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124 results for “robotic dataset”
Dataset for Robot-mounted digital 3D-microscope versus standard microscope for spinal decompression surgery
<p>Compilation of all data collected in the study.</p> <p>- Depth Perception Robotc: Data collected during depth perception experiments with the BHS RoboticScope</p> <p>- Depth Perception Cons: Data collected during depth perception experiments with the OM</p> <p>- Both Scopes Combined: Combined data regarding depth perception experiments</p> <p>- Questionaire Combined: Combined data regarding cadaveric experiments</p> <p>- Questionaire expsurg1: completed questionnaire by the experienced surgeon 1</p> <p>- Questionaire expsurg3: completed questionnaire by the experienced surgeon 3</p>
Dataset - On the performance of online adaptation of robots controlled by nanowire networks
<p>Dataset of the experiments considered in the article in the title</p>
Dataset for multi-locomotion robot Tribot
<p>The dataset contains .mat files with raw and estimated data for plotting a multi-locomotion robot Tribot locomotion performance of the related Nature manuscript. The file also contains detailed data tables for robot specs, materials used, power consumption and comparison table for similar sized robots and insects reported in the literature. </p>
Dataset for AI Co-pilot Bronchoscope Robot
Open the record for dataset details and reuse information.
Acoustic Monitoring Dataset for Robotic Laser Directed Energy Deposition (LDED) of Maraging Steel C300
<p>This dataset presents a set of acoustic signals captured during a single-bead wall experiment in robotic Laser Directed Energy Deposition (LDED) using Maraging Steel C300. The acoustic data was recorded using a high-fidelity Prepolarized microphone sensor (Xiris WeldMIC), capturing the intricate sound profiles associated with the LDED process at a sampling rate of 44,100 Hz.</p><p><strong>Laser Directed Energy Deposition:</strong></p><p>This dataset was generated with a robotic LDED process that consists of a six-axis industrial robot (KUKA KR90) coupled with a two-axis positioner, a laser head, and a coaxial powder-feeding nozzle.</p><p> </p><p><strong>Folder Structure:</strong></p><ul><li><strong>/sample-1</strong>: The main folder for the experiment sample.<ul><li><strong>/audio_files</strong>: Contains 4624 <strong>.wav</strong> audio files, each representing a 40 ms chunk of the LDED process sound.</li><li><strong>/annotations_1.csv</strong>: A CSV file providing annotations for the audio files, labeling each as "Defect-free", "Defective", or "Laser-off".</li></ul></li><li>audio_features.h5: extracted acoustic features in time-domain, frequency-domain, and time-frequency representations (MFCC features). Feature extraction was conducted using Python Essentia Library.</li></ul><p> </p><p><strong>File Naming Convention:</strong></p><ul><li>Audio files within the <strong>audio_files</strong> folder are named following the pattern <strong>sample_ExperimentID_SampleID.wav</strong>. Given that there's only one experiment and one sample, the naming will be consistent, for example, <strong>sample_1_1.wav</strong> for the first file.</li></ul><p><strong>Annotation Details:</strong></p><ul><li>The <strong>annotations_1.csv</strong> file contains detailed labels for each audio file, correlating to the conditions observed during the experiment, aiding in quick identification and analysis.</li></ul><p><strong>Experimental Parameters:</strong> The dataset reflects a controlled experiment setup with the following specifications:</p><ul><li>Geometry: Single bead wall structure</li><li>Dimensions: 90 mm * 42.5 mm</li><li>Number of layers: 50</li><li>Laser beam diameter: 2 mm</li><li>Layer thickness: 0.85 mm</li><li>Stand-off distance: 12 mm</li><li>Laser profile: Gaussian</li><li>Laser wavelength: 1064 nm</li></ul><p><strong>Process Parameters:</strong></p><ul><li>Laser power: 2.3 kW</li><li>Speed: 25 mm/s</li><li>Dwell time: 0 s</li><li>Powder flow rate: 12 g/min</li></ul><p>This dataset aims to facilitate the development and testing of acoustic-based defect detection models for real-time quality monitoring in LDED processes. It can also serve as a reference point for further research on sensor fusion, machine learning, and real-time monitoring of manufacturing processes.</p>
Dataset: Arbe Robotics Ltd. (ARBEW) 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.
[DATASET 2] – BIOINSPIRED ROBOT CONTROL
<p>In the framework of GrowBot project, Task 6.1 aims at developing an embedded control for monitoring stimuli. Task 6.2 and task 6.3 aim at developing a bioinspired control algorithm and the robot behavioural architecture.</p> <p>DS2 aims at collecting the data and results gathered during these activities.</p>
Replicating dynamic humerus motion using an industrial robot dataset
<p>This dataset encompasses all of the data utilized to replicate humeral kinematics, as captured via skin marker motion capture, on a FANUC M20ia industrial robot. The activities replicated include 119 jumping jacks, 15 jug lifts, 105 jogging trials, and 15 rapid internal rotation trials. An Optotrak Certus optical tracking system was utilized to record the kinematics of the humerus as actuated by the robot (also included in this dataset) in order to compare the robotically replicated trajectories against the motion capture trajectories. This dataset is intended to accompany the "Mocap to Robot" software package that provides an algorithmic pipeline for mapping motion capture trajectories to robotically replicated motion.</p> <p>Related publication can be found at: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242005">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242005</a></p>
smallSSD: The Small Robot Company's detection dataset for agriculture
<p>The Small Robot Company's detection dataset for agriculture, consisting of images collected by the Tom robot labelled with bounding boxes to delineate wheat and weeds.</p> <p>This data is shared under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial-4.0 International</a> license.</p> <p>More information about this data is available at <a href="https://github.com/SmallRobotCompany/smallssd">https://github.com/SmallRobotCompany/smallssd</a>.</p>
Robot Motion Dataset
<p>The Robot Motion Dataset contains experimental data from a study investigating human-robot interaction with a leader who used a teleoperated follower robot. So, thirty participants controlled the TFR wearing a virtual reality (VR) headset and using a rudder platform. Thus, the dataset includes sensor data such as accelerometer, gyroscope, trajectory, objects' distance, data questionnaires, and so forth. </p> <p>The dataset is used in the work presented in the following articles:</p> <ul> <li>Di Tecco, A., Genua, A., Serra, F., Camardella, C., Loconsole, C., Ragusa, E., ... & Frisoli, A. (2025, February). Evaluation of a Haptic-Actuated Glove for Remote Human-Robot Interaction (HRI): A Proof of Concept. In European Robotics Forum (pp. 213-219). Cham: Springer Nature Switzerland.</li> <li>Di Tecco, A., Frisoli, A., Loconsole, C. (2025, August). Machine Learning for Predicting User Satisfaction in Human-Robot Interaction (HRI) Teleoperation Tasks. In IEEE ACCESS. </li> </ul>
Loader robot dataset (2024-04-18)
<p>The dataset contains images captured by three RealSense cameras mounted on the back of the loader robot and one Zedx mounted on the front of it. The following topics have been recorded on the bag files:</p> <ul> <li>/loader/ugv0/back_lslidar_packet</li> <li>/loader/ugv0/camera_bc/color/camera_info </li> <li>/loader/ugv0/camera_bc/color/image_raw/compressed</li> <li>/loader/ugv0/camera_bl/color/camera_info</li> <li>/loader/ugv0/camera_bl/color/image_raw/compressed</li> <li>/loader/ugv0/camera_br/color/camera_info </li> <li>/loader/ugv0/camera_br/color/image_raw/compressed </li> <li>/loader/ugv0/gps/fix </li> <li>/loader/ugv0/gps/time </li> <li>/loader/ugv0/gps/vel </li> <li>/loader/ugv0/imu_rion </li> <li>/loader/ugv0/lio_sam/mapping/path </li> <li>/loader/ugv0/zed_node/left/camera_info</li> <li>/loader/ugv0/zed_node/left/image_rect_color </li> <li>/loader/ugv0/zed_node/right/camera_info</li> <li>/loader/ugv0/zed_node/right/image_rect_color</li> <li>/tf</li> <li>/tf_static</li> </ul>
Dataset related to article "Robotic surgery, video-assisted thoracic surgery, and open surgery for early stage lung cancer comparison of costs and outcomes at a single institute"
<p>This record contains raw data related to article "Robotic surgery, video-assisted thoracic surgery, and open surgery for early stage lung cancer comparison of costs and outcomes at a single institute"</p> <p>Background:</p> <p>Robotic surgery is increasingly used to resect lung cancer. However costs are high. We compared costs and outcomes for robotic surgery, video-assisted thoracic surgery (VATS), and open surgery, to treat non-small cell lung cancer (NSCLC).</p> <p>Methods:</p> <p>We retrospectively assessed 103 consecutive patients given lobectomy or segmentectomy for clinical stage I or II NSCLC. Three surgeons could choose VATS or open, the fourth could choose between all three techniques. Between-group differences were assessed by Fisher's exact, two-way analysis of variance (ANOVA), and Wilcoxon-Mann-Whitney test. P values <0.05 were considered significant.</p> <p>Results:</p> <p>Twenty-three patients were treated by robot, 41 by VATS, and 39 by open surgery. Age, physical status, pulmonary function, comorbidities, stage, and perioperative complications did not differ between the groups. Pathological tumor size was greater in the open than VATS and robotic groups (P=0.025). Duration of surgery was 150, 191 and 116 minutes, by robotic, VATS and open approaches, respectively (P<0.001). Significantly more lymph node stations were removed (P<0.001), and median length of stay was shorter (4, 5 and 6 days, respectively; P<0.001) in the robotic than VATS and open groups. Estimated costs were 82%, 68% and 69%, respectively, of the regional health service reimbursement for robotic, VATS and open approaches.</p> <p>Discussion:</p> <p>Robotic surgery for early lung cancer was associated with shorter stay and more extensive lymph node dissection than VATS and open surgery. Duration of surgery was shorter for robotic than VATS. Although the cost of robotic thoracic surgery is high, the hospital makes a profit.</p>
ROS2 bag dataset for tree trunk detection and mapping using an OAK-D mounted on a terrestrial robot in FEUP's garden
<p>A dataset acquired in FEUP's garden with an OAK-D mounted on a mobile terrestrial robot to perform tree trunk mapping.</p>
Dataset related to article "External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era "
<p>This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era"</p> <p>Abstract</p> <p><strong>Introduction: </strong> To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND).</p> <p><strong>Materials and methods: </strong> After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis.</p> <p><strong>Results: </strong> A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (<em>p</em> = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA.</p> <p><strong>Conclusion: </strong> The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.</p>
Dataset for the publication "Deployment of an electrocorticography system assisted with a soft robotic actuation"
<p>Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery, and increasingly explored in brain-machine interface applications. Current devices display limitations that require trade-offs between cortical surface coverage, spatial electrode resolution, aesthetic, and risk consequences, and often limit the use of the mapping technology to the operating room. In this work, we report on a scalable technique for the fabrication of large-area soft robotic electrode arrays and their deployment on the cortex through a square centimeter burr hole using a pressure-driven actuation mechanism called eversion. The deployable system consists of up to six pre-folded soft legs and it is placed subdurally on the cortex using an aqueous pressurized solution and secured to the pedestal on the rim of the small craniotomy. Each leg contains soft, microfabricated electrodes and strain sensors for real-time deployment monitoring. In a proof-of-concept acute surgery, a soft robotic electrode array was successfully deployed on the cortex of a minipig to record sensory cortical activity. This soft robotic neurotechnology opens promising avenues for minimally invasive cortical surgery and applications related to neurological disorders such as motor and sensory deficits.</p>
ROS2 bag multimodal perception dataset acquired in Portuguese forest with Modular-E robot for line vegetation clearing
<p>Rosbag data acquired:</p> <ul> <li>RGB-D data from OAK-D facing sideways;</li> <li>Pointcloud from Hokuyo 2D Lidar facing sideways;</li> <li>Pointcloud from Robosense 360º Lidar facing forward;</li> <li>RTK fix.</li> </ul>
Dataset related to article "Survival outcomes of robotic radical hysterectomy for early stage cervical cancer: A 9-year study."
<p>BACKGROUND:</p> <p>Recently, the results of a RCT have raised concerns on the management of cervical cancer through a minimally invasive approach. This study reports on the outcomes of patients with early stage cervical cancer submitted to robotics.</p> <p>METHODS:</p> <p>Retrospective review of a consecutive series of patients with an early cervical cancer treated with robotics at a single Institution over a 9-year period.</p> <p>RESULTS:</p> <p>A total of 91 women were managed; 39 (41.1%) had cervical adenocarcinoma. One (1.1%) conversion to laparotomy and one (1.1%) intraoperative complication occurred. Five (5.5%) patients experienced postoperative (>G2) complications; 24 (26.4%) patients required further adjuvant therapies. After a median follow-up of 40.7 (3.8-96.6) months, the DFS and OS were 90.4 (95%CI 85.3-95.6)% and 94.5 (95%CI 91.8-97.2)%, respectively.</p> <p>CONCLUSIONS:</p> <p>According to the available literature, the survival outcomes of this series of RRH for ECC are not inferior to what recorded in the past by an open approach.</p>
Dataset related to article "Early Hospital Discharge on Day Two Post Robotic Lobectomy with Telehealth Home Monitoring: A Pilot Study"
<p>This record contains raw data related to article "Early Hospital Discharge on Day Two Post Robotic Lobectomy with Telehealth Home Monitoring: A Pilot Study"</p><p><strong> Abstract</strong></p><p>Despite the adoption of enhanced recovery programs, the reported postoperative length of stay after robotic surgery is 4 days even in highly specialized centers. We report preliminary results of a pilot study for a new protocol of early discharge (on day 2) with telehealth home monitoring after robotic lobectomy for lung cancer. All patients with a caregiver were discharged on postoperative day 2 with a telemonitoring device if they satisfied specific discharge criteria. Teleconsultations were scheduled once in the afternoon of post-operative day 2, twice on postoperative day 3, and then once a day until the chest tube removal. Post-discharge vital signs were recorded by patients at least four times daily through the device and were available for consultation by two surgeons through phone application. In case of sudden variation of vital signs or occurrence of adverse events, a direct telephone line was available for patients as well as a protected re-hospitalization path. Primary outcome was the safety evaluated by the occurrence of post-discharge complications and readmissions. Secondary outcome was the evaluation of resources optimization (hospitalization days) maintaining the standard of care. During the study period, twelve patients satisfied all preoperative clinical criteria to be enrolled in our protocol. Two of twelve enrolled patients were successively excluded because they did not satisfy discharge criteria on postoperative day 2. During telehealth home monitoring a total of 27/427 vital-sign measurements violated the threshold in seven patients. Among the threshold violations, only 1 out of 27 was a critical violation and was managed at home. No postoperative complication occurred neither readmission was needed. A mean number of three hospitalization days was avoided and an estimated economic benefit of about EUR 500 for a single patient was obtained if compared with patients submitted to VATS lobectomy in the same period. These preliminary results confirm that adoption of telemonitoring allows, in selected patients, a safe discharge on postoperative day 2 after robotic surgery for early-stage NSCLC. A potential economic benefit could derive from this protocol if this data will be confirmed in larger sample.</p><p> </p>
Ericsson single robot cellular 5G dataset
Open the record for dataset details and reuse information.
Dataset related to article "Robot-assisted rehabilitation of hand function after stroke: Development of prediction models for reference to therapy"
<p>DATASET #1</p> <p>Il data set è composto da 174 osservazioni riferite ad un campione di n=174 pazienti.</p> <p>Le variabili prese in considerazione per lo studio del data set sono 21:</p> <ul> <li> <p>ID_Pazient: variabile quantitativa continua, indica il numero di identificazione del paziente</p> </li> <li> <p>Sex: variabile dicotomica, indica il sesso del paziente (Maschio=0, Femmina=1)</p> </li> <li> <p>Age: variabile quantitativa continua, indica l'età del paziente nel momento in cui è stata effettuata la valutazione</p> </li> <li> <p>EMG_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del soggetto di controllare il dispositivo con i propri segnali elettromiografici</p> </li> <li> <p>Force_Control: variabile dicotomica, indica la capacità (Si=1) o meno (No=0) del paziente di controllare il dispositivo con la propria forza</p> </li> <li> <p>Month_Injury: variabile quantitativa continua, indica i mesi trascorsi dalla data in cui è avvenuto l'ictus</p> </li> <li> <p>Diagnosis: variabile dicotomica, indica la tipologia di ictus: (Ischemico=0, Emorragico =1)</p> </li> <li> <p>Hemisphere: variabile dicotomica, indica quale emisfero cerebrale è stato colpito dall'ictus (Destro=0, Sinistro=1)</p> </li> <li> <p>FM_UE: variabile quantitativa discreta, indica la misura della funzione motoria dell'arto superiore determinata somministrando la scala Fugl-Meyer Upper Extremity</p> </li> <li> <p>Sensitivity: variabile quantitativa discreta, indica la sezione per la misura della sensibilità della scala Fugl-Meyer</p> </li> <li> <p>Pain_ROM: variabile quantitativa discreta, indica la sezione per la misura di articolarità e dolore della scala Fugl-Meyer</p> </li> <li> <p>FIM: variabile quantitativa discreta, indica la misura di autonomia della persona nelle attività della vita quotidiana, determinata dalla somministrazione della scala Functional Independence Measure</p> </li> <li> <p>RPS: variabile quantitativa discreta, indica la misura della funzione di raggiungimento di un oggetto</p> </li> <li> <p>Peg_Sec: variabile quantitativa continua, indica la misura della destrezza manuale fine e coincide con il rapporto tra il numero di pioli e i secondi impiegati per inserirli in uno specifico supporto</p> </li> <li> <p>PectMaj: variabile qualitativa ordinata, indica la misura della spasticità del pettorale, secondo la Modified Ashworth Scale</p> </li> <li> <p>BicBrach: variabile qualitativa ordinata, indica la misura della spasticità del bicipite, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexCarp: variabile qualitativa ordinata, indica la misura della spasticità del flessore del carpo, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexProfDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore profondo delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>FlexSupDig: variabile qualitativa ordinata, indica la misura della spasticità del flessore superficiale delle dita, secondo la Modified Ashworth Scale</p> </li> <li> <p>Ashworth_TOT: variabile quantitativa discreta, indica la misura totale della Modified Ashworth Scale, data dalla somma delle 5 variabili precedenti</p> </li> <li> <p>BB_par: variabile quantitativa discreta, indica la misura della destrezza manuale grossolana dell'arto paretico</p> </li> </ul>
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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.