO2 (EtCO2), carbon dioxide (CO2) concentration and respiratory rate (RR) were measured via a nasal cannula using a portable capnography monitor (RespSense™, NONIN, Plymouth, USA)</li> <li><strong>Protocol:</strong> After a resting epoch lasting at least 5 minutes (baseline, referred to in the aliases as "B"), a controlled breathing session was initiated. Five-minute recordings were collected at each of the following respiratory rates: 6, 10, or 15 breaths per minute (corresponding to 0.1 Hz, 0.17 Hz, and 0.25 Hz, respectively), guided by a digital metronome (referred to in aliases as "6", "10", "15")</li> </ul> <h1>Data description</h1> <ul> <li>ID</li> <li>Type of database (database 1/database 2)</li> <li>Type of device used for ABP measurement (ECG was measured in the same way in both databases, using a built-in module, attached to photoplethysmography)</li> <li>Metadata including: sex (male M, female F), and age</li> <li>Physiological parameters measured during controlled breathing, including:</li> <ul> <li>end-tidal carbon dioxide: ETCO2</li> <li>Respiratory rate: RR</li> <li>Carbon dioxide concentration: CO2</li> <li>Heart rate: HR</li> <li>Arterial blood pressure: ABP</li> <li>Cerebral blood flow velocity: CBv</li> </ul> <li>Autonomic Nervous System metrics, including:</li> <ul> <li>joint symbolic dynamics, estimated as the relative frequency of baroreflex-like word types (JSD<sub>sym</sub>) and the relative frequency of patterns that are opposed to baroreflex behaviour (JSD<sub>diam</sub>) (Baumert et al., 2015)</li> <li>entropy metrics: MSEn, multiscale entropy; ApEn, approximate entropy; SampEn, sample entropy, FuzzyEn, fuzzy entropy</li> <li>frequency-domain metrics: LFn, HFn, normalized power spectral density of the R-R interval time series in the low-frequency range (LF, 0.04–0.15 Hz) and the high-frequency range (HF, 0.15–0.40 Hz), obtained by dividing the respective power spectra by a total power (TP, 0.04–0.40 Hz); LF/HF; low-to-high frequency ratio; </li> <li>Baroreflex sensitivity estimated using cross-correlation method (xBRS) </li> <li>time-domain metrics: SDNN, standard deviation of the R-R intervals; RMSSD, square root of the mean of the squared successive differences between adjacent R-R intervals; meanNN, mean intervals between normal R-peaks, pNN20 and pNN50, proportion of R-R intervals greater than 20 ms or 50 ms, respectively;</li> </ul> <li>Cerebral autoregulation metrics, including TFA metrics were provided for two frequency ranges: VLF, very low frequency (0.02–0.07 Hz), BF, breathing frequency (determined for each of the participants for spontaneous breathing and 0.10; 0.17; 0.25 Hz±0.02 Hz for controlled breathing); </li> <ul> <li>coherence,</li> <li>phase shift (PS)</li> <li>gain</li> </ul> </ul> <p> </p> <p> </p> <p> </p>
Reaching the limit in autonomous racing: Optimal control versus reinforcement learning
<p>A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.</p>
Autonomous Intraluminal Navigation Dataset
<p>This is a dataset for lumen segmentation. The dataset is composed of 1,873 images taken from endoscopic videos of 10 different phantoms. For each endoscopic image there is its corresponding mask in png format. The dataset is divided by folders in train/val/test. The training and validation data is the one used for training the networks described in <strong>[1]</strong>. The test folder is composed of two folders from a single phantom case. For more details please check the referred publication associated with this dataset. </p><p> </p><p>[1]. J. F. Lazo et al., "Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 6952-6959, doi: 10.1109/IROS47612.2022.9982141.</p>
Maritime international trade and bioinvasions: a three-year long survey of small mammals in Autonomous Port of Cotonou, Benin
<ol> <li>International trade has been favoring the dissemination of a wide panel of invasive alien species. Upstream prevention through the monitoring of entry points is identified as an appropriate strategy to achieve control of bioinvasions and their consequences. Maritime transports have been responsible for the introduction worldwide of exotic rodents that are major pests for crops and food stocks as well as reservoirs of many zoonotic pathogens. In order to limit further dissemination, the International Health Regulation constrains decision makers and socio-economic stakeholders to manage ship-mediated import/export of rodents within seaports. Unfortunately, eco-evolutionary insights into rodent introduction events that could guide preventive actions in seaports are very scarce.</li> <li>In order to bridge this gap, we here describe the results of a three-year-long survey of small mammals conducted in the Port of Cotonou, Benin, that aims at assessing the spatio-temporal distribution, diversity and relative abundance of invasive and native rodents.</li> <li>960 small mammal individuals were captured in nine within-seaport sites. We found (i) a marked predominance of invasive species (84% of the individuals belonging to <em>Mus</em> <em>musculus</em>, <em>Rattus</em> <em>rattus</em>, <em>R. norvegicus</em>), (ii) with native species (i.e., <em>Mastomys natalensis</em> and the shrew <em>Crocidura olivieri</em>) essentially restricted to peripheral non-industrial areas, as well as (iii) a fine-scale spatial segregation stable over time between the invasive Norway rats and house mice on the one hand, and the black rats and shrews on the other hand.</li> <li>Furthermore, trapping before and after two successive rodent anticoagulant-based control campaigns indicates that they were poorly efficient and that subsequent rodent recolonization occurs 6–12 months following intervention.</li> <li> <em>Synthesis and applications</em>: Our results are discussed in terms of ecological processes at play (e.g., interspecific interactions) and operational actions that may be implemented to improve rodent control (e.g., assessment of proper eradication units, environmental modifications) and to limit re-infestation (e.g., rat-proofing of moors and buildings).</li> </ol>
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.