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Dataset results
285 results for “Machine learning dataset”
Dataset de entrenamiento para un Modelo tipológico de juguete para clasificar fragmentos cerámicos basado en Machine Learning
<p>Este dataset contiene información sobre 577 fragmentos de cerámica arqueológica de Colombia usada para entrenar un modelo tipológico de juguete basado en aprendizaje de máquinas</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>
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>
A Comparative Analysis of Machine Learning Approaches to Gap Filling Meteorological Datasets (Results Only)
<p>This dataset contains the results from our evaluation of methodologies for filling gaps in meterological data. Variables were chosen to represent a standard set of measurements for purposes typically performed using Weather Stations installed in urban and rural areas. There are 4 dimensions by which we measure and validate each of the gap filling models across a large set of experimental configurations: meteorological variables <strong>TargetVar </strong>(dewpoint, humidity, leaf wetness, temp); <strong>feature_set</strong> (AWS, AWS-ERA5, ERA5, ERA5_Debias, Spatial); 3 types of machine learning algorithms <strong>ML</strong> (linear regression, random forests, LightGBM) combined with 2 non-ML algorithms; and <strong>gap_length</strong>: 1, 4, 36 and 288. A total of 1,720 experiments were conducted: 1,440 machine learning experiments; 160 using a spatial algorithm and 120 experiments using ERA5. For the 3 machine learning experiments, the average result was selected for each of 10 sites for 4 gap sizes (40 results) with the 3 ML models using 3 different feature sets (120 results).</p> <p>Data is provided in both CSV format and as a MySQL dump.</p> <p>Resultsets are accompanied with the SQL expression used to generate the result.</p>
Manuscript dataset - Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia
<p>This is a dataset associated with submission of the manuscript "Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia"</p>
Result dataset for the paper "Determining Research Priorities Using Machine Learning"
<p>The dataset needed by the paper software to run the notebooks.</p>
Input Dataset for "Determining Reseach Priorities Using Machine Learning" Paper
<p>Input dataset. Used by LDA and other scripts to create the result dataset. Journal data harvested from ADS in 2020 (so it will be different if again harvested in present day given that new papers are being added for various years by ADS staff).</p>
B3DB Dataset adapted for A Transparent Machine Learning Model To Understand Drugs Permeability Through the Blood Brain Barrier
<p>The B3DB dataset which we adapted for use for our paper "A Transparent Machine Learning Model To Understand Drugs Permeability Through the Blood Brain Barrier"</p>
p-IgGen Dataset: Cleaned paired and unpaired antibody sequence data for machine learning applications.
<p>This data is released alongside "p-IgGen: A Paired Antibody Generative Language Model", which contains full details on the data processing and cleaning.</p> <p>p-IgGen Paper: https://www.biorxiv.org/content/10.1101/2024.08.06.606780v1 .</p> <p>OAS: https://opig.stats.ox.ac.uk/webapps/oas/</p> <p> </p>
Datasets for "Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning"
<p>This upload contains the datasets used for the experiments in:</p> <p>Accurate and efficient structure elucidation from routine one-dimensional NMR spectra using multitask machine learning</p> <p>Frank Hu, Michael S. Chen, Grant M. Rotskoff, Matthew W. Kanan, and Thomas E. Markland</p> <p>https://arxiv.org/abs/2408.08284</p> <p> </p> <p>For file descriptions and usage, please refer to the supplied README.md file.</p> <p> </p> <p> </p>
ALPHA-g Preprocessed Simulation Dataset for Machine Learning
<p>Dataset generated for "Vertex Reconstruction with Deep Learning for ALPHA-g Radial Time Projection Chamber" 2024 preprint and "AI Meets Antimatter: Unveiling Antihydrogen Annihilations" paper for Machine Learning and the Physical Sciences workshop at NeurIPS'24 by Ferreira et al.</p> <p>The goal of this project was to create a PointNet-like deep learning model that could learn the relationship between simulated ALPHA-g detector data and the position of an antimatter annihilation on the walls of the ALPHA-g vacuum chamber. If this approach to antimatter annihilation event reconstruction works with real-world data then it can then be used as part of a larger analysis pipeline to better constrain the effect of gravity on antimatter.</p> <p>See the above papers for a much more detailed description of the experiment and data used but generally, the four important terms to know for interacting with this dataset are:</p> <table> <tbody> <tr> <td><strong>Term</strong></td> <td><strong>Meaning</strong></td> </tr> <tr> <td>Event</td> <td>An antimatter annihilation. Each event contains a number of spacepoints and one vertex.</td> </tr> <tr> <td>Spacepoints</td> <td>The many 3D points (x,y,z) that represent the ALPHA-g detector data, if more than 800 this dataset cuts them off at 800, prioritizing the ones with higher amplitudes, and if less than 800 it pads them with zeros such that all events have 800 entries.</td> </tr> <tr> <td>Vertex</td> <td>3D position of the antimatter annihilation. For this initial study, we just use the <em>z</em> coordinate since this is what is most important to measuring the effect of gravity. </td> </tr> <tr> <td>Helix Fit</td> <td>The state-of-the-art method currently used for antimatter annihilation event reconstruction within the ALPHA-g detector. The method doesn't use machine learning and instead involves fitting 3D helix functions to the spacepoints.</td> </tr> </tbody> </table> <p> </p> <p>Data is split into three separate HDF5 datasets: training (2,160,020 events), validation (261,654 events), and testing (288,919 events). Each dataset contains the following columns with each row representing one event:</p> <table> <tbody> <tr> <td><strong>Column Index</strong></td> <td><strong>Value [mm]</strong></td> </tr> <tr> <td>0</td> <td><em>x</em> coordinates of spacepoints</td> </tr> <tr> <td>1</td> <td><em>y</em> coordinates of spacepoints</td> </tr> <tr> <td>2</td> <td><em>z</em> coordinates of spacepoints</td> </tr> <tr> <td>3</td> <td> <p><em>z</em> coordinate of simulated vertex</p> </td> </tr> <tr> <td>4</td> <td> <p><em>z</em> coordinate of Helix Fit vertex prediction</p> </td> </tr> </tbody> </table> <p> </p> <p>The code used to generate the raw dataset is available at <code><a href="bitbucket.org/expalpha/alphasoft">bitbucket.org/expalpha/alphasoft</a></code> and the code used to process it into this preprocessed dataset as well as the downstream training and evaluation are available at <code><a href="gitlab.triumf.ca/alpha-ai/rTPC-AI">gitlab.triumf.ca/alpha-ai/rTPC-AI</a></code>.</p>
Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations: Preprocessed Satellite and In-situ observation datasets
<p>This record includes all of the prepared data used in the manuscript, "Probabilistic Machine Learning Estimation of Ocean Mixed Layer Depth from Dense Satellite and Sparse In-Situ Observations" (citation information forthcoming). As a part of this manuscript, we analyzed the ability for machine learning models to extract sea surface information (from salinity, temperature, sea height anomaly) to predict mixed layer depth. In this manuscript there are two experimental datasets: (1) info derived from CESM POP2 ocean model dataset (1989-1998), and (2) info derived from a combination of satellite sources and MLD from Argo profiles. More details below. </p> <p>All of these data files are preprocessed and organized to be used with the ml-ocean-bl github code found at https://github.com/NCAR/ml-ocean-bl/mloceanbl/.</p> <ul> <li><strong>CESM POP2 Ocean model dataset</strong></li> </ul> <p>Preprocessed sea surface salinity (SSS), temperature (SST), sea surface height anomalies (SSH), and ocean mixed layer depth (MLD, or HMXL) derived from the CESM POP2 Ocean model. Specifically, CESM POP2 model in a hindcast forced by JRA55do atmospheric reanalysis from 1958 to present and initialized with an oceanic climatology as in e.g. <a href="https://journals.ametsoc.org/view/journals/phoc/aop/JPO-D-20-0217.1/JPO-D-20-0217.1.xml">Deppenmeier et al. (2021)</a>. The model outputs include the ocean mixed layer depth (MLD), sea surface salinity (SSS), sea surface temperature (SST), and sea height anomaly (SSH) at a temporal frequency of 5-days and an approximate latitude and longitude resolution of 0.1 degrees.</p> <p>Relevant files:</p> <ol> <li>full_EPO.nc, full_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, MLD for the equatorial Pacific Ocean (EPO) and southern Indian Ocean (SIO) (see manuscript for details). Data is regridded onto a 1/2 degree lat/lon 5 day grid to correspond with data used for Argo datasets (see below).</li> </ul> </li> <li>clim_EPO.nc, clim_SIO.nc, clim_std_EPO.nc, std_clim_EPO.nc, std_clim_SIO.nc <ul> <li>NetCDF4 containing mean and standard deviation climatologies of SSS, SST, SSH, and MLD for EPO and SIO.</li> </ul> </li> <li>std_anomalies_EPO.nc, std_anomalies_SIO.nc <ul> <li>NetCDF4 containing SSS, SST, SSH, and MLD standardized anomalies for EPO and SIO. This is the dataset directly used for training in aforementioned manuscript. Use with ml-ocean-bl/ml-ocean-test/data. </li> </ul> </li> </ol> <ul> <li><strong>Satellite and Argo datasets</strong></li> </ul> <p>Preprocessed satellite sea surface salinity (SSS), temperature (SST), and sea surface height anomalies (SSH) and Argo-based mixed layer depth (MLD) profiles. Original data can be found at:</p> <p>(SST): Remote Sensing Systems. 2017. MW optimum interpolated SST data set. Ver. 5.0. PO.DAAC, CA, USA. Further information available at at <a href="https://doi.org/10.5067/GHMWO-4FR05">https://doi.org/10.5067/GHMWO-4FR05</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/allData/ghrsst/data/GDS2/L4/GLOB/REMSS/mw_OI/v5.0/.</p> <p>(SSS): Oleg Melnichenko. 2018. Aquarius L4 Optimally Interpolated Sea Surface Salinity. Ver. 5.0. PO.DAAC, CA, USA. Further information at <a href="https://doi.org/10.5067/AQR50-4U7CS">https://doi.org/10.5067/AQR50-4U7CS</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SalinityDensity/aquarius/L4/IPRC/v5/7day. </p> <p>(SSH): Zlotnicki, Victor; Qu, Zheng; Willis, Joshua. 2019. SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL1609. Ver. 1812. PO.DAAC, CA, USA. Information available at <a href="https://doi.org/10.5067/SLREF-CDRV2">https://doi.org/10.5067/SLREF-CDRV2</a>. Data can be accessed at https://podaac-tools.jpl.nasa.gov/drive/files/SeaSurfaceTopography/merged_alt/L4/cdr_grid</p> <p>(MLD) Argo-based ocean surface mixed layer depths using the buoyancy gradient definition of Whitt Nicholson and Carranza (2019) processed dataset available at https://doi.org/10.5281/zenodo.4291175.</p> <p>Relevant files:</p> <ol> <li>https://github.com/NCAR/ml-ocean-bl/mloceanbl/preprocess_mld.py and .../preprocess_sss_sst_ssh.py. <ul> <li>Preprocessing code</li> </ul> </li> <li>sss_sst_ssh_anomalies.nc. <ul> <li>Regridded and resampled SSS, SST, SSH onto a 1/2 degree lat/lon 7day grid. Contains preprocessed seasonal data along with anomalies.</li> </ul> </li> <li> mldb_climatology_climatologystd_binned.nc <ul> <li>Smoothed argo-based mixed layer depths are used to calculate climatologies and standardized climatologies. 4 degree lat/lon gridded climatologies.</li> </ul> </li> <li>mldb_full_anomalies_stdanomalies_climatology_stdclimatology.nc <ul> <li>Contains the Argo profile-derived MLD, anomalies, standard anomalies, climatologies, and standardized climatologies with corresponding argo locations, times, and corresponding weeks. </li> </ul> </li> <li>equatorial_pacific_model_oi_re.nc, southern_indian_model_oi_re.nc <ul> <li>Model outputs for the Equatorial Pacific Ocean and Southern Indian Ocean. These gridded files contain the model outputs (vlcnn, vlcnn variance, OI, OI variance, reanalysis, and reanalysis variance - see manuscript for nomenclature details) at each of the 200 weeks available. It should be noted that, in the equatorial Pacific Ocean, the lat/lon location of (-138.75, -9.75) is masked during the training and filled with a NaN in the .nc files. </li> </ul> </li> </ol> <p> </p> <p> </p> <p>Contact D. Foster with any questions.</p> <p> </p>
Dataset for the publication of "WRF model parameter calibration to improve the prediction of tropicalcyclones over the Bay of Bengal using Machine Learning-basedMultiobjective Optimization"
<p>The dataset consists of the modified WRF model software, that can be extracted and used in any Linux system with preinstalled required software.</p> <p>The namelists_file.zip consists of the namelist.input files that are used for the default and calibration simulations with different driving data namely, FNL files at 1deg with two nested domains, ERA files at 1deg with two nested domains, ERA files at 0.25deg with a single domain, and the ERA files at 0.25deg with two nested domains.</p>
Dataset for "Comparative Analysis of Machine Learning Models to Forecast Flaring Capability of Solar Active Regions: A Parameter Based Approach"
<p>This CSV file contains the values of 14 selected magnetic features along with the active region class for all the regions used in our study. As discussed in the paper, these 14 magnetic features characterize the properties of active regions. All these magnetic features are obtained from the HMI SHARP data series which provides open-sourced vector magnetic field information of solar active regions. the column named 'AR_class' carries information about the class of active regions i.e., 1 for flaring regions and 0 for non-flaring regions.</p>
Generating a Labeled Dataset to Train Machine Learning Algorithms for Lithological Classification of Drill Cuttings
<p>This dataset contains 16,700 fully labeled SEM images of rock chips isolated from 14 thin sections of drill cutting samples. These samples come from a low-permeability reservoir in western Canada.</p>
Machine Learning for Bird Song Learning (ML4BL) dataset
<p><strong>General description</strong></p> <p>This dataset contains Zebra Finch decisions about perceptual similarity on song units. All the data and files are used for reproducing the results of the paper 'Bird song comparison using deep learning trained from avian perceptual judgments' by the same authors. </p> <p><strong>Git repo on Zenodo:</strong> <a href="https://doi.org/10.5281/zenodo.5545932">https://doi.org/10.5281/zenodo.5545932</a><br> <strong>Git repo access: </strong><a href="https://github.com/veronicamorfi/ml4bl/tree/v1.0.0">https://github.com/veronicamorfi/ml4bl/tree/v1.0.0</a></p> <p><strong>Directory organisation:</strong><br> ML4BL_ZF<br> |_files<br> |_Final_probes_20200816.csv - all trials and decisions of the birds (aviary 1 cycle 1 data are removed from experiments)<br> |_luscinia_triplets_filtered.csv - triplets to use for training<br> |_mean_std_luscinia_pretraining.pckl - mean and std of luscinia triplets used for trianing<br> |_*_cons_* - % side consistency on triplets (train/test) - train set contains both train and val splits<br> |_*_gt_* - cycle accuracy for triplets of the specific bird (train/test) - train set contains both train and val splits<br> |_*_trials_* - number of decisions made for a triplet (train/test) - train set contains both train and val splits<br> |_*_triplets_* - triplet information (aviary_cycle-acc_birdID, POS, NEG, ANC) (train/test) - train set contains both train and val splits<br> |_*_low*_ - low-margin (ambiguous) triplets (train/val/test)<br> |_*_high_ - high-margin (unambiguous) triplets (train/val/test)<br> |_*_cycle_bird_keys_* - unique aviary_cycle-acc_birdID keys (train/test) - train set contains both train and val splits<br> |_TunedLusciniaV1e.csv - pairwise distance of two recordings computed by Luscinia<br> |_training_setup_1_ordered_acc_single_cons_50_70_trials.pckl - dictionary containing everything needed for training the model (keys: 'train_keys', 'train_triplets', 'val_keys', 'vali_triplets', 'test_triplets', 'test_keys', 'train_mean', 'train_std')<br> |_melspecs - *.pckl - melspectrograms of recordings<br> |_wavs - *wav - recordings<br> |_README.txt</p> <p><strong>Recordings</strong></p> <p>887 syllables extracted from zebra finch song recordings, with a sampling rate of 48kHz and high pass filtered (100Hz), with a 20ms intro/outro fade. </p> <p><strong>Decisions</strong></p> <p>Triplets were created from the recordings and the birds made side based decisions about their similarity (see 'Bird song comparison using deep learning trained from avian perceptual judgments' for further information).</p> <p><strong>Training dictionary Information</strong></p> <p>Dictionary keys:<br> 'train_keys', 'train_triplets', 'val_keys', 'vali_triplets', 'test_triplets', 'test_keys', 'train_mean', 'train_std'</p> <p>train_triplets/vali_triplets/test_triplets: <br> Aviary_Cycle_birdID, POS, NEG, ANC, Decisions, Cycle_ACC(%), Consistency(%)<br> <br> train_keys/val_keys/test_keys:<br> Aviary_Cycle_birdID</p> <p>train_mean/train_std:<br> shape: (1, mel_bins)</p> <p> </p> <p><strong>Open Access</strong></p> <p>This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><br> <strong>Contact info</strong></p> <p>Please send any questions about the recordings to:<br> Lies Zandberg: Elisabeth.Zandberg@rhul.ac.uk</p> <p>Please send any feedback or questions about the code and the rest of the data to:<br> Veronica Morfi: g.v.morfi@qmul.ac.uk</p>
OUTCAR dataset for machine learning potential about a h-BN growth on Pt(111) surface
<p>The growth of monolayer h-BN from boron and nitrogen atoms on Pt(111) is investigated using molecular dynamics combined with machine-learning potentials trained based on first-principles data. The MD simulation can be performed to investigate the h-BN growth on the Pt(111) surface. The training dataset and machine learning potential have been made by the active learning method [1].</p> <p> </p> <p>[1] L. Zhang, D.-Y. Lin, H. Wang, R. Car, E. Weinan, Active learning of uniformly accurate interatomic potentials for materials simulation, Physical Review Materials 3 (2019) 023804.</p>
cropped diBas dataset for machine learning
<p>This zip file contains a collection of 244 x 244 images of various gram-stained bacteria strains, cropped from the diBas dataset (Bartosz Zieli ́nski et al. 2017). Each folder is named by the species of the bacteria images it contains. </p> <p>Potential uses for the dataset include machine learning. An example neural network for bacteria classification using this dataset is deployed at <a href="https://huggingface.co/spaces/qile0317/Bacteria-Classification">https://huggingface.co/spaces/qile0317/Bacteria-Classification</a></p> <p>Note that the data is not completely optimized, as it contains trace amounts of almost blank images. </p>
Identifying serpentine minerals by their chemical compositions with machine learning (dataset and python code)
<p>The dataset and python code for the manuscript of Identifying serpentine minerals by their chemical compositions with machine learning (submitted to American Geologist)</p>
Curated Dataset of Association Constants Between a Cyclodextrin and a Guest for Machine Learning: Raw Data and Generation Script
<p>Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices. It is necessary to make them available in a computer friendly format and to curate them. Furthermore, the raw data need to be enriched with physicochemical information about each molecule and when such information does not allow to discriminate molecules, some additional data is needed. We present a dataset built from data gathered from the literature. The dataset contains both the original raw data from the articles and the enriched ones. We also provide the scripts used to curate and enrich the raw data.</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.