{
  "Name": "MUniverse Grison et al 2025",
  "DatasetType": "raw",
  "License": "CC0 BY 4.0",
  "Authors": [
    "Agnese Grison",
    "Irene Mendez Guerra",
    "Alexander Kenneth Clarke",
    "Silvia Muceli",
    "Jaime Ibanez Pereda",
    "Dario Farina"
  ],
  "EthicsApprovals": [
    "The Ethics Committee at Imperial College London reviewed and approved all procedures and protocols (no. 19IC5640)"
  ],
  "Funding": [
    "UK Research and Innovation (UKRI Centre for Doctoral Training in AI for Healthcare grant number EP/S023283/1)",
    "Huawei Technologies Research & Development (UK) Ltd.",
    "Engineering and Physical Sciences Research Council (EPSRC) Doctoral Prize Fellowship",
    "HybridNeuro (HORIZON-WIDERA-2021-ACCESS-03- 101079392)",
    "ECHOES (ERC Starting Grant 101077693)",
    "Consolidación Investigadora grant (CNS2022-135366) funded by MCIN/AEI/10.13039/501100011033",
    "NextGenerationEU/PRTR",
    "Non-Invasive Single Neuron Electrical Monitoring (NISNEM Technology) Grant EP/T020970/1)"
  ],
  "ReferencesAndLinks": [
    "Grison, A., Mendez Guerra, I., Clarke, A. K., Muceli, S., Ibanez Pereda, J., & Farina, D. (2025). Unlocking the full potential of high-density surface EMG: novel non-invasive high-yield motor unit decomposition Journal of Physiology, 603.8 (2025)",
    "https://doi.org/10.1113/JP287913"
  ],
  "BIDSVersion": "1.11.1",
  "GeneratedBy": [
    {
      "Name": "MUniverse",
      "Version": "1.0",
      "CodeURL": "https://github.com/dfarinagroup/muniverse/scripts/",
      "ScriptName": "grison2025_to_bids.py",
      "Description": "Semi-automated conversion of the original data into BIDS format"
    }
  ],
  "SourceDatasets": [
    {
      "DOI": "https://doi.org/10.7910/DVN/ID1WNQ"
    }
  ],
  "DatasetDOI": "10.82901/nemar.nm000165",
  "Version": "1.0.1"
}