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356 results for “Machine learning models”
MME/CRPS models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select 50. To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME/CRPS models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select 50. To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>The tar file contains two directories: data and models. Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data). The data in these directories are unnormalized. The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file. These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively. To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths. The other arguments should be as follows:</p><p>--uniformize=1</p><p>--predictor_norm_type_string="z_score"</p><p>--vector_target_norm_type_string=""</p><p>--scalar_target_norm_type_string=""</p><p> </p><p>Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data. To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
Landslide susceptibility maps using ensemble machine learning models on basin and regional level in Lombardy, Italy
<p>A selection of landslide susceptibility maps computed through ensemble machine learning models for the basins of Val Tartano, Upper Valtellina and Valchiavenna, and on a regional level for the Lombardy region in Italy.</p> <p>A list of the used base machine learning methods:</p> <ul> <li>Random Forest,</li> <li>AdaBoost,</li> <li>Neural Networks.</li> </ul> <p>A list of the used ensemble models:</p> <ul> <li>Stacking,</li> <li>Blending,</li> <li>Soft Voting.</li> </ul> <p>A full list of the model combinations can be found in the "Case Studies" document.</p> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632).</p> <p>The map production process details are discussed in Xu et al. 2024. If you use the dataset, please, cite also the paper:</p> <p><em>Qiongjie Xu, Vasil Yordanov, Lorenzo Amici & Maria Antonia Brovelli (2024) Landslide susceptibility mapping using ensemble machine learning methods: a case</em><br><em>study in Lombardy, Northern Italy, International Journal of Digital Earth, 17:1, 2346263, DOI:10.1080/17538947.2024.2346263</em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001.</p> <p> </p>
Innovative Strategies for Blood-Brain Barrier (BBB) Permeability Modeling: Harnessing the Power of Machine Learning-based q-RASAR Approach
<p>In the current research, we have unveiled an advanced technique termed the quantitative Read-Across Structure-Activity Relationship (q-RASAR) framework to harnesses the power of machine learning (ML) for significantly enhancing the precision of predictions related to blood-brain barrier (BBB) permeability. It is important to emphasize that the central objective of this study is not to introduce another model for predicting BBB permeability. Instead, our focus is on highlighting the improvement in predicting the BBB permeability of organic compounds by introducing the q-RASAR approach. This innovative methodology strives to enhance the precision of evaluating neuropharmacological implications and streamline the drug development process. In this investigation, we developed an ML-based q-RASAR PLS model using a dataset comprising 1012 compounds of diverse classes of heterocyclic and aromatic hydrocarbons, obtained from the freely accessible B3DB database (accessible at <a href="https://github.com/theochem/B3DB">https://github.com/theochem/B3DB</a>) to predict BBB permeability during the lead discovery phase for central nervous system (CNS) drugs. The model's predictive capability underwent validation using two external sets, encompassing a total of 1,130,315 compounds, including synthetic compounds and natural products (NPs) for data gap filling and other two external sets comprising 116 drug-like/drug compounds from FDA and ChEMBL databases to assess the model's reliability. This study aimed to bridge the data gap by employing a predictive model to estimate the impact of brain-plasma concentration ratios on BBB permeability for both synthetic compounds and natural products (NPs). To further enhance predictability, we have developed various other ML-based q-RASAR models. The insights from the developed model highlight the pivotal roles played by hydrophobicity, electronic effects, degree of ionization and steric factors as essential features facilitating the traversal of the blood-brain barrier. This research not only advances our understanding of the molecular determinants influencing the permeability of central nervous system drugs but also establishes a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in the realms of drug development and design.</p>
Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites
<p>Data for reproducibility of the paper "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites"</p> <p>Corresponding paper:</p> <p>ShanPing Liu, Romain Dupuis, Dong Fan, Salma Benzaria, Mickaele Bonneau, Prashant Bhatt, Mohamed Eddaoudi, and Guillaume Maurin. "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites." <em>Chemical Science</em> (2024). https://doi.org/10.1039/D3SC05612K</p>
Data and code for Causality analysis and prediction of riverine algal blooms by combining empirical dynamic modeling and machine learning techniques
<p>Hydrological data (including daily water levels, flow velocities, and streamflow discharges) from two hydrological stations, the Hankou Station in the Yangtze River (YR) and the Hanchuan Station in the Han River (HR), were obtained from Hubei Province Hydrology and Water Resources Center.</p> <p>Water quality data (i.e., total nitrogen (TOTN), total phosphorus (TOTP), and water temperature in the Han River) and algae densities at three sections (Baihezui, Qinduankou and Zongguan) were acquired from the Yangtze River Basin Ecological and Environmental Supervision Authority. </p> <p><span>The R script(s) for machine learning models can also be found at <a href="../api/records/10901736/draft/files/Code%20for%20machine%20learning%20classification%20model.R/content" target="_blank" rel="noopener noreferrer">Code for machine learning classification model.R</a>.</span></p> <p> </p>
Field and Lab_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Field and Lab observations_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Orthomosaic_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Orthomosaic of the aerial images_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
AIMD training data for H2 adsorption on MoxCy facets for machine learning model
<p>This data is used to train the MACE model for modeling hydrogen dissociation on molybdenum carbide surfaces.</p>
Dataset and machine learning models for seismic response predictions of small-to-medium continuous girder bridges
<p>This upload includes the dataset and machine learning models (based on Matlab platform) for longitudinal seismic response predictions of multi-span highway girder bridges, which have a typical span length of 30 m supported by reinforced concrete (RC) bridge bents and abutments through spherical steel bearings. The input variables (features) are five structural parameters of studied bridges and seven intensity measures of earthquakes. The output variables (labels) are peak column drifts and peak bearing deformations. The dataset is developed by conducting a total number of 720 nonlinear time-history analyses considering the uncertainty of bridges and earthquakes. Machine learning models are developed using two popular machine learning algorithms named artificial neural network (ANN) and support vector regression (SVR).</p>
Supplementary data: Optimising centralisation and decentralisation in distribution networks for perishable products through mathematical modelling, parametric analysis, and machine learning
<p><span>The success of distribution companies for perishable products is enabled by optimally configuring distribution networks, which allows for reducing total logistic costs while ensuring reduced product spoilage and high service levels. Since customer demand for perishable products varies over time, the network configuration should not be optimised once, but periodically reviewed. Among the decisions to be reviewed, determining whether to centralise or decentralise inventory (i.e., stock allocation in distribution centres) is crucial. However, the literature overlooks stock allocation decisions, and existing methodologies to compare the economic performance of centralised, decentralised, and hybrid policies neglect important cost items, also requiring advanced computational technologies and skills to be applied. This paper addresses these gaps by providing two contributions. In this dataset, a comparison has been made between the cost performance of centralized, decentralized, and hybrid stock allocation policies in distribution networks for perishable products. The dataset comprises 100,000 realistic case studies generated through a Sobol quasi-random low discrepancy series.</span></p>
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations: Data and Visualization Notebooks
<p>The data, jupyter notebooks, and saved model weights for the "Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations" Hu et al. (2025) arxiv preprint: <a href="https://arxiv.org/abs/2407.00124">arXiv:2407.00124</a>. This updated version contains more analysis notebooks together with related data/model.</p>
Data for "Simulating and analysing seabird flyways: an approach combining least-cost path modelling and machine learning"
Open the record for dataset details and reuse information.
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks
<p>Solvent removal stability and thermal stability associated with structurally characterized metal organic frameworks.</p>
Code for: Comparison and interpretability of machine learning models to predict severity of chest injury
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective:</b> Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at non-trauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Materials and Methods:</b> A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models: logistic regression with elastic net regularization (EN), extreme gradient boosted machines (XGB), and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results:</b> Of 8,952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax." </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Discussion: </b>The CNN model featured optimal discrimination, calibration, and clinically relevant features selected. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusion:</b> NLP and ML methods to populate trauma registries for quality analyses are feasible.</span></span></span></span></span></span></span></span></span></span></span></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.