Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,943
datasets available to search
ShareScore release 0.9.0
Dataset results
1,943 results for “machine learning”
Dataset for Characterizing Distributed Machine Learning Workloads on Apache Spark
<div> <div> <div> <ul> <li> <p><span>YasmineDjebrouni,IsabellyRocha,SaraBouchenak,LydiaChen,PascalFelber,Vania Marangozova, and Valerio Schiavoni. 2023. Characterizing Distributed Machine Learning Workloads on Apache Spark. In Proceedings of the 24th International Middleware Conference (Middleware ’23). Association for Computing Machinery, New York, NY, USA, 151–164. </span></p> </li> </ul> </div> </div> </div>
Dynamical ising dataset for the paper Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
<p>This dataset provide the evolution in time for the magnetizaion in the 2D Ising model evolved with Gluber dynamics for a lattice of size 64 x 64.</p>
Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
ProbioSML: Probiotic Sequences database based on Machine Learning
<p>ProbioSML is a database comprising 1,071 genes associated with microbial genera that have been demonstrated to possess probiotic properties.</p>
An Integrated Machine Learning Approach Delineates an Entropic Expansion Mechanism for the Binding of a Small Molecule to α-Synuclein
<p>The <a href="https://zenodo.org/uploads/14177307" target="_blank" rel="noopener noreferrer">TRAJECTORY.zip</a> contains two trajectories. One for the apo simulation (traj_mw_c4.xtc) and the other for the water simulation in .gro format (traj.gro). The run1.tpr is the binary file for running the apo simulation in gromacs. For water simulation, the raw DOSPT files are also included to calculate the entropy.</p> <p>The <a href="https://zenodo.org/uploads/14177307" target="_blank" rel="noopener noreferrer">figure_raw_data.zip</a> file contains the raw data that was used for plotting the figures in the main text and in the supplementary material.</p> <p>For any further inquiries or requests for additional data, please contact <a rel="noopener">jmondal@tifrh.res.in</a>.</p>
Data from:Modelling the spatiotemporal dynamics of soil nitrogen in croplands of Northeast China from 1980 to 2023 using multisource data and machine learning
<p>This dataset include the spatiotemporal distribution and uncertainty of cropland soil total nitrogen content at 0-30, 30-60, 60-100 cm depths in Northeast China from 1980 to 2023. The long-time series of TN were estimated by using an space-time automatic machine learning. The detail information on the products were given below:</p> <p>Period: 1980-2023</p> <p>Spatial resolution: 0.004166667 degree (~500 m)</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Data format: GeoTIFF</p>
Machine learning prediction of enzyme optimal pH
<p>- Enzyme catalytic optimum pH dataset (pHopt, 9855 proteins)</p> <p>- Secreted bacterial optimum environment pH dataset (pHenv, 1.9 million proteins)</p> <p>- Model for predicting pHopt of enzymes (EpHod)</p> <p>- Code for using the EpHod model are in <a href="https://github.com/beckham-lab/EpHod">GitHub</a>. Paper in <a href="https://doi.org/10.1101/2023.06.22.544776">BioRxiv</a></p> <p> </p>
Data for: A comprehensive dataset of forest above-ground biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir Himalaya
<p>The repository contains observed Above Ground Biomass (AGB) estimates at about 275 sample plots chosen for AGB assessment in the forests of Kashmir Himalaya. The AGB is assessed as a fucntion of dbh using various allometric equations developed specifically for the region. It also contains the AGB for years 1978, 1990, 2000, 2010 and 2021 predicted using topographcally augmeneted multivariate regression model. The extent of forest, delineated using on-screen digitization using Landsat and Sentinel image collection at decadal scale is also provided for the years 1978, 1990, 2000, 2010 and 2021.</p>
Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.
<p>ML_AB_H: Datasets of 64 water molecules and a single proton with 64 water molecules.</p> <p>ML_AB_VP2: Datasets of a single V^2+ ion with 64 water molecules.</p> <p>ML_AB_VP3: Datasets of a single V^3+ ion with 64 water molecules.</p> <p>ML_AB_FeP2: Datasets of a single Fe^2+ ion with 64 water molecules.</p> <p>ML_AB_FeP3: Datasets of a single Fe^3+ ion with 64 water molecules.</p> <p>ML_AB_CuP1: Datasets of a single Cu^+ ion with 64 water molecules.</p> <p>ML_AB_CuP2: Datasets of a single Cu^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP2: Datasets of a single Ru^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP3: Datasets of a single Ru^3+ ion with 64 water molecules.</p> <p>ML_AB_AgP1: Datasets of a single Ag^+ ion with 64 water molecules.</p> <p>ML_AB_AgP2: Datasets of a single Ag^2+ ion with 64 water molecules.</p> <p>ML_AB_O2: Datasets of a single O2 ion with 64 water molecules.</p> <p>ML_AB_O2N1: Datasets of a single O2^- ion with 64 water molecules.</p> <p>ML_AB_water: Datasets of 64 water molecules presenting bulk water and 64 water molecules representing water slab.</p> <p>All datasets were generated by VASP using PAW, plane wave basis sets with cutoff energy of 520 eV and RPBE+D3 exchange-correlation functional with zero-damping. All ab initio calculations were done on extended systems with periodic boundary conditions. See details in <a href="https://doi.org/10.48550/arXiv.2409.11000">https://doi.org/10.48550/arXiv.2409.11000</a>.</p>
An improved long-term high-resolution surface pCO2 data product for the Indian Ocean using machine learning
<p>This dataset contains two improved surface pCO2 products, along with surface pCO2 from the INCOIS-BIO-ROMS model (pCO2_model) and other input variables. It is a long-term, high-resolution dataset developed for the Indian Ocean region (30°E - 120°E, 30°S - 30°N), covering the period from 1980 to 2019. The dataset features a monthly temporal resolution and a spatial resolution of 1/12 degree.</p> <p> The file includes INCOIS-BIO-ROMS model outputs (sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), nitrate (NO3), dissolved inorganic carbon (DIC), and chlorophyll-a (CHL)). These variables are used as inputs for machine learning models to improve the pCO2_model. The machine learning model predicts the surface pCO2 deviants (pCO2_obs - pCO2_model). The file also provides spatiotemporally varying uncertainties associated with the predicted pCO2 deviants.</p> <p><strong>**Users are advised to download Version v2 of the data product, as Version v1 has been deprecated and is no longer recommended for use.</strong></p>
Modeling surface pCO2 variability in two contrasting basins of North Indian Ocean using advanced machine learning algorithms
<p>The dataset contains surface ocean <em>p</em>CO2, uncertainty and air-sea CO2 flux for the North Indian Ocean region. The data is available from 1993 to 2020 on a monthly time scale. Each of these data has a spatial resolution of 1/12º. Air-sea CO2 flux is calculated using a bulk parameterization, which is a function of wind speed. A positive CO2 flux value signifies CO2 outgassing, while a negative value indicates atmospheric CO2 uptake. </p> <p><strong>**It is recommended to use the latest version V3. Previous versions are depricated.</strong></p>
Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score
<h2>ML-GPS: Machine Learning-Assisted Genetic Priority Score</h2> <p>This Zenodo repository contains data and code associated with the publication:</p> <p>Chen R, Duffy Á, Petrazzini BO, Vy HM, Stein D, Mort M, Park JK, Schlessinger A, Itan Y, Cooper DN, Jordan DM, Rocheleau G, Do R. Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score. Nat Commun. 2024 Oct 15;15(1):8891. doi: <a href="https://doi.org/10.1038/s41467-024-53333-y">10.1038/s41467-024-53333-y</a>.</p> <h3>Important notes</h3> <ul> <li>You can interactively view the top 10% of ML-GPS predictions without download at <a href="https://rstudio-connect.hpc.mssm.edu/mlgps/">https://rstudio-connect.hpc.mssm.edu/mlgps/</a>.</li> <li>For running Jupyter notebooks, please follow the instructions in the README of the GitHub repository at <a href="https://github.com/robchiral/ML-GPS">https://github.com/robchiral/ML-GPS</a>.</li> </ul> <h3>Repository contents</h3> <p>Files needed to train ML-GPS and ML-GPS DOE:</p> <ul> <li><strong>Files needed for Jupyter notebooks.zip</strong>: Data files required for preprocessing and training.</li> <li><strong>Jupyter notebooks.zip</strong>: Notebooks for cleaning data, training models, and generating predictions.</li> </ul> <h3>Other files:</h3> <ul> <li><strong>Predictions for all gene-phecode pairs.zip</strong>: ML-GPS and ML-GPS DOE scores for all analyzed gene-phecode pairs.</li> <li><strong>Summary statistics.zip</strong>: Genetic association summary statistics for all tested gene-phecode pairs.</li> </ul> <h3>Updated performance metrics</h3> <table> <tbody> <tr> <td><strong>Model</strong></td> <td><strong>Open Targets AUPRC</strong></td> <td><strong>SIDER AUPRC</strong></td> </tr> <tr> <td>ML-GPS (non-DOE)</td> <td>0.074</td> <td>0.080</td> </tr> <tr> <td>ML-GPS DOE (activator predictions)</td> <td>0.029</td> <td>0.042</td> </tr> <tr> <td>ML-GPS DOE (inhibitor predictions)</td> <td>0.067</td> <td>0.064</td> </tr> </tbody> </table> <h3>Zenodo versions</h3> <ul> <li><strong>Version 4: </strong>Updated notebooks and external data to use Open Targets 2024.9; summary statistics are unchanged</li> <li><strong>Version 3: </strong>Corrected error where DOE for rare and ultrarare variants was incorrectly incorporated</li> <li><strong>Version 2: </strong>Original release accompanying the publication</li> </ul>
Machine Learning for predicting chaotic systems – Data
<p>The data used in our article "Machine Learning for Predicting Chaotic Systems" - <a href="https://arxiv.org/abs/2407.20158">https://arxiv.org/abs/2407.20158</a></p> <p>DeebDbDysts*.zip contain the Dysts database, DeebDbLorenz*.zip the DeebLorenz database (with DeebDbLorenzBig*.zip being the "extension" dataset for Lorenz63std with different time series lengths).</p> <p>The observation and truth data of the Dysts database originates from <a href="https://github.com/williamgilpin/dysts">https://github.com/williamgilpin/dysts</a> (we converted the data format from json to csv).</p> <p>For DeebLorenz, we used the R package <a href="https://github.com/chroetz/DEEBdata">DEEBdata</a> to create it.</p>
An Audit of Machine Learning Experiments on Software Defect Prediction - Dataset
<p><strong>ML_Audit_20250328_anon.csv</strong>:<br>This CSV file contains anonymized data used in the audit of machine learning experiments on software defect prediction. The dataset includes variables and performance metrics extracted from studies published between 2019 and 2023. It supports the audit's evaluation of study reproducibility and issues related to experimental design and statistical analysis. This data can be used for replication and further analysis of the trends and reproducibility issues identified in the paper.</p> <p><strong>ML_Audit_March2025.Rmd</strong>:<br>This RMarkdown file contains the analysis script used for the statistical analysis and audit of the machine learning experiments reviewed in the study. It includes the procedures for data preprocessing, statistical evaluations, and reproducibility assessments. The script is integral for replicating the audit results presented in the paper and can be used by other researchers to perform similar audits or extend the analysis on different datasets.</p>
Dataset for: Anniés et al., "Accessing structural, electronic, transport and mesoscale properties of Li-GICs via a complete DFTB-model with machine-learned repulsion potential"
<p>GPrep training data, GPrep jupyter notebook, .skf files.</p> <p>The GPrep code is available at https://doi.org/10.5281/zenodo.3697913</p>
Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes
<p>This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.</p>
Biologically informed machine learning for identifying human placental cellular heterogeneity and preeclampsia discovery
<p>Single-cell transcripts of 20,518 placental cells and identified 12 major placental cell clusters were collected from the European Bioinformatics Institute (EBI; accession no. EGAS00001002449) (29). Based on the same parameters, we clustered and visualized highly similar cells using t-distribution random neighbourhood embedding (T-SNE) to identify 17 cell subpopulations. we selected nine placental cell clusters that have received more attention from biologists for our study according to the literature survey (Table 1). Considering the sample balance, 7178 single-cell transcriptome data were used to identify human placental cell subpopulations. The samples were randomly divided into a 4809-sample training set and a 2369-sample testing set. The same strategy was applied to split single-cell transcriptomic datasets from healthy and preeclampsia patients (EBI; accession no. EGAS00001002449), with 9852 samples (healthy 4705, preeclampsia 5147) in the training set and 5305 samples (healthy 2473, preeclampsia 2832) in the independent test set</p>
Self-Directed Online Machine Learning for Topology Optimization
<p>Code and results of the paper "Self-Directed Online Machine Learning for Topology Optimization".</p> <p>See latest updates at https://github.com/deng-cy/deep_learning_topology_opt</p>
An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects - Dataset
<p>This repository contains the dataset of the manuscript:</p> <p>"An Empirical Study on the Usage and Availability of Machine Learning Libraries in Open-Source Python Projects"</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.