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1,943 results for “machine learning”
Machine-learning convection model output file for use in convection pattern estimation.
<p>Output file from the model described in:</p> <p>Bristow, W. A., Topliff, C. A., & Cohen, M. B. (2022). Development of a high-latitude convection model by application of machine learning to SuperDARN observations. <em>Space Weather</em>, 20, e2021SW002920. <a href="https://doi.org/10.1029/2021SW002920">https://doi.org/10.1029/2021SW002920</a></p> <p>The file provides model convection patterns for the period 0000 UT to 2359 UT on 26 March 2014</p> <p> </p>
Data for EMO2023 Paper "Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective"
<p><strong>Data for Paper "Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective"</strong></p> <p><br> The file <strong>dbmopp_dataset_perf.csv</strong> contains results from the 945 x 30 instances, with the following columns:</p> <ul> <li><em>design_id</em>: problem identifier</li> <li><em>n_var</em>: number of variables {2, ..., 20}</li> <li><em>n_obj</em>: number of objectives {2, ..., 10}</li> <li><em>nonident_ps</em>: non-identical Pareto sets {0 (no), 1 (yes)}</li> <li><em>var_density</em>: varying density {0 (no), 1 (yes)}</li> <li><em>n_discon_ps</em>: number of disconnected Pareto sets {0, ..., 6}</li> <li><em>n_local_fronts</em>: number of local fronts {0, ..., 6}</li> <li><em>n_resist_regions</em>: number of dominance resistance regions {0, ..., 6}</li> <li><em>instance_id</em>: instance (fold) identifier {1, ..., 30}</li> <li><em>budget</em>: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}</li> <li><em>algo</em>: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}</li> <li><em>hypervolume</em>: hypervolume reached by the algorithm [0.0, 1.0]</li> </ul> <p> </p> <p>The file <strong>dbmopp_dataset_perf_aggregated.csv</strong> contains average results from the 945 problems, with the following columns:</p> <ul> <li><em>design_id</em>: problem identifier</li> <li><em>n_var</em>: number of variables {2, ..., 20}</li> <li><em>n_obj</em>: number of objectives {2, ..., 10}</li> <li><em>nonident_ps</em>: non-identical Pareto sets {0 (no), 1 (yes)}</li> <li><em>var_density</em>: varying density {0 (no), 1 (yes)}</li> <li><em>n_discon_ps</em>: number of disconnected Pareto sets {0, ..., 6}</li> <li><em>n_local_fronts</em>: number of local fronts {0, ..., 6}</li> <li><em>n_resist_regions</em>: number of dominance resistance regions {0, ..., 6}</li> <li><em>budget</em>: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}</li> <li><em>algo</em>: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}</li> <li><em>hypervolume_avg</em>: average hypervolume reached by the algorithm [0.0, 1.0]</li> <li><em>best</em>: 1 if the corresponding algorithm obtains the best average hypervolume, 0 otherwise</li> </ul> <p> </p>
Trained Machine Learning Model to Detect Surface Events from Seismograms
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ExioML: Emission Factor Database for Scope 3 Emission Estimation Machine Learning Benchmarks
<h1>🙋‍♂️ Introduction</h1> <p>ExioML is the first ML-ready benchmark dataset in eco-economic research, designed for global sectoral sustainability analysis. It addresses significant research gaps by leveraging the high-quality, open-source EE-MRIO dataset ExioBase 3.8.2. ExioML covers 163 sectors across 49 regions from 1995 to 2022, overcoming data inaccessibility issues. The dataset includes both factor accounting in tabular format and footprint networks in graph structure.</p> <p>We demonstrate a GHG emission regression task using a factor accounting table, comparing the performance of shallow and deep models. The results show a low Mean Squared Error (MSE), quantifying sectoral GHG emissions in terms of value-added, employment, and energy consumption, validating the dataset's usability. The footprint network in ExioML, inherent in the multi-dimensional MRIO framework, enables tracking resource flow between international sectors.</p> <p>ExioML offers promising research opportunities, such as predicting embodied emissions through international trade, estimating regional sustainability transitions, and analyzing the topological changes in global trading networks over time. It reduces barriers and intensive data pre-processing for ML researchers, facilitates the integration of ML and eco-economic research, and provides new perspectives for sound climate policy and global sustainable development.</p> <h1>📊 Dataset</h1> <p>ExioML supports graph and tabular structure learning algorithms through the Footprint Network and Factor Accounting table. The dataset includes the following factors in PxP and IxI:</p> <p>- Region (Categorical feature)<br>- Sector (Categorical feature)<br>- Value Added [M.EUR] (Numerical feature)<br>- Employment [1000 p.] (Numerical feature)<br>- GHG emissions [kg CO2 eq.] (Numerical feature)<br>- Energy Carrier Net Total [TJ] (Numerical feature)<br>- Year (Numerical feature)</p> <h2>☁️ Factor Accounting</h2> <p>The Factor Accounting table shares common features with the Footprint Network and summarizes the total heterogeneous characteristics of various sectors.</p> <h2>🚞 Footprint Network</h2> <p>The Footprint Network models the high-dimensional global trading network, capturing its economic, social, and environmental impacts. This network is structured as a directed graph, where directionality represents sectoral input-output relationships, delineating sectors by their roles as sources (exporting) and targets (importing). The basic element in the ExioML Footprint Network is international trade across different sectors with features such as value-added, emission amount, and energy input. The Footprint Network helps identify critical sectors and paths for sustainability management and optimization. The Footprint Network is hosted on Zenodo.</p> <h1>🔗 Code and Data Availability</h1> <p>The ExioML development toolkit in Python and the regression model used for validation are available on the GitHub repository: (https://github.com/YVNMINC/ExioML). The complete ExioML dataset is hosted by Zenodo: (https://zenodo.org/records/10604610).</p> <h1>💡 Additional Information</h1> <p>More details about the dataset are available in our paper: *ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability*, accepted by the ICLR 2024 Climate Change AI workshop: (https://arxiv.org/abs/2406.09046).</p> <h1>📄 Citation</h1> <pre>@article{guo2024exioml, title={ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability}, author={Guo, Yanming and Guan, Charles and Ma, Jin}, journal={arXiv preprint arXiv:2406.09046}, year={2024} }</pre> <h1>🌟 Reference</h1> <p>Stadler, Konstantin, et al. "EXIOBASE 3." Zenodo. Retrieved March 22 (2021): 2023.</p>
Data set used for comparison of different machine learning approaches, for predicting aircraft departure delays, due to the circumstances of the defrosting process.
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Machine Learning for Analyzing Atomic Force Microscopy (AFM) Images Generated from Polymer Blends
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Dataset for accelerating the calculation of electron-phonon coupling strength with machine learning
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Datasets for "Leveraging Machine Learning and Natural Language Processing Techniques for Agriculture Experiment Station Project Classification"
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Enhancing Streamflow Prediction through Multi-model Ensemble Framework and Machine Learning Techniques
<p>This file contains python code used in this study and data used to plot figures. </p>
Models from "Applying Machine Learning to Characterize and Extrapolate the Relationship Between Seismic Structure and Surface Heat Flow"
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Exploring Community Smells in Machine Learning Applications: Analytical Insights and Their Association with Self-Admitted Technical Debt
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Exploring Community Smells in Machine Learning Applications: Analytical Insights and Their Association with Self-Admitted Technical Debt
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Machine Learning-Enabled Software and System Architecture Frameworks
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Data from: Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning
<p><strong>Paper Abstract:</strong></p> <p>Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R<sup>2</sup>: 0.90, RMSE: 10.1%), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R<sup>2</sup>: 0.67, RMSE: 2.73%), and in residential areas, capturing the totality of canopy cover variation (R<sup>2</sup>: 0.85, RMSE: 5.66%). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R<sup>2</sup>: 0.86, RMSE: 11.4%), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34% canopy cover). The two cities (Brampton and Mississauga) have 15.9% and 17.5% canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper. </p>
Computational Chemistry and Machine Learning-assisted Screening of Supported Amorphous Metal Oxide Nanoclusters for Methane Activation
<p>Gaussian input and output files for reproducibility of the results.</p>
Machine learning reveals the influence of the Changbaishan mantle plume sourced from the mantle transition zone on Cenozoic intraplate magmatism in NE China
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X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones
<p>Deidentified dataset, which includes raw data used in the study:</p> <p>Gitto S, Annovazzi A, Nulle K, et al. X-rays radiomics-based machine learning classification of atypical cartilaginous tumor and high-grade chondrosarcoma of long bones. EBioMedicine 2024; 101:105018. doi: 10.1016/j.ebiom.2024.105018.</p>
Raw dataset for evaluating the effects of M sand and fly ash on concrete strength using experimental and machine learning approaches
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A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM): Datasets
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Online machine learning algorithms to predict link quality in community wireless mesh networks
<p>FunkFeuer raw topology data from 15-01-2016 to 28-01-2016 retrieved on 17-02-2016 from http://opendata.confine-project.eu/dataset/funkfeuer-topology-data. This dataset was employed in "Miguel L. Bote-Lorenzo, Eduardo Gómez-Sánchez, Carlos Mediavilla-Pastor, Juan I. Asensio-Pérez, Online machine learning algorithms to predict link quality in community wireless mesh networks, Computer Networks, Volume 132, 2018, Pages 68-80, https://doi.org/10.1016/j.comnet.2018.01.005."</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.