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285
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Dataset results
285 results for “Machine learning dataset”
Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1950-1974)
<p>The data contains simulation results from 1950-1974, 25 years total.</p> <p>You can access the remaining part of the dataset via Qingchen Xu and Lu Li (2025) using the following reference:<br>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (1975-1999) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671253</p> <p>Qingchen Xu, & Lu Li. (2025). Data for "A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022" (2000-2024) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15671254</p>
Dataset for "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena"
<p>This dataset was used to fit a general purpose machine-learning interatomic potential for Mo-Si alloys based on the Atomic Cluster Expansion (ACE) formalism. It supports the paper "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena".</p>
Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion
<p><span>This data includes the pre-processed dataset, along with a novel workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of machine learning and ionospheric physics science, as well as being useful to other researchers for diverse objectives. </span></p> <p><span><span>Acknowledgements:</span></span></p> <p><span><span>The WALDO database (<a href="https://waldo.world"><span>https://waldo.world</span></a>, accessed on October 1, 2023) provides VLF data. It is run collaboratively by the University of Colorado Denver and the Georgia Institute of Technology, utilizing data gathered from Stanford University and those two institutions. It has been made possible by numerous grants from the Department of Defense, NASA, and the NSF.<span> </span></span></span></p> <p> </p> <p> </p>
PPARδ dataset curated and enriched using the Enalos tools and Enalos KNIME nodes for machine learning analysis (SCENARIOS project)
<p><span>A curated and enriched dataset for PPAR</span>δ<span>, suitable for in silico model development, was obtained from PubChem BioAssay under the numeric identifier AID 469785 using Enalos tools and Enalos KNIME nodes. This dataset comprises 136 compounds that induce luciferase activity, serving as an indicator of agonist activity against the human PPAR</span>δ<span> ligand-binding domain. These molecules were tested in a human embryonic kidney cell line (293T), co-transfected with a chimeric plasmid containing the yeast GAL4 DNA-binding domain (DBD). All 136 oxazole-based compounds retrieved from the dataset are accompanied by their half-maximal effective concentration (EC50) and enriched with 777 molecular descriptors extracted from their 2D structure using EnalosMold2 KNIME nodes</span></p>
Tandem Mass Spectrometry Dataset for Machine Learning in Metabolomics
<p>This dataset contains tandem mass spectrometry data cleaned and processed from the publicly available GNPS Spectral Library. We aim to continuously update this dataset with new data points as the spectral libraries expand.</p>
RAM-SCB ROPE Dataset and Machine Learning Models with Code
<p>This repository contains the machine learning datasets and programs used in the development of the RAM-SCB ROPE.</p>
dataset for Machine Learning Surrogates for Surface Complexation Model of Uranium Sorption to Oxides
<p>The data sets are used to train, validate, and test SCM surrogates for uranium sorption on oxide surfaces. </p> <p>train_norm.csv, val_norm.csv, and test_norm.csv are used to train, validate, and evaluate DNN-SCM surrogate. </p> <p>test_set1.csv and test_set2.csv are two additional datasets to evaluate the model's performance. </p>
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>
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>
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>
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>
Messenger magentometer and coordinates dataset for Machine learning
<p>This dataset is based on the original MESSENGER mission magnetometer and coordinate data as made available at PDS PPI. It introduces a number of improvements upon the original [1 sec temporal resolution]:</p> <ol> <li>Magnetometer calibration signals have been removed.</li> <li>Coordinates and magnetic field measurements have been merged together.</li> <li>Additional fields, such as model dipole magnetic field and planetary position in a heliocentric coordinate system have been added.</li> <li>The dataset has been split up into files by orbit numbers, with each file centered on the periapsis.</li> <li>Data for a number of partially recorded orbits has been removed.</li> </ol>
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>
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>
Dataset: Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering
<p>This dataset contains all relevant data for the manuscript (in submission) "<em>Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering</em>".</p> <p>Code written to analyse this dataset (which may be adapted for other flow cytometry datasets) is found at https://zenodo.org/record/999747</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Naming convention for raw flow cytometry data files (located in /Script 3. Generating raw data subset/input/):</p> <p>[Allparameters] _ [Year] - [Month] - [Date] [Hour] [u] [Minute] _ [Depth]</p> <p>e.g: Allparameters_2014-07-31 08u08_1.0m</p> <p>The date, time and depth indicate the location and time at which the measurement was taken.</p>
SDO 2H Machine Learning Dataset
<p>This dataset provides a compact Machine Learning ready dataset of SDO EUV and HMI medium-resolution (1024x1024 pixels) images, for a total of 56,664 samples from May 14, 2010, to April 18, 2023, with a temporal cadence of 2 hours.</p> <p>EUV images are provided at the following wavelength : 1600A, 304A, 211A, 193A, 171A and 94A.<br>They are processed from the level 1.5 AIA-synoptic dataset (http://jsoc.stanford.edu/data/aia/synoptic/) and are successively:</p> <ul> <li>corrected for instrument degradation</li> <li>normalised by exposure time</li> <li>log-transformed (x->log(1+x)), symetrically on positive and negative values</li> <li>saturated to the 99.9 percentile maximum pixel value of the dataset, up to 2020*, for each channel</li> <li>linearly scaled between 0 and 255, converted to 8bit integers and compressed as jpegs</li> </ul> <p>The HMI's line-of-sight magnetograms (blos.zip) are retrieved from JSOC from the level 1.5 45-second line-of-sight serie and are successively :</p> <ul> <li>downscaled to 1024x1024 pixels</li> <li>standardized to a 2.4 arcec-to-pixel resolution (equal to the EUV images)</li> <li>aligned with the EUV images</li> <li>log-transformed (x->log(1+x))</li> <li>saturated to the 99.9 percentile maximum pixel value of the dataset, up to 2020* </li> <li>linearly scaled between 0 and 255, with 127 representig original null values, 0 and 255 respecivelly the negative and positive saturation value (approximately 4644G before log-transformation)</li> <li>converted to 8bit integers and compressed as jpegs</li> </ul> <p>Downscaled and cropped images (224x448 pixels) used in <a title="Francisco et al., 2023" href="https://doi.org/10.22541/essoar.170688972.24631782/v1" target="_blank" rel="noopener">Francisco et al., 2023</a> are aso provided in pcnn_images.zip</p> <p>An outlier study is also provided in anomalies.zip, from which '{wavelength}_anomalies_grades.csv' files can be used to exclude the dates where abnormal samples of a given type ('anomalies_grade_scale.txt') are identified.</p> <p>*The percentile values are computed on the pixels joint distribution using all sample from 2010 to 2019-12 included, so that the period starting from 2020-01 can be used as a completely independant test set.<br>Original exposure and instrument degradation corrected values can be retrieved using the saturation values provided belows. Althought the JPEG encoding results in the loss of small scale information, the dataset processing preserve the physical intensity of the original inputs so that the provided compressed images can efficiently be used to estimate large and medium-scale Active Regions physical features.</p> <table> <tbody> <tr> <td>HMI / BLOS</td> <td>±4644 G</td> </tr> <tr> <td>1600</td> <td>9,360 DN</td> </tr> <tr> <td>304</td> <td>44,488 DN</td> </tr> <tr> <td>171</td> <td>29,599 DN</td> </tr> <tr> <td>193</td> <td>81,139 DN</td> </tr> <tr> <td>211</td> <td>8,179 DN</td> </tr> <tr> <td>94</td> <td>6,099 DN</td> </tr> </tbody> </table> <p> </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.