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ShareScore release 0.9.0
Dataset results
24 results for “AI algorithm”
F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms
<p>Practical research in AI often lacks of available and reliable datasets so the practitioners can try different algorithms. The field of predictive maintenance is particularly challenging in this aspect as many researchers don't have access to full-size industrial equipment or there is not available datasets representing a rich information content in different evolutions of faults.</p> <p>This dataset presents the evolution of typical faults (commutator, winding and brush wear) in inexpensive DC motors under extensive monitoring (vibration, temperature, voltage, current and noise). These motors exhibit a particularly short useful life when operating out of nominal conditions (from 30 minutes to 6 hours) which make them very interesting to test different signal processing algorithms and introduce students and researchers into signal processing, fault detection and predictive maintenance.</p> <p>The data-set comprises two main elements:</p> <ul> <li>A spreadsheet with the processing of each raw data file</li> <li>4 folders with raw data files in HDF5 format</li> </ul> <p>The spread sheet contains the following columns</p> <ul> <li>filename of the raw data file</li> <li>timestamp of the raw data file</li> <li>speed of the motor in rpm</li> <li>speed of the motor in Hz</li> <li>Current of the moter (A)</li> <li>Voltage supply (V)</li> <li>surface motor temperature (ºC)</li> <li>ambient temperature (ºC)</li> <li>For each measured signal (Vibration, current, voltage) the vibration of the main harmonic (at the speed of the motor) and it's first 10 multiple.</li> <li>For each measured signal (Vibration, current, voltage) the vibration in 4 bands: 0-4kHz, 4kHz-8kHz, 8kHz-16kHz, 16kHz-26kHz</li> </ul> <p>The raw data files in HDF5 format contains the instantaneous measured vibration (g), current (A) and voltage (volts) of the DC motor at 51.200 Hz.</p>
Replication package for: "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty"
<p>Package to the following paper:</p> <p>Leib, M; Köbis, N; Rilke, R M; Hagens, M; Irlenbusch, B (2023) Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty</p>
Using AI Algorithms for Predictive Analysis in Personalized Medicine
<p><strong><span>This study explored the factors influencing patients' willingness to adopt AI-powered personalized medicine. This research found the problems. Integrating AI and personalized medicine has the potential to revolutionize healthcare. However, public trust in AI for healthcare applications remains a challenge. This research examines the factors determining people's views toward using artificial intelligence for predictive analytics in personalized medicine. A cross-sectional design was employed through a survey distributed via Google Forms in April 2024 using purposive sampling. The target respondents included residents of the Jabodetabek area (Jakarta, Bogor, Depok, Tangerang, Bekasi- cities in Indonesia) with prior experience seeking medical consultation or checkups. A total of 267 responses were collected after removing outliers. The study used a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach to analyze the data. </span></strong><strong><span>The study considered six independent variables: AI knowledge, trust in AI, attitude towards data privacy, personalized medicine expectations, personalized medicine understanding, and perceived risk of discrimination in AI. The dependent variable was the intention to use AI in personalized medicine. It found five of six hypotheses have significant impact. </span></strong></p>
AI Algorithm for Surveillance of Deep Surgical Site Infections After Elective Colorectal Surgery.
ClinicalTrials.gov study NCT07130656. IPD Sharing: NO. Countries: 1. Publications: 1.
AI Algorithms to Automate the TMI by VMAT Optimization Using WB-CT/MRI and Synthetic WB-CT - The AuToMI Project
ClinicalTrials.gov study NCT04976205. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
High-quality Image (NIR and RGB) Dataset Synchronized With Contact Vital Sings Recordings and Clinical Data of Stratified Healthy Population. Algorithms and AI Models to Obtain a Set of Vital Signs Im
ClinicalTrials.gov study NCT05947721. IPD Sharing: NO. Countries: 1. Publications: 2.
Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
ClinicalTrials.gov study NCT06796283. IPD Sharing: NO. Countries: 1. Publications: 1.
DOACT Algorithm Versus AI-Based Decision Models in Oral Anticoagulant Therapy for Vascular Patients
ClinicalTrials.gov study NCT07290608. IPD Sharing: NO. Countries: 1. Publications: 4.
Prospective Validation Study of AI-based Prediction Algorithm for the Prediction of Paroxysmal Atrial Fibrillation
ClinicalTrials.gov study NCT05725187. IPD Sharing: NO. Countries: 1. Publications: 5.
[5G-IANA] UC1 - Data processed by the AI object detection algorithm
<p>Bounding boxes of objects detected by the artificial intelligence algorithm for each video frame received.</p>
An AI Algorithm for Lymphocyte Focus Score of Minor Salivary Gland Biopsy Samples for Diagnosing Sjogren's Syndrome
ClinicalTrials.gov study NCT06437652. IPD Sharing: NO. Countries: 1. Publications: 0.
AI Algorithm-Informed Biopsy for Prostate Cancer Detection With Indeterminate and Low-Risk Prostate MRI Lesions
ClinicalTrials.gov study NCT07231627. IPD Sharing: Not stated. Countries: 0. Publications: 0.
AI Algorithms in Prediction of ACS Based on Leukocyte Properties
ClinicalTrials.gov study NCT06384846. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Observational Clinical Investigation of EKG Diagnostic Performance of the Apple Watch Augmented With an AI Algorithm
ClinicalTrials.gov study NCT05045456. IPD Sharing: NO. Countries: 1. Publications: 0.
An Observational Study Using Artificial Intelligence (AI) Algorithms on Electrocardiography (ECG), Point-of-care Ultrasound (POCUS), and Transthoracic Echocardiophy (TTE) to Estimate the Under-diagnos
ClinicalTrials.gov study NCT07062848. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of a COVID-19 Pneumonia CXR AI Detection Algorithm
ClinicalTrials.gov study NCT04561024. IPD Sharing: NO. Countries: 1. Publications: 0.
Identification of Outcome Predictors and Stratification of Responder Profiles Implanted With Spinal Cord Stimulation. An AI-based-pathway & Algorithmic Approach to Treat Failed Back Surgery Syndrome P
ClinicalTrials.gov study NCT04587674. IPD Sharing: NO. Countries: 1. Publications: 0.
The Accuracy and Efficacy of AI-driven tVNS Algorithm
ClinicalTrials.gov study NCT05215756. IPD Sharing: NO. Countries: 1. Publications: 0.
Pilot Study: AI Algorithm for Dermatology Referral Optimization
ClinicalTrials.gov study NCT06228014. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
LVEF Prediction During ACS Using AI Algorithm Applied on Coronary Angiogram Videos
ClinicalTrials.gov study NCT05317286. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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.