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285 results for “Machine learning dataset”
OQM9HK: A Large-scale Graph Dataset for Machine Learning in Materials Science
<p>This is a large-scale graph dataset of materials science based on the Open Quantum Materials Database (OQMD) v1.5 .</p> <p><a href="https://storage.googleapis.com/rimcs_cgnn/oqm9hk_dataset_Sep_30_2022.pdf" target="_blank" rel="noopener">Technical Report</a></p> <p><a href="https://www.rimcs.co.jp" target="_blank" rel="noopener">RIMCS Website</a></p> <p><strong>Data Loading</strong></p> <p>A Python code example:</p> <pre><code>import sys sys.path.append('/your/path/to/data/OQM9HK_BEL') import OQM9HK bel_path='/your/path/to/data/OQM9HK_BEL' config = OQM9HK.load_config(path=bel_path) print(config['atomic_numbers']) split = OQM9HK.load_split(path=bel_path) print(len(split['train']), len(split['val']), len(split['test'])) graph_data = OQM9HK.load_graph_data(path=bel_path) name = next(iter(graph_data)) # Frist entry's name graph = graph_data[name] # Graph object print(graph.nodes) print(graph.edge_sources) print(graph.edge_targets) dataset = OQM9HK.load_targets(path=bel_path) # Pandas dataframe print(dataset) train_set = dataset.iloc[split['train']] val_set = dataset.iloc[split['val']] test_set = dataset.iloc[split['test']]</code></pre> <p> </p>
Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"
<p>Dataset and results for "Comparing machine learning and deep learning models for probabilistic post-processing of satellite precipitation-driven streamflow simulation"</p> <p>Yuhang Zhang1, Aizhong Ye1*, Phu Nguyen2, Bita Analui2, Soroosh Sorooshian2, Kuolin Hsu2</p> <p>1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.</p> <p>2 Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, CA 92697, USA.</p> <p>## Dataset </p> <p>Streamflow simulations from one observed precipitation (CMA) and three satellite precipitation products (PDIR, IMERG-F, and GSMaP) for 522 sub-basins.</p> <p>- Q-CMA (streamflow reference)<br> - Q-PDIR (uncorrected)<br> - Q-IMERGF (uncorrected)<br> - Q-GSMAP (uncorrected)</p> <p>### Data structure</p> <p>- Head section (row1-row5)<br> - SubNO: 522 <br> - BeginT: 2003-01-01 00:00 <br> - EndT: 2019-12-31 00:00 <br> - Interval: 1440s (daily)<br> - Revise: 10 (scaling factor to keep int datatype)<br> - Point1 Point2 ... (Subbasin No.)<br> - Data section<br> - 6209 rows, 522 cols</p> <p>## Results</p> <p>Two post-processing model results for test period (2015-1-1 to 2018-12-31).</p> <p>### Data structure</p> <p>- 1462 rows, every row denotes each day from 2015-1-1 to 2018-12-31</p> <p>- 100 columns, every column denotes each quantile from 0.005 to 0.995, total 100 quantiles.</p> <p>### qrf-output</p> <p>- pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)</p> <p>### lstm-output</p> <p>- pdir (single input)<br> - imergf (single input)<br> - gsmap (single input)<br> - all (multiple inputs)</p> <p> </p>
UCI Machine Learning- Adult Dataset
https://archive.ics.uci.edu/ml/machine-learning-databases/adult/
Graph Machine Learning Dataset LinkedMDB (LinkedMDB-GML)
<p>LinkedMDB-GML is a heterogeneous graph machine learning dataset based on the full RDF knowledge graph <a href="https://ceur-ws.org/Vol-538/ldow2009\_paper12.pdf" target="_blank" rel="noopener">LinkedMDB</a> (6.1 million RDF triples).</p> <p>LinkedMDB-GML contains node types, such as movies and related entities, such as actors and directors.</p> <p>LinkedMDB-GML was created with <a href="https://github.com/davidlamprecht/AutoRDF2GML" target="_blank" rel="noopener">AutoRDF2GML</a>.</p>
Clinical Dataset for the Paper 'Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis'
<p>This dataset accompanies the manuscript titled "Machine Learning Prediction of Treatment Response to Biological Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis." It includes clinical data used for training and evaluating the machine learning models described in the paper. The dataset contains baseline clinical data of 154 RA patients who were treated with bDMARDs. The labels for remission, and effectiveness (remission and low disease activity) were applied after a 6-month follow-up based on EULAR criteria on DAS28ESR. The sustained effectiveness label indicates maintaining effectiveness within 6 months after initially achieving effectiveness.</p> <p><strong>Crossponder Authors:</strong></p> <ul> <li>Fatemeh Salehi (email: <a rel="noreferrer">fatemeh.salehihafshejni@fau.de</a>)</li> </ul>
Chest X-Ray Image Dataset: A Resource for Medical Diagnosis and Machine Learning
<p>The Chest X-Ray Image Dataset is an extensive collection designed to support medical research and the development of diagnostic tools for COVID-19 detection. It consists of two distinct classes: COVID-19 affected X-ray images and normal X-ray images of the chest area, each covering the full lungs. This dataset provides a diverse range of X-ray images, capturing the unique characteristics of both healthy and COVID-19 affected lungs, making it an invaluable resource for training and testing machine learning models in medical image classification and analysis.</p>
A 1-km resolution monthly mean air temperature (Ta) dataset across the Tibetan Plateau during 2001-2015, related with the article "Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods".
<p>We present a 1-km resolution monthly mean air temperature (Ta) dataset across the Tibetan Plateau from 2001 to 2015. It ranges from 25°-45°N, 70°-105°E, covering a total area of ~7,045,000 km2. To develop this dataset, 10 machine learning algorithms were applied to 11 environmental variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data and topographic index data. The best model generated by Cubist algorithm was finally selected to calculate monthly mean Ta, and achieved an overall accuracy of RMSE= 1.00 °C and MAE= 0.73 °C. To get details of this dataset, please refer to the manuscript "Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods". This Ta dataset provides spatially continuous coverage compared with station observed data, and has much higher accuracy and spatial resolution than reanalysis datasets, making it a useful dataset for climate change and environmental studies in the Tibetan Plateau.</p> <p>Xu Y., Knudby A., Shen Y., Liu Y., Mapping monthly air temperature in the Tibetan Plateau from MODIS data based on machine learning methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(2): 345-354. (DOI: 10.1109/jstars.2017.2787191).</p> <p>The Ta dataset is provided in ENVI standard format. The coordinate system is WGS84 Geographic Coordinate System.</p>
Dataset for: Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning.
<p>The data presented in this article is related to the research article entitled ``Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning." \citep{Nice2019UC}.</p> <p>The dataset consists of a trained Inception V3 neural network model as well as the configuration files to train the neural network and run the inferences. The dataset also contains two sets of outdoor imagery (from Skyfinder and Google Street View) used to train the neural network and validate the sky pixel detection system in the linked article. The original images are included as well as rescaled imagery used to train the neural network, and sky masks used for validation.</p>
Dataset for Machine Learning-Based Prediction and Optimization of As-Extruded Viability in Extrusion-Based 3D Bioprinting
<p>The dataset supports the findings presented in the paper "Machine learning-based prediction and optimization framework for as-extruded cell viability in extrusion-based 3D bioprinting."</p> <ul> <li>Sodium alginate viscosity data: "alg_i1g_viscosity_data.zip"</li> <li>Cross Power Law parameter fitting results: "alg_i1g_viscosity_fittings.zip"</li> <li>Rheological stability measurement: "alg_i1g_contact_angle_data.zip"</li> <li>OpenFOAM simulation results: "alg_i1g_simulation_data.zip"</li> <li>Post-extrusion cell viability results: "cell_viability_data.zip"</li> </ul> <p><strong>Keywords:</strong> 3D bioprinting; cell viability; shear stress; numerical analysis; machine learning; alginate<br><br></p> <p><strong>Code availability statement</strong><br>The scripts used for data analysis, machine learning models, and numerical simulations in this study are available on GitHub at: <a href="https://github.com/KORINZ/in-silico-bioink-viability-prediction">https://github.com/KORINZ/in-silico-bioink-viability-prediction</a></p>
Datasets for "Scalable interpolation of satellite altimetry data with probabilistic machine learning"
<p>Elevation (radar freeboard and sea-level anomaly) fields from CryoSat-2, Sentinel-3A, and Sentinel-3B, over the period December 1st 2018 - April 30th 2019. These data were processed for the Arctic domain using the European Space Agency's Grid Processing on Demand (GPOD) service. Processing follows the steps outlined in Lawrence et al., 2021 (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2019.10.011" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asr.2019.10.011</a>). These data are provided at along-track, 5 km and 50 km resolution, where gridded data follow the EASE grid definition (<a href="https://doi.org/10.3390/ijgi1010032">https://doi.org/10.3390/ijgi1010032</a>).</p> <p>These data were used to develop the open-source Python programming library GPSat (https://github.com/CPOMUCL/GPSat), which uses local Gaussian Process models to perform scalable interpolation of non-stationary satellite altimetry data. The 'Source_data.xlsx' file contains the data corresponding to figures in the published Nature Communications article 'Scalable interpolation of satellite altimetry data with probabilistic machine learning'.</p>
VacSol-ML(ESKAPE) Machine Learning DataSet
Open the record for dataset details and reuse information.
Dataset for "Machine Learning-Aided High Throughput Examination of Block Copolymer Processing Conditions"
Open the record for dataset details and reuse information.
Dataset for the research paper "Automated machine learning in research – a literature review"
<p>This repository contains the literature used in the research paper "Automated Machine Learning in Research – A Literature Review."</p> <p>The four BibTeX files contain the following collections of literature:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> <td><strong>Method</strong></td> </tr> <tr> <td><strong>01_Initial_papers.bib</strong></td> <td>385 Initial papers</td> <td>Keyword search in scientific databases Scopus and Web of Science</td> </tr> <tr> <td><strong>02_Primary_papers.bib</strong></td> <td>267 Primary papers</td> <td>Removing duplicate articles and filtering for full articles (i.e., conference and journal papers)</td> </tr> <tr> <td><strong>03_Possibly_relevant_papers.bib</strong></td> <td>54 Possibly relevant papers</td> <td>Identifying possibly relevant papers through abstract scan and application of inclusion criteria</td> </tr> <tr> <td><strong>04_Relevant_papers.bib</strong></td> <td>49 Relevant papers</td> <td>Inclusion of relevant papers after full-text analysis and backward and forward searches</td> </tr> </tbody> </table> <p>The following inclusion criteria were used to identify the 54 possibly relevant papers during the abstract scan:</p> <ul> <li>The abstract must mention autoML or related concepts such as low-code ML, meta-learning, or automated hyperparameter tuning and their potential use in research.</li> <li>The abstract must mention reproducibility or related concepts such as transparency or explainability in the context of autoML or the related concepts mentioned above</li> </ul>
DFT datasets for training machine-learning potential to model Cl-doped lithium borosilicate glasses using DeePMD
<h2><strong>Li diffusion in oxygen-chlorine mixed anion borosilicate glasses using</strong></h2> <h2><strong>a machine-learning simulation</strong></h2> <h5>Shingo Urata, Noriyoshi Kayaba</h5> <ul> <li>DFT_Data_for_Cl-doped_LBSCl_glass.zip inlucudes atom configurations, energies, forces, box size, atom types, atom kinds, and virial in coord.raw, energy.raw, force.raw, type.raw, type_map.raw, and virial.raw, respectively. </li> <li>All DFT data were evaluated using PBE with a cutoff energy of 600 eV by VASP.</li> <li>The other detasets are available from https://doi.org/10.5281/zenodo.10577559</li> <li>LBSCl_DMD_model.pb is force field developed using DeePMD-kit.</li> <li>LBSCl_DMD_model_c.pb.zip is the compressed version of LBSCl_DMD_model.pb.</li> </ul>
Dataset for the paper 'Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning'
<div> <div>This dataset contains the data produced for the above paper. The dataset consists of:</div> <div> </div> <div>- input nanopillars before deformation (molecular_dynamics/nanopillars)</div> <div>- stress-strain curves acquired by deforming the nanopillars (molecular_dynamics/stress_strain_curves)</div> <div>- weights of the CNNs trained to predict mechanical properties of the nanopillars (machine_learning/train_CNN)</div> <div>- Grad-CAM fields of the predictions (machine_learning/train_CNN)</div> <br> <div>Codes used for creating and analyzing the dataset are available at https://github.com/tekoivisto/nanopillar-ML</div> </div>
DSM Water Level: An UAV photogrammetry dataset for determination of river surface level using machine learning
<p>Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the water surface level of a river. However, this task is difficult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. Machine Learning can be used to correct these disturbances as well as to extract a single water surface elevation value. The presented dataset contains raw photogrammetric orthophotos and DSMs of areas representing parts of a small river and the corresponding DSMs with corrected water surface disturbances. Also a single ground truth value of mean water surface level for each DSM sample is provided. This allows the dataset to be used for supervised training of a neural network performing a denoising or regression task.</p> <p>Acknowledgement: some of the samples were extracted from photogrammetric data acquired by Bandini et. al (https://doi.org/10.5281/zenodo.3519888)</p>
Dataset used in "Machine-learning Kondo physics using variational autoencoders"
<p>Spectral functions from the single-impurity Anderson model, generated on a log-linear frequency mesh using the numerical renormalization group algorithm. Used in both https://doi.org/10.1103/PhysRevB.103.245118 (where a detailed description of dataset generation can be found) and https://arxiv.org/abs/2107.08013.</p>
Dataset for puplication: Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose Tissue-Derived Human Mesenchymal Stem cells employing 1H-1H TOCSY NMR
<p>Data set used in the publication: <strong>Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose Tissue-Derived Human Mesenchymal Stem cells employing <sup>1</sup>H-<sup>1</sup>H TOCSY NMR. </strong></p> <p>Abstract: In this work, the dynamic evolution of adipose tissue-derived human MSCs (AT-derived hMSCs) after fourteen days of cultivation, adiobocytes and osteocytes differentiation has been inspected based on 2D NMR TOCSY using machine learning techniques. Multi-class classification in addition to novelty detection of metabolites was established based on the profile of a control hMSCs sample at four days cultivation and successively detect the absence and the abundance of metabolites in differentiated MSCs following a set of <sup>1</sup>H-<sup>1</sup>H TOCSY profiles. The uploaded files are:</p> <p>File: metabolites_names.xlsx contain the names of the used metabolites.</p> <p>File: metabolites.xlsx</p> <p> column 1: metabolite abbreviation</p> <p>column 2: 2D NMR TOCSY horizontal and vertical frequencies of metabolites in the control group at 4 days cultivation (Ct d4)</p> <p>column 3: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after 14 days of cultivation (Ct d14)</p> <p>column 4: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after 14 days of differentiation into adipocytes (AT d14)</p> <p>column 5: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after14 days of differentiation into osteocytes (OS d14)</p> <p>column 6: 2D NMR TOCSY horizontal and vertical standard frequencies of all metabolites measured at broadband high resolution 600.13 MHz NMR</p>
Digital rock dataset for use in machine learning research of permeability prediction
<p>Datasets and codes for the paper "Hierarchical homogenization with deep-learning-based surrogate model for rapid estimation of effective permeability from digital rocks"</p>
Relevant Datasets and Software Used for Paper "KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description"
<p>This repository contains relevant datasets and software used in a paper "KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description". They are used to run the code of <em>KGML-xDTD </em>stored on <a href="https://github.com/chunyuma/KGML-xDTD">Github</a> and support the results of this paper.</p> <p><strong>About the datasets</strong></p> <p>1. <em>bkg_rtxkg2c_v2.7.3.tar.gz</em></p> <p>This tar.gz file contains three sub-folders: tsv_files, scripts, and relevant_dbs. The "tsv_files" sub-folder has the input files that the neo4j software uses. The "scripts" sub-folder contains a shell script with a relevant python script to construct the biomedical knowledge graph. The "relevant_dbs" sub-folder stores two auxiliary databases that <em>KGML-xDTD</em> needs to use. </p> <p>2. <em>indication_paths.yaml</em></p> <p>This file contains the <a href="https://sulab.github.io/DrugMechDB">DrugMechDB</a> MOA paths that we used to evaluate the predicted MOA paths by <em>KGML-xDTD. </em>It is downloaded from the official <a href="https://github.com/SuLab/DrugMechDB">GitHub repository</a> of DrugMechDB.</p> <p>3. <em>training_data.tar.gz</em></p> <p>This tar.gz file contains the processed training data of four data sources (e.g., <a href="https://mychem.info">MyChem</a>, <a href="https://lhncbc.nlm.nih.gov/ii/tools/SemRep_SemMedDB_SKR/SemMedDB_download.html">SemMedDB</a>, <a href="https://bioportal.bioontology.org/ontologies/NDFRT">NDF-RT</a>, <a href="https://unmtid-shinyapps.net/shiny/repodb/">RepoDB</a>) mentioned in the paper. These processed drug-disease pairs have been matched to the identifiers of biological entities used in our biomedical knowledge graph and respectively split into true positive (tp) sets and true negative (tn) sets. We also provide the names of these drug identifiers and disease identifiers under a sub-folder "translated _to_name".</p> <p><strong>About the software</strong></p> <p><em>neo4j-community-3.5.26.tar.gz</em></p> <p>This tar.gz is the Neo4j community version 3.5.26 downloaded from <a href="https://neo4j.com/download-center/#community">Neo4j Download Center</a>. Although the newer versions are available, due to their big changes in the Neo4j setting that are not compatible with our scripts on Github, we provide the version that we used in our research. If you would like to use the newer version, modifications to our script will be required to import the biomedical knowledge graph into your local Neo4j database with the new setting.</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.