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161
datasets available to search
ShareScore release 0.9.0
Dataset results
161 results for “unsupervised”
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>
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>
Unsupervised Acoustic Modeling using Autoencoder-DNN with HMM Posteriograms (system #3)
<p>DNN trained with Autoencoder features with HMM posteriograms.</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
Open the record for dataset details and reuse information.
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>
DSMBind: SE(3) denoising score matching for unsupervised binding energy prediction and nanobody design
<p>This record provides the training and evaluation dataset for DSMBind</p> <p>Paper link: https://www.biorxiv.org/content/10.1101/2023.12.10.570461v1.abstract</p> <p>Github repo: https://github.com/wengong-jin/DSMBind</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>
Datasets for "Discourse-Aware Unsupervised Summarization for Long Scientific Documents"
<p>Datasets used in <a href="https://aclanthology.org/2021.eacl-main.93.pdf">Discourse-Aware Unsupervised Summarization for Long Scientific Documents</a></p>
Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder
<p>This is the public dataset of synthetic trajectories and MOT17-3T, which is used in paper <a href="https://arxiv.org/abs/2202.09315">Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder</a>. The source code can be found <a href="https://gitlab.inria.fr/xilin/dvae-umot-release">here</a>.</p>
RIGA+ Dataset for Unsupervised Domain Adaptation in Medical Image Segmentation
<p>Different from the previous combined multi-domain dataset for unsupervised domain adaptation (UDA) in medical image segmentation, this multi-domain fundus image dataset contains annotations made by the same group of ophthalmologists. Hence the annotator bias among different datasets can be mitigated. Therefore, this dataset can provide a relatively fair benchmark for evaluating UDA methods in fundus image segmentation.</p> <p>This dataset is based on the RIGA[1] dataset and MESSIDOR[2] dataset. We appreciate their efforts devoted by the authors of [1] and [2].</p> <p>The six duplicated cases in the RIGA dataset are filtered out according to the <a href="https://www.adcis.net/en/third-party/messidor/">Errata</a>. We also remove the duplicated cases that exist in both the RIGA dataset and the MESSIDOR dataset by hash value matching.</p> <table align="center"> <caption>Details of the RIGA+ dataset</caption> <thead> <tr> <th scope="row">Domain</th> <th scope="col">Dataset</th> <th scope="col"> <p>Labeled Samples</p> <p>(Train+Test)</p> </th> <th scope="col"> <p>Unlabeled</p> <p>Samples</p> </th> </tr> </thead> <tbody> <tr> <th scope="row">Source</th> <td>BinRushed</td> <td>195 (195+0)</td> <td>0</td> </tr> <tr> <th scope="row">Source</th> <td>Magrabia</td> <td>95 (95+0)</td> <td>0</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE1</td> <td>173 (138+35)</td> <td>227</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE2</td> <td>148 (118+30)</td> <td>238</td> </tr> <tr> <th scope="row">Target</th> <td>MESSIDOR-BASE3</td> <td>133 (106+27)</td> <td>252</td> </tr> </tbody> </table> <p>[1] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonics, 2018, 10579: 105790B.</p> <p>[2] Decencière E, Zhang X, Cazuguel G, et al. Feedback on a publicly distributed image database: the Messidor database[J]. Image Analysis & Stereology, 2014, 33(3): 231-234.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code>@inproceedings{hu2022domain, title={Domain Specific Convolution and High Frequency Reconstruction based Unsupervised Domain Adaptation for Medical Image Segmentation}, author={Shishuai Hu and Zehui Liao and Yong Xia}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, year={2022}, organization={Springer} }</code></pre> <p> </p>
AGP data for the project "Over-optimism in unsupervised microbiome analysis"
<p>Datasets from the American Gut Project (AGP) that were used by Ullmann et al. for the research project "Over-optimism in unsupervised microbiome analyis".</p> <p>The datasets were published by McDonald et al.:</p> <p>McDonald D, Hyde E, Debelius JW, Morton JT, Gonzalez A, Ackermann G,<br> et al. American gut: an open platform for citizen science microbiome research.<br> Msystems. 2018;3(3). DOI: <a href="https://doi.org/10.1128/mSystems.00031-18">https://doi.org/10.1128/mSystems.00031-18</a></p> <p>We accessed the files otu_table__BODY_HABITAT_UBERON_feces_json.biom and metadata__BODY_HABITAT_UBERON_feces__.txt at ftp://ftp.microbio.me/AmericanGut/ag-2017-12-04.</p> <p>The phyloseq object ag.genus.rds results from these files after preprocessing. (For the preprocessing code, see our Github repository.) </p>
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>
Dataset for: Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks
<p>This repository contains the output of all birth-death neural network simulations and the empirical analyses of Proboscidean diversification. For details see the Readme of the individual directories.</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>
The replication page for paper: Revisiting Supervised and Unsupervised Models for Effort-Aware Just-in-Time Defect Prediction
<p>The replication page for the paper submitted to EMSE: Revisiting Supervised and Unsupervised Models for Effort-Aware Just-in-Time Defect Prediction.</p> <p>There are four java files in the package named "model". Each file corresponds to one specific model (CBS and CBS+ are in the same file). Each file has a main function and you will get the experiment results when running the main function.</p> <p> </p>
Unsupervised Acoustic Segmentation and Clustering using Siamese Network Embeddings
<p>The spoken term discovery system outputs and evaluations for track 2 on Zero Speech 2017. </p>
Simulation data for "Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing"
<p>Simulation data used in analysis of single cell variant calling softwares in manuscript "Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing"</p> <p>Two sets of simulation experiments were performed, one with varying sequencing depth (simulation.tar.gz) and one with a fixed depth at 30 (simulation_fixedDP.tar.gz).</p> <p> </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>
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.