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.
2,639
datasets available to search
ShareScore release 0.9.0
Dataset results
2,639 results for “Robotic”
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 20
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 21
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 18
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 16
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 15
<p>These data were collected from robot controller during solar tanks welding operation.</p>
Supplementary figures for paper "Should robots have accents?" published at IEEE RO-MAN 2020
<p>These plots show the preference towards a robot's accent (displayed on the x-axis), broken down by participants' region of origin in the UK. For example, "plot_Wales" shows that around 25% of the 23 participants from Wales indicated that they would like a robot to have an SSBE accent, around 15% a Welsh accent, etc.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 26
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 29
<p>These data were collected from robot controller during solar tanks welding operation.</p>
It Takes a Village to Build a Robot: An Empirical Study of The ROS Ecosystem - Replication Package
<p>Over the past eleven years, the Robot Operating System (ROS), has grown from a small research project into the most popular framework for robotics development. Composed of packages released on the Rosdistro package manager, ROS aims to simplify development by providing reusable libraries, tools and conventions for building a robot. Still, developing a complete robot is a difficult task that involves bridging many technical disciplines. Experts who create computer vision packages, for instance, may need to rely on software designed by mechanical engineers to implement motor control. As building a robot requires domain expertise in software, mechanical, and electrical engineering, as well as artificial intelligence and robotics, ROS faces knowledge based barriers to collaboration.</p> <p>In this paper, we examine how the necessity of domain specific knowledge impacts the open source collaboration model. We create a comprehensive corpus of package metadata and dependencies over three years in the ROS ecosystem, analyze how collaboration is structured, and study the dependency network evolution. We find that the most widely used ROS packages belong to a small cluster of foundational working groups (FWGs), each organized around a different domain in robotics. We show that the FWGs are growing at a slower rate than the rest of the ecosystem, in terms of their membership and number of packages, yet the number of dependencies on FWGs is increasing at a faster rate. In addition, we mined all ROS packages on GitHub, and showed that 82% rely exclusively on functionality provided by FWGs. Finally, we investigate these highly influential groups and describe the unique model of collaboration they support in ROS.</p>
Data Set of Focus Groups with Industry Representatives on Smart Dust and Micro Robots
<p>Data Set of Focus Groups with Industry Representatives on Smart Dust and Micro Robots</p> <p>This data set comprises the following documents:</p> <ul> <li>Coding scheme</li> <li>Focus group guidelines</li> <li>Focus group 01-03</li> </ul>
Social cues of a robot and cognitive control
<p>The data presented here are reported in Ciardo & Wykowska 2020 submitted to Jornal of Cognition. <br> Please refer to that paper for context and method. We intend to upload the raw data shortly, which until then will be available upon request.</p> <p>The file includes Correct mean reaction times.</p>
[Additional Materials] - Characterization of the Growing From the Tip as Robot Locomotion Strategy
<p>Additional materials related to the paper "Characterization of the Growing From the Tip as Robot Locomotion Strategy" (<a href="https://doi.org/10.3389/frobt.2019.00045">https://doi.org/10.3389/frobt.2019.00045</a>).</p>
The effect of an end-effector type of robot-assisted gait training on patients with Guillain-Barré syndrome
<p>Background: Guillain-Barre syndrome (GBS) is a peripheral nerve injury caused by a post-infectious immune response. Although the prognosis of GBS is relatively good, some patients have severe impairments such as walking disabilities. Robot-assisted gait training (RAGT) is used to improve gait function in various neurologic disorders; however, no studies have reported its effectiveness in GBS patients. We aimed to evaluate the effect of gait training using an end-effector type robotic device on GBS patients.</p> <p>Methods: This was a retrospective study of patients diagnosed with GBS who received RAGT using Morning Walk® at the inpatient department. The main outcome measures evaluated before and after RAGT were: Medical Research Council scale, Functional Ambulation Categories, Modified Barthel Index score, Rivermead Mobility Index, and 2-minute walk test.</p> <p>Results: In total, 15 patients underwent RAGT 24 times. The mean age was 55.7 (±15.3) years and the average time from onset was 3.9 (±3.6) months. When compared to the baseline, all outcome measures associated with gait function were improved after RAGT.</p> <p>Conclusions: RAGT can improve walking ability in GBS patients. RAGT can be considered as one of the gait training tools to recover gait function in GBS patients.</p>
Figure 6 from: Hardy H, van Walsum M, Livermore L, Walton S (2020) Research and development in robotics with potential to automate handling of biological collections. Research Ideas and Outcomes 6: e61366. https://doi.org/10.3897/rio.6.e61366
Figure 6 An example of a biomechanical exoskeleton (© Yuichiro C. Katsumoto / Wikimedia Commons / CC-BY 2.0).
Figure 5 from: Hardy H, van Walsum M, Livermore L, Walton S (2020) Research and development in robotics with potential to automate handling of biological collections. Research Ideas and Outcomes 6: e61366. https://doi.org/10.3897/rio.6.e61366
Figure 5 VERRSABALL 'jamming' gripper capable of pressure-sensitivity and picking up irregularly shaped objects (© Empire Robotics.
Figure 2 from: Hardy H, van Walsum M, Livermore L, Walton S (2020) Research and development in robotics with potential to automate handling of biological collections. Research Ideas and Outcomes 6: e61366. https://doi.org/10.3897/rio.6.e61366
Figure 2 Example of robot arm with a camera. The object is placed on a surface or turntable, and the robot does not physically interact with the object (© CultLab3D).
Figure 10 from: Hardy H, van Walsum M, Livermore L, Walton S (2020) Research and development in robotics with potential to automate handling of biological collections. Research Ideas and Outcomes 6: e61366. https://doi.org/10.3897/rio.6.e61366
Figure 10 The ROBOT(E) remotely operable digital imaging system from Wheeler et al. 2012 (©Erik Holsinger, Arizona State University).
Figure 11 from: Hardy H, van Walsum M, Livermore L, Walton S (2020) Research and development in robotics with potential to automate handling of biological collections. Research Ideas and Outcomes 6: e61366. https://doi.org/10.3897/rio.6.e61366
Figure 11 ROBOCASE with robot arm and automated retrieval designed for closer inspection of items in an exhibition (© Bruns).
Figure 4 from: Hardy H, van Walsum M, Livermore L, Walton S (2020) Research and development in robotics with potential to automate handling of biological collections. Research Ideas and Outcomes 6: e61366. https://doi.org/10.3897/rio.6.e61366
Figure 4 mGrip, the soft robotic modular tooling system - potentially suitable for picking up pressure-sensitive and small objects (© Soft Robotics).
Figure 3 from: Hardy H, van Walsum M, Livermore L, Walton S (2020) Research and development in robotics with potential to automate handling of biological collections. Research Ideas and Outcomes 6: e61366. https://doi.org/10.3897/rio.6.e61366
Figure 3 Robot arm with vacuum gripper, potentially suitable for lifting containers of NH specimens (© Universal Robots).
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.