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Dataset results
56 results for “operant conditioning”
Paracetamol Metabolism in Post Operative Conditions
ClinicalTrials.gov study NCT01152502. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using EEG Operant Conditioning to Improve Trait Self-Control and Promote Healthy Behavior
ClinicalTrials.gov study NCT01801254. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Operant Conditioning of Reflex Pathways to Improve Walking in Individuals Post-stroke
ClinicalTrials.gov study NCT05801744. IPD Sharing: NO. Countries: 1. Publications: 0.
Neuropathic Pain and Operant Conditioning of Cutaneous Reflexes After SCI
ClinicalTrials.gov study NCT05492188. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Compare the Operating Condition of Two Levels of Muscle Relaxation on Facial Nerve MEP Monitoring in CPA Surgery
ClinicalTrials.gov study NCT04148534. IPD Sharing: NO. Countries: 1. Publications: 0.
The Role of the Pre-operative Condition and Artificial Intelligence-Assisted Diagnosis in Predicting the Success of Full Pulpotomy in Symptomatic Permanent Mature Molars
ClinicalTrials.gov study NCT07340606. IPD Sharing: YES. Countries: 1. Publications: 0.
Operant Conditioning After ACL Reconstruction
ClinicalTrials.gov study NCT05519345. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Nasal Gel Under Military Operational Conditions for the Prevention of Nausea Associated With Motion Sickness
ClinicalTrials.gov study NCT04272255. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Influence of Analgesia and Neuromuscular Block on Abdominal Distension and Operating Conditions Laparoscopic Operations
ClinicalTrials.gov study NCT03605628. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Operant Conditioning of Spinal Reflexes Training System--Reflex Operant Down Conditioning
ClinicalTrials.gov study NCT05094362. IPD Sharing: NO. Countries: 1. Publications: 0.
This Three-year Observational Study Involves Patients of All Ages Undergoing Endovascular Treatment for Various Cerebral or Spinal Vascular Conditions. It Aims to Assess Pre- and Post-operative Clinic
ClinicalTrials.gov study NCT06805721. IPD Sharing: NO. Countries: 1. Publications: 0.
European starling operant conditioning
Drawing uploaded to scidraw.io on: 18 August 2019
European starling operant conditioning
Drawing uploaded to scidraw.io on: 18 August 2019
The Effect of Intubation Without Muscle Relaxation on Post Operative Recovery Conditions: Double Blind Randomized Controlled Study
ClinicalTrials.gov study NCT03824470. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Adaptive Contingency Control: Wind Turbine Operation Integrated with Blade Condition Monitoring
We report here on first steps towards integrating systems health monitoring with adaptive contingency controls. In the scenario considered, the adaptive controller receives specific information about damage to a blade and reacts to turbulent conditions where the blade might incur additional damage. The controller uses an observer to determine when the generator set point should be lowered, thereby causing the blades to be pitched at a lower wind speed. The loads on the blades are reduced when the blades are set at larger pitch angles. This is meant to decrease the damage propagation. The adaptive contingency controller can be tuned to find the optimal trade-off between maximized damage suppression and maximized power generation. Future work will address the assessment of blade health in the presence of turbulent wind conditions.
Dataset of Vibration, Temperature and Speed Measurements for Multiple Types of Localized Defects on Spherical Roller Bearings across Multiple Operating Conditions
<p><strong>Description:</strong></p> <p>This dataset has been created by the ISED group of Politecnico di Torino to address the lack of data available for developing fault detection models and/or predictive maintenance systems for medium/large-sized spherical roller bearings, commonly used in industrial settings.</p> <p>The data were collected using <a href="https://www.skf.com/it/products/rolling-bearings/roller-bearings/spherical-roller-bearings/productid-22240%20CCK%2FW33">SKF 22240 CCK/W33 spherical roller bearings</a> through an extensive experimental campaign on the medium/large-scale bearing test rig (capable of testing bearings with an outer diameter up to 420 mm), developed by the ISED research group at Politecnico di Torino (<a href="https://www.dimeas.polito.it/la_ricerca/gruppi/ised_industrial_systems_engineering_and_design">ISED Research Group</a>). The technical details of the test rig can be found in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>.</p> <p>The dataset contains data for individual localized defects applied to one of the four bearings tested simultaneously on the rig, which is capable of independently applying both axial and radial loads.</p> <p><strong>Dataset Structure:</strong></p> <p>The dataset is organized into four folders:</p> <ul> <li><code>Undamaged</code></li> <li><code>InnerRaceDamage</code></li> <li><code>OuterRaceDamage</code></li> <li><code>RollerDamage</code></li> </ul> <p>The <code>Undamaged</code> folder contains data for all bearings in healthy condition. The other folders contain data from tests where one of the bearings presents a localized defect. The defects, introduced by chip removal, have a diameter of 2 mm and a depth of 0.5 mm, affecting either the <strong>inner race</strong> (IR), <strong>outer race</strong> (OR), or <strong>roller elements</strong> (B). More detailed information about the defect geometry and location can be found in the following publications:</p> <ul> <li><a href="https://www.mdpi.com/1424-8220/23/1/211">Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/4/2038">Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/22/12458">Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks</a></li> </ul> <p>Each folder contains <code>.mat</code> files named according to the following format:</p> <p><code>(<em>Nominal_Rotation_Speed</em>)rpm_(<em>Radial_Force</em>)kN_(<em>Axial_Force</em>)kN.mat</code></p> <p>Where:</p> <ul> <li><strong>Nominal_Rotation_Speed</strong> is the machine's nominal rotational speed</li> <li><strong>Radial_Force</strong> is the radial force applied to the bearing</li> <li><strong>Axial_Force</strong> is the axial force applied to the bearing</li> </ul> <p>The dataset includes measurements from 10 different nominal rotation speeds and four load conditions, one of which includes an axial load. In some cases, "ramp" files are included, containing data where the rotational speed was linearly varied during the test.</p> <p>Each <code>.mat</code> file contains multiple structures, depending on the type of test. Each structure is labeled as <code>Signal_</code> followed by a number (0, 1, 2, 3, 4), where each represents a specific signal extracted during the test. There is no fixed correspondence between the signal number and the type of measurement. For example, <code>Signal_2</code> does not always represent the accelerometer signal. Users are encouraged to inspect the <code>y_values.quantity</code> field to identify the signal's unit and nature. For instance, if <code>y_values.quantity.label</code> shows "g", the signal corresponds to an accelerometric measurement.</p> <p>All signals have been exported using the <strong>MKS system</strong>, so <code>y_values.values</code> contains data in units of m/s² for acceleration signals. To convert the values to the unit indicated in <code>y_values.quantity.label</code>, users can apply the multiplication factor and offset provided in <code>y_values.quantity.unit_transformation</code>. In the case of accelerometric data, the multiplication factor is 0.1020, converting <code>y_values.values</code> from m/s² to g.</p> <p>A more detailed description of the data structure can be found in the Test.Lab documentation.</p> <p>In addition to acceleration data, the files include temperature signals, tacho sensor signals measuring shaft speed, and tacho impulse signals. In some cases, there is also a signal measured in "N", representing the frictional force generated by the bearings (as detailed in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>). This friction force signal is not always present, as the load cell could go into overload during certain tests.</p> <p><strong>Sensor Data Organization:</strong></p> <p>Acceleration and temperature data are presented in tables with four columns, each corresponding to one of the four bearings. In the damaged condition tests, the damaged bearing is always the one corresponding to sensor 4 (i.e., <code>y_values.values(:, 4)</code>).</p>
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.