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31 results for “dimensionality reduction”
Codes and datasets for a brief introduction to clustering and dimensionality reduction
<p>Python codes and datasets for the examples on unsupervised learning presented in Joris Paret's doctoral thesis « Hidden order in disordered materials » (2021).</p>
Data for "Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis"
<p>The files named <code>df_scran.csv</code>, <code>df_seurat.csv</code>, <code>df_zinbwave.csv</code>, <code>df_dca.csv</code>, and <code>df_scvi.csv</code> contain one row per configuration that we ran successfully.</p> <p>The files named <code>DATASET.METHOD.h5ad</code> are encoded with anndata <code>v0.7.0</code> (be careful as they are not readable with previous versions) and contain 100 embeddings each. The embeddings are in the <code>obsm</code> attribute of the object. All the embeddings can be listed with the <code>obsm_keys()</code> method. The name of the embedding contains the parameters used to generate that embedding and are written like that <code>method=zinbwave.dims=10.epsilon=1000.features=300.gene_covariate=0</code>.</p> <p> </p> <p>For questions on this dataset please contact fraimundo@google.com</p>
Original data for article "Is Unsupervised Dimensionality Reduction Sufficient to Decode the Complexities of Electrochemical Impedance Spectra?"
<p>The uploaded Jupyter notebooks contain original data generation and processing methods used in the article "Is Unsupervised Dimensionality Reduction Sufficient to Decode the Complexities of Electrochemical Impedance Spectra?" by A. Makogon, F. Kanoufi, and V. Shkirskiy</p>
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
<p>Result Files for the Paper "A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations" to be published at IEEE Vis 2024</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Representative Testing/Validation WSIs used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction
<p>Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"</p>
Nonlinear methods for dimensionality reduction and clustering of bacterial single-cell sequencing data - intermediate data and figures (MSc thesis)
<p>Data, intermediate results and figures for analyses of my master's thesis in biostatistics at LMU Munich. I took a look on how to use Nonlinear Matrix Decomposition (NMD) (<a href="https://doi.org/10.1137/21M1405769">Saul, L., 2022</a>) in the context of bacterial scRNA-seq analysis (Heumos, L., et. al. 2023), replacing Principal Component Analysis in the optimized workflow, as outlined in Ostner, J. (2024).</p> <p>My thesis was structured along the following objectives:</p> <ul> <li>implement the algorithms from <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> in the Python module <a href="https://github.com/flatironinstitute/nomad/">nomad</a> in cooperation with <a href="https://www.simonsfoundation.org/flatiron/" rel="nofollow">Flatiron Institute</a></li> <li>code for the simulation study of the algorithms in <a href="https://arxiv.org/abs/2305.08687">Seraghiti, G., et. al. (2023)</a> with varying sparsity can be found in <code>/simulation</code></li> <li>apply NMD in the context of the BacSC workflow (<a href="https://www.biorxiv.org/content/10.1101/2024.06.22.600071v1">Ostner, J., et. al. (2024)</a>) on raw and normalized counts (found in <code>/application/analysis</code>), also for manually set number of latent dimensions</li> <li>explore NMD's potential for imputation of <a href="https://www.nature.com/articles/s41467-021-27729-z" rel="nofollow">sampling zeros</a> (check <code>/application/NMD_zero_imputation /</code>)</li> <li>potential of Poisson-Hurdle model-based clustering (<a href="https://academic.oup.com/bioinformatics/article/39/1/btac782/6873739">Qiao, Z., et. al. (2023)</a>) for scRNA-seq (<code>/application/poisson_hurdle</code>).</li> </ul>
Dimensionality reduction of local structure in glassy binary mixtures
<p>This dataset is associated with "<em>Dimensionality reduction of local structure in glassy binary mixtures</em>", by D. Coslovich, R. L. Jack, J. Paret. It includes data and workflow to allow for the replication of the analysis and figures of the manuscript.</p> <p>To reproduce the workflow and the figures, download and extract the package sprouts.tar.gz, then execute</p> <pre><code class="language-bash">./make all</code></pre> <p>This will create the figures under plots/paper and recompile project.pdf. If the workflow fails because of missing dependencies, read the project.pdf file below and check the requirements, or download the <a href="http://www.docker.com">docker</a> image sprouts-docker.tar.gz, load the image and execute the same command within the container.</p> <p><em>Make sure you have at least 20 Gb of free space on your disk if you use the package, and 22 Gb if you use the docker image.</em></p> <p>To speed up the execution of the workflow, download the data cache (cache.tar) and extract it at the root of the project folder.</p> <p>See project.pdf below for full details about the workflow.</p> <p><strong>Changelog</strong>:</p> <ul> <li>1.0.2: use more portable she-bang in scripts</li> <li>1.0.1: fix typos, remove some dead code</li> <li>1.0.0: initial submission</li> </ul>
An Empirical Study of Word Embedding Dimensionality Reduction
<p>In order to analyze the impact on model quality while reducing the number of dimensions, strictly controlled trainings of word embedding are performed on Wikipedia corpora of 170 languages. The specially designed word embedding training tool makes use of processed corpus and intermediate results to accelerate the training, while keeping the consistency of negative sampling.</p> <p>Tests of semantic relatedness show that, except for some corpora of poor scale, the margin gain from extra dimensions significantly decreases above 200.</p>
Research data supporting: "A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields"
<p>This repository contains the data used in the paper of Capelli <em>et al. </em>"A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields", published on Journal of Physical Chemistry B (DOI: 0.1021/acs.jpcb.1c02503).<br> <br> The archive traj_processed.tar.gz contains the trajectories converted in xyz format with the dimensions of the box.</p> <p>The archive trajectories_xtc.tar.gz contains the raw trajectories (of the membranes without solvent) in gromacs xtc format with a .tpr binary file. <br> </p>
Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction
<p>Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction</p> <p>This work has been developed to discover the usage of Multi-Objective Evolutionary computation to reduce the dimensionality of synset-based datatsets. </p> <p>The objective of this code is to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using Multi-Objective Evolutionary Algorithms (MOEA).</p>
CH2O coordinate manifolds and their analysis for the paper "A global view of reactive coordinate manifolds from nonlinear dimensionality reduction"
<p>This dataset contains the the following data created for the paper "A global view of reactive coordinate manifolds from nonlinear dimensionality reduction" by Dmitrij Rappoport.</p> <p>The data was generated using the manic open-source code that is publicly accessible at https://bitbucket.org/rappoport/manic/.</p> <p>All the data in this dataset is for the CH2O coordinate manifold</p> <p>Data listing:</p> <p>MCMC chains (using uniform sampling, average-distance weighted sampling, Jacobian-norm weighted sampling)<br> - mcmc/uniform-sampling<br> - mcmc/ave-distance-weighted<br> - mcmc/jacobian-norm-weighted</p> <p>MLE dimension estimates (from uniform sampling, average-distance weighted sampling, Jacobian-norm weighted sampling)<br> - mle/uniform-sampling<br> - mle/ave-distance-weighted<br> - mle/jacobian-norm-weighted</p> <p>Isomap embeddings for d = 2, 3, 4, 6 (from uniform sampling, average-distance weighted sampling, Jacobian-norm weighted sampling)<br> - isomap/uniform-sampling<br> - isomap/ave-distance-weighted<br> - isomap/jacobian-norm-weighted</p> <p>LLE embeddings for d = 2 (from uniform sampling)<br> - isomap/uniform-sampling</p> <p>Embedding of critical points of the CH2O PES in coordinate manifolds (from uniform sampling, average-distance weighted sampling, Jacobian-norm weighted sampling)<br> - isomap-nystrom/uniform-sampling<br> - isomap-nystrom/ave-distance-weighted<br> - isomap-nystrom/jacobian-norm-weighted</p>
Initial results in dimensionality reduction of taxi DropOut-PickUp regions
<p>Initial results with respect to dimensionality reduction of taxi PickUp-DropOut regions from New York City, Manhattan region, YellowCab company (2018 year, first 7 months). The dimensionality reduction is done separately for all working days and weekends using t-SNE, an SVD, and a simple deep autoencoder. The clustering quality assessment in two-dimensional space in which dimensionality reduction is done is conducted by using Silhouette, Calinski-Harabasz, and Davies-Bouldin metrics. Furthermore, the 15-minute taxi data aggregation is used.</p>
Cooling bosons by dimensional reduction
<p>All data for the preprint "Cooling bosons by dimensional reduction" </p>
Data and code for comparison of different machine learning methods and dimensionality reduction for classification astrocytoma and glioblastoma tissues by mass spectra
<p>This upload contains all replication material for "Comparison of different machine learning methods and dimensionality reduction for classification astrocytoma and glioblastoma tissues by mass spectra" (forthcoming).</p> <p><strong>Authors:</strong> E.S. Zhvansky, A.A. Sorokin, V.A. Shurkhay, V.A. Eliferov, D.S. Bormotov, D.G. Ivanov, D.S. Zavorotnyuk, A.A. Potapov.</p> <p><strong>Code and data are located within data_and_code.zip.</strong> Code is written in Python 3.7.7 using Jupyter Notebook, MATLAB R2019b, and Python 3.5.2.</p> <p>Please find the readme.txt for code using and the code to replicate the main findings of the paper described below:</p> <ul> <li>venn_diagramm.py for Venn diagram figures.</li> <li>SSM.m for SSM calculation and visualization.</li> <li>DR_ML.ipynb for dimensionality reduction and machine learning algorithms comparing on the datasets.</li> </ul> <p> </p>
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
<p>A detailed list of the selected scRNA-seq datasets used in the paper, also provided in Additional file <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1898-6#MOESM1">1</a>: Table S1-S2.</p>
TIE-GCM ROPE - Dimensionality Reduction: Part I
<p>This Repository contains the dataset and models used to obtain the results published in the paper</p>
Supplementary data for "Dimensionality reduction distills complex evolutionary relationships in seasonal influenza and SARS-CoV-2"
Open the record for dataset details and reuse information.
Dimensional reduction of phenotypes from 53,000 mouse models reveals a diverse landscape of gene function - data bundle
<p>This bundle is an archive of data files, configuration files, and scripts related to the manuscript "Dimensional reduction of phenotypes from 53,000 mouse models reveals a diverse landscape of gene function".</p> <p> </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)
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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.