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.
11
datasets available to search
ShareScore release 0.9.0
Dataset results
11 results for “unsupervised classification”
8 years of dayside Magnetospheric Multiscale (MMS) unsupervised clustering plasma regions classifications
<p>These files contain the 1-minute resolution dataset (“labeled_sunside_data.csv”) and 15 minute or longer region list (“<region_name>_region_list.csv”) for Toy-Edens et al.'s Classifying 8 years of MMS Dayside Plasma Regions via Unsupervised Machine Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, probe name (mms1, mms2, mms3, mms4), features that go into clustering and post-cleansing methods, spacecraft positions (in GSE, GSM, and magnetic latitude/local time), raw and cleansed clustering labels, and transition name. The 15+ minute region lists contain the name of the plasma region type, the probe name (mms1, mms2, mms3, mms4), and the start and stop epoch of >= 15 minute epoch where the probe is solidly within that region. NOTE: for the 15+ minute region lists we are only looking for changes in plasma regions, this means that missing data may artificially inflate the duration of the epoch, we suggest looking at the full 1-minute resolution dataset to confirm the region timing.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.'s Classifying 8 years of MMS Dayside Plasma Regions via Unsupervised Machine Learning (DOI:10.1029/2024JA032431).</p> <p>This work was funded by grant 2225463 from the NSF GEM program.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data.csv description</strong></p> <table> <tbody> <tr> <td> <p><strong>Column Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Epoch</p> </td> <td> <p>Epoch in datetime</p> </td> </tr> <tr> <td> <p> probe</p> </td> <td> <p>MMS probe name</p> </td> </tr> <tr> <td> <p> ratio_max_width</p> </td> <td> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr> <td> <p> ratio_high_low</p> </td> <td> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr> <td> <p> norm_Btot</p> </td> <td> <p>Magnitude of the total magnetic field normalized to 50nT. See paper for more information</p> </td> </tr> <tr> <td> <p> small_energy_mean</p> </td> <td> <p>The denominator in ratio_high_low</p> </td> </tr> <tr> <td> <p> large_energy_mean</p> </td> <td> <p>The numerator in ratio_high_low</p> </td> </tr> <tr> <td> <p> temp_total</p> </td> <td> <p>Total temperature from the DIS moments. See paper for more information</p> </td> </tr> <tr> <td> <p> r_gse_x</p> </td> <td> <p>x position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gse_y</p> </td> <td> <p>y position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gse_z</p> </td> <td> <p>z position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gsm_x</p> </td> <td> <p>x position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> r_gsm_y</p> </td> <td> <p>y position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> r_gsm_z</p> </td> <td> <p>z position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> mlat</p> </td> <td> <p>magnetic latitude of spacecraft</p> </td> </tr> <tr> <td> <p> mlt</p> </td> <td> <p>magnetic local time of spacecraft</p> </td> </tr> <tr> <td> <p> raw_named_label</p> </td> <td> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind, ion foreshock)</p> </td> </tr> <tr> <td> <p> modified_named_label</p> </td> <td> <p>Cleansed cluster assigned plasma region label (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr> <td> <p> transition_name</p> </td> <td> <p>Transition names (e.g. quasi-perpendicular bow shock, magnetopause). See paper for more information</p> </td> </tr> </tbody> </table> <p> </p> <p><strong><region_name>_region_list.csv description</strong></p> <table> <tbody> <tr> <td> <p><strong>Column Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>start</p> </td> <td> <p>Starting Epoch in datetime</p> </td> </tr> <tr> <td> <p>stop</p> </td> <td> <p>Stopping Epoch in datetime</p> </td> </tr> <tr> <td> <p>probe</p> </td> <td> <p>MMS probe name</p> </td> </tr> <tr> <td> <p>region</p> </td> <td> <p>Cleansed cluster name associated with 1-minute resolution “modified_named_label”</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Datasets for "Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Classification Algorithms in Reiner Gamma and Mare Ingenii"
<p>Final surface reflectance data at 2.6 m/pixel resolution with floating point values are available as GeoTiff and ASCII text files. Definition files for the K-Means and MLC algorithms in classifying swirl units are also available as ASCII text files. See README file for further details.</p> <p>Data used in the research article:</p> <p>Chuang, F.C., M.D. Richardson, J.R. Weirich, A.A. Sickafoose, and D.L. Domingue, 2022. Mapping Lunar Swirls with Machine Learning: The Application of Unsupervised and Supervised Image Classification Algorithms in Reiner Gamma and Mare Ingenii. The Planetary Science Journal, 3:231. doi://10.3847/PSJ/ac8f43</p> <p> </p>
Automated MESSENGER Plasma Region Classifications via Unsupervised Transfer Learning
<p>This file contains the 1-minute resolution dataset (“labeled_sunside_data_3labels.csv”) for Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, features that go into clustering and post-cleaning methods, spacecraft positions (in MSO), total magnetic field, raw and cleaned clustering labels, and raw and cleaned transition name.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning (DOI: 10.3389/fspas.2025.1608091).</p> <p>This work was supported by NASA grants 80NSSC19K0789 and 80NSSC22K0993.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data_3labels.csv description</strong></p> <table style="width: 100.063%; height: 851.2px;"> <tbody> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p><strong>Column Name</strong></p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p><strong>Description</strong></p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> Epoch</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Epoch in datetime (YYYY-MM-DD HH:MM:SS)</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> x_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>x position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> y_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>y position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> z_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>z position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> btot_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Total magnetic field [nT]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> norm_Btot</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Magnitude of the total magnetic field normalized to 150nT. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_max_width</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_high_low</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> high_intensity</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Boolean if there is a peak with a higher minimum intensity threshold. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> spectra_counts</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>A ratio of spectra bins with non-zero counts to all possible spectra bins (i.e. way to determine if too much missing spectra data). See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> raw_named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind)</p> </td> </tr> <tr> <td style="width: 17.3792%;"> <p>intermediate_named_label</p> </td> <td style="width: 78.9512%;"> <p>Cleaned cluster assigned plasma region label with only relabeling rules applied. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned cluster assigned plasma region label with relabeling rules and post-processing applied (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> raw_transition_name</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Raw transition names (e.g. bow shock, magnetopause) based on "raw_named_label" cluster labels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> transition_name</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned transition names (e.g. bow shock, magnetopause) after removing likely transient transitions based on "named_label" cluster labels. See paper for more information</p> </td> </tr> </tbody> </table> <p> </p>
Materials for the "Unsupervised classification (clustering) of satellite images" workshop
<p>Dataset for the "<strong>Unsupervised classification (clustering) of satellite images</strong>" workshop on <a href="https://opengeohub.org/summer-school/opengeohub-summer-school-poznan-2023/">OpenGeoHub Summer School 2023</a>. The repository with the code can be found on GitHub: <a href="https://github.com/kadyb/OGH2023">https://github.com/kadyb/OGH2023</a>.</p> <p>In the .zip archive there are two catalogs: "<em>data</em>" and "<em>task</em>", which include:</p> <ul> <li>Landsat 8 scene (7 spectral bands) + metadata;</li> <li>polygons with coverage of Poznań and Szamotuły counties.</li> </ul> <p>The satellite data was downloaded from <a href="https://earthexplorer.usgs.gov/">EarthExplorer</a>.</p>
Towards geomorphometry of plains - country level unsupervised classification of low-relief areas (Poland)
<p>Catalog "input_rasters" contains ten downsampled geomorphometric variables with 500 m resolution.</p> <p>There are serialized models in .rds format including Gaussian mixture model (<em>GMM_model.rds</em>) and data transformator (<em>transformator.rds</em>) in "models" catalog. The mclust package is required to load the GMM model, and the recipes package is required for the transformator.</p> <p>The map with geomorphological units is saved in the <em>geomorphometry_map.tif</em> file. The map with the calculated uncertainty of these units is saved in the <em>uncertainty_map.tif</em> file. In addition, a separate .xml file with class names is included.</p> <p>All rasters are in the ETRS89 / Poland CS92 (EPSG:2180) coordinate system and GeoTIFF format.</p> <p>The code to reproduce the results can be found in the GitHub repository: <a href="https://github.com/kadyb/geomorph_clustering">https://github.com/kadyb/geomorph_clustering</a></p>
Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring
<p>1. Passive acoustic monitoring (PAM) has the potential to greatly improve our ability to monitor cryptic yet vocal animals. Advances in automated signal detection have increased the scope of PAM, but distinguishing between individuals— which is necessary for density estimation— remains a major challenge. When individual identity is known, supervised classification techniques can be used to distinguish between individuals. Supervised methods require labeled training data, whereas unsupervised techniques do not. If the acoustic signals of individuals are sufficiently different, the number of clusters might represent the number of individuals sampled. The majority of applications of unsupervised techniques in animal vocalizations have focused on quantifying species-specific call repertoires. However, with increased interest in PAM applications, unsupervised methods that can distinguish between individuals are needed. <br> 2. Here, we use an existing dataset of Bornean gibbon female calls with known identity from five sites on Malaysian Borneo to test the ability of three different unsupervised clustering algorithms (affinity propagation, K-medoids, and Gaussian mixture model-based clustering) to distinguish between individuals. Calls from different gibbon females are readily distinguishable using supervised techniques. For internal validation of unsupervised cluster solutions, we calculated silhouette coefficients. For external validation, we compared clustering results with female identity labels using a standard metric: normalized mutual information. We also calculated classification accuracy by assigning unsupervised cluster solutions to females based on which cluster had the highest number of calls from a particular female.<br> 3. We found that affinity propagation clustering consistently outperformed the other algorithms for all metrics used. In particular, classification accuracy of affinity propagation clustering was more consistent as the number of females increased, and when we randomly sampled females across sites. <br> 4. We conclude that unsupervised techniques may be useful for providing additional information regarding individual identity for PAM applications. We stress that although we use gibbons as a case study, these methods will be applicable for any individually-distinct vocal animal. <br> </p>
From Galaxy Zoo DECaLS to BASS+MzLS: detailed galaxy morphological classification with unsupervised domain adaption
<p>This repository contains the data released in the paper "From Galaxy Zoo DECaLS to BASS+MzLS: Detailed Galaxy Morphological Classification with Unsupervised Domain Adaption".</p> <p>We release detailed galaxy morphological classification in DESI Legacy Imaging Surveys (LIS) DECaLS, BASS, MzLS for m_r<17.77 galaxies and z<0.15.</p> <p>-morphology_GZD.csv contains prediction for DESI LIS DECaLS footprint.</p> <p>-morphology_BMz.csv contains prediction for DESI LIS BASS+MzLS footprint.</p> <p>They include the information about: ra, dec, {question}_{answer}_alpha,{question}_{answer}_prob,{question}_{answer}_var</p> <p>Predictions of the Dirichlet parameter alpha for each galaxy on each feature of each problem, with all the original multiple MC Dropout results, are included to make it easier for you to know all the original predictions.</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>
Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring
Open the record for dataset details and reuse information.
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 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.