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1,943 results for “machine learning”
Replication Package of the Paper "How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow"
<p>This replication package contains datasets and scripts related to the paper: "<em>How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow</em>"</p> <ul> <li> <p><code>Contributor_Classification.csv</code>: contains the assignment of each contributor to a specific classification. The file contains the following columns:</p> <ul> <li><em>Date</em> : contains the date of each comment</li> <li><em>Type</em>: describe the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC)</li> <li><em>ID</em> : specific ID of the comment</li> <li><em>Body</em> : contains the body of the comment analyzed</li> <li><em>Url</em> : link at each comments</li> <li><em>NumberPR</em> : number of a PR</li> <li><em>Name_Contributor</em>: contains the name of a contributor for each comment</li> <li><em>Contributor_classification</em>: contains the assignment of a specific classification (academic, bot, ML expert, software engineer, unknown), obtained after manual analysis, for each contributor of a comment</li> </ul> </li> <li> <p><code>Contributors_ManualAnalysis.csv</code>: contains the manual analysis performed by two authors to assign a classification for each contributor. The file contains the following columns:</p> <ul> <li><em>Contributors</em>: contains the name of the contributor for each comment</li> <li><em>Link GitHub</em>: contains the link to the GitHub page for each contributor</li> <li><em># PR</em>: contains the number of PRs in which a specific contributor is involved</li> <li><em>Annotator1</em>: manual classification of the first annotator</li> <li><em>Annotator2</em>: manual classification of the second annotator</li> <li><em>Final Classification</em>: contains the final label (academic, bot, ML expert, software engineer, unknown)assigned for each contributor</li> <li><em>Organization</em>: contains the organization, if any (Google, Hugging Face, Microsoft, OpenAI)</li> </ul> </li> <li> <p><code>ManualAnalysis.csv</code>: contains the manual analysis performed regarding Comment Type, Nature of Comment and Artifact. The .csv contains the following columns:</p> <ul> <li><em>URL</em>: contains the link to each comment</li> <li><em>NumberPR</em> : number of pull request</li> <li><em>CommentType1</em>: contains the classification of the first annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other)</li> <li><em>CommentType2</em>: contains the classification of the second annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other)</li> <li><em>NatureComment1</em>: contains the classification of the first annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment)</li> <li><em>NatureComment2</em>: contains the classification of the second annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment)</li> <li><em>Artifact1</em>: contains the classification of the first annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other)</li> <li><em>Artifact2</em>: contains the classification of the second annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other)</li> <li><em>FINALCommentType</em>: contains the final classification of the comment type after the resolution of the conflicts</li> <li><em>FINALNatureComment</em>: contains the final classification of the nature of the comment after resolution of conflicts</li> <li><em>FINALArtifact</em>: contains the final classification of the artifact after the resolution of conflicts</li> </ul> </li> <li> <p><code>Summary_PR.csv</code>: contains the details about the composition of each PRs. The file contains the following columns:</p> <ul> <li><em>#PullRequest</em>: contains the number of all pull requests analyzed</li> <li><em>#events</em>: contains the number of all the events analyzed for each PR</li> <li><em>#comments</em>: contains the number of all comments for each PR</li> <li><em>#Commit</em>:contains the number of Commit for each PR</li> <li><em>PC</em>: contains the number of PC for each PR</li> <li><em>RC</em>: contains the number of RC for each PR</li> </ul> </li> <li> <p><code>Total_PR_Comments.csv</code>: contains information about all comments analyzed. The columns are:</p> <ul> <li><em>Date</em>: contains the date of each comment</li> <li><em>Type</em>: describes the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC)</li> <li><em>ID</em>: SHAn of the comment</li> <li><em>NumberPR</em>: PR number</li> <li><em>Name_Contributor</em>: contains the name of a contributor for each comment</li> <li><em>Body</em>: contains the body of the comment analyzed</li> <li><em>Url</em>: link at each comment</li> </ul> </li> </ul> <p>The replication also contains a directory <code>results</code> in which there are quantitative results. The directory contains:</p> <ul> <li> <p><code>Artifacts.csv</code>: This file contains the results for the artifact. The columns are:</p> <ul> <li><em>#PullRequest</em>: number of the pull request analyzed</li> <li><em>Article</em>: contains the percentage of the occurrences of the article in the specific pull request</li> <li><em>Code</em>: contains the percentage of the occurrences of the code in the specific pull request</li> <li><em>Commit</em>: contains the percentage of the occurrences of the commit in the specific pull request</li> <li><em>Issue reference</em>: contains the percentage of the occurrences of the issue reference in the specific pull request</li> <li><em>External link</em>: contains the percentage of the occurrences of the external link in the specific pull request</li> <li><em>Review</em>: contains the percentage of the occurrences of the review in the specific pull request</li> <li><em>Other</em>: contains the percentage of the occurrences of the other in the specific pull request</li> </ul> <p>ßAlso, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (Article, Code, Commit, Issue reference, External link, Review, Other)</p> </li> <li> <p><code>CommentType.csv</code>: this file contains the results for the comment type. The columns are:</p> <ul> <li><em>#PullRequest</em>:number of the pull request analyzed</li> <li><em>Conventional review</em>: contains the percentage of the occurrences of the conventional review in the specific pull request</li> <li><em>Initial implementation</em>: contains the percentage of the occurrences of the initial implementation in the specific pull request</li> <li><em>Management</em>: contains the percentage of the occurrences of the management in the specific pull request</li> <li><em>ML review</em>: contains the percentage of the occurrences of the ML review in the specific pull request</li> <li><em>Other</em>: contains the percentage of the occurrences of the Other in the specific pull request</li> </ul> <p>Also, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (Conventional review, Initial implementation, Management, ML review, Other)</p> </li> <li> <p><code>Contributor.csv</code>: this file contains the results for the contributors. The columns are:</p> <ul> <li><em>#PullRequest</em>: number of the pull requests analyzed</li> <li><em>academic</em>: contains the percentage of the occurrences of the academic in the specific pull request</li> <li><em>bot</em>: contains the percentage of the occurrences of the bot in the specific pull request</li> <li><em>ML expert</em>: contains the percentage of the occurrences of the ML expert in the specific pull request</li> <li><em>software engineer</em>: contains the percentage of the occurrences of the software engineer in the specific pull request</li> <li><em>unknown</em>: contains the percentage of the occurrences of unknown in the specific pull request</li> </ul> <p>Also, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (academic, bot, ML expert, software engineer, unknown)</p> </li> </ul>
Supplementary Data Sets for Machine Learning Tracks the Role of Slab-Derived Fluids in Large Igneous Province/CUGBZL
<p>Supplementary Dataset S1: All data collcted from both the GEOROC database and recent literature published in Chinese and English journals and books.<span></span></p> <p>Supplementary Dataset S2: Data after cleaning.</p> <p>Supplementary Dataset S3: Prediction results.</p>
AIMD training data for H2 adsorption on MoxCy facets for machine learning model
<p>This data is used to train the MACE model for modeling hydrogen dissociation on molybdenum carbide surfaces.</p>
Dataset and machine learning models for seismic response predictions of small-to-medium continuous girder bridges
<p>This upload includes the dataset and machine learning models (based on Matlab platform) for longitudinal seismic response predictions of multi-span highway girder bridges, which have a typical span length of 30 m supported by reinforced concrete (RC) bridge bents and abutments through spherical steel bearings. The input variables (features) are five structural parameters of studied bridges and seven intensity measures of earthquakes. The output variables (labels) are peak column drifts and peak bearing deformations. The dataset is developed by conducting a total number of 720 nonlinear time-history analyses considering the uncertainty of bridges and earthquakes. Machine learning models are developed using two popular machine learning algorithms named artificial neural network (ANN) and support vector regression (SVR).</p>
Code and Training Data for "Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands based on In-situ Internet of Things Monitoring"
Open the record for dataset details and reuse information.
Supplementary data: Optimising centralisation and decentralisation in distribution networks for perishable products through mathematical modelling, parametric analysis, and machine learning
<p><span>The success of distribution companies for perishable products is enabled by optimally configuring distribution networks, which allows for reducing total logistic costs while ensuring reduced product spoilage and high service levels. Since customer demand for perishable products varies over time, the network configuration should not be optimised once, but periodically reviewed. Among the decisions to be reviewed, determining whether to centralise or decentralise inventory (i.e., stock allocation in distribution centres) is crucial. However, the literature overlooks stock allocation decisions, and existing methodologies to compare the economic performance of centralised, decentralised, and hybrid policies neglect important cost items, also requiring advanced computational technologies and skills to be applied. This paper addresses these gaps by providing two contributions. In this dataset, a comparison has been made between the cost performance of centralized, decentralized, and hybrid stock allocation policies in distribution networks for perishable products. The dataset comprises 100,000 realistic case studies generated through a Sobol quasi-random low discrepancy series.</span></p>
Appendix B and C of "The Galaxy Activity, Torus, and Outflow Survey (GATOS). Black hole mass estimation using machine learning"
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Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 2 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 2/2 of a dataset used for running and training the ML model.</p> <p>Part 1 is available at <a href="https://doi.org/10.5281/zenodo.14513933">10.5281/zenodo.14513933</a></p> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
DRAMMA: A multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data (dataset 1 of 2)
<p>Dataset for the support of a journal publication. </p> <p>Part 1/2 of a dataset used for running and training the ML model.</p> <div> <div>Part 2 is available at <a href="https://doi.org/10.5281/zenodo.14524613">10.5281/zenodo.14524613</a></div> <div> </div> </div> <p>The code associated with this publication is available at: https://github.com/burstein-lab/DRAMMA </p>
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</p>
Data for "Simulating and analysing seabird flyways: an approach combining least-cost path modelling and machine learning"
Open the record for dataset details and reuse information.
Evaluation of Machine Learning-Assisted Directed Evolution Across Diverse Combinatorial Landscapes
<p>Refer to the <a href="https://github.com/fhalab/SSMuLA">SSMuLA Github repository</a> for the associated code</p> <p>See our <a href="https://doi.org/10.1101/2024.10.24.619774">paper</a> titled "Evaluation of Machine Learning-Assisted Directed Evolution Across Diverse Combinatorial Landscapes"</p> <p> </p>
Supplementary Data and Codes for "Machine Learning Based Long-term Water Quality in the Turbid Pearl River Estuary, China"
<p>The file "Ma_AGUSupplementary.xlsx" contains the in situ data used to develop the ANN model.</p> <p>The files "example_Chla.zip" and "example_TSS.zip" contain codes for retrieving the concentration of Chl-a and TSS on the sea surface of the Pearl River Estuary.</p> <p> </p>
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks
<p>Solvent removal stability and thermal stability associated with structurally characterized metal organic frameworks.</p>
A Machine Learning-Based Method for Classifying Well Test Responses in Naturally Fractured Reservoirs
<p>Complete dataset (raw data), processing codes (MATLAB v2020b) and numerical simulation model (Petrel v2017, ECLIPSE) for clustering of pressure derivatives in Naturally Fractured Reservoirs</p> <p> </p> <p> </p>
Analytical prediction of scattering properties of spheroidal dust particles with machine learning
<p>This respository includes the data used in the paper "Analytical prediction of scattering properties of spheroidal dust particles with machine learning".</p> <ol> <li>"alldata.zip" represents the extinction and absorption coefficients, and phase matrix elements of spheroids dust particles for both training and test data. These data comes from the Oleg Dubovik's group: <a href="https://www.grasp-open.com/products/spheroid-package-release">https://www.grasp-open.com/products/spheroid-package-release</a>/.</li> <li>"<a href="/api/files/7c2cdcdd-dc47-4a19-adc8-59c944e76e54/tmat_Jacall_norm_intg.pickle?versionId=59294030-26a8-4683-9b26-8e8584c2b44b">tmat_Jacall_norm_intg.pickle</a>" contains the Jacobians simulated from linearized T-matrix model used for training in the paper and "<a href="/api/files/7c2cdcdd-dc47-4a19-adc8-59c944e76e54/tmat_fine_n_intg.pickle?versionId=3d20954d-4318-40bb-a900-a93627915c2e">tmat_fine_n_intg.pickle</a>" involves Jacobians used for testing.</li> </ol>
Datasets for: Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy
<p>The datasets and supplementary materials for the manuscript "Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy". <strong>Citations should refer directly to the manuscript (refer to the DOI </strong><a href="https://doi.org/10.1021/acs.jcim.1c01103">10.1021/acs.jcim.1c01103</a><strong>)</strong>.</p> <p>The preprint version of of the manuscript is also available at: <a href="https://doi.org/10.33774/chemrxiv-2021-djd3d-v2">10.33774/chemrxiv-2021-djd3d-v2</a></p> <p> </p> <p>Regarding "<strong>Solvation_data-1.0.0.zip</strong>":</p> <p>The datasets include the curated data for: (1) Abraham solute parameters, (2) solvation free energy, (3) solvation enthalpy, (4) gas-water partition coefficient (logKw), (5) water-1-octanol partition coefficient (logPow). The fitted Abraham and Mintz solvent parameters are also included.</p> <p>Detailed information can be found in the "README.txt" file of the zip file.</p> <p> </p> <p>Regarding "<strong>ML_model_files.zip</strong>":</p> <p>This contains the machine learning model files for SoluteML and DirectML. For the instruction on how to use it, please refer to the <em>chemprop_solvation</em> git repository (<a href="https://github.com/fhvermei/chemprop_solvation">https://github.com/fhvermei/chemprop_solvation</a>)</p> <p> </p>
Data archive for paper "Machine Learning Emulation of 3D Cloud Radiative Effects"
<p><strong>Overview</strong></p> <p>This archive contains models, data, and the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002550">Machine Learning Emulation of 3D Cloud Radiative Effects</a>".</p> <p>For the Python tool to generate synthetic data, please refer to the <a href="https://github.com/dmey/synthia">Synthia repository</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/singularity/">Singularity</a> (tested with version 3.6.3-1.el8)*.</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler**.</li> </ul> <p>*Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p>**Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=24:00:00</code>).</p> <p><strong>Initialization</strong></p> <p>Deflate the data archive with:</p> <pre><code>./init.sh </code></pre> <p>Compile ecRad with Singularity:</p> <pre><code>./tools/singularity/compile_ecrad.sh </code></pre> <p><strong>Usage</strong></p> <p>To reproduce the results as described in the paper, run the following commands from the <code>hpc</code> folder:</p> <pre><code>qsub -v JOB_NAME=mlp_default ./submit_grid_search_default.sh qsub -v JOB_NAME=mlp_synthia ./submit_grid_search_synthia.sh qsub submit_benchmark.sh </code></pre> <p>then, to plot stats and identify notebooks run:</p> <pre><code>qsub submit_stats.sh </code></pre> <p><strong>License</strong></p> <p>Paper code released under the <a href="./LICENSE.txt">MIT license</a>. Data released under <a href="./data/LICENSE.txt">CC BY 4.0</a>. <a href="https://confluence.ecmwf.int/display/ECRAD">ecRad</a> released under the <a href="./ecrad/LICENSE">Apache 2.0 license</a>.</p>
K-Nearest-Neighbor algorithm to predict the survival time and classification of various stages of Oral Cancer: A machine learning approach
<p>This project predicts the survival time of a cancer patient in terms of the number of days and also classifies the dataset into various stages of cancer</p> <p>This is executed on the SPYDER platform using Python 3.7 on Anaconda Navigator.</p> <p>The dataset includes the oral cancer patient's record of 4 countries</p> <p> </p>
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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.