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.
161
datasets available to search
ShareScore release 0.9.0
Dataset results
161 results for “unsupervised”
Unsupervised Therapy After Stroke in the Home Setting with a Hand Rehabilitation Device (ReHandyBot)
ClinicalTrials.gov study NCT06057129. IPD Sharing: NO. Countries: 1. Publications: 0.
Unsupervised Physical Activity in Elderly
ClinicalTrials.gov study NCT02976506. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effects of a Pedometer-based Unsupervised Exercise Program Upon Lifestyle Physical Activity of Asthmatic Adults
ClinicalTrials.gov study NCT01984281. IPD Sharing: Not stated. Countries: 1. Publications: 0.
COVID-19 Clinical Status Associated With Outcome Severity: An Unsupervised Machine Learning Approach
ClinicalTrials.gov study NCT05119465. IPD Sharing: NO. Countries: 1. Publications: 0.
Impact of Additional Unsupervised Peddle Bike Physiotherapy on Acutely Hospitalized Medicine Inpatients
ClinicalTrials.gov study NCT04565626. IPD Sharing: NO. Countries: 1. Publications: 0.
Performances and Safety of MuCopilot, a Digital Tool for the Unsupervised Objective Assessment of Cystic Fibrosis
ClinicalTrials.gov study NCT06147778. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Dragon Boat Race Versus Family Unsupervised Training to Improve the Physical Function and Quality of Life in Breast Cancer Patients
ClinicalTrials.gov study NCT07162298. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy of Preoperative Prehabilitation With a Home-based Supervised Exercise Program Against an Unsupervised Exercise Program for Frail Elderly Patients Undergoing Major Abdominal Surgery
ClinicalTrials.gov study NCT04892368. IPD Sharing: NO. Countries: 1. Publications: 0.
Supervised Online Resistance Training vs Unsupervised Self-administered Resistance Training for Elders
ClinicalTrials.gov study NCT05885165. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data
Open the record for dataset details and reuse information.
Unsupervised cell interaction profiling based on multiplet RNA sequencing reveals major architectural differences between small intestinal and colonic epithelium.
GEO Series GSE143639. Mus musculus. 7501 samples. Type: Expression profiling by high throughput sequencing.
Blood mRNA expression signatures derived from unsupervised analyses identify prostate cancers with poor outcome
GEO Series GSE37199. Homo sapiens. 107 samples. Type: Expression profiling by array.
Risk of Serious Adverse Events Following Unsupervised vs Supervised Rehabilitation After Knee Arthroplasty
ClinicalTrials.gov study NCT07067060. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Unsupervised Clustering Reveals Noncanonical Myeloid Cell Subsets in the Brain Tumor Microenvironment
GEO Series GSE251900. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Unsupervised clustering and epigenetic classification of single cells
GEO Series GSE107651. Mus musculus. 96 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Unsupervised Anomaly Detection for Liquid-Fueled Rocket Prop...
Title: Unsupervised Anomaly Detection for Liquid-Fueled Rocket Propulsion Health Monitoring. Abstract: This article describes the results of applying four unsupervised anomaly detection algorithms to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms.
Comparison of Unsupervised Anomaly Detection Methods
Several different unsupervised anomaly detection algorithms have been applied to Space Shuttle Main Engine (SSME) data to serve the purpose of developing a comprehensive suite of Integrated Systems Health Management (ISHM) tools. As the theoretical bases for these methods vary considerably, it is reasonable to conjecture that the resulting anomalies detected by them may differ quite significantly as well. As such, it would be useful to apply a common metric with which to compare the results. However, for such a quantitative analysis to be statistically significant, a sufficient number of examples of both nominally categorized and anomalous data must be available. Due to the lack of sufficient examples of anomalous data, use of any statistics that rely upon a statistically significant sample of anomalous data is infeasible. Therefore, the main focus of this paper will be to compare actual examples of anomalies detected by the algorithms via the sensors in which they appear, as well the times at which they appear. We find that there is enough overlap in detection of the anomalies among all of the different algorithms tested in order for them to corroborate the severity of these anomalies. In certain cases, the severity of these anomalies is supported by their categorization as failures by experts, with realistic physical explanations. For those anomalies that can not be corroborated by at least one other method, this overlap says less about the severity of the anomaly, and more about their technical nuances, which will also be discussed.
Unsupervised hierarchical clustering of iHPCs induced by 9 or 10 TFs
GEO Series GSE29730. Mus musculus. 5 samples. Type: Expression profiling by array.
UTDRM: unsupervised method for training debunked-narrative retrieval models (Dataset)
<p>The repository contains T5 and ChatGPT generated claims for the paper "<strong>UTDRM: Unsupervised Method for Training Debunked-Narrative Retrieval Models</strong>" (EPJ Data Science Journal).</p> <p>The synthetically generated false claims dataset is solely for evaluation purposes and will only be made available to academic researchers following careful vetting and a signed contract, in order to prevent public harm or the spread of misinformation.</p> <p><strong>Paper link</strong>:</p> <p><a href="https://link.springer.com/article/10.1140/epjds/s13688-023-00437-y" target="_blank" rel="noopener">https://link.springer.com/article/10.1140/epjds/s13688-023-00437-y</a></p> <p><strong>Model links</strong>:</p> <p><a href="https://huggingface.co/iknoor/UTDRM-MPNet">https://huggingface.co/iknoor/UTDRM-MPNet</a> </p> <p><a href="https://huggingface.co/iknoor/UTDRM-RoBERTa">https://huggingface.co/iknoor/UTDRM-RoBERTa</a></p> <p><strong>Please use the following reference if you use the dataset or models:</strong></p> <pre><code>@article{singh2023utdrm, title={UTDRM: unsupervised method for training debunked-narrative retrieval models}, author={Singh, Iknoor and Scarton, Carolina and Bontcheva, Kalina}, journal={EPJ Data Science}, volume={12}, number={1}, pages={59}, year={2023}, publisher={Springer Berlin Heidelberg} }</code></pre>
Unsupervised Analysis of Array Comparative Genomic Hybridization Data from Early-Onset Colorectal Cancer Reveals Equivalence with Molecular Classification and Phenotypes
GEO Series GSE108220. Homo sapiens. 60 samples. Type: Genome variation profiling by genome tiling array.
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.