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161
datasets available to search
ShareScore release 0.9.0
Dataset results
161 results for “unsupervised”
Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment
ClinicalTrials.gov study NCT03752489. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Comparison of Supervised and Unsupervised Physical Activity Programs During a Weight Loss Intervention for Adults
ClinicalTrials.gov study NCT02693132. IPD Sharing: NO. Countries: 1. Publications: 0.
Feasibility of Unsupervised Therapy After Neurological or Orthopaedical Injury in the Home Setting With a Hand Rehabilitation Device (ReHandyBot) and an Assistive Smartphone Application (RehabCoach)
ClinicalTrials.gov study NCT07182734. IPD Sharing: NO. Countries: 0. Publications: 0.
Effects of an Exercise Program Under Supervision and Unsupervised in the Treatment of Low Back
ClinicalTrials.gov study NCT02703402. IPD Sharing: UNDECIDED. Countries: 0. Publications: 9.
Data from: The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis
Open the record for dataset details and reuse information.
An unsupervised and broadly applicable method for physical cell interaction profiling of complex tissues
GEO Series GSE175664. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Unsupervised analysis of flow cytometry data in a clinical setting captures cell diversity and allows population discovery
GEO Series GSE162177. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Unsupervised hierarchical clustering of iNPCs induced by 6 or 5 TFs
GEO Series GSE29724. Mus musculus. 2 samples. Type: Expression profiling by array.
Data and results from Spage2vec: Unsupervised representation of localized spatial gene expression signatures
<p>This repository contains spatial gene expression data for reproducing analysis results of spage2vec manuscript [1].</p> <p>[1] Partel G, Wählby C. Spage2vec: Unsupervised representation of localized spatial gene expression signatures. The FEBS Journal. 2020.</p> <p>Acknowledgement:</p> <p>We thank Xiaoyan Qian, Simone Codeluppi, Jeffrey Moffitt, Kok Hao Chen, Alistair N. Boettiger and Dhananjay Bambah-Mukku for generating and sharing in situ sequencing [2], osmFISH [3] and MERFISH [4-5] datasets. And Jeongbin Park, Wonyl Choi for uploading osmFISH [3] and MERFISH [4] data to public repository.</p> <p> </p> <p>[2] Qian, Xiaoyan, et al. "Probabilistic cell typing enables fine mapping of closely related cell types in situ." Nature methods 17.1 (2020): 101-106.</p> <p>[3] Codeluppi, Simone, et al. "Spatial organization of the somatosensory cortex revealed by osmFISH." Nature methods 15.11 (2018): 932-935.</p> <p>[4] Moffitt, Jeffrey R., et al. "Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region." Science 362.6416 (2018): eaau5324.</p> <p>[5] Chen, Kok Hao, et al. "Spatially resolved, highly multiplexed RNA profiling in single cells." <em>Science</em> 348.6233 (2015): aaa6090.</p>
Data for the preprint of "Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems"
<p>README: description of the files. </p> <p>This Zenodo repository contains all the data and original code necessary to reproduce the results of the paper https://doi.org/10.48550/arXiv.2402.07786. The code (continuously mantained and updated) is available open-source as a Python package at https://pypi.org/project/onion-clustering/ and on GitHub (https://github.com/matteobecchi/timeseries_analysis). </p> <p>The repository contains the folders "Fig1", "Fig2" etc, which contain the corresponding Datasets, together with the code to reproduce the figures. Additionally, the folder "FigS1 contains code and data for FigS1. </p> <p>The repository also contains the Supplementary Movies S1 to S4, in .mp4 format. </p>
AnoShift: A distribution shift benchmark for unsupervised anomaly detection
<p>Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models. The existing benchmarks are focused on supervised learning, and to the best of our knowledge, there is none for unsupervised learning. Therefore, we introduce an unsupervised anomaly detection benchmark with data that shifts over time, built over Kyoto-2006+, a traffic dataset for network intrusion detection. This type of data meets the premise of shifting the input distribution: it covers a large time span (10 years), with naturally occurring changes over time (eg users modifying their behavior patterns, and software updates). We first highlight the non-stationary nature of the data, using a basic per-feature analysis, t-SNE, and an Optimal Transport approach for measuring the overall distribution distances between years. Next, we propose AnoShift, a protocol splitting the data in IID, NEAR, and FAR testing splits. We validate the performance degradation over time with diverse models, ranging from classical approaches to deep learning. Finally, we show that by acknowledging the distribution shift problem and properly addressing it, the performance can be improved compared to the classical training which assumes independent and identically distributed data (on average, by up to 3% for our approach).</p>
Comparing of Supervised and Unsupervised Home-Based Modified Poco-Poco Exercise in Elderly
ClinicalTrials.gov study NCT07029308. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Effects of Unsupervised Inspiratory Muscle Training on Ventilation Variability in Post-covid-19 Patients.
ClinicalTrials.gov study NCT06362499. IPD Sharing: NO. Countries: 1. Publications: 0.
Acceptability and Feasibility of Unsupervised HIV Self-Testing Among Networks of Men
ClinicalTrials.gov study NCT03891407. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Usability Validation of the MuCopilot Mobile Application, a Digital Tool for the Unsupervised Objective Assessment of Cystic Fibrosis
ClinicalTrials.gov study NCT06132048. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of Supervised Versus Unsupervised Strength Training on Balance and Functional Performance Among Post Stroke Ambulatory Patients
ClinicalTrials.gov study NCT07010185. IPD Sharing: YES. Countries: 1. Publications: 0.
Quantifying the Benefits of Supervised vs. Unsupervised Pre-habilitation for Patients With Acute ACL Tears
ClinicalTrials.gov study NCT04593264. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of an Unsupervised Multi-domain Intervention on Injury Risk Reduction in Athletics (I-ReductAI).
ClinicalTrials.gov study NCT06805162. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prospective Study for 24-months of Physical Training Introduced in Lifestyle of Patients With FSHD : Tolerance, Sustainability and Efficiency of Unsupervised Training Program.
ClinicalTrials.gov study NCT01689480. IPD Sharing: NO. Countries: 1. Publications: 0.
Performance and Safety of a Digital Tool for Unsupervised Self-assessment of NMOSD
ClinicalTrials.gov study NCT05566769. IPD Sharing: NO. Countries: 3. 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.