.zenodo.json

{
  "title": "A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning",
  "description": "<p>PyNets harnesses the flexibility of Nipype, Nilearn, Dipy, and Networkx packages to automatically generate graphical ensembles on a subject-by-subject basis, using any combination of graph-generating hyperparameters. PyNets utilities can be integrated with any existing preprocessing workflow, and a docker container is provided to facilitate complete reproducibility of executions.</p>",
  "maintainers": [
    {
      "name": "Pisner, Derek",
      "orcid": "0000-0002-1228-0201"
    }
  ],
  "keywords": [
    "workflow",
    "nipype",
    "dipy",
    "nilearn",
    "tractography",
    "fmri",
    "dmri",
    "gaussian-graphical-models",
    "ensemble-sampling",
    "ensemble-learning",
    "networks",
    "graph-analysis",
    "connectomics",
    "brain-connectivity",
    "structural-connectomes",
    "gridsearch",
    "functional-connectomes",
    "networkx"
  ],
  "related_identifiers": [
    {
      "identifier": "https://pynets.org",
      "relation": "documents",
      "scheme": "url"
    }
  ],
  "license": "GNU AFFERO GENERAL PUBLIC LICENSE",
  "upload_type": "software"
}