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Dataset results
161 results for “unsupervised”
Illuminating the Hierarchical Segmentation of Faults through an Unsupervised Learning Approach applied to clouds of earthquake hypocenters [Dataset]
<p>Data repository to the preprint “Illuminating the Hierarchical Segmentation of Faults through an Unsupersived Larning Approach applied to clouds of earthquake hypocenters” by Piegari et al. (2023), including the datasets for the three analyzed earthquake catalogs.</p>
Datasets and Trained Models of the paper: The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows
<p><strong>Training Data and Trained models corresponding to the publication: 'The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows' (<a href="https://arxiv.org/abs/2309.09743">arXiv:2309.09743</a>).</strong></p> <p>The files cointain the relevant resources for 3 trained Likelihood functions: The Toy-Likelihood, the EW-Likelihood and the Flavor-Likelihood. In each corresponding directory, the training data is found in \data. The trained model and generated samples are found in \NFmodel.</p> <p>To reproduce the published results, git clone https://github.com/NF4HEP/NFLikelihoods, and plug in the provided resources in the corresponding directories of the code.</p> <p> </p> <p> </p>
Pretrained models and simulated data for MICCAI paper Unsupervised Domain Transfer with Conditional Invertible Neural Networks
<p>Simulated data and the pretrained models used for the publication "Unsupervised Domain Transfer with Conditional Invertible Neural Networks", see https://link.springer.com/chapter/10.1007/978-3-031-43907-0_73 published at MICCAI 2023.</p>
Data from: Long-term unsupervised recalibration of cursor BCIs
Open the record for dataset details and reuse information.
Data from: Unsupervised discovery of family specific vocal usage in the Mongolian gerbil
Open the record for dataset details and reuse information.
Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval (Evaluation datasets)
<p>This dataset contains all the runs, pools, plots and analyses to reproduce the results presented in the paper: "Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval ", 2020.</p>
SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
<p><strong>Authors</strong></p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky, and Nina Tahmasebi</p> <p><strong>Description</strong></p> <p>This data collection contains the <strong>post-evaluation</strong> data for <a href="https://languagechange.org/semeval">SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>:</p> <ul> <li>the starting kit to download data, and examples for competing in the CodaLab challenge including baselines</li> <li>the true binary change scores of the targets for Subtask 1, and their true graded change scores for Subtask 2 (<code>test_data_truth/</code>),</li> <li>the scoring program used to score submissions against the true test data in the evaluation and post-evaluation phase (<code>scoring_program/</code>),</li> <li>the results of the evaluation phase including <ul> <li>the final rankings of the participating teams by their best submission (<code>results/rankings_teams.csv</code>),</li> <li>the submitted files of each team (<code>results/submissions/</code>),</li> <li>an overview of the results for each submission ordered by team (<code>results/submissions_results.csv</code>),</li> <li>analysis plots (<code>plots/</code>) displaying the results: <ul> <li>under <code>per_target/</code> we provide the gold change scores and the normalized prediction error of target words plotted against their frequency and polysemy statistics,</li> <li>under <code>per_team/</code> we provide the model predictions from the best submission per team (per subtask) plotted against frequency/polysemy statistics and performance on gold data (gray lines give the correlation with the respective variable in the gold data); we also provide plots of visualizing the teams' prediction similarities.</li> </ul> </li> </ul> </li> </ul> <p>Some remarks:</p> <ul> <li>the paper referenced below remains the only source for the rankings between teams,</li> <li>some teams were disqualified, and are thus removed from the analyses and the rankings present in the paper,</li> <li>some teams have changed names, resulting in a discrepancy between team names under <code>results/</code> and team names in the paper. The paper contains a key to match old names with new names.</li> </ul> <p><strong>Test Data </strong>for SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection can be found using the links below:</p> <ul> <li><a href="https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd-eng/">English</a></li> <li><a href="https://www.ims.uni-stuttgart.de/en/research/resources/corpora/sem-eval-ulscd-ger/">German</a></li> <li><a href="https://zenodo.org/record/3734089">Latin</a></li> <li><a href="https://zenodo.org/record/3730550">Swedish</a></li> </ul> <p>Please find more information on the provided data in the paper referenced below.</p> <p><strong>Reference</strong></p> <p>Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, Haim Dubossarsky and Nina Tahmasebi. 2020. <a href="https://languagechange.org/semeval">SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection</a>. SemEval@COLING2020.</p> <p>The resources are freely available for education, research and other non-commercial purposes.</p> <pre><code>@inproceedings{schlechtweg2020semeval, title = "{S}em{E}val-2020 {T}ask 1: {U}nsupervised {L}exical {S}emantic {C}hange {D}etection", author = "Schlechtweg, Dominik and McGillivray, Barbara and Hengchen, Simon and Dubossarsky, Haim and Tahmasebi, Nina", booktitle = "To appear in Proceedings of the 14th International Workshop on Semantic Evaluation", year = "2020", address = "Barcelona, Spain", publisher = "Association for Computational Linguistics"}</code></pre> <p> </p>
Molecular dynamics simulations data for "Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes".
<p>Molecular dynamics simulation data created and used in "Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes".</p> <p> </p>
Total Ortholog Median Matrix (TOMM): an alternative unsupervised approach for phylogenomics based on evolutionary distance between protein coding genes
<p>The increasing number of available genomic data allowed the development of phylogenomic analytical tools. Current methods compile information from single gene phylogenies, whether based on topologies or multiple sequence alignments. Generally, phylogenomic analyses elect gene families or genomic regions to construct phylogenomic trees. Here, we presented an alternative approach for Phylogenomics, named TOMM (Total Ortholog Median Matrix), to construct a representative phylogram composed by amino acid distance measures of all pairwise ortholog protein sequence pairs from desired species inside a group of organisms. The procedure is divided two main steps, (1) ortholog detection and (2) creation of a matrix with the median amino acid distance measures of all pairwise orthologous sequences. We tested this approach within three different group of organisms: Kinetoplastida protozoa, hematophagous Diptera vectors and Primates. Our approach was robust and efficacious to reconstruct the phylogenetic relationships for the three groups. Moreover, novel branch topologies could be achieved, providing insights about some phylogenetic relationships between some taxa.</p>
Data from: A demonstration of unsupervised machine learning in species delimitation
One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats. Many fields of science are now using machine learning, and in evolutionary biology supervised machine learning has recently been used to infer species boundaries. These supervised methods require training data with associated labels. Conversely, unsupervised machine learning (UML) uses inherent data structure and does not require user-specified training labels, potentially providing more objectivity in species delimitation. Here we demonstrate the utility of three UML approaches (random forests, variational autoencoders, t-distributed stochastic neighbor embedding) for species delimitation in an arachnid taxon with high population genetic structure (Opiliones, Laniatores, Metanonychus). We find that UML approaches successfully cluster samples according to species-level divergences and not high levels of population structure, while model-based validation methods severely over-split putative species. UML offers intuitive data visualization in two-dimensional space, the ability to accommodate various data types, and has potential in many areas of systematic and evolutionary biology. We argue that machine learning methods are ideally suited for species delimitation and may perform well in many natural systems and across taxa with diverse biological characteristics.
Data from: Unsupervised machine learning reveals mimicry complexes in bumble bees occur along a perceptual continuum
Müllerian mimicry theory states that frequency dependent selection should favour geographic convergence of harmful species onto a shared colour pattern. As such, mimetic patterns are commonly circumscribed into discrete mimicry complexes each containing a predominant phenotype. Outside a few examples in butterflies, the location of transition zones between mimicry complexes and the factors driving mimicry zones has rarely been examined. To infer the patterns and processes of Müllerian mimicry, we integrate large-scale data on the geographic distribution of colour patterns of social bumble bees across the contiguous United States and use these to quantify colour pattern mimicry using an innovative, unsupervised machine learning approach based on computer vision. Our data suggest that bumble bees exhibit geographically clustered, but sometimes imperfect colour patterns and that mimicry patterns gradually transition spatially, rather than exhibit discrete boundaries. Additionally, examination of colour pattern transition zones of three comimicking, polymorphic species, where active selection is driving phenotype frequencies, revealed their transition zones to differ in location within a broad region of poor mimicry. Potential factors influencing mimicry transition zone dynamics are discussed.
Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation
<p>This dataset contains the models for interpretable Word Sense Disambiguation (WSD) that were employed in Panchenko et al. (2017; the paper can be accessed at https://www.lt.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_LangTech/publications/EACL_Interpretability___FINAL__1_.pdf).</p> <p>The files were computed on a 2015 dump from the English Wikipedia. Their contents:</p> <ul> <li>Induced Sense Inventories: <strong>wp_stanford_sense_inventories.tar.gz</strong><br> This file contains 3 inventories (coarse, medium fine)</li> <li>Language Model (3-gram): <strong>wiki_text.3.arpa.gz</strong><br> This file contains all n-grams up to n=3 and can be loaded into an index</li> <li>Weighted Dependency Features: <strong>wp_stanford_lemma_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000.gz</strong><br> This file contains weighted word--context-feature combinations and includes their count and an LMI significance score</li> <li>Distributional Thesaurus (DT) of Dependency Features: <strong>wp_stanford_lemma_BIM_LMI_s0.0_w2_f2_wf2_wpfmax1000_wpfmin2_p1000_simsortlimit200_feature expansion.gz</strong><br> This file contains a DT of context features. The context feature similarities can be used for context expansion</li> </ul> <p>For further information, consult the paper and the companion page: http://jobimtext.org/wsd/</p> <p>Panchenko A., Ruppert E., Faralli S., Ponzetto S. P., and Biemann C. (2017): Unsupervised Does Not Mean Uninterpretable: The Case for Word Sense Induction and Disambiguation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL'2017). Valencia, Spain. Association for Computational Linguistics.</p> <p> </p> <p> </p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from Switchboard. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair information was the ground truth from Switchboard. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 3)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) on all corpora of five languages. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 2)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) only on the give ENGLISH corpus. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Dataset and source code for ICSME2017 paper "Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction"
<p>Dataset and source code for ICSME2017 paper “Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction”</p> <p>There are four different models in the paper (i.e., EALR, LT, CBS and OneWay). Each model was implemented in a single Java file in the model package. To reproduce the experiment results of each model in the paper, just run the main method in the corresponding Java file. </p> <p> </p>
Unsupervised Blink Detection Using Eye Aspect Ratio Values
<p>This datastore supports an open-source blink detection software which acts as both an implementation of methods proposed in the (unpublished) paper "Unsupervised Blink Detection Using Eye Aspect Ratio Values" and a validation of the results provided.</p> <p>The software can be accessed at the <strong>GitHub repository</strong>: <a href="https://github.com/mi3nts/tobiiBlinkDetection">https://github.com/mi3nts/tobiiBlinkDetection</a></p> <p>Although the study results can be displayed readily from the files provided at the GitHub, this datastore contains the required resources to apply the proposed blink detection algorithm to new data. Simply download this file and replace the resources folder of the cloned repository.</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>
Organisation of gene programs revealed by unsupervised analysis of diverse gene-trait associations
<p>Data used for manuscript 'Organisation of gene programs revealed by unsupervised analysis of diverse gene-trait associations'.<br> </p> <p> </p> <p> </p>
Research data supporting: "Classifying soft self-assembled materials via unsupervised machine learning of defects"
<p>Research data supporting: "Classifying soft self-assembled materials via unsupervised machine learning of defects".</p> <p>The root folder contains 5 folders:</p> <ol> <li>FIBERS</li> <li>MEMBRANES_and_MICELLES</li> <li>NANOPARTICLES</li> <li>COMPARISON</li> <li>paper_images</li> </ol> <p>The folders 1. to 3. contain the data for every soft-matters architecture used to produce the results discussed in the main paper. Each of these folders contain additional sub-fordels: TRAJ, SOAP, PCA, CLUSTERING, containing the files discussed in the main paper.</p> <p>Folder 4. contains the data of the comparison between different classes of materials (SOAP, PCA, and CLUSTERING sub-folders).</p> <p>Folder 5. contains the images that are showed in the main paper and in the Supporting Information.</p>
ScienceDex guides
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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.