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356 results for “Machine learning models”
Surrogate-modelling & machine learning dataset : finite element stress analysis of biaxial specimen with random elastic properties - 1000 samples
<p>Dataset finite element stress analysis of biaxial specimen with random elastic properties</p> <p>Unzip and execute dataset.py to visualise data samples. PyVista is needed.</p>
Surrogate-modelling & machine learning dataset : finite element stress analysis of biaxial specimen with random elastic properties - 100 samples
<p>Dataset finite element stress analysis of biaxial specimen with random elastic properties</p> <p>Unzip and execute dataset.py to visualise data samples. PyVista is needed</p>
Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling
<p>The datasets contain input data and data emulated by Neural networks (NN) used in the study 'Dynamics of the euphotic zone in the Black Sea: The synergy of data from profiling floats, machine learning and numerical modeling'</p> <p>- <strong>NN2018_CMEMS_ARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) along ARGO float paths in 2018 with vertical resolution taken from CMEMS (13 depth levels in the depth range studied here); NN was applied either on CMEMS physics ('C') or on ARGO physics ('A'); additionally, CMEMS BGC model data (Chlorophyll-a and Oxygen) along these paths are included; (filenames follow the names of floats given in Table 1:<em> floatname</em>_2018_NNARGOCMEMS.mat)</p> <p>- <strong>NNalongARGO.tar.gz</strong>: archive contains Matlab binary files consisting of NN-derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700 nm) along ARGO float paths together with input ARGO data (time, latitude, longitude, salinity, temperature, sigma_T and BGC variables); all variables are mapped with 1m vertical resolution; depth range is [1m 150m] ; additionally, float ogs7 data include NO3, float hzg1 data does not contain Chlorophyll-a and backscatter at 700 nm; (filenames follow the names of floats given in Table 1:<em> floatname</em>_euph_1mRes_150mALLINCLNN.mat)</p> <p>- <strong>NNReconBlackSea.tar.gz</strong>: archive contains Matlab binary files consisting of basin wide NN derived BGC variables (Chlorophyll-a, Oxygen and backscatter at 700nm) for the years 2015-19 and 2010/11 (weekly mean data); additionally CMEMS data (time, latitude, longitude, salinity, temperature, sea surface height) are provided for the photic zone; (filenames are reconNNCMEMS_2015_2019.mat and reconNNCMEMS_2010_2011.mat, respectively)</p>
Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response
<p><span>Most cancer patients are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a novel machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in TCGA, which agreed well with existing clinical reports. Finally, feature analysis implicated factors associated with ICB response. In summary, our novel computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.</span></p>
Mapping Phyllosilicates on the Asteroid Bennu Using Thermal Emission Spectra and Machine Learning Model Applications Datasets
<p>We provide laboratory spectra of pure minerals and mineral mixtures and the corresponding metadata. Derived from the laboratory data, we include the PLS model through coefficients. Through the application of the model, we provide the prediction values in volume% for Mg-rich serpentine, cronstedtite, and saponite for the BBD1, EQ3, and TAG datasets.</p>
Mapping Phyllosilicates on the Asteroid Bennu Using Thermal Emission Spectra and Machine Learning Model Applications
<p>We provide the laboratory spectra and metadata that was used to construct the PLS model coefficients. Through the application of the model, we provide the prediction values in volume% for Mg-rich serpentine, cronstedtite, and saponite for the BBD1, EQ3, and TAG datasets.</p>
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
<p>Storm-resolving climate models (SRMs) have gained international interest for their unprecedented detail with which they globally resolve convection. However, this high resolution also makes it difficult to quantify the emergent differences or similarities among complex atmospheric formations induced by different parameterizations of sub-grid information. This paper uses modern unsupervised machine learning methods to analyze and intercompare SRMs based on their high-dimensional simulation data, learning low-dimensional latent representations and assessing model similarities based on these representations. To quantify such inter-SRM ``distribution shifts'', we use variational autoencoders in conjunction with vector quantization. Our analysis involving nine different global SRMs reveals that only six of them are aligned in their representation of atmospheric dynamics. Our analysis furthermore reveals regional and planetary signatures of the convective response to global warming in a fully unsupervised, data-driven way. In particular, this approach can help elucidate the effects of climate change on rare convection types, such as ``Green Cumuli''. Our study provides a path toward evaluating future high-resolution global climate simulation data more objectively and with less human intervention than has historically been needed.</p>
Preprocessed Data for "Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning"
<p>Preprocessed Data (training and test) for 3 SRMs used in "Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning". Here we included ICON, SPCAM, and SPCAM with sea surface tem[eratures warmed by +4K. Additionally we include lat/lon information for the test data.</p>
Data for Developing Machine Learning Models to Predict Base Resistance of Pile Foundation
<p>This data was collected from 86 static pile load tests across 37 different high-rise buildings in Vietnam, especially soft soil region in Mekong Delta (Ho Chi Minh City). The data was used to develop machine learning models to predict base resistance of piles. Further details can be found in publication: "<strong>Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments</strong>", Link: https://www.mdpi.com/2673-7094/4/2/25.</p> <p>Recommended citation: Nguyen, Thanh T., Viet D. Le, Thien Q. Huynh, and Nhu H.T. Nguyen. 2024. "Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments" <em>Geotechnics</em> 4, no. 2: 447-469. https://doi.org/10.3390/geotechnics4020025</p> <p> </p>
The code and data for the paper entitled 'Facilitating Efficient Discovery: A GUI-Oriented Approach for Exploring Functionality using Machine Learning Model'
<h2><strong>Code</strong></h2> <p><strong><span>dataCollection.py</span></strong></p> <p><span>The collection of app data is primarily accomplished by processing and storing XML files and screenshots of the app. Firstly, the uiautomator is utilized to connect to the smart device and obtain screenshots and XML files. Subsequently, the XML files are analyzed to identify clickable functions within the GUI, and the textual information contained within the functions, along with their specific locations, is extracted. Finally, the Python file also includes handling of interface elements, such as determining if elements are obscured and verifying the legitimacy of the text.</span></p> <p><strong><span>tagData.py</span></strong></p> <p><span>The main implementation involves volunteers annotating app functionalities, including HTML generation, user data analysis, and retrieval. Flask framework is employed, presenting one GUI to the user each time while randomly prompting them to click on three functionalities. Ultimately, the time taken by users to locate these three functionalities is collected.</span></p> <p><strong><span>userPersonalization.py</span></strong></p> <p><span>Separating out the data annotated by each user facilitates personalized analysis. This process involves extraction, storage, and loading of individual user annotations.</span></p> <p><strong><span>dataPreprocessing.py</span></strong></p> <p><span>For a user-annotated functionality, completing the conversion from user time to either "hard-to-find" or "easy-to-find" involves several steps. First, the functionalities are vectorized, extracting relevant parameters from the XML files and computing their correlation with the time users spent searching for the functionalities. These parameters are then normalized to obtain feature vectors for the functionalities. Additionally, an initial determination is made regarding whether the annotated functionalities are "hard-to-find" or "easy-to-find" for each user. Subsequently, clustering is performed on all annotated data from users, and based on the clustering results, the outcomes are filtered and adjusted.</span></p> <p><strong><span>difficultFindClassifier.py</span></strong></p> <p><span>Train the classifier and use it to predict "hard-to-find" functionalities, then display the results.</span></p> <h2><span>Data</span></h2> <p><span>The data is located in the "static" folder:</span></p> <p><span>- The "persistentData" folder contains the trained classifier.</span></p> <p><span>- The "picture" folder contains screenshots of the app.</span></p> <p><span>- The "requestTime" folder stores data for when volunteers annotate only one function in a GUI.</span></p> <p><span>- The "threeResponseTime" folder saves data for when volunteers annotate three functions in a GUI.</span></p> <p><span>- The "userData" folder stores personalized user data.</span></p> <p><span><span>- The "xml_information" folder stores XML files of the app.</span></span></p>
A Machine Learning Approach for Multiscale Modeling of Biological Tissues, Companion Dataset
<p>This dataset includes the response of a Delaunay fiber network to various deformation gradients. The dataset is in adios2 format and includes stress, strain, strain energy, stiffness, and the applied deformation gradients.</p> <p>This also includes the pytorch implementation of our neural network.</p>
SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models
<p>This repository contains the documentation, test datasets and scripts used in the following study:</p> <p>"SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models"<br><br>- `sonic-manuscript-master.zip` contains all the scripts to reproduce the study, including those for generating the figures and tables included in the manuscript.</p> <p>- <a href="../api/records/11361985/draft/files/sonicparanoid2.wiki.tar.xz/content" target="_blank" rel="noopener noreferrer">sonicparanoid2.wiki.tar.xz</a> contains a snapshot fo the wiki for SonicParanoid2 as of May 30, 2024</p>
Cloud-resolving model for machine learning buoyant cloudy updraught
<p>Atmospheric model data from simulations carried out using the Unified Model nesting suite u-cj161.</p> <p>This repository includes scripts to perform all the required steps.</p> <p>Retrieval of the model output files from the archive: ml_cape_pdf_1_retrieve_lam_files.py </p> <p>Coarse-graining of the 1.5 km atmospheric model data to a series of different "global model" resolutions by calculating mean profiles of key thermodynamic variables and the calculation of the fraction of that coarse-volume in which the 1.5 km pixels meet the criterion of being bouyant cloudy updraughts: ml_cape_pdf_2d_coarse_grain_bcu.py </p> <p>Subsampling to rebalance the data set since so much of the raw data consists of examples of no convectie activity: ml_cape_pdf_3e_sub_sample_bcu.py </p> <p>There was too much data to process all the raw data in one go, so it was done is several steps. There is then a need to join multiple files together: ml_cape_pdf_3g_stitch_together_bcu_files.py </p> <p>The data is on the Unified Model L70 grid (counting from the bottom up), but the goal is to deploy the machine-learnt algorithm on the E3SM grid (72 levels counting from the top down), so some regirddign is required: ml_cape_pdf_3h_regrid_um_to_e3sm.py </p> <p>Training of 1 two-headed 1d CNN is done using: ml_cape_pdf_4c_bcu_cnn.py </p> <p>Evaluation against witheld data is done using: ml_cape_pdf_5b_validate_BCU.py </p> <p> </p>
Trained Potentials for Article "Computationally Efficient Machine-Learned Model for GST Phase Change Materials via Direct and Indirect Learning"
<p>We provide 8 files here to get started using our trained potentials:</p> <p>1) *.yaml files for each trained potential. These are the outputs of the PACE training process.</p> <p>2) *.yace files for each trained potential. These are read by LAMMPS to use the trained potential. They can be obtained from the *.yaml files using the command line command: "pace_yaml2yace *.yaml".</p> <p>3) GST_config.data -- a starting configuration of GST to be read by LAMMPS. This configuration contains 504 atoms at density 5.85 g/cm^3.</p> <p>4) sample.inp -- a sample LAMMPS input file using the trained potentials. This currently uses "ACE-Indir2.yace" to run the starting configuration "GST_config.data" for 10 ps at 1200 K. When run, it outputs a log file "test.log" and a dump file "test.dump". The choice of trained potential can be changed in the "pair_coeff" section.</p>
PiezoTensorNet: Crystallography informed multi-scale hierarchical machine learning model for rapid piezoelectric performance finetuning
<h2>Description:</h2> <p>a. <strong>Feature Engineering</strong>:</p> <p> The file <strong>data_of_145_features.csv</strong> consists of the datasets of the features. The datasets correspond to Fig. 1(a) of the paper.</p> <p>b. <strong>HierCrystalNet of PiezoTensorNet</strong>:</p> <p> The saved models after training HierCrystalNet are placed inside the zip folders, namely, (i)<strong> classification_saved_models.zip</strong>, (ii) PG1_cubic_saved_models.zip, (iii) PG2_tetragonal42m_saved_models.zip, (iv) PG3_orthorhombic222_saved_models.zip, (v) PG4_hex6tetra4mm_saved_models.zip, and (vi) PG5_orthorhombicmm2_saved_models.zip.</p> <p>c. <strong>ModularEnsembleNet of PiezoTensorNet </strong>:</p> <p> The prediction_results.zip folder consists the prediction results of the ModularEnsembleNet. </p> <p>d. <strong>Input data of piezoelectric tensors and stiffness tensors for the</strong> <strong>finite element analysis</strong>: </p> <p>(i) <em><strong>piezoelectric_tensors_AlN_B0.3Er0.5Al0.2N_alloy.zip</strong></em>: The coefficients of piezoelectric tensors (array shape: 3x6) for AlN alloy and B0.3Er0.5Al0.2N alloy are respectively presented in <strong>undoped_AlN_piezoelectric_tensor.csv</strong> and <strong>B0.3Er0.5Al0.2N_alloy_unrotated_piezoelectric_tensor.csv</strong> files. Both of these files contain the data for samples whose laboratory coordinate system is same with that of the orientation of crystal lattice (all of the three Euler angles theta, psi and phi = 0 degree ), and so the compositional effect on the piezoelectric behavior can be assessed solely without considering the orientation effect. Rotation or other transformation can cause the vector value of (theta, phi, phis) other than (0,0,0) which indicates that the laboratory coordinate system of a sample being different than its lattice orientation. The effect of two different magnitudes of rotation on the coefficients of piezoelectric tensors for B0.3Er0.5Al0.2N alloy are revealed through the files <strong>B0.3Er0.5Al0.2N_alloy_rotated_at_orientation_1_piezoelectric_tensor.csv</strong> and <strong>B0.3Er0.5Al0.2N_alloy_rotated_at_orientation_2_piezoelectric_tensor.csv</strong>. Orientation 1 refers to<strong> theta = 0 degree, psi = 268.47 degree, 91.52 degree, 0 <= phi <= 360 degree</strong> whereas <strong> theta = 180 degree, psi = 268.47 degree, 91.52 degree, 0 <= phi <= 360</strong> for orientation 2. The units of the coefficients are C/m^2. The description of the methodology on how to generate this data in this format is provided at https://piezoelectrictensorsdatabase.streamlit.app/ .</p> <p>(ii) <em><strong>stiffness_tensors_AlN_B0.3Er0.5Al0.2N_alloy.csv</strong></em>: The stiffness tensors (array shape: 6x6) for the two materials AlN alloy and B0.3Er0.5Al0.2N alloy at room temperature are presented through the four csv files. The elements of stiffness tensors in the datasets are presented in GPa. The Cijkl tensors are provided first for isotropy and then for orthotropy assumptions. The description of these tensors for AlN and B0.3Er0.5Al0.2N materials are provided in the web apps: https://isotropic-elasticity.streamlit.app/ and https://orthotropic-elasticity.streamlit.app/</p>
2D Ising model Monte Carlo configurations for the paper Entropy from Machine Learning
<p>Monte Carlo configurations for the 2D Ising model on a periodic 20x20 lattice used in the paper <em>Entropy from Machine Learning.</em></p> <p>Contains:</p> <ul> <li>20000 configurations for each temperature in the range T=1.0 to T=4.0 in 0.1 intervals</li> <li>40000 configurations at T=Tc</li> <li>README.md file with information about reading into Python</li> </ul> <p>See repository <a href="https://github.com/rmldj/ml-entropy">github.com/rmldj/ml-entropy.</a></p>
Machine learning pipeline to train toxicity prediction model of FunTox-Networks
<p>Machine Learning pipeline used to provide toxicity prediction in FunTox-Networks</p> <p>01_DATA # preprocessing and filtering of raw activity data from ChEMBL<br> - Chembl_v25 # latest activity assay data set from ChEMBL (retrieved Nov 2019)<br> - filt_stats.R # Filtering and preparation of raw data<br> - Filtered # output data sets from filt_stats.R<br> - toxicity_direction.csv # table of toxicity measurements and their proportionality to toxicity</p> <p>02_MolDesc # Calculation of molecular descriptors for all compounds within the filtered ChEMBL data set<br> - datastore # files with all compounds and their calculated molecular descriptors based on SMILES<br> - scripts<br> - calc_molDesc.py # calculates for all compounds based on their smiles the molecular descriptors<br> - chemopy-1.1 # used python package for descriptor calculation as decsribed in: https://doi.org/10.1093/bioinformatics/btt105</p> <p>03_Averages # Calculation of moving averages for levels and organisms as required for calculation of Z-scores<br> - datastore # output files with statistics calculated by make_Z.R<br> - scripts<br> -make_Z.R # script to calculate statistics to calculate Z-scores as used by the regression models<br> <br> 04_ZScores # Calculation of Z-scores and preparation of table to fit regression models<br> - datastore # Z-normalized activity data and molecular descriptors in the form as used for fitting regression models<br> - scripts<br> -calc_Ztable.py # based on activity data, molecular descriptors and Z-statistics, the learning data is calculated</p> <p>05_Regression # Performing regression. Preparation of data by removing of outliers based on a linear regression model. Learning of random forest regression models. Validation of learning process by cross validation and tuning of hyperparameters.</p> <p>- datastore # storage of all random forest regression models and average level of Z output value per level and organism (zexp_*.tsv)<br> - scripts<br> - data_preperation.R # set up of regression data set, removal of outliers and optional removal of fields and descriptors<br> - Rforest_CV.R # analysis of machine learning by cross validation, importance of regression variables and tuning of hyperparameters (number of trees, split of variables)<br> - Rforest.R # based on analysis of Rforest_CV.R learning of final models</p> <p>rregrs_output<br> # early analysis of regression model performance with the package RRegrs as described in: https://doi.org/10.1186/s13321-015-0094-2</p>
A machine learning model reveals expansive downregulation of ligand-receptor interactions enhancing lymphocyte infiltration in melanoma with developed resistance to Immune Checkpoint Blockade
<p>Data repository containing the data to reproduce the results and findings that are published in:</p> <p>Sahni, S., Wang, B., Wu, D. <em>et al.</em> A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. <em>Nat Commun</em> <strong>15</strong>, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4</p>
Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C
<p>Supplementary example data for the work presented in "<em>Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C</em>". </p> <p><strong>Abstract: </strong></p> <p>Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.</p> <p> </p>
Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification
<p>This dataset contains gravitational wave (GW) data used in our research work <a href="https://doi.org/10.1088/2632-2153/ada33a" target="_blank" rel="noopener">Abylkairov et al. (2024)</a>. The first 10,000 columns represent the gravitational wave strain <em>D · h</em> [cm] for the corresponding time values ranging from -993 ms to 6.9 ms, with a step size of 0.1 ms. The zero time refers to the time of core bounce. Each row within these first 10,000 columns corresponds to 864 different gravitational wave signals.</p> <p>Columns 10,001 to 10,005 contain the following additional parameters:</p> <ul> <li><strong>T/|W|</strong>: The rotational parameter.</li> <li><strong>GR_or_GREP</strong>: Binary indicator for the signal type, where 0 denotes GR and 1 denotes GREP.</li> <li><strong>EOS</strong>: The equation of state (EOS) model, where 0 corresponds to SFHo, 1 to LS220, 2 to HSDD2, and 3 to GShenFSU2.1.</li> <li><strong>f_peak</strong>: The peak frequency [Hz].</li> <li><strong>D Delta h</strong>: <em>D · ∆h</em> [cm].</li> </ul> <p>For each row (representing a single gravitational wave signal), these parameters provide information about the signal's rotational parameter, type (GR or GREP), EOS model, peak frequency, and <em>D · ∆h</em>.</p> <p><strong>Note</strong>: In the f_peak calculation procedure, we truncated the GW signal at 4.5 ms after the end of the core bounce (see <a href="https://doi.org/10.1103/PhysRevD.95.063019">Richers et al. (2017)</a> for details).</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.