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69 results for “Representation learning”
Example data for "Characterization of tumour heterogeneity through segmentation-free representation learning on multiplexed imaging data"
<p>This repository includes two example dataset and configurations for running CANVAS (https://github.com/tanjimin/CANVAS).</p> <p>The repostory is structured as follows:</p> <p>├── Kim_2022<br>│ ├── configs<br>│ │ ├── config.yaml<br>│ │ └── preprocess<br>│ │ ├── channels_vis_strength.yaml<br>│ │ └── selected_channels_w_color.yaml<br>│ └── data<br>│ └── raw_data<br>│ ├── common_channels.txt<br>│ └── image_files<br>└── Sorin_2023<br> ├── configs<br> │ ├── config.yaml<br> │ └── preprocess<br> │ ├── channels_vis_strength.yaml<br> │ └── selected_channels_w_color.yaml<br> └── data<br> └── raw_data<br> ├── common_channels.txt<br> └── image_files</p> <p> </p> <p>The source IMC data from this repository are from Kim et al. 2022 (https://www.nature.com/articles/s41592-022-01657-2) and Sorin et al. 2023 (https://www.nature.com/articles/s41586-022-05672-3). They are avaiable at: https://zenodo.org/records/4110560 and https://zenodo.org/records/7760826.</p>
Harnessing large-scale herbarium image datasets through representation learning (application images)
<p>Herbarium specimen images used in the manuscript <a href="https://www.frontiersin.org/articles/10.3389/fpls.2021.806407/full"><em>Harnessing large-scale herbarium image datasets through representation learning</em></a> for application tasks 2 and 3. These tasks were formulated to test the applicability of representations learned by three different neural networks trained on <a href="https://www.kaggle.com/c/herbarium-2021-fgvc8">the Half-Earth Challenge dataset</a>.</p> <p>All images are for specimens held by Royal Botanic Gardens, Kew. The images from Kew's HerbCat service, using URLs collated by <a href="https://www.idigbio.org/portal/search">iDigBio</a>. We first search iDigBio for the specimens we wanted, then downloaded the associated occurrence and image metadata files. We then used the URLs in the image metadata files to download the specimen images. The code used to download these images can be found in the <a href="https://doi.org/10.5281/zenodo.5776894">GitHub repository for the paper</a>.</p> <p>We searched iDigBio using these parameters:</p> <pre><code>[x] Must have media Genus: Dendrobium Institution Code: K Basis of Record: PreservedSpecimen</code></pre> <p> </p> <p>©<em> All images copyright of the Board of Trustees of the Royal Botanic Gardens, Kew.</em></p>
Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide Susceptibility
<p>This upload contains 1) the used thematic maps in the study, 2) the samples to train and validate the proposed model, and 3) the samples used to predict landslide susceptibility of Fengjie County and Fuling District. The code related to the methods is available on the website: <a href="https://github.com/Young-Excavator/Meta_LSM">https://github.com/Young-Excavator/Meta_LSM</a>.</p>
SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning
<p>This is a dataset for our paper: "SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning" published at FASE 2022. Find more details at https://github.com/uiuc-arc/sixthsense</p>
DIFAIR: Towards learning DIFerentiAted Image Representations
<p>DIFAIR (DIFferentiAted Image Representations) is an approach to learn a specific representation for deep neural networks applied to image classification. The objectives are to obtain representations exhibiting: (i) class separability, through predefined class positions in the representation space; (ii) the extraction of distinct features, which remain inactive if not present in the image; and (iii) semantic meaning when comparing representations. A distance-based loss function is proposed to optimize a network, in a supervised way, to obtain the desired representation. </p> <p>This resource contains additional figures containing examples of representations for different images.</p>
Information-Theoretic Distraction-Free Representation Learning for Visual Offline RL
<p>Cheetah and Walker dataset with Clean, Single Video, Multiple Videos, and 2x2 Grid distractions used as one of the tasks in the paper.</p> <p>We also provide the pretrained encoder for the given dataset.</p> <p>Since the dataset is quite large, we will release the code to generate the dataset. However, we are still in the process of cleaning the code.</p>
Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties using Machine Learning
<p>This dataset accompanies a study that investigates the use of machine learning (ML) approaches for predicting seven different physical properties of 140 binary copolymers. Two computational methods were employed: a random forest (RF) model based on molecular descriptors and a Graph Neural Network (GNN) using 2D polymer graphs. These methods were applied in both single- and multi-task settings to explore the strengths of each approach in capturing various polymer properties.</p> <p>The dataset includes two files:</p> <ol> <li> <p><strong>Dataset.xlsx</strong>: Contains the following information for each of the 140 copolymers:</p> <ul> <li>Polymer names</li> <li>SMILES notation of the monomers</li> <li>Fraction of monomers in each copolymer</li> <li>Simulated values (calculated using molecular dynamics simulation) and experimental values of various physical properties, including density, specific heat capacity at constant pressure ad volume ,radius of gyration, linear expansion coefficient, volume expansion coefficient, and bulk modulus .</li> </ul> </li> <li> <p><strong>Descriptors.xlsx</strong>: Provides the molecular descriptors calculated using PaDEL-Descriptor software, which were used as input for the RF model to predict polymer properties.</p> </li> </ol> <p>This work provides insight into the comparative strengths of descriptor- and graph-based representations in machine learning models for predicting material properties. It also highlights the importance of selecting appropriate molecular representations based on the nature of the properties being predicted.</p>
MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"
<p>This MATLAB code is part of the study titled <em>"Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"</em>, which has been accepted for publication in the <em>Journal of Imaging (MDPI)</em>. The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper:</p> <p>Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. <em>Journal of Imaging</em>. 2024; 10(10):245. <a href="https://doi.org/10.3390/jimaging10100245">https://doi.org/10.3390/jimaging10100245 </a></p> <p>The datsets use in this work are:</p> <p>Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1<br>Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1</p>
Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer
<p>Dataset for the specified study.</p>
Data to support "Physics-based representations for machine learning properties of chemical reactions
<p>4 datasets of reaction data: </p> <p>1. SN2-20 dataset adapted from https://iopscience.iop.org/article/10.1088/2632-2153/aba822/meta</p> <p>2. Proparg-21-TS dataset from https://pubs.rsc.org/en/content/articlehtml/2021/sc/d1sc00482d</p> <p>3. GDB7-22-TS dataset from https://www.nature.com/articles/s41597-020-0460-4</p> <p>4. Our Hydroform-22-TS dataset of 2,350 structures of reactant and product structures and associated barriers</p> <p>In all cases, there are xyz files of reactant(s) and product(s) structures, and a csv file of associated properties (reaction energies for the first case, e.e. values for the second, and barriers for the third and fourth).</p> <p>For example usage see https://github.com/lcmd-epfl/b2r2-reaction-rep</p>
Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images
<p>A partially synthetic histopathology dataset containing image patches of colon tissue from 3 staining and scanning conditions.</p> <p>This dataset can be used to develop novel histopathology image analysis algorithms that are better able to generalise to novel data domains.</p> <p>See repo for more information.</p>
Unlabeled Sentinel 2 time series dataset (training, T30TXT): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p> <strong> T30TXT unlabeled S2 dataset </strong></p> <p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TXT</strong> are available. To download the full pretraining dataset, see : <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TYS): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYS</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning
<p>Dataset associated with the manuscript entitled "Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning". </p> <p>See Readme file (markdown format) for details on how the data is structured in the "database" file.</p>
A deep learning framework combining molecular image and protein structural representation identifies candidate drugs for chronic pain
<p>Official dataset for <i>A deep learning framework combining molecular image and protein structural representation identifies candidate drugs for chronic pain</i>. </p><p>The related code can be found at <a href="https://github.com/yuxin212/GPCR-public">here</a> and <a href="https://github.com/ChengF-Lab/LISA-CPI">here</a>. </p><p>The dataset is now public. </p>
Bottom-up Graph Representation Learning for Symbolic Expression Solving
Open the record for dataset details and reuse information.
Deep Representation Learning Improves Prediction of LacI-mediated Transcriptional Repression
GEO Series GSE175456. Escherichia coli. 12 samples. Type: Other.
Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 2
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 2.</p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Data to Replicate paper Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks part 1
<p>Data to Replicate paper "Improving Bug Detection via Context-based Code Representation Learning and Attention-based Neural Networks" part 1. Part 2 accessible here: <a href="https://doi.org/10.5281/zenodo.3719225">https://doi.org/10.5281/zenodo.3719225</a></p> <p>The author of the paper uploaded dataset to Google Drive. These are the same files, uploaded to Zenodo. Since detection_data.tar.gz exceeded zenodo limits, I split the data into 2 parts <em>detection_data.tar.gz</em> and <em>detection_data.tar.gz</em>. This is the first part. Splitting was achieved on OS X with:</p> <pre><code>split -b 31000m "detection_data.tar.gz" "detection_data.tar.gz."</code></pre> <p>To get original file back, run</p> <pre><code>cat detection_data.tar.gz.* > detection_data.tar.gz</code></pre> <p>GitHub link to the project: <a href="https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection">https://github.com/OOPSLA-2019-BugDetection/OOPSLA-2019-BugDetection</a></p>
Online repository for Paper "GTE: A Framework for Learning Code AST Representation Efficiently and Effectively"
<p>The online repository for the under review IJCAI2024 paper "<strong>GTE: A Framework for Learning Code AST Representation Efficiently and Effectively</strong>"</p><p><strong>GTE-main.zip</strong> contains the source code of GTE, please see <strong>README.md</strong> in GTE-main.zip<strong> </strong>for more guidance.</p><p><strong>Appendix.pdf </strong>contains more<strong> </strong>details about the dataset and probing task design.</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.