{
 "format": "oscr-script-manifest/1",
 "repository": "github.com/kmanoli/normcerebellum",
 "url": "https://github.com/kmanoli/NormCerebellum",
 "host": "github.com",
 "commit": "4355bdd8545b3df693b1d141f86543fda3fa222d",
 "license": "MIT",
 "license_confirmed_by": "license file LICENSE",
 "redistribution": "yes",
 "files": [
  {
   "path": "LICENSE",
   "sha256": "9876942f18a0e1a8b49a4820fc1c796795ecf7b44982a96f762828124099bb83",
   "language": "License",
   "lines": 21,
   "truncated": false,
   "block": 6,
   "row": 1383
  },
  {
   "path": "README.md",
   "sha256": "5d458c289330a2a09adfdf7d1430edc20503a6ab0fb2862f6ddf3d734b2c6e46",
   "language": "Text",
   "lines": 66,
   "truncated": false,
   "block": 8,
   "row": 10478
  },
  {
   "path": "scripts/1_segmentation/anat/1_run_acapulco.sh",
   "sha256": "07210cebce4be8e23268c84ef43d50ae42773fdb44d224ece885ea0b6ff0c9e8",
   "language": "Shell",
   "lines": 20,
   "truncated": false,
   "block": 8,
   "row": 7216
  },
  {
   "path": "scripts/1_segmentation/anat/2_acapulco_native_vols.py",
   "sha256": "73b70b4061c222626cf362d540a64c16a639029509b4075d5f2e2adcee9cf11d",
   "language": "Python",
   "lines": 69,
   "truncated": false,
   "block": 6,
   "row": 34288
  },
  {
   "path": "scripts/1_segmentation/func/1_fsl_func_warp.sh",
   "sha256": "ebc5137d11320dbd0f34ad52bf78c194e556837f2309d397dd0d7145ecd4fee4",
   "language": "Shell",
   "lines": 45,
   "truncated": false,
   "block": 8,
   "row": 7854
  },
  {
   "path": "scripts/1_segmentation/func/2_func_native_vols.py",
   "sha256": "30da8d1ee3114ca743073d721fcf0c2e6d8d11dfc899d461211396e1854f9c63",
   "language": "Python",
   "lines": 123,
   "truncated": false,
   "block": 6,
   "row": 39992
  },
  {
   "path": "scripts/2_normative_modeling/1_qc_euler_yeo17.py",
   "sha256": "4bf86400c935dc19832cd79914e2269ef85b689b619f65dfdd73119c4a0edd81",
   "language": "Python",
   "lines": 110,
   "truncated": false,
   "block": 6,
   "row": 40292
  },
  {
   "path": "scripts/2_normative_modeling/2_data_prep.py",
   "sha256": "f10c2fc77677ebeea9dc046e4caf8b8924c7de326dae932dc6489bed318da911",
   "language": "Python",
   "lines": 47,
   "truncated": false,
   "block": 6,
   "row": 30807
  },
  {
   "path": "scripts/2_normative_modeling/3_norm_modeling_hbr.py",
   "sha256": "e3ee0936ed5aa59725894101814a94134ca2cfe5513335a6429c0b971b898992",
   "language": "Python",
   "lines": 254,
   "truncated": false,
   "block": 7,
   "row": 2911
  },
  {
   "path": "scripts/2_normative_modeling/4_loocv.py",
   "sha256": "56ef8236490d7b528ac04b47a8a40fd65410eeb72919a26134e2faec7f59050e",
   "language": "Python",
   "lines": 187,
   "truncated": false,
   "block": 6,
   "row": 44671
  },
  {
   "path": "scripts/3_cerebral_associations/cerebral_assoc.py",
   "sha256": "fb312b8133868cab893df711865cacf6d7c7b4a8927f183758bb8d9991415743",
   "language": "Python",
   "lines": 592,
   "truncated": false,
   "block": 7,
   "row": 9250
  },
  {
   "path": "scripts/4_behavioral_outcomes/1_behav_pls.py",
   "sha256": "d93eba07cdab6f6818074e215c634777f8a092e7492dd32b202a0bbcc31d44a7",
   "language": "Python",
   "lines": 618,
   "truncated": false,
   "block": 7,
   "row": 9390
  },
  {
   "path": "scripts/4_behavioral_outcomes/2_behav_regressions.py",
   "sha256": "5caaa27c2d1d92d415d1f4340f9cda0104563bcb90d10ebadc8b06acc1705d77",
   "language": "Python",
   "lines": 134,
   "truncated": false,
   "block": 6,
   "row": 39575
  },
  {
   "path": "scripts/4_behavioral_outcomes/3_behav_cereb_cortex.py",
   "sha256": "f8bca94b51ad8809bb2a8bace7673dab0ef1429c4104f25c5a0da5151378d739",
   "language": "Python",
   "lines": 456,
   "truncated": false,
   "block": 7,
   "row": 7252
  }
 ]
}