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117 results for “Machine-learning”
Experimental and Computational Study Towards Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials
<p>SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however, it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, we first enhance its CO2RR performance by supporting SnO2 nanoparticles on two common supports, Vulcan Carbon and TiO2 . Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C.<br>Furthermore, our study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO 2 reduction and establish a correlation between SnO x structures and their CO 2 RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2−, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx -based electrocatalysts for CO2RR.</p>
Navigating the Maze of Mass Spectra: A Machine-Learning Guide to Identifying Diagnostic Ions in O-Glycan Analysis
<p>FragmentFactory_dataset is a pickled pandas DataFrame and should be loaded using the following code:</p> <pre><code>import pandas as pd FF_data = pd.read_pickle('/my_directory/FragmentFactory_dataset.pkl')</code></pre> <p> </p>
Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks
<p>Scripts and data for the publication</p>
Data and machine-learning model for fcc-FeHx at the CMB conditions
<p>Data including melting temperatures and sound velocities. The machine-learning model was trained by DeePMD-kit. The initial configuration for two-phase coexistence simulations is provided.</p>
DFT datasets for training machine-learning potential to model Cl-doped lithium borosilicate glasses using DeePMD
<h2><strong>Li diffusion in oxygen-chlorine mixed anion borosilicate glasses using</strong></h2> <h2><strong>a machine-learning simulation</strong></h2> <h5>Shingo Urata, Noriyoshi Kayaba</h5> <ul> <li>DFT_Data_for_Cl-doped_LBSCl_glass.zip inlucudes atom configurations, energies, forces, box size, atom types, atom kinds, and virial in coord.raw, energy.raw, force.raw, type.raw, type_map.raw, and virial.raw, respectively. </li> <li>All DFT data were evaluated using PBE with a cutoff energy of 600 eV by VASP.</li> <li>The other detasets are available from https://doi.org/10.5281/zenodo.10577559</li> <li>LBSCl_DMD_model.pb is force field developed using DeePMD-kit.</li> <li>LBSCl_DMD_model_c.pb.zip is the compressed version of LBSCl_DMD_model.pb.</li> </ul>
Supplementary initial and final MD configurations for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>Extended XYZ files for the initial and final configurations of the molecular dynamics simulations from the article 'General-purpose machine-learned potential for 16 elemental metals and their alloys' (https://arxiv.org/abs/2311.04732).</p> <p><span>The Supplementary Data is contained in the folder named:<br>1) Polycrystalline-MoTaVW: <span> </span>The plasticity MD simulations in multi-principal element alloys.</span></p> <p><span>2) MoTaVW-radiation: The primary radiation damage MD simulations in multi-principal element alloys.</span></p> <p><span>3) Goldene: Comparisons between UNEP-v1 and EAM models in MD simulations.</span></p> <p><span>4) NiAlMo: Comparisons between UNEP-v1 and EAM models in MCMD simulations.<br>5) AlCrCuNiV: Comparisons between UNEP-v1 and EAM models in MCMD simulations.</span></p>
Source Data for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>***Source Data***<br>This folder contains multiple .txt files that provide the source data for the figures and tables presented in the paper: "General-purpose machine-learned potential for 16 elemental metals and their alloys."</p> <p>The source data are organized in the following folders and files:</p> <p>1) Fig2<br>2) Fig3<br>3) Fig4<br>4) Fig5<br>5) Fig6<br>6) FigS1<br>7) FigS2<br>8) FigS3<br>9) FigS4-6-pure<br>10) FigS7-9-binary<br>11) FigS10-12-ternany<br>12) FigS13-15-quaternary<br>13) FigS16-17-quinary<br>14) FigS18-20<br>15) FigS21<br>16) FigS22<br>17) FigS23<br>18) FigS26<br>19) Table1-Element-atoms-GPU-Speed.txt<br>20) Table-S1-DFT-EAM-UNEP-Elastic.txt<br>21) Table-S2-DFT-EAM-UNEP-Mono-vacanc.txt<br>22) Table-s3-Surface-100-110-111-DFT-EAM-UNEP.txt<br>23) Table-S4-Melting-EAM-UNEP-Exp.txt</p>
Data related to the publication "structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations"
<p>Reference training and test datasets, trained ML potential models, and input scripts for the training (Allegro) and MD simulations (LAMMPS).</p>
[Supplementary Data] PowerModel-AI: A First On-the-fly Machine-Learning Predictor for AC Power Flow Solutions.
<h1><strong>Abstract</strong></h1> <p>The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to autonomously build models while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce <em>PowerModel-AI</em>, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures the load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model's performance is sub-optimal, based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using <em>PowerModel.jl</em>, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.</p> <div> <h1><strong>Overview</strong></h1> <p>This dataset, provided as supplementary material for the above-referenced study, includes a comprehensive collection of images (plots) from the study’s analyses, along with Jupyter notebooks containing Python scripts used for the training, validation, and testing phases of PowerModel-AI. Additionally, it includes all training and external test data used in this work, generated via LANL-based open-source power flow solver, PowerModels.jl.</p> <p>The primary objective of this dataset is to ensure full reproducibility of the study’s analyses and facilitate critical examination by the scientific community, thereby maximizing the overall impact of the work.</p> <h1>Directory Structure</h1> <p>The hierarchy of folders and file organization of the dataset is illustrated in the chart below. The directory contains a README.md file which contains the information provided here and a requirements.txt that contains all libraries necessary to run the python scripts or Jupyter notebooks in this directory. A brief description of the folders and what they contain are provided below: </p> </div> <div> <strong>.</strong></div> <div>├── <strong>Models/</strong></div> <div>│ ├── _PM_AI_Models/</div> <div>│ │ ├── Type1/</div> <div>│ │ ├── Type2/</div> <div>│ │ └── Type3/</div> <div>│ │</div> <div>│ ├── _PM_AI_Module/</div> <div>│ │ ├── __init__.py</div> <div>│ │ └── PM_Methods.py</div> <div>│ │</div> <div>│ ├── _PM_JL_Data/</div> <div>│ ├── C1_Model/</div> <div>│ ├── C2_Model/</div> <div>│ ├── C3_Model/</div> <div>│ ├── M1_Model/</div> <div>│ ├── M2_Model/ </div> <div>│ └── M3_Model/</div> <div>│</div> <div>├── <strong>NodeSensitivityAnalysis/</strong></div> <div>│ ├── Sensitivity_Plots/</div> <div>│ ├── Sensitivity_PM_JL_Data/</div> <div>│ ├── get_sensitivity_PMJL_data.py</div> <div>│ └── NodeSensitivityAnalysis.ipynb</div> <div>│</div> <div>├── <strong>PlotsForOnTheFlyAnalysis/</strong></div> <div>│ ├── BaseModel_A/</div> <div>│ ├── BaseModel_B/ </div> <div>│ └── BaseModel_C/</div> <div>│ </div> <div>├── <strong>README.md</strong></div> <div>└── <strong>requirements.txt</strong></div> <p> </p> <h2>Files Description </h2> <h3><strong>1. </strong><strong>Models/</strong></h3> <p><strong><em>_PM_AI_Models/</em></strong> contains the ML models discussed in the manuscript. Type1, Type2, and Type3 refers to the C and M models with numbers "1", "2," and "3". Each Type folder contains individual subfolders for the synthetic grids discussed.</p> <p><strong> <em>_PM_AI_Module/</em></strong> contains a python script that has all the functions used in model training and analysis. It is imported in the Jupyter notebooks in the C and M subfolders in this directory.</p> <p><strong><em>_PM_JL_Data/</em> </strong>contains the following subfolders:</p> <p>a) <strong> </strong><em>_</em><strong><em><strong>G</strong>eneratePowerModelData/</em></strong> has a python script (<u>get_PowerModelJLData.py)</u> that is used to parse <u>PowerModels.jl</u> to compute AC power flow solutions for different power demand configurations. It also contains a subfolder, <strong><em>BusData_MATLAB/</em></strong>, that has all the synthetic grids used in the study and in MATLAB format.</p> <p>b) It also contains other subfolders (not shown in the chart above) that contains AC power flow solutions generated using LANL’s PowerModels.jl, for each synthetic grids and other grid-related data.</p> <p>The folders starting with C and M are the control and candidate models (more details in manuscript). Each folder contains Jupyter notebooks (for each power grid) that has python algorithms used in model training and testing, as well as functions to analyze and plot the prediction performance of the models. It accesses (if already trained) or stores (if newly trained) the models in the <strong><em>_PM_AI_Models/</em></strong> directory. Additionally, they contain 2 subfolders (not shown in the chart above): <strong>AbsoluteErrorPlots/</strong> and <strong>PredictionPlots/</strong> were the results (plots) from the analyses are stored. Some of these results are shown in the manuscript (<em>Figures 4</em>,<em> 5</em>, <em>6</em>, and <em>7</em>).</p> <h3><strong>2. </strong><strong>NodeSensitivityAnalysis/</strong></h3> <p>This folder contains the tools used for the node dependency analysis in Section 3.1.1 of the manuscript.</p> <p>It contains a python script called get_sensitivity_PMJL_data.py, which has similar operation like the <u>get_PowerModelJLData.py</u> script but only compute changes for one bus at a time (see details in manuscript). There is also a Jupyter notebook called <u>NodeSensitivityAnalysis.ipynb </u>that analyzes the bus node dependencies and produces the plots that are shown in Figure 3 in the manuscript. It contains 2 additional sub-folders:</p> <p>a) <strong> <em>Sensitivity_PM_JL_Data/</em></strong> where PowerModels.jl generated AC power flow solution data are stored, and</p> <p>b) <em><strong>Sensitivity_Plots/</strong> </em>where the results from <u>NodeSensitivityAnalysis.ipynb</u> are stored.</p> <h3><strong>3. </strong><strong>PlotsForOnTheFlyAnalysis/</strong></h3> <p>This directory contains only the results for the discussion in Section 3.2 in the manuscript, which is the on-the-fly implementation of PowerModel-AI. The on-the-fly algorithm will be provided and distributed separately in the PowerModel-AI package, which will be publicly available through LANL’s <a href="https://github.com/lanl-ansi" target="_blank" rel="noopener">The Advanced Network Science Initiative</a> (Github). It contains 3 subfolders with similar names but ends with "A", "B," and "C" which correspond to the designations shown and discussed in <em>Figure 8</em> of the manuscript.</p> <h1>Summary</h1> <h3><strong> </strong><strong>Models/</strong></h3> <p><strong>1. _PM_AI_Models/: </strong></p> <p>Contains the machine learning models discussed in the manuscript. The Type1, Type2, and Type3 folders refer to C and M models labeled "1", "2," and "3". Each type folder includes subfolders for the corresponding synthetic grids analyzed.</p> <div><strong>2. _PM_AI_Module/: </strong> </div> <div>Contains Python scripts for model training and analysis. PM_Methods.py is the script that defines functions for model training and analysis, which are imported in the Jupyter notebooks in the C and M subfolders. </div> <div> </div> <div><strong>3. _PM_JL_Data/: </strong></div> <div>-_GeneratePowerModelData/: Contains a Python script (get_PowerModelJLData.py) used to parse PowerModels.jl and compute AC power flow solutions for various power demand configurations. This folder also includes BusData_MATLAB/, which holds the synthetic grid data in MATLAB format. </div> <div>- Other Subfolders: Contain PowerModels.jl AC power flow solutions for each synthetic grid, as well as related data.</div> <div> </div> <div><strong>4. C1_Model/, C2_Model/, C3_Model/, M1_Model/, M2_Model/, M3_Model/: </strong></div> <div>These folders contain the control and candidate models (refer to manuscript details). Each folder includes pre-run Jupyter notebooks for model training and analysis, as well as two subfolders: </div> <div> - <em>AbsoluteErrorPlots/</em>: Contains saved analysis results for each grid.</div> <div> <strong> </strong>- <em>PredictionPlots/</em>: Stores model prediction results. </div> <div>Some results are shown in <em>Figures 4, 5, 6,</em> and <em>7</em> of the manuscript and can be reproduced using the notebooks.</div> <h3>NodeSensitivityAnalysis/</h3> <div><strong>1. Node Dependency Analysis Tools: </strong> </div> <div> This folder contains scripts and data used for the node dependency analysis in Section 3.1.1 of the manuscript.</div> <div> </div> <div><strong>2. Scripts: </strong></div> <div> - get_sensitivity_PMJL_data.py: Computes power flow changes for individual buses (refer to the manuscript for details). </div> <div> - NodeSensitivityAnalysis.ipynb: Analyzes dependencies and generates plots for Figure 3 in the manuscript. </div> <div> </div> <div><strong>3. Subfolders:</strong> </div> <div> - Sensitivity_PM_JL_Data/: Stores PowerModels.jl data for sensitivity analysis. </div> <div> - Sensitivity_Plots/: Contains the generated results from the Jupyter notebook.</div> <h3>PlotsForOnTheFlyAnalysis/</h3> <div><strong>On-the-Fly Learning Results: </strong>Contains the results for the on-the-fly learning analysis discussed in Section 3.2 of the manuscript. The subfolders BaseModel_A/, BaseModel_B/, and BaseModel_C/ correspond to the designations in <em>Figure 8</em> of the manuscript. These subfolders contain plots generated during analysis.</div> <h3>Additional Files</h3> <div>- README.md: This file.</div> <div>- requirements.txt: Contains a list of necessary Python libraries required to run the scripts and Jupyter notebooks.</div> <h3>Notes:</h3> <div>- The PowerModel-AI code is publicly available on GitHub.</div> <div>- The supplementary materials include additional Jupyter notebooks and results for each power grid analysis. </div> <div>- The generated results and predictions in this repository are consistent with those discussed in the manuscript.</div>
Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.
<p>Raw-data related to a manuscript submitted to "Cancers" journal - MDPI - https://www.mdpi.com/journal/cancers</p> <p><strong>Manuscript Title</strong>: Clinical Categorization Algorithm (Clical) and Machine-Learning Approach (Srf-clical) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-world Evidence from Istituto Nazionale Tumori Irccs Fondazione Pascale, Napoli, Italy.</p> <p><strong>Authors:</strong> Gabriele Madonna1,#, Giuseppe V. Masucci2,3,#, Mariaelena Capone1, Domenico Mallardo1, Antonio Maria Grimaldi1, Ester Simeone1, Vito Vanella1, Lucia Festino1, Marco Palla1, Luigi Scarpato1, Marilena Tuffanelli1, Grazia D’angelo1, Lisa Villabona2, Isabelle Krakowski2,4, Hanna Eriksson2,3, Felipe Simao5, Rolf Lewensohn2,3, Paolo Antonio Ascierto1,+</p> <p><strong>Affiliations</strong>:</p> <p>1 Cancer Immunotherapy and Development Therapeutics Unit, Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale", Napoli, Italy</p> <p>2 Theme Cancer, Karolinska University Hospital, Stockholm, Sweden</p> <p>3 Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden</p> <p>4 Theme Inflammation, Karolinska University Hospital Stockholm, Sweden</p> <p>5 Genevia technologies OY, Tampere, Finland</p> <p># these authors equally contributed</p> <p>+ Corresponding author</p> <p><strong>Abstract of submitted Manuscript:</strong> The real-life application of immune checkpoint inhibitors (ICI) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICI at Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli Italy (INT-NA). To compare patients’ clinical variables (age, Lactate Dehydrogenase (LDH), Neutrophil-Lymphocyte Ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive non-hierarchical way, a Clinical Categorization Algorithm (CLICAL) was defined and validated by the application of machine learning, Survival Random Forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms convened into predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with a 95% accuracy.</p> <p><strong>Funding: </strong>This research was funded by Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2-2. Additional funding [N#184093) from the Stockholm Cancer Society and King Gustav V’s Jubilee foundation Stockholm.</p>
Data archive for paper "Copula-based synthetic data augmentation for machine-learning emulators"
<p><strong>Overview</strong></p> <p>This is the data archive for paper "<a href="https://doi.org/10.5194/gmd-14-5205-2021">Copula-based synthetic data augmentation for machine-learning emulators</a>". It contains the paper’s data archive with model outputs (see <code>results</code> folder) and the Singularity image for (optionally) re-running experiments.</p> <p>For the Python tool used to generate synthetic data, please refer to <a href="https://github.com/dmey/synthia">Synthia</a>.</p> <p><strong>Requirements</strong></p> <ul> <li><a href="https://sylabs.io/singularity/">Singularity</a> >= 3</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler*</li> <li>Today's high-performance computer (e.g. ~ 32 CPUs @ 2 500 MHz with 64 GB of RAM )</li> </ul> <p>*Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=72:00:00</code>).</p> <p><strong>Usage</strong></p> <p>To reproduce the results from the experiments described in the paper, first fit all copula models to the reduced NWP-SAF dataset with:</p> <pre><code>qsub hpc/fit.sh</code></pre> <p>then, to generate synthetic data, run all machine learning model configurations, and compute the relevant statistics use:</p> <pre><code>qsub hpc/stats.sh qsub hpc/ml_control.sh qsub hpc/ml_synth.sh</code></pre> <p>Finally, to plot all artifacts included in the paper use:</p> <pre><code>qsub hpc/plot.sh</code></pre> <p><strong>Licence</strong></p> <p>Code released under <a href="./LICENSE.txt">MIT license</a>. Data from the reduced NWP-SAF dataset released under <a href="./data/LICENSE.txt">CC BY 4.0</a>.</p>
Dataset used in "Machine-learning Kondo physics using variational autoencoders"
<p>Spectral functions from the single-impurity Anderson model, generated on a log-linear frequency mesh using the numerical renormalization group algorithm. Used in both https://doi.org/10.1103/PhysRevB.103.245118 (where a detailed description of dataset generation can be found) and https://arxiv.org/abs/2107.08013.</p>
Data for the study: "Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional"
<p>This set of files contains the raw data generated by the study titled:</p> <p>"Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional"</p> <p>Contains:</p> <p>- A set of Jupyter Notebooks to analyzed the data and produce the figures presented in the paper.</p> <p>- runs: Contains the training and validation scripts for each of the systems studied in this work. Also holds the model weights and validation data.</p> <p>Three folders are found: Hchain, noninteracting and Atoms.</p> <p>- datasets: Holds all the datasets generated and employed in this work.</p>
AneuploidyML_Datasets: Machine-learning analysis of factors that shape cancer aneuploidy landscapes reveals an important role for negative selection
<p>This is raw data that were used to build the processed dataset for training and testing the machine learning</p>
data for "A machine-learned approach to monitor chemical reaction via in-situ infrared spectroscopy"
<p>Source spectral and structural data of the AIMD trajectory and NEB calculation</p> <p>1. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/180-structure-IR.zip">180-structure-IR</a>.zip Source spectral and structural data of the AIMD trajectory for the selected 180 configurations</p> <p>2. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/md-pos-1.xyz?versionId=1a43760b-5cba-4548-85e6-6a45252403fc">md-pos-1.xyz</a> AIMD trajectories</p> <p>3. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-0-5100.tar.gz</a> <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-5101-7500.tar.gz</a> <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/ML-0-5100.tar.gz?versionId=ed65c63f-aa96-4be4-abef-e7a5bb7bf22f">ML-7501-9500.tar.gz</a> Source spectral and structural data of the AIMD trajectory for the extracted 9500 configurations</p> <p>4. <a href="https://zenodo.org/api/files/2d59762e-fb91-4c71-be73-fc9b18275144/split-neb-75.zip?versionId=a30f0f34-3ce2-4619-abd3-62e71174069b">split-neb-75.zip</a> Source spectral and structural data of the AIMD trajectory for the CO-CO dimerization reaction</p>
Machine-Learning Algorithm for Prediction of Blood Pressure, Glycated Haemoglobin and Estimated Glomerular Filtration Rate
ClinicalTrials.gov study NCT04814680. IPD Sharing: YES. Countries: 1. Publications: 0.
Machine-Learning Based EEG Biomarkers for Personalized Interventions
ClinicalTrials.gov study NCT06531317. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Machine-Learning Prediction and Reducing Overdoses With EHR Nudges
ClinicalTrials.gov study NCT06806163. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach
ClinicalTrials.gov study NCT05789953. IPD Sharing: NO. Countries: 1. Publications: 11.
Data from: QTG-Finder2: a generalized machine-learning algorithm for prioritizing QTL causal genes in plants
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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.