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285 results for “machine learning dataset”
Atmospheric rivers dataset for machine learning training
<p>A thorough description of the data and how it was created can be found: <a title="http://climate-cms.org/CNN-Atmospheric-Rivers/" href="http://climate-cms.org/CNN-Atmospheric-Rivers/" target="_blank" rel="noopener">http://climate-cms.org/CNN-Atmospheric-Rivers/</a></p> <p>A Jupyter notebook has also been created where we'll show you how to use this data to train a deep learning model to identify whether an Integrated Vapor Transport map contains an atmosperhic river. it can be found: <a title="CNN_AR_tutorial.ipynb" href="https://github.com/coecms/CNN-Atmospheric-Rivers/blob/main/CNN_AR_tutorial.ipynb" target="_blank" rel="noopener">CNN_AR_tutorial.ipynb</a> and had been published here: </p> <p>Mesto, M., Hobeichi, S., & Green, S. (2024). CNN-Atmospheric-Rivers (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.12538779" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12538779</a></p> <p>The data is organised in three folders:</p> <ul> <li>IVT_ERA5_2Deg: Contains global IVT data.</li> <li>AR_Global: Contains polygons representing AR objects identified and hand-labelled in each IVT map.</li> <li>Training_Testing_tiles: Contains tiles of IVT data with an annotation file that classifies each tile as one of the following: ‘Atmospheric River’, ‘Ambiguous’. The ‘Ambiguous’ class refers to objects that are not clearly identifiable as atmospheric rivers.</li> </ul> <p><strong>Integrated Vapor Transport maps:</strong></p> <p>These maps were computed using the magnitude of the vertical integral of northward and eastward water vapour flux variables from ERA5. The IVT values are expressed in units of kg m^-1 s^-1. All the IVT TIFF files were loaded into ArcGIS software and displayed using a colour scheme that allows for the visual identification of atmospheric rivers. Data details:</p> <ul> <li>Folder: <strong><em>IVT_ERA5_2Deg</em></strong></li> <li>File format: TIFF</li> <li>Spatial resolution: 2 degrees</li> <li>Spatial coverage: Global (longitude: -180 to 180 , latitude: -90 to 90)</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1<sup>st</sup> – 5<sup>th</sup> day of January, April, July, October for 2010, 2013, 2015; these years correspond to La Niña, neutral, and El Niño year respectively</li> <li>Temporal resolution: Daily</li> <li>Naming of files: ivt_2deg_ddmmyyyy.tif</li> <li>Number of files: 60 (5 days × 4 months × 3 years)</li> <li>Number of channels in each file: 1</li> </ul> <p> </p> <p><strong>Atmospheric Rivers in IVT maps:</strong></p> <p>The annotation tool ‘Label Objects for Deep Learning’ was used to draw polygons to cover the shape of atmospheric rivers on each IVT map. Each polygon was assigned one of two labels: 'Atmospheric Rivers' or 'Ambiguous'. The polygons were drawn based on visual identification of the shape of atmospheric rivers, guided by IVT values close to 500kg m^-1 s^-1 as in Reid et al (2020). The 'Ambiguous' label was assigned to objects that were unclear in their classification as ARs. This ambiguity arose from objects that were shorter, wider, had slightly lower IVT values, or it was hard to tell if they were ARs of tropical cyclones during the early stages of their formation. Data details:</p> <ul> <li>Folder: <strong><em>AR_Global</em></strong></li> <li>File format: SHP (shapefile)</li> <li>Spatial coverage: Global</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1<sup>st</sup> – 5<sup>th</sup> day of January, April, July, October for 2010, 2013, 2015 (corresponding to La Niña, neutral, and El Niño year respectively)</li> <li>Temporal resolution: Daily</li> <li>Naming of files: ivt_2deg_ddmmyyyy_labelled.shp</li> <li>Number of files: 60 (5 days × 4 months × 3 years)</li> </ul> <p> </p> <p><strong>Dataset for deep learning training:</strong></p> <p>The tool 'Export Training Data for Deep Learning' uses the IVT maps in the 'AR_Global' folder and the shapefiles in the 'IVT_ERA5_2Deg' folder to create labelled tiles for deep learning training. Each tile in the map is assigned a label: 'Atmospheric River', 'Ambiguous', or no label if it doesn’t contain any AR or ambiguous shape. The generated map chips are stored in folder <em>Training_Testing_tiles/ RCNN_Masks_All_Tiles (no 3-10-15)/images</em>, and the labels are provided in the textfile 'map.txt' file in folder Training_Testing_tiles/ RCNN_Masks_All_Tiles (no 3-10-15)/. Data details:</p> <ul> <li>Folder: <strong><em>Training_Testing_tiles/</em> RCNN_Masks_All_Tiles (no 3-10-15)/images</strong></li> <li>File format: TIFF</li> <li>Spatial resolution: 2 degrees</li> <li>Spatial coverage: varies. Width of tile = 40 gridcells. Height of tile = 20grid cells</li> <li>Geographic Coordinate System: GCS_WGS_1984</li> <li>Temporal coverage: 1<sup>st</sup> – 5<sup>th</sup> day of January, April, July, October for 2010, 2013, 2015. These years correspond to La Niña, neutral, and El Niño year respectively. Please note that data for certain days are missing; these omissions correspond to days with no or only a single atmospheric river detected.</li> <li>Temporal resolution: Daily</li> <li>Number of files: varies</li> <li>Number of channels in each file: 1</li> </ul>
Dataset for "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions"
<p>This dataset is a part of the paper "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions", accepted for publication in the Journal of Advances in Modeling Earth Systems (JAMES).</p> <h2>Contents</h2> <p>This dataset includes:</p> <ul> <li><strong>lhs-gen-190.csv</strong>: Training input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>lhs-gen-50-test.csv</strong>: Test input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>190-elm-samples.csv</strong>: Training input perturbed parameter samples for performing ELM simulations.</li> <li><strong>50-elm-test-samples.csv</strong>: Test input perturbed parameter samples for performing ELM simulations.</li> <li><strong>train_CH-CHA.csv</strong>: Contains the five ELM simulation output flux values for 240 samples (190 train + 50 test).</li> <li><strong>lhsgen.py</strong>: Script for generating Latin Hypercube Samples.</li> <li><strong>gpr-fit-new.py</strong>: Script for fitting Gaussian Process Regression (GPR) models.</li> <li><strong>sobol-new.py</strong>: Script for performing Sobol sensitivity analysis.</li> </ul> <h2>Usage</h2> <ol> <li><strong>lhsgen.py</strong>: <ul> <li>Use this script to generate the Latin Hypercube Samples for parameter sampling.</li> </ul> </li> <li><strong>gpr-fit-new.py</strong>: <ul> <li>This script fits GPR models using the training samples provided in <code>lhs-gen-190.csv</code>.</li> <li>It tests the models using the input testing samples in <code>lhs-gen-50-test.csv</code>.</li> <li>The fitted GPR models are stored as <code>.joblib</code> files in the <code>gpr_models</code> directory.</li> <li>Corresponding cross-validation and R-squared values are stored in <code>.xlsx</code> files.</li> </ul> </li> <li><strong>sobol-new.py</strong>: <ul> <li>This script performs Sobol sensitivity analysis using the fitted GPR models by reading the .joblib files.</li> <li>The Sobol indices are written to <code>.xlsx</code> files in the <code>results</code> directory.</li> </ul> </li> </ol>
Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia - Datasets
<p>Our research leverages advanced deep learning methods to classify cuneiform tablets by their historical periods, focusing on shape analysis rather than textual content. Utilizing a dataset of over 94,000 images from the Cuneiform Digital Library Initiative, we introduce a novel toolset powered by Variational Auto-Encoders (VAEs) to enhance model interpretability. By highlighting the predictive power of tablet silhouettes for historical period classification with a ResNet model reaching 61\% macro-F1 score,, our approach allows researchers to explore changes in tablet shapes across different eras. This methodology not only complements traditional archaeological methods but also enriches the field of document analysis and diplomatics, offering valuable tools for historians and epigraphists to understand ancient Mesopotamian cultures.</p> <p>The Datasets attached include:</p> <ul> <li>All CDLI IDs used and their URLs on CDLI</li> <li>All tablet silhouettes - Black and white representations</li> <li>A height-to-width ratio table, of the largest component extracted from the silhouettes.</li> <li>VAE encodings per tablet, extracted from the VAE model</li> </ul>
Bio-ML: Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
<p> </p> <blockquote> <p><strong>This version is used in the Bio-ML track of the OAEI 2024; the only change compared to the OAEI 2023 is the deletion of certain training subsumption mappings.</strong></p> </blockquote> <p> </p> <h3><strong>Overview</strong></h3> <p>The purpose of these datasets is to support <em>equivalence</em> and <em>subsumption</em> ontology matching.</p> <p>There are five ontology pairs extracted from MONDO and UMLS:</p> <table> <tbody> <tr> <td>Source</td> <td>Task</td> <td>Category</td> <td>#SrcCls</td> <td>#TgtCls</td> <td>#Ref (equiv)</td> <td>#Ref (subs)</td> </tr> <tr> <td>Mondo</td> <td>OMIM-ORDO</td> <td>Disease</td> <td>9,648</td> <td>9,275</td> <td>3,721</td> <td>103</td> </tr> <tr> <td>Mondo</td> <td>NCIT-DOID</td> <td>Disease</td> <td>15,762</td> <td>8,465</td> <td>4,686</td> <td>3,338 (-1)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-FMA</td> <td>Body</td> <td>34,418</td> <td>88,955</td> <td>7,256</td> <td>5,453 (-53)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-NCIT</td> <td>Pharm</td> <td>29,500</td> <td>22,136</td> <td>5,803</td> <td>4,224 (-1)</td> </tr> <tr> <td>UMLS</td> <td>SNOMED-NCIT</td> <td>Neoplas</td> <td>22,971</td> <td>20,247</td> <td>3,804</td> <td>213</td> </tr> </tbody> </table> <p>The "-" numbers reflect the changes due to lthe deletion of certain training subsumption mappings.</p> <p>The main track is available at "bio-ml", where each pair is associated with a task folder, containing the source and target ontologies, reference equivalence mappings (in "refs_equiv"), reference subsumption mappings ("refs_subs"). </p> <p>The special sub-track is available at "bio-llm", where each pair is associated with a task folder, containing the source and target ontologies, and the test candidate mappings. </p> <p> </p> <h3><strong>Citation</strong></h3> <p><strong>Bio-ML (Main Track)</strong></p> <pre>```<br>@inproceedings{he2022machine, title={Machine learning-friendly biomedical datasets for equivalence and subsumption ontology matching}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Jim{\'e}nez-Ruiz, Ernesto and Hadian, Ali and Horrocks, Ian}, booktitle={International Semantic Web Conference}, pages={575--591}, year={2022}, organization={Springer} }<br>```</pre> <p><strong>Bio-LLM (Sub-track)</strong></p> <pre>```<br>@article{he2023exploring, title={Exploring large language models for ontology alignment}, author={He, Yuan and Chen, Jiaoyan and Dong, Hang and Horrocks, Ian}, journal={arXiv preprint arXiv:2309.07172}, year={2023} }<br>```</pre> <p> </p> <h3><strong>Important Links</strong></h3> <ul> <li>See detailed documentation at: <a href="https://krr-oxford.github.io/DeepOnto/bio-ml">https://krr-oxford.github.io/DeepOnto/bio-ml</a>.</li> <li>See the OAEI Bio-ML track at: <a href="https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/">https://www.cs.ox.ac.uk/isg/projects/ConCur/oaei/</a></li> <li>See our resource paper for the original Bio-ML at <a href="https://arxiv.org/abs/2205.03447">arxiv</a> or <a href="https://link.springer.com/chapter/10.1007/978-3-031-19433-7_33">springer</a> (accepted at <em>ISWC-2022</em> and nominated as the <em>best resource paper candidate</em>). See our poster paper for the Bio-LLM sub-track at <a href="https://arxiv.org/abs/2309.07172">arxiv </a>(accepted at <em>ISWC-2023 Posters & Demos</em>).</li> </ul> <p> </p> <h3><strong>Changelog</strong></h3> <p>The only change in this version compared to the OAEI 2023 is the deletion of certain training subsumption mappings that can be directly exploited through deductive reasoning.</p>
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
Initial dataset used in SIMON, an automated machine learning approach
<p>The 7-Zip file contains raw data in the CSV file downloaded from Stanford Data Miner and used for further analysis using mulset algorithm and SIMON, as described in the publication:</p> <p>Tomic A, Tomic I, Rosenberg-Hasson Y, Dekker CL, Maecker HT, and Davis MM. SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses. <em>JImmunol</em>, doi: 10.4049/jimmunol.1900033, 2019.</p> <p>File was compressed using 7-Zip available at https://www.7-zip.org/.</p>
EELS dataset used for machine learning in github.com/trygvrad/RNN-on-EELS-data
<p>EELS dataset used for machine learning.</p> <p>The dataset shows a GaN substrate with an AlGaN film and SiNX top layer</p> <p>The data is courtesy of Andrew Lang and Mitra Taheri, and was uploaded by Trygve M. Ræder</p> <p>Code available at doi:10.5281/zenodo.2580160<br> or github.com/trygvrad/RNN-on-EELS-data<br> Data available at doi:10.5281/zenodo.2580185<br> Presentation available at doi:10.5281/zenodo.2580206</p>
DeepThought DPR: Distributed peer review enhanced with natural language processing and machine learning - Dataset I
<p>This is the anonymized dataset obtained from the DPR Experiment run at ESO in Fall 2018. If this dataset is used both this DOI as well as the main paper need to be cited. </p>
Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning
<p>The dataset titled <strong>"Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning</strong><strong>"</strong> was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.</p>
Datasets, trained models and supporting results for machine learning tensorial properties of atomic systems via XPaiNN model.
Open the record for dataset details and reuse information.
FireSafetyNet: An Image-Based Dataset with Pretrained Weights for Machine Learning-Driven Fire Safety Inspection
<p>This dataset offers a diverse collection of images curated to support the development of computer vision models for detecting and inspecting Fire Safety Equipment (FSE) and related components. Images were collected from a variety of public buildings in Germany, including university buildings, student dormitories, and shopping malls. The dataset consists of self-captured images using mobile cameras, providing a broad range of real-world scenarios for FSE detection.</p> <p>In the journal paper associated with these image datasets, the open-source dataset FireNet (Boehm et al. 2019) was additionally utilized for training. However, to comply with licensing and distribution regulations, images from <a href="https://www.firenet.xyz/">FireNet</a> have been excluded from this dataset. Interested users can visit the FireNet repository directly to access and download those images if additional data is required. The provided weights (.pt), however, are trained on the provided self-made images and FireNet using YOLOv8.</p> <p>The dataset is organized into six sub-datasets, each corresponding to a specific FSE-related machine learning service:</p> <ol> <li> <p><strong>Service 1: FSE Detection</strong> - This sub-dataset provides the foundation for FSE inspection, focusing on the detection of primary FSE components like fire blankets, fire extinguishers, manual call points, and smoke detectors.</p> </li> <li> <p><strong>Service 2: FSE Marking Detection</strong> - Building on the first service, this sub-dataset includes images and annotations for detecting FSE marking signs.</p> </li> <li> <p><strong>Service 3: Condition Check - Modal</strong> - This sub-dataset addresses the inspection of FSE condition in a modal manner, focusing on instances where fire extinguishers might be blocked or otherwise non-compliant. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>3_1_FSE Condition Check_modal_train_data (containing training images and annotations) </em>and <em>3_1_FSE Condition Check_modal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 4: Condition Check - Amodal</strong> - Extending the modal condition check, this sub-dataset involves amodal detection to identify and infer the state of FSE components even when they are partially obscured. This dataset includes semantic segmentation annotations of fire extinguishers. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>4_1_FSE Condition Check_amodal_train_data (containing training images and annotations) </em>and <em>4_1_FSE Condition Check_amodal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 5: Details Extraction - Inspection Tags</strong> - This sub-dataset provides a detailed examination of the inspection tags on fire extinguishers. It includes annotations for extracting semantic information such as the next maintenance date, contributing to a thorough evaluation of FSE maintenance practices.</p> </li> <li> <p><strong>Service 6: Details Extraction - Fire Classes Symbols</strong> - The final sub-dataset focuses on identifying fire class symbols on fire extinguishers.</p> </li> </ol> <p>This dataset is intended for researchers and practitioners in the field of computer vision, particularly those engaged in building safety and compliance initiatives.</p>
Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072
<p>Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072.</p> <p>VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs </p> <p>EC_LCG_bits.txt: bits from the linear congruential generator on elliptic curves</p> <p>LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits</p> <p>VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs</p> <p> </p>
GalaxiesML: an imaging and photometric dataset of galaxies for machine learning
<div># GalaxiesML README</div> <p> </p> <div>Version 6.1</div> <p> </p> <div>## Overview</div> <p> </p> <div>GalaxiesML is a machine learning-ready dataset of galaxy images, photometry, redshifts, and structural parameters. It is designed for machine learning applications in astrophysics, particularly for tasks such as redshift estimation and galaxy morphology classification. The dataset comprises **286,401 galaxy images** from the Hyper-Suprime-Cam (HSC) Survey PDR2 in five filters: g, r, i, z, y, with spectroscopically confirmed redshifts as ground truth.</div> <p> </p> <div>This dataset is particularly useful for developing machine learning models for upcoming large-scale surveys like **LSST** and **Euclid**.</div> <p> </p> <div>## Features</div> <p> </p> <div>- **286,401 galaxy images** in five photometric bands (g, r, i, z, y).</div> <div>- Spectroscopic redshifts for each galaxy, with redshift values ranging from **0.01 to 4**.</div> <div>- Morphological parameters derived from galaxy images, including **Sérsic index**, **half-light radius**, and **ellipticity**.</div> <div>- **Machine learning-friendly formats**: images are provided in **HDF5** format, along with CSV metadata.</div> <p><br><br></p> <div>## Examples of Using GalaxiesML</div> <p> </p> <div>Examples of uses of GalaxiesML are outlined in Do et al. (2024). The repository for example code are here:</div> <p> </p> <div><a href="https://github.com/astrodatalab/galaxiesml_examples">https://github.com/astrodatalab/galaxiesml_examples</a></div> <p> </p> <div>## Citation</div> <p> </p> <div>Please cite the following papers if you use this dataset in your work:</div> <p> </p> <div>1. **GalaxiesML Dataset**:</div> <div> <div> <div>Do, T. et al., *GalaxiesML: A Dataset of Galaxy Images, Photometry, Redshifts, and Structural Parameters for Machine Learning*. arXiv:2410.00271, <a href="https://arxiv.org/abs/2410.00271">https://arxiv.org/abs/2410.00271</a> (2024)</div> </div> </div> <p> </p> <div>2. **Hyper Suprime-Cam Subaru Strategic Program (HSC PDR2)**:</div> <div>- Aihara, H., et al., *Second Data Release of the Hyper Suprime-Cam Subaru Strategic Program*. Publications of the Astronomical Society of Japan, 71(6), 114 (2019). DOI: [10.1093/pasj/psz103](https://doi.org/10.1093/pasj/psz103)</div> <p> </p> <div>3. **Spectroscopic Surveys**:</div> <div>- Several publicly available spectroscopic redshift catalogs were used in creating this dataset. Notable sources include:</div> <div>- **zCOSMOS Survey**: Lilly, S. J., et al., *The zCOSMOS 10k-Bright Spectroscopic Sample*. The Astrophysical Journal Supplement Series, 184(2), 218-229 (2009). DOI: [10.1088/0067-0049/184/2/218](https://doi.org/10.1088/0067-0049/184/2/218)</div> <div>- **VIMOS Public Extragalactic Survey (VIPERS)**: Garilli, B., et al., *The VIMOS Public Extragalactic Survey (VIPERS): First Data Release of 57,204 Spectroscopic Measurements*. Astronomy & Astrophysics, 562, A23 (2014). DOI: [10.1051/0004-6361/201322790](https://doi.org/10.1051/0004-6361/201322790)</div> <div>- **DEEP2 Survey**: Newman, J. A., et al., *The DEEP2 Galaxy Redshift Survey: Design, Observations, Data Reduction, and Redshifts*. The Astrophysical Journal Supplement Series, 208(1), 5 (2013). DOI: [10.1088/0067-0049/208/1/5](https://doi.org/10.1088/0067-0049/208/1/5)</div> <p><br><br></p> <div>## How to Access</div> <p> </p> <div>The dataset is publicly available on **Zenodo** with the DOI: **[10.5281/zenodo.11117528](https://doi.org/10.5281/zenodo.11117528)**.</div> <p> </p> <div>## License</div> <p> </p> <div>This dataset is licensed under a **Creative Commons Attribution 4.0 International License (CC BY 4.0)**. You are free to share and adapt the dataset as long as appropriate credit is given. For more details, visit: **[CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/)**.</div> <p> </p> <div>Please cite the references mentioned above if you use this dataset in your work.</div>
A Dataset of the Operating Station Heat Rate for 806 Indian Coal Plant Units using Machine Learning
<div> <div> <div> <div> <p>India aims to achieve net-zero emissions by 2070 and has set an ambitious target of 500 GW of renewable power generation capacity by 2030. Coal plants currently contribute to more than 60% of India’s electricity generation in 2022. Upgrading and decarbonizing high-emission coal plants became a pressing energy issue. A key technical parameter for coal plants is the operating station heat rate (SHR), which represents the thermal efficiency of a coal plant. Yet, the operating SHR of Indian coal plants varies and is not comprehensively documented. This study extends from several existing databases and creates an SHR dataset for 806 Indian coal plant units using machine learning (ML), presenting the most comprehensive coverage to date. Additionally, it incorporates environmental factors such as water stress risk and coal prices as prediction features to improve accuracy. This dataset, easily downloadable from our visualization platform, could inform energy and environmental policies for India’s coal power generation as the country transitions towards its renewable energy targets.</p> </div> </div> </div> </div>
Four spatial prediction datasets of susceptibility to gully erosion, comparing machine learning models, in the Piraí drainage basin, southeastern Brazil
<p>The data in this repository refer to the article published in the journal Land, entitled: Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba do Sul Middle Valley, Southeast Brazil.</p>
CsSn(Cl/Br/I)3 Perovskite Alloy DFT Dataset for Machine Learning
<p>This upload contains density functional theory (DFT) calculations of CsSn(Cl/Br/I)3 perovskite alloy. The calculations were performed for a study, where the DFT data was used to train an energy predicting machine learning model for CsSn(Cl/Br/I)3. The code related to the study is available through GitLab (https://gitlab.com/cest-group/learnsolar-cssnclbri).</p> <p>The data is divided into four data sets. For each set, the atomic structure data with total energies and forces has been separated into an ASE (Atomic Simulation Environment) extended XYZ file. Additional information on the atomic structures (e.g. space groups) is provided in JSON format. The data sets are:</p> <p><strong>sp_train_set</strong><br>Single point DFT calculations of 16 000 algorithmically generated CsSn(Cl/Br/I)3 structures of four different space groups: Pm-3m, P4/mbm, I4/mcm, and Pnma. Lattice parameters and atomic positions are determined through Vegard's law, but random deviations have been added to the atom positions, tilting angles of the Sn coordination octahedra, cell volume, cell height-to-width ratio, and some lattice vector angles. Cl/Br/I configurations are randomized. This data set was used to fit an initial machine learning model. The atomic structures included were selected using a clustering algorithm to accelerate learning.</p> <p><strong>sp_test_set</strong><br>Single point DFT calculations of 2 600 atomic structures similar to sp_train_set. The Cl/Br/I compositions are uniformly represented, having two atomic structures per composition and space group. This data was used for testing the machine learning model. </p> <p><strong>al_data</strong><br>DFT relaxation structure snapshots from the active learning run that was performed to improve the machine learning model's structure relaxation accuracy. There are 4230 structure snapshots in total.</p> <p><strong>relax_test_set</strong><br>100 DFT relaxations used for testing the machine learning relaxation accuracy. There are 2881 structure snapshots in total. Both initial (relax_test_set_initial.xyz) and final (relax_test_set_relaxed.xyz) atomic geometries are included.</p>
Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
<p>This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript:</p> <p><strong>Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).</strong></p> <p><br> This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.</p>
Datasets for the manuscript "In silico proof of principle of machine learning-based antibody design at unconstrained scale"
<p>The zip file contains dataset files for the manuscript "In silico proof of principle of machine learning-based antibody design at unconstrained scale"</p>
Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets
<p>This dataset accompanies the paper titled</p> <p><em>Data Models for Dataset Drift Controls in Machine Learning with Images</em><br> <br> that appeared in the Transactions on Machine Learning Research<br> <br> <a href="https://openreview.net/forum?id=I4IkGmgFJz">https://openreview.net/forum?id=I4IkGmgFJz</a><br> </p> <pre><code>@article{ oala2023data, title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images}, author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2023}, url={https://openreview.net/forum?id=I4IkGmgFJz}, note={} }</code></pre> <p>We make available two datasets.</p> <p><strong>Raw-Microscopy:</strong></p> <ul> <li><strong>940 raw bright-field microscopy images</strong> of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels).</li> <li><strong>5,640 variations measured at six additional different intensities </strong>(microscopy/images/raw_scale001-raw_scale0075)</li> <li><strong>11,280 images of the raw sensor data processed through twelve different pipelines</strong> (microscopy/images/processed_views)</li> </ul> <p><strong>Raw-Drone:</strong></p> <ul> <li><strong>548 raw drone camera images for car segmentation</strong> (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472.</li> <li><strong>3,288 variations measured at six additional different intensities</strong> (drone/images_tiles_256/raw_scale001-raw_scale075).</li> <li><strong>6,576 images of the raw sensor data processed through twelve different pipelines</strong> (drone/images_tiles_256/processed_views).</li> </ul> <p>Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper.</p> <p>The code repository for this project can be found at <a href="https://github.com/aiaudit-org/raw2logit">https://github.com/aiaudit-org/raw2logit</a></p> <p> </p>
jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research
<p>Jazznet is a dataset of piano patterns for music audio machine learning research. The dataset comprises chords, arpeggios, scales, and chord progressions in all keys of an 88-key piano and in all the inversions, for a total of 162520 labeled piano patterns, resulting in 95GB of data and more than 26k hours of audio. The data is also accompanied by Python scripts to enable the easy generation of new piano patterns beyond those present in the dataset. The data is broken down into small, medium, and large subsets, comprising 21516, 30328, and 52360 patterns, respectively (with all the chords, arpeggios, and scales being present in all subsets). </p> <p>The GitHub page of the dataset, containing details of the dataset and scripts for generating new data is https://github.com/tosiron/jazznet.</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.