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161
datasets available to search
ShareScore release 0.9.0
Dataset results
161 results for “unsupervised”
Unsupervised Machine Learning and Cepstral Analysis with 4D-STEM for Characterizing Complex Microstructures of Metallic Alloys
<p>Raw 4D-STEM data of Ni50Ti26Hf20Al4 used for analysis in the publication "Unsupervised Machine Learning and Cepstral Analysis with 4D-STEM for Characterizing Complex Microstructures of Metallic Alloys". Datasets were collected using the electron microscope pixel array detector (EMPAD) with a Themis Z STEM. Custom python scripts used for data analysis are available upon request to one of the corresponding authors.</p>
Unsupervised detection of semantic correlations in big data
<p>simulated spin configurations from statistical mechanics models at thermal equilibrium. Further details are in our manuscript with the same title. </p>
The replication package for the paper "Unsupervised Learning of General-Purpose Embeddings for Code Changes"
<p>The replication package for the paper "Unsupervised Learning of General-Purpose Embeddings for Code Changes".</p> <p>To get the detailed instructions, please, consult the README file in the archive.</p>
South Atlantic Ocean profile dataset: identification of near-Antarctic profiles using unsupervised classification
<p>This dataset consists of temperature and salinity profiles taken by Argo floats (<a href="http://argo.ucsd.edu">http://argo.ucsd.edu</a>) and ship-based CTDs as recorded in the World Ocean Database (<a href="https://www.ncei.noaa.gov/products/world-ocean-database">https://www.ncei.noaa.gov/products/world-ocean-database</a>). It covers 65°W to 80°E in longitude, 85°S to 30°S in latitude (roughly the South Atlantic Ocean and a part of the Indian Ocean). In the vertical, it covers the top 1000m of the ocean. </p> <p>It was prepared using MITprof, which is a toolbox for Matlab:<br> <br> Gael Forget. (2017). gaelforget/MITprof: various updates and minor improvements (v1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.834078">https://doi.org/10.5281/zenodo.834078</a></p> <p>This dataset should be considered the "initial" dataset that has been classified into five sub-groups using a profile classification model (PCM) built on Gaussian mixture modelling. The five-component PCM consists of these profile types: (1) subtropical Atlantic, (2) subtropical Indian, (3) circumpolar (more northern), (4) circumpolar (more southern), and (5) near-Antarctic.</p>
Label-free data mining of scientific literature by unsupervised syntactic distance analysis
<p>1. OER.zip: literature resources, mined data and manual annotation of OER</p> <p>2. OLED.zip: literature resources and mined data of OLED</p> <p>3. PVK_ligand.zip: literature resources, mined data and manual annotation of perovskite ligand</p> <p>4. PVK_solvent.zip: literature resources, mined data and manual annotation of perovskite solvent</p> <p>5. syn_vec.part01.rar - syn_vec.part01.rar: word vector model trained by synthesis paragraphs in USPTO</p> <p>6. crossref_vec.part01.rar - crossref_vec.part10.rar: word vector trained by abstracts in Crossref</p>
Unsupervised segmentation and clustering time series approach to Southern Africa rainfall regime changes.
<p>The datasets were sourced from Southern Africa countries namely Malawi, Mozambique, South Africa, and Zimbabwe where recorded historical rainfall data was available. In each country different meteorological stations were selected and were then categorized into coastal, sub-humid and semi-arid zones based on agro-ecological regions. The following information was collected for each meteorological service station; country, meteorological service station name, agroecological region, year , recorded rainfall, and stations geographical coordinates. </p>
A Feasibility Study of Unsupervised, Pre-operative Exercise Program for Patients Scheduled for Lung Cancer Surgery
ClinicalTrials.gov study NCT03162718. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Supervised Center-based vs. Unsupervised Home-based Exercise Programs (PRO-Training)
ClinicalTrials.gov study NCT05619250. IPD Sharing: NO. Countries: 1. Publications: 3.
Supervised Physical Therapy Versus Unsupervised Home Exercise After Surgery for Lumbar Disc Herniation
ClinicalTrials.gov study NCT03505918. IPD Sharing: NO. Countries: 1. Publications: 1.
Unsupervised Elastic Band Exercises Performed by Frail Elderly Hospitalized Patients Monitored by the Bandcizer
ClinicalTrials.gov study NCT02702557. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Hospital- and Home-based Supervised Exercise Versus UNsupervised Walk Advice For Patients With InTermittent Claudication
ClinicalTrials.gov study NCT02341716. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Video Dance Class and Unsupervised Physical Activity During Covid-19 Pandemic in People With Parkinson's Disease
ClinicalTrials.gov study NCT04422353. IPD Sharing: NO. Countries: 1. Publications: 18.
Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring
Open the record for dataset details and reuse information.
Dataset for "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"
<p>Dataset to reproduce results of "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"</p> <p> </p> <p><strong>Abstract</strong>: Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, when coordinates are embedded in high-dimensional data such as images, these approaches either lose interpretability or can only be applied to one particular example. We introduce a new unsupervised neural network model that learns Lagrangian dynamics from images, with interpretability that benefits prediction and control. The model infers Lagrangian dynamics on generalized coordinates that are simultaneously learned with a coordinate-aware variational autoencoder (VAE). The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane. By inferring interpretable Lagrangian dynamics, the model learns physical system properties, such as kinetic and potential energy, which enables long-term prediction of dynamics in the image space and synthesis of energy-based controllers.</p>
Data from: The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis
The inference of population genetic structures is essential in many research areas in population genetics, conservation biology and evolutionary biology. Recently, unsupervised Bayesian clustering algorithms have been developed to detect a hidden population structure from genotypic data, assuming among others that individuals taken from the population are unrelated. Because of this hypothesis, markers in a sample taken from a subpopulation can be considered to be in Hardy-Weinberg and linkage equilibrium. However, close relatives might be sampled from the same subpopulation, and consequently, might cause Hardy-Weinberg and linkage disequilibrium and thus bias a population genetic structure analysis. In this study, we used simulated and real data to investigate the impact of close relatives in a sample on Bayesian population structure analysis. We also showed that, when close relatives were identified by a pedigree reconstruction approach and removed, the accuracy of a population genetic structure analysis can be greatly improved. The results indicate that unsupervised Bayesian clustering algorithms cannot be used blindly to detect genetic structure in a sample with closely related individuals. Rather, when closely related individuals are suspected to be frequent in a sample, these individuals should be first identified and removed before conducting a population structure analysis.
Systematic exploration of unsupervised methods for mapping behavior
<p>Raw data and raw movies accompanying initial publication</p>
Unsupervised functional alignment via data-driven ANN-based visual decoding
<p>This repository is for the under-review paper "Unsupervised functional alignment via data-driven ANN-based visual decoding". Xin-Ya Zhang, Hang Lin, Zeyu Deng, Markus Siegel, Earl K. Miller and Gang Yan.</p>
Support videos for the article "Damage categorization in full-scale, full-composite ship hull under high-energy impacts by unsupervised-learning-enabled acoustic emission monitoring and laser shearography inspection"
<p>Video 1: Video showing one of the impact from a general perspective</p> <p>Video 2: Slow-motion video of the second impact</p>
Unsupervised behaviour analysis and magnification (uBAM) using deep learning Dataset
<h3>Abstract</h3> <p>Motor behaviour analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. State-of-the-art instrumented movement analysis is time- and cost-intensive, because it requires the placement of physical or virtual markers. As well as the effort required for marking the keypoints or annotations necessary for training or fine-tuning a detector, users need to know the interesting behaviour beforehand to provide meaningful keypoints. Here, we introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement. Besides discovering and quantifying deviations in behaviour, we also propose a generative model for visually magnifying subtle behaviour differences directly in a video without requiring a detour via keypoints or annotations. Essential for this magnifica-tion of deviations, even across different individuals, is a disentangling of appearance and behaviour. Evaluations on rodents and human patients with neurological diseases demonstrate the wide applicability of our approach. Moreover, combining optoge-netic stimulation with our unsupervised behaviour analysis shows its suitability as a non-invasive diagnostic tool correlating function to brain plasticity</p> <div> </div> <div> <p> </p> </div>
Data for "Defect detection in atomic-resolution images via unsupervised learning with translational invariance"
<p>This dataset accompanies the paper titled <em>Defect detection in atomic-resolution images via unsupervised learning with translational invariance</em> by Yueming Guo<sup>*</sup>, Sergei V. Kalinin, Hui Cai, Kai Xiao, Sergiy Krylyuk, Albert V Davydov, Qianying Guo, Andrew R. Lupini<sup>* </sup></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.