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356 results for “Machine learning models”
Dataset for "Enabling Machine Learning Models in Alarm Fatigue Research: Creation of a Large Relevance-annotated Oxygen Saturation Alarm Data Set"
<p>Chromik and Flint et al. (2024) (under review) propose an algorithm that uses clinical alarm logs, an annotation guideline (Klopfenstein et al. 2023), and routinely collected intensive care data to create a data set of relevance-annotated oxygen saturation alarms. We provide the algorithm's source code and data set of annotated oxygen saturation alarms as supplementary material to the publication.</p> <ul> <li>The algorithm's implementation is open-source and can be re-used on similar data sets.</li> <li>Our implementation used airway management data mappings to identify airway devices (AD), ventilation devices (VD), and ventilation modes (VM). These mappings can be found here: <a href="../doi/10.5281/zenodo.7511031">https://zenodo.org/doi/10.5281/zenodo.7511031</a></li> <li>The data set suggests that the majority of oxygen saturation alarms in the intensive care unit is non-actionable.</li> <li>We are the first to provide such an extensive data set of annotated oxygen saturation alarms.</li> </ul>
Calibration of a pedestrian ingress model based on CCTV surveillance data using machine learning methods: data and code
<p>The package includes a dataset of trajectories obtained from a real-time pedestrian traffic detector at the vaccination centre and the code needed to validate the analyses described in the paper entitled Calibration of a pedestrian ingress model based on CCTV surveillance data using machine learning methods. Version 1.0.1 is a patch that corrects path to data.</p>
Construction of a Predictive Model of Gangrenous Cholecystitis Based on Machine Learning
ClinicalTrials.gov study NCT06399081. IPD Sharing: NO. Countries: 1. Publications: 3.
Volatilome and Single-Lead Electrocardiogram Optimize Ischemic Heart Disease Diagnosis Using Machine Learning Models
ClinicalTrials.gov study NCT06181799. IPD Sharing: NO. Countries: 1. Publications: 8.
The Prediction of Recurrence Lumbar Disc Herniation At L5-S1 Level Through Machine Learning Models Based on Endoscopic Discectomy Via the Interlaminar Approach
ClinicalTrials.gov study NCT06833099. IPD Sharing: NO. Countries: 1. Publications: 20.
Expression-based machine learning models for predicting plant tissue identity
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Using machine learning to model nontraditional spatial dependence in occupancy data
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Coarsened fine-grid model data for: A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
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Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response
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Supplementary material: Machine learning and phylogenetic models identify predictors of genetic variation in Neotropical amphibians
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Modelling the potential distribution of African Wormwood (Artemisia afra) using machine learning algorithm-based approach (MaxEnt) in Sekhukhune District, South Africa
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Supplementary information for: A continuous-score occupancy modeling framework for incorporating uncertain machine learning output in autonomous biodiversity surveys
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A machine learning based prediction model for life expectancy
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Data and scripts for 'Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes'
<p>Data and scripts for "Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes" by Axel Seifert and Stephan Rasp, J. Adv. Modeling Earth Systems, 12, 2020, https://doi.org/10.1029/2020MS002301</p>
Calibration of probability predictions from machine-learning and statistical models
<p>This data set describes the occurrence (yes/no) of a bird, the Southern Whiteface (<i>Aphelocephala leucopsis)</i> in Australia. A suite of environmental variables is provided, which are used in the paper to illustrate a statistical problem. The data are meant to allow reproduction of the analysis in this paper. They are not intended for actual ecological analysis. The data come as .Rdata-file, i.e. as an R-dataset (described technically here: https://www.loc.gov/preservation/digital/formats/fdd/fdd000470.shtml).</p> <p>Here is the paper's abstract:</p> <p><span>Aim: Predictions from statistical models may be uncalibrated, meaning that the predicted values do not have the nominal coverage probability. This is easiest seen with probability predictions in machine-learning classification, including the common species occurrence probabilities. Here, a predicted probability of, say, 0.7 should indicate that out of 100 cases with these environmental conditions, and hence the same predicted probability, the species should be present in 70 and absent in 30.</span><br> <span>Innovation: A simple calibration plot shows that this is not necessarily the case, particularly not for over-fitted models or algorithms that use non-likelihood target functions. As a consequence, "raw" predictions from such model could easily be off by 0.2, are unsuitable for averaging across model types, and resulting maps hence be substantially distorted. The solution, a flexible calibration regression, is simple and can be applied whenever deviations are observed.</span><br> <span>Conclusion: "Raw", uncalibrated probability predictions should be calibrated before interpreting or averaging them in a probabilistic way.</span></p>
Benchmarking parametric and machine learning models for genomic prediction of complex traits
<p>The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.</p>
Soil dissolved organic carbon (DOC) machine learning model code
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Replication Package for 'How do Machine Learning Models Change?'
<h1>Replication Package: How Do Machine Learning Models Change?</h1> <p> </p> <h2>Overview</h2> <div>This replication package accompanies the paper "<strong>How Do Machine Learning Models Change?</strong>". In this study, we conducted a large-scale analysis of <strong>over 680,000 commits from 100,000 models</strong> and <strong>2,251 releases from 202 of these models</strong> on the Hugging Face (HF) platform. Our goal was to understand how machine learning (ML) models evolve by classifying commit types using a detailed ML change taxonomy and analyzing temporal patterns in their activities using Bayesian networks.</div> <p> </p> <div>Our research addresses three main aspects:</div> <div><strong>1. Categorization of Commit Changes:</strong> We classified over 960,000 commits on HF, providing a detailed breakdown of change types and their distribution.</div> <div><strong>2. Analysis of Commit Sequences</strong>: We examined the sequence and dependencies of commit types using Bayesian networks to identify temporal patterns.</div> <div><strong>3. Release Analysis**</strong>: We investigated the distribution and evolution of release types, analyzing how model attributes and metadata change across successive releases.</div> <p> </p> <div>This package provides all the necessary code, data, and documentation to reproduce the results presented in our paper.</div> <p> </p> <h2>Data Collection and Preprocessing</h2> <h3>Data Collection</h3> <div>We collected data from the Hugging Face platform using the Hugging Face Hub API. The data extraction was performed up to <strong>May 2025,</strong> capturing details from over 1 million models available at that time.</div> <p> </p> <div><strong>- Model & Release Information</strong>: We collected model metadata, commit histories, and release information (Git tags) for our sampled models.</div> <div><strong>- Detailed Commit Changes</strong>: To get a detailed list of files modified in each commit, we implemented a direct Git processing approach. For each model, its repository was temporarily cloned to programmatically extract the list of changed files for every commit SHA.</div> <p> </p> <h3>Data Preprocessing</h3> <div><strong>Commit Diffs</strong></div> <div>We computed the differences for key JSON configuration files (e.g., `config.json`) between commits to identify added, deleted, and updated keys, which served as input for classification.</div> <div> </div> <div><strong>Commit Classification</strong></div> <div>We classified each commit according to Bhatia et al.'s ML change taxonomy using the <strong>Gemini 2.5 Flash LLM</strong>. To ensure the reliability of this process, we implemented a rigorous <strong>two-phase validation</strong>:</div> <div><strong>1. Prompt Refinement (Training): </strong>The prompt was iteratively refined over 6 cycles using a curated training set of 143 commits. The process was guided by comparing LLM classifications against a gold standard created by two human annotators (Human-Human IRR on a subset: 𝜅 = 0.7798). The final refined prompt achieved a Kappa of 0.9068 against the training gold standard.</div> <div><strong>2. Final Validation (Testing): </strong>The validated prompt was tested on an independent, statistically significant sample of 384 commits. The LLM's classifications achieved a Cohen's Kappa of 0.8568 when compared against the test set's gold standard, which itself was validated with a human-human IRR of 𝜅 = 0.8150.</div> <p> </p> <div>We also classified commits into Swanson's categories using a fine-tuned DistilBERT model, as detailed in the paper.</div> <p> </p> <h2>Folder Structure</h2> <div>The replication package is organized as follows. The structure has been designed to separate code, data, and metadata for clarity.</div> <p> </p> <ul> <li>`code/`: Contains all Jupyter notebooks for the study. <ul> <li>`Collection/`: Scripts for data extraction from Hugging Face.</li> <li>`HFExtraction.ipynb`: Collects primary model and commit information.</li> <li>`HFReleasesExtraction.ipynb`: Collects release (tag) specific information.</li> </ul> </li> <li>`Preprocessing/`: Scripts for data cleaning, processing, and classification. <ul> <li>`HFCommitsPreprocessing.ipynb`: Processes commits, computes diffs, and prepares data for classification and analysis.</li> <li>`HFReleasesPreprocessing.ipynb`: Processes and classifies release data.</li> </ul> </li> <li>`Analysis/`: Notebooks for reproducing the analysis for each research question. <ul> <li>`HFFileChanges.ipynb`: Contains the preliminary analysis of file change patterns.</li> <li>`RQ1_Analysis.ipynb`: Analysis for Research Question 1.</li> <li>`RQ2_Analysis.ipynb`: Analysis for Research Question 2.</li> <li>`RQ3_Analysis.ipynb`: Analysis for Research Question 3.</li> </ul> </li> <li>`datasets/`: Contains the key final datasets used in the analysis notebooks. <ul> <li>`commits_datasets/`: Contains the main classified commit dataset. <ul> <li>`HFCommitsClassification_final.csv`: The final dataset with over 960,000 classified commits for RQ1 and RQ2.</li> </ul> </li> <li>`releases_datasets/`: Contains the datasets related to releases. <ul> <li>`HFReleasesClassification.csv`: The final dataset of 2,251 classified releases for RQ3.</li> </ul> </li> <li>`model_metadata.csv`: The extracted internal metadata from model files for RQ3.4.</li> </ul> </li> <li>`metadata/`: Contains configuration files and the data used for the validation process. <ul> <li>`validation_data/`: A sub-folder containing the gold standard data. <ul> <li>`Agreement TOSEM Commit Changes.xlsx`: Excel containing details of the classication and validation processes.</li> <li>`prompt_refinement.txt`: The final, validated prompt used for the LLM classification along its previous iterations.</li> <li>`training_set_ground_truth.json`: Gold standard for the 143-commit training set.</li> <li>`training_set_first_classification.json`: First annotator's labels for the training IRR subset.</li> <li>`training_set_second_classification.json`: Second annotator's labels for the training IRR subset.</li> <li>`test_set_ground_truth.json`: Gold standard for the 384-commit test set.</li> <li>`test_first_classification.json`: First annotator's labels for the training IRR subset.</li> </ul> </li> <li>`test_set_second_classification.json`: Second annotator's labels for the test IRR subset.</li> <li>`tags_metadata.yaml`: Auxiliary metadata file used during preprocessing.</li> </ul> </li> <li>`README.md`: This file.</li> <li>`requirements.txt`: Lists the required Python packages.</li> </ul> <p>*Note: Other intermediate CSV files are provided to facilitate re-running specific parts of the analysis without starting from scratch.*</p> <p> </p> <h2>How to Use This Package</h2> <p> </p> <h3>Setup</h3> <div><strong>1. Create and activate a virtual environment</strong> (recommended).</div> <div>```bash</div> <div>python -m venv venv</div> <div>source venv/bin/activate # On Windows: venv\Scripts\activate</div> <div>```</div> <div><strong>2. Install required packages.</strong></div> <div>```bash</div> <div>pip install -r requirements.txt</div> <div>```</div> <p> </p> <h3>Running the Analysis</h3> <div>The Jupyter notebooks in the `code/` directory are numbered and named to be run in a logical sequence: <strong>Collection -> Preprocessing -> Analysis.</strong> We recommend following this order.</div> <div> </div> <div>- <strong>To reproduce our findings directly,</strong> you can start with the notebooks in `code/Analysis/`. They are configured to load the final, processed datasets provided in the `datasets/` folder.</div> <div>- <strong>To re-run the entire pipeline</strong>, start with the notebooks in `code/Collection/`, followed by `code/Preprocessing/`. Please note that running the full data collection and classification pipeline is time-consuming and may require significant computational resources and appropriate API keys for the LLM.</div> <p> </p> <h3>Key Datasets Provided</h3> <div><strong>- For RQ1 & RQ2:</strong> `datasets/commits_datasets/HFCommitsClassification_final.csv` (100,000 models for RQ1; filtered to 14,343 models for RQ2).</div> <div><strong>- For RQ3.1-3.3:</strong>`datasets/releases_datasets/HFReleasesClassification.csv` (2,251 releases from 202 models).</div> <div><strong>- For RQ3.4: </strong>`datasets/releases_datasets/model_metadata.csv` (from 28 models).</div> <p> </p> <h2>Contact</h2> <div>If you have any questions or encounter issues with this package, please contact the corresponding author. If you find our work useful, please consider citing our paper.</div>
MME-only 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-only ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data). To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME-only 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-only ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</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.