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datasets available to search
ShareScore release 0.9.0
Dataset results
46 results for “unsupervised learning”
Dataset: Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering
<p>This dataset contains all relevant data for the manuscript (in submission) "<em>Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering</em>".</p> <p>Code written to analyse this dataset (which may be adapted for other flow cytometry datasets) is found at https://zenodo.org/record/999747</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Naming convention for raw flow cytometry data files (located in /Script 3. Generating raw data subset/input/):</p> <p>[Allparameters] _ [Year] - [Month] - [Date] [Hour] [u] [Minute] _ [Depth]</p> <p>e.g: Allparameters_2014-07-31 08u08_1.0m</p> <p>The date, time and depth indicate the location and time at which the measurement was taken.</p>
An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer
<p>Supplemental data for the paper titled "An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer"</p>
Headcam: Cylindrical Panoramic Video Dataset for Unsupervised Learning of Depth and Ego-Motion
<p>This dataset contains panoramic video captured from a helmet-mounted camera while riding a bike through suburban Northern Virginia. We used the videos to evaluate an unsupervised learning method for depth and ego-motion estimation, as described in our paper:</p> <p>Alisha Sharma and Jonathan Ventura. "Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video." Proceedings of the 2019 IEEE Artificial Intelligence & Virtual Reality Conference, San Diego, CA, 2019.</p> <p>If you make use of this dataset, please cite this paper.</p> <p> </p> <p>The videos are stored as .mkv video files encoded using lossless H.264. To extract the images, we recommend using ffmpeg:</p> <blockquote> <p>mkdir 2018-10-03 ;</p> <p>ffmpeg -i 2018-10-03.mkv -q:v 1 2018-10-03/%05d.png ;</p> </blockquote> <p>Associated code can be found in our <a href="https://github.com/jonathanventura/cylindricalsfmlearner">GitHub repository</a>. </p>
Data for "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer"
<p>These are the data associated with the paper, "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer" (DOI 10.1039/D1SM01012C). Each of the directories contains subdirectories with `GSD` files dumped from HOOMD. Each subdirectory roughly corresponds to one or two of the figures in the paper.</p>
Data for: Defining usual oral temperature ranges in outpatients using an unsupervised learning algorithm
<p><strong>Importance</strong>: Although oral temperature is commonly assessed in medical examinations, the range of usual or "normal" temperature is poorly defined.</p> <p><strong>Objective</strong>: To determine normal oral temperature ranges by age, sex, height, weight and time of day.</p> <p><strong>Design</strong>: We applied a filtering algorithm (LIMIT) to 10 years of outpatient temperature measurements. LIMIT iteratively removed encounters with primary diagnoses overrepresented in the tails of the temperature distribution, leaving only those diagnoses unrelated to temperature. Mixed effects modeling was applied to the remaining temperature measurements to identify independent predictors of normal oral temperature and to generate individualized normal temperature ranges.</p> <p><strong>Setting</strong>: Single large medical care system, divisions of Internal Medicine and Family Medicine.</p> <p><strong>Participants</strong>: All adult outpatient encounters that included temperature measurements, April 2008 - June 2017.</p> <p><strong>Exposures</strong>: Primary diagnoses and medications, age, sex, height, weight, time of day and month, abstracted from each outpatient encounter.</p> <p><strong>Main outcomes and measures</strong>: Normal temperature ranges by age, sex, height, weight, and time of day.</p> <p><strong>Results</strong>: From 618,306 encounters, 36% were removed by LIMIT because they included diagnoses or medications that fell disproportionately in the tails of the temperature distribution. The encounters removed due to overrepresentation in the upper tail were primarily linked to infectious diseases (76.81% of all removed encounters); type 2 diabetes mellitus was the only diagnosis removed for over-representation in the lower tail (15.71% of all removed encounters). Prior to running LIMIT, the mean overall oral temperature was 36.71°C (±0.43); following LIMIT, the mean temperature was 36.64°C (±0.35). Using mixed effects modeling, age, sex, height, weight and time of day accounted for 6.86% (overall) and up to 25.52% (per subject) of the observed variability in temperature. Mean normal oral temperature did not reach 37°C for any subgroup; the upper 99th percentile ranged from 36.8°C (tall underweight old men in the morning) to 37.9°C (short obese young women in the afternoon).</p> <p><strong>Conclusion and relevance</strong>: Normal oral temperature varies in a predictable manner based on sex, age, height, weight and time of day, allowing individualized normal temperature ranges to be established. </p>
Data for: Defining usual oral temperature ranges in outpatients using an unsupervised learning algorithm
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Data from: Generalizable physical descriptors of pool boiling heat transfer from unsupervised learning of images
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Data from: A demonstration of unsupervised machine learning in species delimitation
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Data from: Unsupervised machine learning reveals mimicry complexes in bumble bees occur along a perceptual continuum
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Deep Learning Methods for Unsupervised Acoustic Modeling using HMM posteriograms (system #2)
<p>System combination of HMM-DNN with auto encoder features</p>
Deep Learning Methods for Unsupervised Acoustic Modeling using GMM posteriograms (system #1)
<p>System combination of autoencoder and GMM-DNN features. </p>
Deep Learning Methods for Unsupervised Acoustic Modeling using HMM posteriograms
<p>DNN trained using HMM posteriograms</p>
Leveraging the Power of Unsupervised Learning for Flood Mapping
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spatiAlign: An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics
<p>Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.</p>
Sample data and algorithm implementation for ChiSCAT: Unsupervised Learning of Recurrent Cellular Micromotion Patterns from a Chaotic Speckle Pattern
<p>Sample data and algorithm implementation for the article </p> <div>Trelin, A., Kussauer, S., Weinbrenner, P., Clasen, A., David, R., Rimmbach, C., & Reinhard, F. (2024). ChiSCAT: Unsupervised Learning of Recurrent Cellular Micromotion Patterns from a Chaotic Speckle Pattern. <em>Nano Letters</em>, <em>24</em>(40), 12374-12381.</div>
Supplementary material for the publication: "Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer"
<p><span><span>This dataset contains postprocessed data for the publication „<span>Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer</span>“.</span></span></p>
2024 ACNS AI Tutorial Unsupervised Learning Dataset
<p>Data needed for a tutorial on unsupervised learning for the 2024 ACNS conference.</p> <p>Associated github repository is: <a title="ACNS AI Tutorial Repo" href="https://github.com/CAMM-UTK/acns-AI-tutorial.git">https://github.com/CAMM-UTK/acns-AI-tutorial</a></p>
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>
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>
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>
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.