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575 results for “Modeling Codes”
Code and measurement data - Capacity and internal resistance diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (Matlab code, measurement data, figure files) of the journal article: <a href="https://doi.org/10.1149/1945-7111/ad6938">Wolfgang G. Bessler, “Capacity and resistance diagnosis of batteries with voltage-controlled models,” J. Electrochem. Soc. 171, 080510 (2024), https://doi.org/10.1149/1945-7111/ad6938</a>.</strong><br><br></p> <p><strong>Abstract:</strong></p> <p>Capacity and internal resistance are key properties of batteries determining energy content and power capability. We present a novel algorithm for estimating the absolute values of capacity and internal resistance from voltage and current data. The algorithm is based on voltage-controlled models (VCM). Experimentally-measured voltage is used as input variable to an equivalent circuit model. The simulation gives current as output, which is compared to the experimentally-measured current. We show that capacity loss and resistance increase lead to characteristic fingerprints in the current output of the simulation. In order to exploit these fingerprints, a theory is developed for calculating capacity and resistance from the difference between simulated and measured current. The findings are cast into an algorithm for operando diagnosis of batteries operated with arbitrary load profiles. The algorithm is demonstrated using cycling data from lithium-ion pouch cells operated on full cycles, shallow cycles, and dynamic cycles typical for electric vehicles. Capacity and internal resistance of a “fresh” cell was estimated with high accuracy (mean absolute errors of 0.9 % and 1.8 %, respectively). For an “aged” cell, the algorithm required adaptation of the model’s open-circuit voltage curve in order to obtain high accuracies.</p> <p> </p> <p><strong>Copyright and IP information:</strong></p> <p>Copyright 2024 by Wolfgang G. Bessler. The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license (Creative Commons Attribution Non Commercial 4.0 International). Please note that the algorithms themselves are subject to intellectual property rights, including, but not necessarily limited to, German patent DE102022129314 and international patent WO2024/099513A1. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p><br><strong>Quick start:</strong></p> <p>Copy all files into one folder. Open and run capacityAndResistanceDiagnosisFigures8and10.m with Matlab. Observe the reproduction of Figure 8 of the paper.</p> <p><br><strong>Description of the files:</strong></p> <p>Matlab code (tested using versions R2019a and R2022b):</p> <ul> <li>capacityAndResistanceDiagnosisFigures8and10.m: this is the main Matlab script. It performs the capacity and resistance diagnosis on experimental data V(t) and I(t). The script reproduces Figures 8 ("fresh" cell) or 10 ("aged" cell) of the paper, depending on which lines you uncomment in upper part of the script.</li> <li>calculateDeltaR.m, calculatefC.m: functions that evalue deltaR and fC, which are two key outputs of the diagnosis algorithm. </li> <li>simulateVCMSimple.m, simulateVCMDynamic.m: functions that simulate the voltage-controlled equivalent circuit models (either "simple" of "dynamic"). Input is V(t), output are I(t) and SOC(t).</li> <li>interpolateCurve.m: performs linear interpolation of the OCV(SOC) curve. We use this because Matlab's interp1() function is awfully slow.</li> </ul> <p>Experimental data:</p> <ul> <li>Experimental_data_fresh_cell.csv: Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a "fresh" lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</li> <li>Experimental_data_aged_cell.csv: Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a "pre-aged" lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</li> <li>OCV_vs_SOC_curve_fresh_cell.csv: Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC) of the "fresh" lithium-ion cell. 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</li> <li>OCV_vs_SOC_curve_aged_cell.csv: Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC) of the "aged" lithium-ion cell. 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</li> </ul> <p>Figure files:</p> <ul> <li>Figures.zip: contains .emf (Windows format) and .fig (Matlab format) versions of Figures 2-10 of the paper.</li> </ul>
FRAMEWORK FOR A VOLUME MODEL FOR MONOCLINIC AMPHIBOLE - Data and Code
<p>This upload includes the data and code I used to calibrate a volume model for monoclinic amphoboles.</p>
Codes and data upload for Mesocircuit Model project
<h3>Information</h3> <p>This resource contains source codes and data required to produce the figures from:<br>Senk, J., Hagen, E., van Albada, S. J., & Diesmann, M. (2024). Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space. <em>arXiv preprint arXiv:1805.10235v3<br><br></em>For only source codes, see:</p> <p>Senk, J., & Hagen, E. (2024). Mesocircuit Model (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.13798936" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13798936</a></p> <div> <h3>Instructions</h3> To use this resource, download the mesocircuit-model-1.0.0.tar.gz file. Then, extract the contents of the archive and run the following command in the extracted directory:<br> <div> </div> <div>$ # unzip the archive</div> <div>$ tar -xzf mesocircuit-model-1.0.0.tar.gz</div> <br> <div>$ # create and activate the conda environment</div> <div>$ cd mesocircuit-model</div> <div>$ conda env create -f environment.yml</div> <div>$ conda activate mesocircuit</div> <br> <div>$ # run the figure-generation scripts</div> <div>$ cd scripts</div> <div>$ python ms_figures_simulations.py</div> <div>$ python run_mesocircuit_lfps.py</div> <div> </div> <div>$ # deactivate environment</div> <div>$ conda deactivate </div> </div> <div> </div> <div>Installers for conda may be obtained e.g., from https://github.com/conda-forge/miniforge. <br> <div>The figures produced by ms_figures_simulations.py will be saved in the `ms_figures` directory.</div> <div>The figures produced by run_mesocircuit_lfps.py will be saved in mesocircuit_data/mesocircuit_MAMV1/dd1dcbd4034fbd4f689fbdd2ff7abb3b/lfp/figures.</div> </div>
Do Current Language Models Support Code Intelligence for R Programming Language?
<p>This is the dataset used in the paper: Do Current Language Models Support Code Intelligence for Programming Language?</p> <p> </p> <p>This dataset contains code snippets from R programming language repositories on GitHub, paired with their corresponding natural language (NL) descriptions. It was created for research in software engineering tasks like code summarization and code search. The data was collected using the GitHub REST API and includes over 1,500 public R repositories. To ensure quality, only active, well-structured R packages with proper documentation were included. Roxygen2, a popular documentation framework, was used to extract both the code and its matching NL descriptions.</p> <p>The dataset is organized into three parts: base R functions (Base), functions from the tidyverse (Tidy), and a combined set (RCombine). The dataset follows the CodeSearchNet format, with a split for training, validation, and testing data, ensuring no duplicate functions.</p>
Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS
<p><span>This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.). <br></span></p>
Data and codes for "The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"
<p>Data and codes for the manuscript entitled:</p> <p>"<strong>The impact, costs, and cost-effectiveness of tuberculosis outbreak investigations in the United States: a model-based analysis"</strong></p> <p> </p> <p><strong>Please see Readme.txt for details</strong></p>
Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code
<p>Despite their success, large language models (LLMs) face the critical challenge of hallucinations, generating plausible but incorrect content. While much research has focused on hallucinations in multiple modalities including images and natural language text, less attention has been given to hallucinations in source code, which leads to incorrect and vulnerable code that causes significant financial loss. To pave the way for research in LLMs' hallucinations in code, we introduce Collu-Bench, a benchmark for predicting code hallucinations of LLMs across code generation (CG) and automated program repair (APR) tasks. Collu-Bench includes 13,234 code hallucination instances collected from five datasets and 11 diverse LLMs, ranging from open-source models to commercial ones. <br>To better understand and predict code hallucinations, Collu-Bench provides detailed features such as the per-step log probabilities of LLMs' output, token types, and the execution feedback of LLMs' generated code for in-depth analysis. In addition, we conduct experiments to predict hallucination on Collu-Bench, using both traditional machine learning techniques and neural networks, which achieves 22.03 -- 33.15% accuracy. Our experiments draw insightful findings of code hallucination patterns, reveal the challenge of accurately localizing LLMs' hallucinations, and highlight the need for more sophisticated techniques.</p>
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling (Source code and data)
<p>This dataset contains</p> <ul> <li>the source code</li> <li>the data and examples</li> <li>the material subroutine with examples of uniaxial strain and stress</li> </ul> <p>of the inelastic Constitutive Artificial Neural Network (iCANN) enhanced by the concept of homeostatic surfaces to discover tensional homeostasis.</p> <p>The corresponding publication is:</p> <p>Holthusen, H., Brepols, T., Linka, K., & Kuhl, E..<em> </em></p> <p><em>Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling.</em></p> <p> </p> <p><strong>Standalone_Materialroutine</strong></p> <ul> <li>00_Materialroutine: Contains the material subroutine implemented in FORTRAN</li> <li>01_uniaxial_strain: Example of the material subroutine in a uniaxial strain driven manner</li> <li>02_uniaxial_stress: Example of the material subroutine in a uniaxial stress driven manner</li> </ul> <p> </p> <p><strong>TensorFlow</strong></p> <ul> <li> <p>iCANN:</p> <ul> <li> <p>01_Biax/biax_l1: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L1 (Lasso) regularization</p> </li> <li> <p>01_Biax/biax_l2: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L2 (ridge) regularization</p> </li> <li> <p>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L1 (Lasso) regularization</p> </li> <li>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L2 (ridge) regularization</li> </ul> </li> <li> <p>iCANN_ABS_activation: Same four examples as above, however, with the absolute value activation function</p> </li> <li> <p>installed_packages: File containing a list of installed Python modules used to implement the iCANN</p> </li> </ul> <p>The TensorFlow implementations in all 01_Biax/ and 02_Uniax/ sub-directories are the same.</p> <p>The implementation in iCANN_ABS_activation is different with respect to the activation functions of the pseudo potential.</p> <p> </p> <p>The experimental data for the cross and stripe specimen are taken from the literature:</p> <p>Eichinger, J. F., Paukner, D., Szafron, J. M., Aydin, R. C., Humphrey, J. D., & Cyron, C. J. (2020).</p> <p>Computer-controlled biaxial bioreactor for investigating cell-mediated homeostasis in tissue equivalents. <em>Journal of biomechanical engineering</em>, <em>142</em>(7), 071011.</p> <p><a href="https://doi.org/10.1115/1.4046201">https://doi.org/10.1115/1.4046201</a></p>
Code and Data for Sturm and Silva (2024) A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction
<p>This record contains the Julia photochemical model (https://doi.org/10.5281/zenodo.13385541) output in csv format used for training XGBoost in ProjectionConservationRF.py to emulate ozone photochemical formation. Nonphysical predictions that violate conservation of atoms are corrected using a closed-form, constrained least-squares approach that factors in uncertainty and scale using species-level weights. The file ozoneNOx_visualization.py contains an example and visualization for a smaller system, the primary photolytic cycle from which the Leighton relationship can be derived.</p> <p>The corresponding preprint is available here: <a href="https://doi.org/10.48550/arXiv.2408.16109">https://doi.org/10.48550/arXiv.2408.16109</a></p>
Official Code and Dataset of Table Tennis Coaching System Based on a Multimodal Large Language Model with Knowledge Base
<p>Official Code and Dataset of Table Tennis Coaching System Based on a Multimodal Large Language Model with Knowledge Base</p>
Data and Code from: Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients
<div> <div> <div> <p>Abstract</p> <p>Spinocerebellar ataxia type 7 (SCA7) is a genetic neurodegenerative disorder caused by a CAG- polyglutamine repeat expansion. Purkinje cells (PCs) are central to the pathology of ataxias, but their low abundance in the cerebellum underrepresents their transcriptomes in sequencing assays. To address this issue, we developed a PC enrichment protocol and sequenced individual nuclei from mice and patients with SCA7. Single-nucleus RNA sequencing in SCA7-266Q mice revealed dysregulation of cell identity genes affecting glia and PCs. Specifically, genes marking zebrin-II PC subtypes accounted for the highest proportion of DEGs in symptomatic SCA7-266Q mice. These transcriptomic changes in SCA7-266Q mice were associated with increased numbers of inhibitory synapses as quantified by immunohistochemistry and reduced spiking of PCs in acute brain slices. Dysregulation of zebrin-II cell subtypes was the predominant signal in PCs of SCA7-266Q mice and was associated with the loss of zebrin-II striping in the cerebellum at motor symptom onset. We furthermore demonstrated zebrin-II stripe degradation in additional mouse models of polyglutamine ataxia and observed decreased zebrin-II expression in cerebellum of patients with SCA7. Our results suggest that a breakdown of zebrin subtype regulation is a shared pathological feature of polyglutamine ataxias.</p> <p>Data and Code Availability</p> <p>Here you will find data and code associated with our manuscript "Dysregulation of zebrin-II cell subtypes is a shared feature across polyglutamine ataxia mouse models and human patients", Bartelt et al., <em>Sci. Trans. Med. </em>16, eadn5449 (2024).</p> <p>The data file labeled "HuCb_filtered.rds" is a processed and annotated single-nucleus RNA-seq Seurat object, containing the gene-level count data for the multiplexed snRNA-seq experiment performed on post-mortem human cerebellar tissues from patients with SCA7 and unaffected controls. Data obtained from WT and SCA7-266Q mice as described in our paper can be accessed in the NIH Gene Expression Omnibus under accession number GSE269430.</p> <p>There are three code files numbered 00 through 02 which contain analysis code for snRNA-seq data applied to both the mouse and human datasets. These files are sequential and will take the user from CellRanger output, to filtered and annotated Seurat objects, and include details for subclustering analysis as well as our pseudobulk DEseq2 differential expression approach. There are places where the user may need to modify the code based on their computer system, version of R or Seurat, and whether they are processing the 5 week, 8 week, or human data sets; these locations in the code are marked with comments.</p> <ul> <li>The first file, 00_Preprocessing_MULTIseq, begins with CellRanger filtered_feature_barcode_matrix output, extracts cell barcodes, utilizes the MULTIseq deMULTIplex software to match cell barcodes to oligo barcodes from MULTIseq fastq files, and annotates the Seurat file with metadata. Cell type identification and annotation also takes place in this file. Note: the deMULTIplex step will likely need to be run on a high performance compute cluster.</li> <li>The second file, 01_Seurat_Analysis, uses the filtered and annotated Seurat file to calculate useful QC metrics, investigate disease signals, and perform cell type subclustering analyses.</li> <li>The third file, 02_Pseudobulk_DEseq2, contains custom analysis code to extract raw counts for each cell type and each animal from the Seurat file, and uses the DEseq2 package to calculate DEGs, taking into account biological replicates, and raw read count differences between control and SCA7 animals.</li> </ul> </div> </div> </div>
Dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling"
<p>This dataset and R code support the manuscript titled "Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling" submitted to the Journal of Chemical Information and Modeling.</p> <p>Table S 1: Chemicals with their experimental values of logKch , and values of logKow and<span> </span>logKaw used to calibrate chicken muscle protein-water 2p-LFER model.</p> <p>Table S 2: Chemicals with their experimental values of logKfish and values of logKow and<span> </span>logKaw used to calibrate fish muscle protein-water 2p-LFER model.</p> <p>Table S 3: Chemicals with their experimental values of logKBSA and values of logKow and<span> </span>logKaw used to calibrate bovine serum albumin-water 2p-LFER model.</p> <p>Table S 4: Chemicals with their experimental values of logKpw and values of logKow and<span> </span>logKaw used to calibrate combined chicken and fish muscle protein-water 2p-LFER model.</p> <p>Table S 5: Diversity of data for logKpw.</p> <p>Table S 6: Diversity of data for logKBSA.</p> <p>Table S 7: Comparison of Experimental and 2p-LFER Predicted Partition Coefficients for ionizable PFAS Compounds.</p> <p>Table S 8: List of neutral fluorotelomer PFAS Compounds.</p> <p>Table S 9: List of experimental in vivo and in vitro partitioning data for different tissues and species.</p> <p>Table S 10: List of experimental Milk-water partition coefficient and predicted values of Milk-water partitioning.</p> <p>Table S 11: Training set for logKpw</p> <p>Table S 12: Validation set for log Kpw</p> <p>Table S 13: Training set for log KBSA</p> <p>Table S 14: Validation set for log KBSA</p>
Data and code for "Mapping hotspots of zoonotic pathogen emergence: an integrated model- and participatory-based approach"
<p>Code for "Mapping hotspots of zoonotic pathogen emergence: an integrated model- and participatory-based approach". Note shapefiles will need to be downloaded from GADM (https://gadm.org/), or from the gadm package in R (https://rdrr.io/github/rspatial/geodata/man/gadm.html). </p> <p>Data are available in Version 1.0 of this record</p>
Code and data for publication "pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models"
<p>This dataset contains the code of the three pre-processing tools <a href="https://github.com/tum-ens/pyGRETA">pyGRETA</a>, <a href="https://github.com/tum-ens/pyPRIMA">pyPRIMA</a> and <a href="https://github.com/tum-ens/pyCLARA">pyCLARA</a> and an examplary database for the scope of Austria.</p> <p>To run the code with full functionality additional data is needed. Check the documentation of the tools for further information.</p> <p> </p> <p>Sources for data can be found here: </p> <p>pyGRETA: https://pygreta.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyPRIMA: https://pyprima.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyCLARA: https://pyclara.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p>
Conformance Assessment of Architectural Design Decisions on the Mapping of Domain Model Elements to APIs and API Endpoints: Dataset and Code
<p>This is the dataset and related code artifact for the article "Conformance Assessment of Architectural Design Decisions on the Mapping of Domain Model Elements to APIs and API Endpoints".</p> <p><strong>Abstract of the article:</strong></p> <p> Microservice APIs are often identified and designed based on Domain-Driven Design (DDD). To help in the continuous analysis of mappings of domain model elements to APIs and API endpoints, we aim to automate the assessment of conformance to Architectural Design Decision (ADD) options. The ADDs, their decision options, and relevant decision drivers studied in this paper originate from an empirical study on the mapping of domain model elements to APIs and API endpoints in practice. We propose automated detectors to detect the decision options of the ADDs taken in a given microservice API model, and an assessment scoring scheme based on the empirical knowledge codified in the ADDs. We evaluate our work, by first manually creating a ground truth for 14 cases in a multi-case study, and then comparing the results of our automated detectors to the ground truth for each of the 14 cases. In the cases we were able to identify 86\% of the decision points correctly, and a statistical analysis of our data shows only a negligible effect size for differences to the ground truth.</p>
daleihao/Topographic_Effects: Codes and data for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"
<p>Codes and data to reproduce all results and plot all figures for GMD paper "A Parameterization of Sub-grid Topographical Effects on Solar Radiation in the E3SM Land Model (Version 1.0): Implementation and Evaluation Over the Tibetan Plateau"</p>
Code and data for Yasso_Myco modelling
<p>This file includes code and data used in the paper 'Implementation of mycorrhizal mechanisms into soil carbon model improves the prediction of long-term processes of plant litter decomposition'.</p>
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code
<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model output and code used to produce Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M., Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content, <strong>Geoscientific Model Development</strong>, 14, 1–21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A more detailed description of the data are included in README.md. The code and data is also available under version control at <a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</p>
Data and code for the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration
<p>Data and code for the manuscript <em>Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration</em> accepted for publication to<em> </em> <em>Earth and Space Science</em> in October 2021.</p> <p>The dataset contains 7 month of total losses (transmitted - received power levels) and retrieved water vapor density from a 4.87 km long full-duplex E-band commercial microwave link (CML) operating at 73.5 and 83.5 GHz in Prague, CZ. The CML was operated as a part of a mobile phone backhaul. Furthermore, observations of air temperature, and air relative humidity from sites close to the CML end nodes are provided. Finally, theoretical gaseous attenuation calculated from the air temperature and relative humidity is included as a part of the dataset.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. All time series are regular and have 5-min temporal resolution. Metadata are stored in text files.</p> <p>The code is in a form of R Markdown files and html notebooks. Results presented in the manuscript Retrieving Water Vapor From an E-band Microwave Link With an Empirical Model Not Requiring In-situ Calibration and in its Supporting information are fully reproducible using this dataset.</p>
Model and the related code for gravity field determination from the third invariant of the GOCE gravity gradient tensors: I3GG V1.0th
<p>The model and the related code for gravity field determination from the third invariant of the GOCE gravity gradient tensors: I3GG V1.0th</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.