Scripts of 2026-09-29: 4 new block(s), 1817 manifest(s), 0 removed (part 7)

manifests/fc/github.com__malhawarat__hh-dfc-orbitmemory.json

@@ -0,0 +1,111 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/malhawarat/hh-dfc-orbitmemory",
+ "url": "https://github.com/malhawarat/HH-DFC-OrbitMemory",
+ "host": "github.com",
+ "commit": "465b302c206d7fcb197fc7c26ab2b35914991471",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "b26b38ba8fe2107035131fe34aa95cdb55d961c792302b19bd5bc2175a6eb857",
+   "language": "License",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 1475
+  },
+  {
+   "path": "README.md",
+   "sha256": "b9037e07c503784d81dc1ee042070d777a31b43e30455d96c4d05d0b6b358297",
+   "language": "Text",
+   "lines": 147,
+   "truncated": false,
+   "block": 8,
+   "row": 10456
+  },
+  {
+   "path": "experiments/PS0_OrbitCatalogue.ipynb",
+   "sha256": "3967bf9f1afcb78c09a6c2156a1d87e9464c9823c3314dddd86f7d146371fdb3",
+   "language": "Jupyter",
+   "lines": 1038,
+   "truncated": false,
+   "block": 6,
+   "row": 1085
+  },
+  {
+   "path": "experiments/PS0_SecondaryIbias.ipynb",
+   "sha256": "b59689bff59ea9337e5e1b4d7802c39116e7408353773aac25e377d622a6651c",
+   "language": "Jupyter",
+   "lines": 752,
+   "truncated": false,
+   "block": 6,
+   "row": 1031
+  },
+  {
+   "path": "experiments/PS0b_FloquetValidation.ipynb",
+   "sha256": "6c6474223dcab84ac575812dce9a633058233dd8246f24094da465d308c3fa09",
+   "language": "Jupyter",
+   "lines": 843,
+   "truncated": false,
+   "block": 6,
+   "row": 1045
+  },
+  {
+   "path": "experiments/PS1_WriteProtocol.ipynb",
+   "sha256": "00550b48a8358df2a38ef9a0d78d02e1c8e5a770deb16f36d90e99a553ca278f",
+   "language": "Jupyter",
+   "lines": 1109,
+   "truncated": false,
+   "block": 6,
+   "row": 1124
+  },
+  {
+   "path": "experiments/PS2_ReadProtocol.ipynb",
+   "sha256": "48375e858a87e7184bc00d6dfa0da0e847ef23a7e90971af98261bd45ac82048",
+   "language": "Jupyter",
+   "lines": 1183,
+   "truncated": false,
+   "block": 6,
+   "row": 1131
+  },
+  {
+   "path": "experiments/PS3_FullDemo_n_100.ipynb",
+   "sha256": "65c6903051e142783f18c16fe4e6e102f911b0b873d639ba884b79274957bf1a",
+   "language": "Jupyter",
+   "lines": 1161,
+   "truncated": false,
+   "block": 6,
+   "row": 1115
+  },
+  {
+   "path": "experiments/PS3_FullDemo_n_20.ipynb",
+   "sha256": "27a06942213345fee5ae71fc4009ccbec7c67187158ba8c0d61104186751db95",
+   "language": "Jupyter",
+   "lines": 1148,
+   "truncated": false,
+   "block": 6,
+   "row": 1109
+  },
+  {
+   "path": "experiments/PS4_RateBaseline.ipynb",
+   "sha256": "8f318a209b22274dc4c602974063436cac0a0508afcd6f83364371e5e2421299",
+   "language": "Jupyter",
+   "lines": 605,
+   "truncated": false,
+   "block": 6,
+   "row": 875
+  },
+  {
+   "path": "experiments/PS5_MaxCapacity.ipynb",
+   "sha256": "cd0aa060e69fec2d42dd9f4f5defdf1ab5f962d275f58a77a826e5c2c9a736ad",
+   "language": "Jupyter",
+   "lines": 653,
+   "truncated": false,
+   "block": 6,
+   "row": 921
+  }
+ ]
+}

manifests/fc/github.com__ptindex__mndgnn.json

@@ -0,0 +1,102 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/ptindex/mndgnn",
+ "url": "https://github.com/PTINDEX/MNDGNN",
+ "host": "github.com",
+ "commit": "23482e9b546be0b3a1dcea7bc012727ce4f163f8",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "69cef97bae98c10d69d134cacaf6bb8dac059ad7acbda09e3ccf2955d3fb4a2c",
+   "language": "License",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 1380
+  },
+  {
+   "path": "README.md",
+   "sha256": "5c9f97a7f339cde79be8ed9a647c3331a105547bba0835cc3d560b34804e6394",
+   "language": "Text",
+   "lines": 82,
+   "truncated": false,
+   "block": 8,
+   "row": 10075
+  },
+  {
+   "path": "main.py",
+   "sha256": "c13a03fe75d9e71a447a1826cde28ea9c6955bb24dd777112d0fd90c5de1822f",
+   "language": "Python",
+   "lines": 269,
+   "truncated": false,
+   "block": 7,
+   "row": 4523
+  },
+  {
+   "path": "model.py",
+   "sha256": "2c0d84b2b35255784723e1a9d9f2a44a1760cc7e066d80bdbf1fa85dd2feab82",
+   "language": "Python",
+   "lines": 327,
+   "truncated": false,
+   "block": 7,
+   "row": 5555
+  },
+  {
+   "path": "utils/augmentation_util.py",
+   "sha256": "82180e5ff1e347bdd332d64633b461937235cdf8e964d2aee8dbd014b011fcdd",
+   "language": "Python",
+   "lines": 87,
+   "truncated": false,
+   "block": 6,
+   "row": 38280
+  },
+  {
+   "path": "utils/data_augmentation.py",
+   "sha256": "a89800456994bed8c9b025949e6bc9ac232b4b876d0950f0e0eaf52cfe226ebe",
+   "language": "Python",
+   "lines": 50,
+   "truncated": false,
+   "block": 6,
+   "row": 32357
+  },
+  {
+   "path": "utils/data_loading.py",
+   "sha256": "3078a506de6f3ffcc8ae5d355b3ce83e38c2c8a5df9b9db9199dd0174b5d2847",
+   "language": "Python",
+   "lines": 63,
+   "truncated": false,
+   "block": 6,
+   "row": 33347
+  },
+  {
+   "path": "utils/data_utils.py",
+   "sha256": "dbd501e9cecf21995ba26531e6f5fc6e0cddf3b2f2dd8796b704dc18bc0b5380",
+   "language": "Python",
+   "lines": 173,
+   "truncated": false,
+   "block": 6,
+   "row": 42706
+  },
+  {
+   "path": "utils/dataset.py",
+   "sha256": "a6ca1b8bd22d830d86184b8570ccf4420b2553fa502cb23174c5e31c3a4ba72c",
+   "language": "Python",
+   "lines": 29,
+   "truncated": false,
+   "block": 6,
+   "row": 25173
+  },
+  {
+   "path": "utils/predictions_utils.py",
+   "sha256": "c446620ebc196f1fb67acaa85813229361a0b390cab878c3454de7abe222176a",
+   "language": "Python",
+   "lines": 48,
+   "truncated": false,
+   "block": 6,
+   "row": 30518
+  }
+ ]
+}

manifests/fc/github.com__wbxbky__bn-nerf-inference.json

@@ -0,0 +1,39 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/wbxbky/bn-nerf-inference",
+ "url": "https://github.com/wbxbky/BN-NeRF-Inference",
+ "host": "github.com",
+ "commit": "4cd8004c8e5558fe189c5b2f1fea71daf170ee75",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "61386cf1d3f7ab5dcafcdf04cec887d6f62fbe59b317cbc9b4ca2d2447b5e743",
+   "language": "License",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 1353
+  },
+  {
+   "path": "README.md",
+   "sha256": "5339398df4b5f2712b88c0a4898791a6a36a20799bee9f52c36fc984fda48e64",
+   "language": "Text",
+   "lines": 26,
+   "truncated": false,
+   "block": 8,
+   "row": 9744
+  },
+  {
+   "path": "inference.py",
+   "sha256": "5acaef397ea7f8b88b322f4ff5e23f990fb77005c450c6815ad012fbe1759724",
+   "language": "Python",
+   "lines": 83,
+   "truncated": false,
+   "block": 6,
+   "row": 35951
+  }
+ ]
+}

manifests/fc/zenodo__19966318.json

@@ -0,0 +1,93 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "zenodo:19966318",
+ "url": "10.5281/zenodo.19966318",
+ "host": "zenodo.org",
+ "commit": "",
+ "license": "CC-BY-4.0",
+ "license_confirmed_by": "license of the archive's record",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "envelop_extra_exp1.m",
+   "sha256": "5624b68c734a6333b8aefcc9960cdbf492e42a73d174efa58dda79ace9f321bb",
+   "language": "MATLAB",
+   "lines": 89,
+   "truncated": false,
+   "block": 6,
+   "row": 12767
+  },
+  {
+   "path": "freesurfer_fangxiang_envelope_corr.m",
+   "sha256": "1fd78aae7ffb70a2f5aa694790f66999af23ef25b206b8a923a2a18d42632b55",
+   "language": "MATLAB",
+   "lines": 135,
+   "truncated": false,
+   "block": 6,
+   "row": 16174
+  },
+  {
+   "path": "freesurfer_fangxiang_envelope_statistic.m",
+   "sha256": "813d7f09bebb0945d29230fdca8e4cb1a0802f0c114a4f2ce1b0e86549e0bbe2",
+   "language": "MATLAB",
+   "lines": 93,
+   "truncated": false,
+   "block": 6,
+   "row": 15037
+  },
+  {
+   "path": "freesurfer_fangxiang_regress_corr.m",
+   "sha256": "7979dcec06d5931f18b8fb149f38cf2acab0bddbe82c32eefddccdda4ebaa52b",
+   "language": "MATLAB",
+   "lines": 145,
+   "truncated": false,
+   "block": 6,
+   "row": 16380
+  },
+  {
+   "path": "freesurfer_fangxiang_statistic.m",
+   "sha256": "08c6ddf1f3f61d8708b29f3b14636629aa3bfc2454cba085a690f516a180311b",
+   "language": "MATLAB",
+   "lines": 102,
+   "truncated": false,
+   "block": 6,
+   "row": 15785
+  },
+  {
+   "path": "freesurfer_interbrain_fangxiang_corr_lianxu.m",
+   "sha256": "e50e5147738246340f2e284b7c75507a38c9b2d7b48d8fd660b0a948a76542a1",
+   "language": "MATLAB",
+   "lines": 210,
+   "truncated": false,
+   "block": 6,
+   "row": 17915
+  },
+  {
+   "path": "freesurfer_inverse_analysis.m",
+   "sha256": "07606b51657d6d7e8ec1658f2f55a5ea645b543b4d3cd77d94b5a6d770d1f4a3",
+   "language": "MATLAB",
+   "lines": 159,
+   "truncated": false,
+   "block": 6,
+   "row": 15528
+  },
+  {
+   "path": "freesurfer_speak_ROI_signal_bilateral.m",
+   "sha256": "a1af08b6cdf97888c53f7749dccb4821a2c81004eea69c921d3391bb174df063",
+   "language": "MATLAB",
+   "lines": 503,
+   "truncated": false,
+   "block": 6,
+   "row": 18763
+  },
+  {
+   "path": "freesurfer_speaker_GLM_analysis_code.m",
+   "sha256": "ed1589216be5e44c86d3861e249d3a9cd6cfece479dc7bf29b3e12239b25c67d",
+   "language": "MATLAB",
+   "lines": 650,
+   "truncated": false,
+   "block": 6,
+   "row": 18727
+  }
+ ]
+}

manifests/fd/github.com__benshahary__drosophila-courtship.json

@@ -0,0 +1,597 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/benshahary/drosophila-courtship",
+ "url": "https://github.com/benshahary/drosophila-courtship/tree/v1.0.0",
+ "host": "github.com",
+ "commit": "a40249ee538a73f66b25d55c47e06f8505ba10f2",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "e06794c25c36225a6652f292de9d5f171a5b7a85396515aff7485f37b1ddf44b",
+   "language": "License",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 1608
+  },
+  {
+   "path": "README.md",
+   "sha256": "0a83b6496beea5ac009eee07781091d59a37cf73d2651b6fb55860d812e8057a",
+   "language": "Text",
+   "lines": 60,
+   "truncated": false,
+   "block": 8,
+   "row": 9792
+  },
+  {
+   "path": "courtship/__init__.py",
+   "sha256": "ea17ea353d987198217f304054771b4e004e2058bc47a580338f66565f4361e2",
+   "language": "Python",
+   "lines": 6,
+   "truncated": false,
+   "block": 6,
+   "row": 20721
+  },
+  {
+   "path": "courtship/app/__init__.py",
+   "sha256": "7376a37be5e4c2b241345d8d667055d92323dbc2791761dae58f3a2706cf95c7",
+   "language": "Python",
+   "lines": 1,
+   "truncated": false,
+   "block": 6,
+   "row": 20114
+  },
+  {
+   "path": "courtship/app/arena.py",
+   "sha256": "72416fc95ddb791f4db143a5a4868cdeccc7b942d43f1bb93c9de38bbab624a3",
+   "language": "Python",
+   "lines": 201,
+   "truncated": false,
+   "block": 6,
+   "row": 43639
+  },
+  {
+   "path": "courtship/app/dialogs/__init__.py",
+   "sha256": "da4fd97aa53b16fa99305a869be11846257df2ec54c668b2e705f282f2286cf5",
+   "language": "Python",
+   "lines": 1,
+   "truncated": false,
+   "block": 6,
+   "row": 20107
+  },
+  {
+   "path": "courtship/app/dialogs/batch.py",
+   "sha256": "3be36bbc4448597fba5143bb00c6ac2e52754207381198a774b17433507c1432",
+   "language": "Python",
+   "lines": 179,
+   "truncated": false,
+   "block": 6,
+   "row": 44304
+  },
+  {
+   "path": "courtship/app/dialogs/statproc.py",
+   "sha256": "e8d4e582a006906f9ee6ec8925e9a94c9f40c070b0764a88acb39298febfbac5",
+   "language": "Python",
+   "lines": 305,
+   "truncated": false,
+   "block": 7,
+   "row": 4310
+  },
+  {
+   "path": "courtship/app/dialogs/videozoom.py",
+   "sha256": "e22b952611ad02f0d482a9cc1ebedf0a0408fc2a911cb0541372a0150e866b3d",
+   "language": "Python",
+   "lines": 76,
+   "truncated": false,
+   "block": 6,
+   "row": 32878
+  },
+  {
+   "path": "courtship/app/drawing.py",
+   "sha256": "48d5e85e9d2e10efb3c44658c0d0dd11976b83ef143a2b88d72474c94e514826",
+   "language": "Python",
+   "lines": 63,
+   "truncated": false,
+   "block": 6,
+   "row": 31087
+  },
+  {
+   "path": "courtship/app/entry.py",
+   "sha256": "477d0865bcf586dd6e18b1696eef003937a76e0af92a720bab4826868208aaba",
+   "language": "Python",
+   "lines": 552,
+   "truncated": false,
+   "block": 7,
+   "row": 8689
+  },
+  {
+   "path": "courtship/app/errors.py",
+   "sha256": "d7d6b8a1741c1cc737c870c15fe037a9c652515d7cc9cd1b4fa92fe348d244ee",
+   "language": "Python",
+   "lines": 35,
+   "truncated": false,
+   "block": 6,
+   "row": 24970
+  },
+  {
+   "path": "courtship/app/female.py",
+   "sha256": "77e08edba88fae42d6b95e299ae00cef4aa83258784da4597394ab20ecd73910",
+   "language": "Python",
+   "lines": 168,
+   "truncated": false,
+   "block": 6,
+   "row": 41689
+  },
+  {
+   "path": "courtship/app/settings.py",
+   "sha256": "44e830ec2bc8aaab14bdc1b0d6260c4a5903fa26a78cd5c3d7dc72166659a9d4",
+   "language": "Python",
+   "lines": 40,
+   "truncated": false,
+   "block": 6,
+   "row": 25997
+  },
+  {
+   "path": "courtship/app/threads.py",
+   "sha256": "cefefc8f624693df5358372fa60d0390c829327d8df43f58b1a460232c1d9182",
+   "language": "Python",
+   "lines": 180,
+   "truncated": false,
+   "block": 7,
+   "row": 307
+  },
+  {
+   "path": "courtship/app/tracking.py",
+   "sha256": "6a7e44ecf0aa8e790a4feac2a2c08bb3916730ff46cb0919a65ca477cd043db6",
+   "language": "Python",
+   "lines": 838,
+   "truncated": false,
+   "block": 7,
+   "row": 10210
+  },
+  {
+   "path": "courtship/app/transforms.py",
+   "sha256": "de694ac46c9136a8be69e040a7fae32aa942766131567d1a87bc60610beb9338",
+   "language": "Python",
+   "lines": 385,
+   "truncated": false,
+   "block": 7,
+   "row": 5262
+  },
+  {
+   "path": "courtship/app/utils.py",
+   "sha256": "637053bc6ef7374cc21d3aeb902144806974a9eaf04579d247c66d8751323f3b",
+   "language": "Python",
+   "lines": 88,
+   "truncated": false,
+   "block": 6,
+   "row": 31656
+  },
+  {
+   "path": "courtship/app/widgets/__init__.py",
+   "sha256": "da4fd97aa53b16fa99305a869be11846257df2ec54c668b2e705f282f2286cf5",
+   "language": "Python",
+   "lines": 1,
+   "truncated": false,
+   "block": 6,
+   "row": 20107
+  },
+  {
+   "path": "courtship/app/widgets/batch.py",
+   "sha256": "5904536d81e898e49e33662d8d744892d36a952b3df16c2c8bebb26542b0fdba",
+   "language": "Python",
+   "lines": 1661,
+   "truncated": false,
+   "block": 8,
+   "row": 621
+  },
+  {
+   "path": "courtship/app/widgets/fileio.py",
+   "sha256": "e97017bda7d082d7eae2b23ad80b14948dcde5299e8e7dd2d6a0e3d18dc3ced6",
+   "language": "Python",
+   "lines": 63,
+   "truncated": false,
+   "block": 6,
+   "row": 31415
+  },
+  {
+   "path": "courtship/app/widgets/statistics.py",
+   "sha256": "5ed5c3a5a5edcb369b599d6104ad315eae71ca8a00c6e298a24d39431f943480",
+   "language": "Python",
+   "lines": 414,
+   "truncated": false,
+   "block": 7,
+   "row": 6288
+  },
+  {
+   "path": "courtship/app/widgets/text.py",
+   "sha256": "08d8863c0485bde285bab0f25b6ad3e7eb8eeb4cd36fc38f872d44a2b85ca0d6",
+   "language": "Python",
+   "lines": 48,
+   "truncated": false,
+   "block": 6,
+   "row": 29297
+  },
+  {
+   "path": "courtship/app/widgets/video.py",
+   "sha256": "63b0c5f97849e4a66de244c5baecadcf70cee256c9a4ba42db33ff0902b1d43c",
+   "language": "Python",
+   "lines": 302,
+   "truncated": false,
+   "block": 7,
+   "row": 3137
+  },
+  {
+   "path": "courtship/behavior.py",
+   "sha256": "205f38a15374ddbff6ee305fa98fe6beb05204155a4863f29d19c76f4582aeca",
+   "language": "Python",
+   "lines": 268,
+   "truncated": false,
+   "block": 7,
+   "row": 412
+  },
+  {
+   "path": "courtship/experiment.py",
+   "sha256": "cb1a0693a5af95210a0f74474e0822389feea88c88895fd994104c10e9505305",
+   "language": "Python",
+   "lines": 1575,
+   "truncated": false,
+   "block": 8,
+   "row": 558
+  },
+  {
+   "path": "courtship/fly.py",
+   "sha256": "5cc7f2d05e545c9a3ec4425b2e72e00b48ce2eaeace0091cf87ff9a006a84a9b",
+   "language": "Python",
+   "lines": 550,
+   "truncated": false,
+   "block": 7,
+   "row": 7798
+  },
+  {
+   "path": "courtship/meta.py",
+   "sha256": "522976dba3786982431817b753d4defebbeb1c8a61a25bc4183b8bc53d48b728",
+   "language": "Python",
+   "lines": 158,
+   "truncated": false,
+   "block": 6,
+   "row": 41515
+  },
+  {
+   "path": "courtship/ml/__init__.py",
+   "sha256": "7376a37be5e4c2b241345d8d667055d92323dbc2791761dae58f3a2706cf95c7",
+   "language": "Python",
+   "lines": 1,
+   "truncated": false,
+   "block": 6,
+   "row": 20114
+  },
+  {
+   "path": "courtship/ml/classifiers.py",
+   "sha256": "a9dae7efa8a476de4fb9a34af7d4328fc4a75d1b43ec29d0f4708226070067cd",
+   "language": "Python",
+   "lines": 365,
+   "truncated": false,
+   "block": 7,
+   "row": 4591
+  },
+  {
+   "path": "courtship/ml/errors.py",
+   "sha256": "2072c6845517b53dc283c5bc36cc8c29ccc57b224e6585c9de50aa9bbb780ea5",
+   "language": "Python",
+   "lines": 13,
+   "truncated": false,
+   "block": 6,
+   "row": 21339
+  },
+  {
+   "path": "courtship/ml/features.py",
+   "sha256": "23403b37da87cfe0a40f81227da72b23da09354c957385a067dba0c69baea38e",
+   "language": "Python",
+   "lines": 479,
+   "truncated": false,
+   "block": 7,
+   "row": 6750
+  },
+  {
+   "path": "courtship/plots/__init__.py",
+   "sha256": "60a53888c02ef226f01c338bc0f9493d6ee18979cd84cb6b5df62c7769b85775",
+   "language": "Python",
+   "lines": 15,
+   "truncated": false,
+   "block": 6,
+   "row": 21297
+  },
+  {
+   "path": "courtship/plots/categorical.py",
+   "sha256": "5aad87d20fac3124fafe4b3499d25fc718d1a2acc8c514bfa3e8a42001d73c47",
+   "language": "Python",
+   "lines": 206,
+   "truncated": false,
+   "block": 6,
+   "row": 42354
+  },
+  {
+   "path": "courtship/plots/misc.py",
+   "sha256": "353500f3ab49c605dfdcd0dce1b7bde869b6d95f51360346ac8a174de6fdea23",
+   "language": "Python",
+   "lines": 1338,
+   "truncated": false,
+   "block": 7,
+   "row": 11492
+  },
+  {
+   "path": "courtship/plots/polar.py",
+   "sha256": "4aaea9d1a0a068208859658fd3d06d368c8723ef63d7cfc8b4892b2acba4690b",
+   "language": "Python",
+   "lines": 301,
+   "truncated": false,
+   "block": 6,
+   "row": 45483
+  },
+  {
+   "path": "courtship/plots/utils.py",
+   "sha256": "2423ec3fbd96c9f3e577bab1d87d732555c07f10b53aae7ff9f0a62352875ec4",
+   "language": "Python",
+   "lines": 106,
+   "truncated": false,
+   "block": 6,
+   "row": 35459
+  },
+  {
+   "path": "courtship/stats/__init__.py",
+   "sha256": "7376a37be5e4c2b241345d8d667055d92323dbc2791761dae58f3a2706cf95c7",
+   "language": "Python",
+   "lines": 1,
+   "truncated": false,
+   "block": 6,
+   "row": 20114
+  },
+  {
+   "path": "courtship/stats/_signal.py",
+   "sha256": "d1bda39bba318d9e2e0cb2993c66a44bdb03cf19723fc9efb1c8f26360e163b4",
+   "language": "Python",
+   "lines": 412,
+   "truncated": false,
+   "block": 7,
+   "row": 4575
+  },
+  {
+   "path": "courtship/stats/_wavelet.py",
+   "sha256": "2cb74ad7a2412b360a1abf197aa377d8cbf7be8f53331d324fbfe36a24e4b7f4",
+   "language": "Python",
+   "lines": 108,
+   "truncated": false,
+   "block": 6,
+   "row": 35773
+  },
+  {
+   "path": "courtship/stats/behaviors.py",
+   "sha256": "3a661afcf34c8d9ee3bdb111cc6db252f4aa53f21f0d9564038a1d51c4da67d4",
+   "language": "Python",
+   "lines": 605,
+   "truncated": false,
+   "block": 7,
+   "row": 7785
+  },
+  {
+   "path": "courtship/stats/centroid.py",
+   "sha256": "39036ee0a1230c79535d67636d87a033f88babb4bf12361ffa6265f8a3ae9db8",
+   "language": "Python",
+   "lines": 275,
+   "truncated": false,
+   "block": 7,
+   "row": 730
+  },
+  {
+   "path": "courtship/stats/markov.py",
+   "sha256": "251f0ba998fed2428c1976ee37874659fc7982d9c7a7934f7b54e4c036840ce6",
+   "language": "Python",
+   "lines": 175,
+   "truncated": false,
+   "block": 6,
+   "row": 42280
+  },
+  {
+   "path": "courtship/stats/spatial.py",
+   "sha256": "8a60e0e4741cab507980045dde31f73a97c9eeb1f6f06562df8b395842ffc169",
+   "language": "Python",
+   "lines": 799,
+   "truncated": false,
+   "block": 7,
+   "row": 10070
+  },
+  {
+   "path": "courtship/stats/transforms.py",
+   "sha256": "5e4ec5399ca06147afb375d210bf3263fc7f1e56bed6f79fa90aed6376194599",
+   "language": "Python",
+   "lines": 201,
+   "truncated": false,
+   "block": 6,
+   "row": 43159
+  },
+  {
+   "path": "courtship/stats/utils.py",
+   "sha256": "769ce397b15130b6943ea63fd94dbd464cb55ddecd5e9d65b3c3fb25cb65ce78",
+   "language": "Python",
+   "lines": 150,
+   "truncated": false,
+   "block": 6,
+   "row": 39419
+  },
+  {
+   "path": "courtship/stats/wing.py",
+   "sha256": "55995ce1b650746f37fc1e3aa2ddf6e96e2e2ba884421e65bc37430f785b275b",
+   "language": "Python",
+   "lines": 125,
+   "truncated": false,
+   "block": 6,
+   "row": 37247
+  },
+  {
+   "path": "courtship/ts.py",
+   "sha256": "f2a3a49a61b8cb563debb05f6965880be92591bfa9497c786ab161557fc81d16",
+   "language": "Python",
+   "lines": 229,
+   "truncated": false,
+   "block": 6,
+   "row": 45264
+  },
+  {
+   "path": "docs/source/conf.py",
+   "sha256": "21c4507668e97821db063d830b55ecff73ad5f5fdd0810495f8c7106406e87c7",
+   "language": "Python",
+   "lines": 176,
+   "truncated": false,
+   "block": 6,
+   "row": 42243
+  },
+  {
+   "path": "setup.py",
+   "sha256": "f807408b00c5f66afc250cf453eb6712eb717952e6305e8532acc34e7f7d65df",
+   "language": "Python",
+   "lines": 81,
+   "truncated": false,
+   "block": 6,
+   "row": 32981
+  },
+  {
+   "path": "tests/context.py",
+   "sha256": "28610370d80149c4d21ea4999a447b2e6de412769ba6af45bfe15a536b3469c1",
+   "language": "Python",
+   "lines": 29,
+   "truncated": false,
+   "block": 6,
+   "row": 24725
+  },
+  {
+   "path": "tests/test_behavior.py",
+   "sha256": "18b70115b5f2523b599dc2564e5dbb61e7eb7627012206d948c01cf9721a4a43",
+   "language": "Python",
+   "lines": 174,
+   "truncated": false,
+   "block": 6,
+   "row": 44457
+  },
+  {
+   "path": "tests/test_entry.py",
+   "sha256": "9044c67117e978acb44bf70c36347d68eba06e514e78974ff4b4e07c36f489b2",
+   "language": "Python",
+   "lines": 4,
+   "truncated": false,
+   "block": 6,
+   "row": 20339
+  },
+  {
+   "path": "tests/test_experiment.py",
+   "sha256": "0946bc384a0480e8005310162abfac5b91689272b426ae9881aa24678e0f9bc8",
+   "language": "Python",
+   "lines": 50,
+   "truncated": false,
+   "block": 6,
+   "row": 30878
+  },
+  {
+   "path": "tests/test_fly.py",
+   "sha256": "86afe4b007e283a7bb817763686cf52ff2968f5c9b9a6e8cb28dc376d28808bd",
+   "language": "Python",
+   "lines": 245,
+   "truncated": false,
+   "block": 7,
+   "row": 2097
+  },
+  {
+   "path": "tests/test_misc_plots.ipynb",
+   "sha256": "3796f0b2f662102e3a95d3cee9bb01ac7736a447b906347067c149330e7617a4",
+   "language": "Jupyter",
+   "lines": 93,
+   "truncated": false,
+   "block": 5,
+   "row": 16366
+  },
+  {
+   "path": "tests/test_ml_features.py",
+   "sha256": "d2c8aa944cb08f374bc106a2d5f6dae961c093bd73b12776e2c31dbf423591fd",
+   "language": "Python",
+   "lines": 28,
+   "truncated": false,
+   "block": 6,
+   "row": 24054
+  },
+  {
+   "path": "tests/test_plots.ipynb",
+   "sha256": "b2925ce62ff0de2096ae47ddc0465e464f9d9744aa429806262020a001a7a917",
+   "language": "Jupyter",
+   "lines": 178,
+   "truncated": false,
+   "block": 5,
+   "row": 16910
+  },
+  {
+   "path": "tests/test_stats_behaviors.py",
+   "sha256": "14f771dabd3dbaf163285f1c0088a12701807e40a514c4ad77d4a3d89ff8332c",
+   "language": "Python",
+   "lines": 330,
+   "truncated": false,
+   "block": 7,
+   "row": 3003
+  },
+  {
+   "path": "tests/test_stats_centroid.py",
+   "sha256": "4d6d0fd7512c7e17a1db2fbd44a3f97b37957ab26f9ea3fa5255f257c5b29662",
+   "language": "Python",
+   "lines": 186,
+   "truncated": false,
+   "block": 6,
+   "row": 44257
+  },
+  {
+   "path": "tests/test_stats_spatial.py",
+   "sha256": "3d6efcb10e01c3f903f0b938013b371231ab4dddb0e70f78093519eca68f98be",
+   "language": "Python",
+   "lines": 80,
+   "truncated": false,
+   "block": 6,
+   "row": 32984
+  },
+  {
+   "path": "tests/test_trk_arena.py",
+   "sha256": "72cd6a0718beb9800529dd04b402b7f1e1346aa1f861b087756625de054389a8",
+   "language": "Python",
+   "lines": 67,
+   "truncated": false,
+   "block": 6,
+   "row": 32937
+  },
+  {
+   "path": "tests/test_trk_female.py",
+   "sha256": "f2b0a138db8b0f9308b6b0dd075a1aaad915912555fe9e0cc17525a4bbaec1bb",
+   "language": "Python",
+   "lines": 78,
+   "truncated": false,
+   "block": 6,
+   "row": 33443
+  },
+  {
+   "path": "tests/test_trk_tracking.py",
+   "sha256": "d85331160b8a873d150b7513d7eca30e345f35d24546ae9e5159449f014bae2d",
+   "language": "Python",
+   "lines": 139,
+   "truncated": false,
+   "block": 6,
+   "row": 40390
+  },
+  {
+   "path": "tests/test_ts.py",
+   "sha256": "62deba290b2b363c98733bc10d9ec3ca9a43cd6a8fd6f889c21e5ea72dbf1e21",
+   "language": "Python",
+   "lines": 41,
+   "truncated": false,
+   "block": 6,
+   "row": 27354
+  }
+ ]
+}

manifests/fd/github.com__nozomi-asaoka__custom-code-for-asaoka-et-al-2026.json

@@ -0,0 +1,57 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/nozomi-asaoka/custom-code-for-asaoka-et-al-2026",
+ "url": "https://github.com/Nozomi-Asaoka/Custom-code-for-Asaoka-et-al-2026.git",
+ "host": "github.com",
+ "commit": "2c6a0619548ce23514943ee7c35ab83d8bbc3e20",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "FIg5&7_Event_extraction.ipynb",
+   "sha256": "01305208d1e06e11217c40b45670b4a1eded31fe717b02899ae97c4fbc6400ae",
+   "language": "Jupyter",
+   "lines": 176,
+   "truncated": false,
+   "block": 5,
+   "row": 16763
+  },
+  {
+   "path": "Fig7_Classification.ipynb",
+   "sha256": "aa9a04d61ddf5f922a28a82559cf42a07356f5278ca948262f94e26f5509ee90",
+   "language": "Jupyter",
+   "lines": 79,
+   "truncated": false,
+   "block": 5,
+   "row": 16205
+  },
+  {
+   "path": "Fig7_Clustering.ipynb",
+   "sha256": "3fb6e816801dcd7ef8b3478176a7a4ba69c7421215386b71c8ce5b5f335e6306",
+   "language": "Jupyter",
+   "lines": 48,
+   "truncated": false,
+   "block": 5,
+   "row": 16071
+  },
+  {
+   "path": "LICENSE",
+   "sha256": "1f6f8dc5bf3b43acfeb231cc1f5e3e79830c3b3f74339189ff29df6e293483b5",
+   "language": "License",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 1581
+  },
+  {
+   "path": "README.md",
+   "sha256": "85d1c8234d2e189bad095945cd37604c6b2d533fc2e67fcc13b646a5f2acf4ca",
+   "language": "Text",
+   "lines": 40,
+   "truncated": false,
+   "block": 8,
+   "row": 9882
+  }
+ ]
+}

manifests/fd/github.com__qinzhou-work__micnet.json

@@ -0,0 +1,300 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/qinzhou-work/micnet",
+ "url": "https://github.com/QinZhou-work/MicNet",
+ "host": "github.com",
+ "commit": "b53907ce22da06a7c78a1a5bbfe9803205527897",
+ "license": "Apache-2.0",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4",
+   "language": "License",
+   "lines": 201,
+   "truncated": false,
+   "block": 1,
+   "row": 2216
+  },
+  {
+   "path": "README.md",
+   "sha256": "2a191f18e73238dd27bf22de4c6e2a16f135b7683173306d11bad90b38ce310f",
+   "language": "Text",
+   "lines": 129,
+   "truncated": false,
+   "block": 8,
+   "row": 10369
+  },
+  {
+   "path": "main.py",
+   "sha256": "a21da8678d04b90d9775c1fb2f85f494808f978542b151a14ad9438186f27659",
+   "language": "Python",
+   "lines": 437,
+   "truncated": false,
+   "block": 7,
+   "row": 8305
+  },
+  {
+   "path": "tutorial/Dataset_HBC/MicNet_1_data_check_and_preprocessing.ipynb",
+   "sha256": "d24d27de4aa1ebc32a323b3e6a0433b5f2887197b24c2b95e823ddc7de719c67",
+   "language": "Jupyter",
+   "lines": 105,
+   "truncated": false,
+   "block": 5,
+   "row": 16410
+  },
+  {
+   "path": "tutorial/Dataset_HBC/MicNet_2_train_MicNet.ipynb",
+   "sha256": "528b6b9ea979f49c380333408734f54af7377e269a22cbc0f84e6637c1782244",
+   "language": "Jupyter",
+   "lines": 246,
+   "truncated": false,
+   "block": 5,
+   "row": 17368
+  },
+  {
+   "path": "tutorial/Dataset_HBC/MicNet_3_Inference.ipynb",
+   "sha256": "5d24dcdaf92b82f2b8fbe15792d309685fbea5a1e74fc764188ecb1967c47180",
+   "language": "Jupyter",
+   "lines": 234,
+   "truncated": false,
+   "block": 5,
+   "row": 17281
+  },
+  {
+   "path": "tutorial/Dataset_HBC/MicNet_4_clustering_with_MicNet.ipynb",
+   "sha256": "a5f92db5d38e68d456cd0dee6cc59de0fc207f5d21ec5350e310139a3b5edcde",
+   "language": "Jupyter",
+   "lines": 270,
+   "truncated": false,
+   "block": 5,
+   "row": 17379
+  },
+  {
+   "path": "tutorial/Dataset_HOC/MicNet_1_data_check_preprocessing.ipynb",
+   "sha256": "15989e980febee763951956fb6f9e4e012c3168ea78083f1f5ac0f7d4c559702",
+   "language": "Jupyter",
+   "lines": 146,
+   "truncated": false,
+   "block": 5,
+   "row": 16645
+  },
+  {
+   "path": "tutorial/Dataset_HOC/MicNet_2_train_MicNet.ipynb",
+   "sha256": "b46cb03784ee1250f3ca7f96fcc76f31641ca8115d76458688cc8dafb9cf9d7f",
+   "language": "Jupyter",
+   "lines": 464,
+   "truncated": false,
+   "block": 6,
+   "row": 350
+  },
+  {
+   "path": "tutorial/Dataset_HOC/MicNet_3_Inference_Clustering.ipynb",
+   "sha256": "a52f004447a9cd8b701ec5d65b4a2a3069e1a13c783708c036467e3152a34011",
+   "language": "Jupyter",
+   "lines": 438,
+   "truncated": false,
+   "block": 6,
+   "row": 234
+  },
+  {
+   "path": "tutorial/Dataset_HPC/MicNet_1_data_check_preprocessing.ipynb",
+   "sha256": "a8be819684d439ceec9547d2239f04f52adbcacc16b2a0bcbc7cac000097548f",
+   "language": "Jupyter",
+   "lines": 276,
+   "truncated": false,
+   "block": 5,
+   "row": 17128
+  },
+  {
+   "path": "tutorial/Dataset_HPC/MicNet_2_build_train_MicNet.ipynb",
+   "sha256": "2efd03847dd69be5511c0ad43bd3ac8ea2a9f2ec6cbfd0c26cfb95098f06c75b",
+   "language": "Jupyter",
+   "lines": 459,
+   "truncated": false,
+   "block": 6,
+   "row": 376
+  },
+  {
+   "path": "tutorial/Dataset_HPC/MicNet_3_inference_Clustering.ipynb",
+   "sha256": "a4d2df247753616316a60bcbc1be2224823c1d068c08f239edfbe4d767ca22d9",
+   "language": "Jupyter",
+   "lines": 416,
+   "truncated": false,
+   "block": 6,
+   "row": 85
+  },
+  {
+   "path": "tutorial/Dataset_MERFISH/MicNet_1_data_check_preprocessing.ipynb",
+   "sha256": "7135a2d73905e978a101d00aa2992073d78733f376487329f57fb7baaf30fdd9",
+   "language": "Jupyter",
+   "lines": 223,
+   "truncated": false,
+   "block": 5,
+   "row": 17358
+  },
+  {
+   "path": "tutorial/Dataset_MERFISH/MicNet_2_train.ipynb",
+   "sha256": "1b3cf5fc87990a2a9244eba50c77147e1b3d5804eff95b128839a603dd59540f",
+   "language": "Jupyter",
+   "lines": 615,
+   "truncated": false,
+   "block": 6,
+   "row": 745
+  },
+  {
+   "path": "tutorial/Dataset_MERFISH/MicNet_3_Inferece.ipynb",
+   "sha256": "1e8b6b826f897af002e211b29056dfe867aee112b8153b7d30585bd0c57949ab",
+   "language": "Jupyter",
+   "lines": 445,
+   "truncated": false,
+   "block": 6,
+   "row": 357
+  },
+  {
+   "path": "tutorial/Dataset_MERFISH/MicNet_4_Clustering.ipynb",
+   "sha256": "f157a3bebe942a4b64d3ada290412b565c2222b18bade2764c608cdbacf0e6e3",
+   "language": "Jupyter",
+   "lines": 226,
+   "truncated": false,
+   "block": 5,
+   "row": 17423
+  },
+  {
+   "path": "tutorial/Dataset_MOB/MicNet_1_data_check_preprocessing.ipynb",
+   "sha256": "940f482fd4333a4a58292000a9f76cd8970837dec93b94b12f8a29210f6ddbdd",
+   "language": "Jupyter",
+   "lines": 187,
+   "truncated": false,
+   "block": 5,
+   "row": 16835
+  },
+  {
+   "path": "tutorial/Dataset_MOB/MicNet_2_train.ipynb",
+   "sha256": "ae4750c781cc7785b6add70f917273788ed5fb9ef6e93938593a9da282d10b3a",
+   "language": "Jupyter",
+   "lines": 469,
+   "truncated": false,
+   "block": 6,
+   "row": 395
+  },
+  {
+   "path": "tutorial/Dataset_MOB/MicNet_3_inference_clustering.ipynb",
+   "sha256": "20833d480d4cb644c79c0961fcf012554dbaeb05e810f271755ee66ece3d80ee",
+   "language": "Jupyter",
+   "lines": 473,
+   "truncated": false,
+   "block": 6,
+   "row": 360
+  },
+  {
+   "path": "tutorial/Dataset_MPB/MicNet_1_data_check_preprocessing.ipynb",
+   "sha256": "38c964b583b439607df51bfe8c53b25a1604b4a51d28a8acff21d987cf116aa7",
+   "language": "Jupyter",
+   "lines": 236,
+   "truncated": false,
+   "block": 5,
+   "row": 17278
+  },
+  {
+   "path": "tutorial/Dataset_MPB/MicNet_2_train_MicNet.ipynb",
+   "sha256": "c69d7520929598cfcb7a34b163c0d082e3a9e0fe7ce0257fe852e727a275d61e",
+   "language": "Jupyter",
+   "lines": 507,
+   "truncated": false,
+   "block": 6,
+   "row": 461
+  },
+  {
+   "path": "tutorial/Dataset_MPB/MicNet_3_Inference_clustering.ipynb",
+   "sha256": "46353ea482dba61267ef80ab358db6ae510307c600eb43ff5da5a9a58ca07c3f",
+   "language": "Jupyter",
+   "lines": 251,
+   "truncated": false,
+   "block": 5,
+   "row": 17546
+  },
+  {
+   "path": "tutorial/MicNet_1_data_check_and_preprocessing.ipynb",
+   "sha256": "38407d0cced423e83aa7da8676611ff5d04b32637807f85d0ed81bdca4c06aac",
+   "language": "Jupyter",
+   "lines": 105,
+   "truncated": false,
+   "block": 5,
+   "row": 16363
+  },
+  {
+   "path": "tutorial/MicNet_2_train_MicNet.ipynb",
+   "sha256": "6e45dc392f4447f9a4f7ec47266f37019a0d577a9d75348be8688384fcbaf463",
+   "language": "Jupyter",
+   "lines": 247,
+   "truncated": false,
+   "block": 5,
+   "row": 17341
+  },
+  {
+   "path": "tutorial/MicNet_3_Inference.ipynb",
+   "sha256": "baf86b517cb35a962014395c88111b055749b60a6f71515dd9c464895c651737",
+   "language": "Jupyter",
+   "lines": 233,
+   "truncated": false,
+   "block": 5,
+   "row": 17265
+  },
+  {
+   "path": "tutorial/MicNet_4_clustering_with_MicNet.ipynb",
+   "sha256": "d62f981a3a28282b6d4506928366de42ef844f043daf0bf82274b9b4ede5c402",
+   "language": "Jupyter",
+   "lines": 270,
+   "truncated": false,
+   "block": 5,
+   "row": 17366
+  },
+  {
+   "path": "utils/MicNet_model.py",
+   "sha256": "50bc8077a9b4fe18a32cf2cac49d9b089ff3a9b6d77406c462a9695c1d77aeb2",
+   "language": "Python",
+   "lines": 380,
+   "truncated": false,
+   "block": 7,
+   "row": 6269
+  },
+  {
+   "path": "utils/alias_multinomial.py",
+   "sha256": "9e0ae6ce3a44b92a69e2721199cc3d9d3e682d103545e5cfbe41d828a1b1c34e",
+   "language": "Python",
+   "lines": 69,
+   "truncated": false,
+   "block": 6,
+   "row": 32620
+  },
+  {
+   "path": "utils/utils_data.py",
+   "sha256": "dd35b0176270395c3520a94df7962585bf77f90585289b8701fd708935bd392e",
+   "language": "Python",
+   "lines": 223,
+   "truncated": false,
+   "block": 6,
+   "row": 45444
+  },
+  {
+   "path": "utils/utils_eval.py",
+   "sha256": "195964d2fe2755c44758716f9b3c895d47e920b22f35af7d125c506f0cfd3e7e",
+   "language": "Python",
+   "lines": 70,
+   "truncated": false,
+   "block": 6,
+   "row": 30777
+  },
+  {
+   "path": "validate.py",
+   "sha256": "bdf81dba80c94134c4a8bcd744caddb445816c7bd9e34a5ef896343e15c60c2d",
+   "language": "Python",
+   "lines": 286,
+   "truncated": false,
+   "block": 7,
+   "row": 4123
+  }
+ ]
+}

manifests/fd/github.com__rodallab__paz-analysis.json

@@ -0,0 +1,93 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/rodallab/paz-analysis",
+ "url": "https://github.com/rodallab/paz-analysis",
+ "host": "github.com",
+ "commit": "93478cfa4b1449ebf54b6008f456116bb09d7340",
+ "license": "GPL-3.0",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "Analysis/Aggregate_PAZ_Data.py",
+   "sha256": "0a10640b529ed6331741ac56efe88115a74494841ea08f74976768269bcbb941",
+   "language": "Python",
+   "lines": 361,
+   "truncated": false,
+   "block": 7,
+   "row": 6595
+  },
+  {
+   "path": "Analysis/CentroidAnalysis.py",
+   "sha256": "7584806fe52504438d3286c0ad8cbe388b5cba439f2cc90c113724a235021e3b",
+   "language": "Python",
+   "lines": 368,
+   "truncated": false,
+   "block": 7,
+   "row": 5068
+  },
+  {
+   "path": "Analysis/Example_NMJ-level_analysesStats.ipynb",
+   "sha256": "e42b910c1991b59953cd2ee1c52ce8390269b565bd28011aec9dda3164f3b340",
+   "language": "Jupyter",
+   "lines": 142,
+   "truncated": false,
+   "block": 5,
+   "row": 16811
+  },
+  {
+   "path": "Analysis/Example_PAZ_Analyses.ipynb",
+   "sha256": "0c9deb8069c8eb844b48bd5b3d7f20f03672586ded1a45b4e166353e3715ddbd",
+   "language": "Jupyter",
+   "lines": 536,
+   "truncated": false,
+   "block": 6,
+   "row": 709
+  },
+  {
+   "path": "Analysis/Example_PAZ_DescriptiveAnalyses.ipynb",
+   "sha256": "bf40ba38d2893bf4b0f09c8b8b69382203b421099fed70b5a7e6ab083dcd78aa",
+   "language": "Jupyter",
+   "lines": 548,
+   "truncated": false,
+   "block": 6,
+   "row": 722
+  },
+  {
+   "path": "Analysis/PAZ_Processing.py",
+   "sha256": "afcfe89aeeacfe0d1aaff0cb444415257d7a828dd0f2683cf1f67b1442e41a15",
+   "language": "Python",
+   "lines": 35,
+   "truncated": false,
+   "block": 6,
+   "row": 25376
+  },
+  {
+   "path": "Analysis/paz-analysis_manuscript_analyses.ipynb",
+   "sha256": "c91beb9bf70df0bf947bd22b92ea86231e8934f7e42dd0817e73785d51e52b7f",
+   "language": "Jupyter",
+   "lines": 1515,
+   "truncated": false,
+   "block": 6,
+   "row": 1207
+  },
+  {
+   "path": "LICENSE",
+   "sha256": "3972dc9744f6499f0f9b2dbf76696f2ae7ad8af9b23dde66d6af86c9dfb36986",
+   "language": "License",
+   "lines": 674,
+   "truncated": false,
+   "block": 1,
+   "row": 2235
+  },
+  {
+   "path": "README.md",
+   "sha256": "9822d52cc824171d1ffb392f8ad086b9f73d235242c29b6eeac9e4a985f2d023",
+   "language": "Text",
+   "lines": 20,
+   "truncated": false,
+   "block": 8,
+   "row": 9669
+  }
+ ]
+}

manifests/fd/gitlab.dzne.de__ag-ulas__iglon5-autoimmune-antibody-influence-on-neurons.json

@@ -0,0 +1,39 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "gitlab.dzne.de/ag-ulas/iglon5-autoimmune-antibody-influence-on-neurons",
+ "url": "https://gitlab.dzne.de/ag-ulas/iglon5-autoimmune-antibody-influence-on-neurons",
+ "host": "gitlab.dzne.de",
+ "commit": "39b95ee856d3cbf4189243e201bc7cf6be8bf980",
+ "license": "LGPL-3.0",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "250327_publication.Rmd",
+   "sha256": "a40cacd42679b8efa449e6df4b27cb34de04669a904b631668c561bd114fae7b",
+   "language": "R",
+   "lines": 1350,
+   "truncated": false,
+   "block": 8,
+   "row": 5138
+  },
+  {
+   "path": "LICENSE",
+   "sha256": "425347eb68a83b13fe14e5a93c518c7a939cdfd3062c33b8e6bfc4236d1ea928",
+   "language": "License",
+   "lines": 165,
+   "truncated": false,
+   "block": 6,
+   "row": 2038
+  },
+  {
+   "path": "README.md",
+   "sha256": "875f8ef53767e8ca1f0acaef83a5af30b4b4236f9ececb915116f7d03401127c",
+   "language": "Text",
+   "lines": 10,
+   "truncated": false,
+   "block": 8,
+   "row": 9714
+  }
+ ]
+}

manifests/fd/zenodo__1039580.json

@@ -0,0 +1,21 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "zenodo:1039580",
+ "url": "https://doi.org/10.5281/zenodo.1039580",
+ "host": "zenodo.org",
+ "commit": "",
+ "license": "CC-BY-SA-4.0",
+ "license_confirmed_by": "license of the archive's record",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "README.txt",
+   "sha256": "76306229f7f750ecc062880c3f948f70c2dfa94ba6d5a8d7c2cc9e5f4399ae51",
+   "language": "Text",
+   "lines": 49,
+   "truncated": false,
+   "block": 8,
+   "row": 10212
+  }
+ ]
+}

manifests/fd/zenodo__18678186.json

@@ -0,0 +1,21 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "zenodo:18678186",
+ "url": "10.5281/zenodo.18678186",
+ "host": "zenodo.org",
+ "commit": "",
+ "license": "CC-BY-4.0",
+ "license_confirmed_by": "license of the archive's record",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "README.md",
+   "sha256": "2e27b055e8c1b0646c70c78051bce3ea933ca34e7a00dd6eab54420478845797",
+   "language": "Text",
+   "lines": 262,
+   "truncated": false,
+   "block": 8,
+   "row": 10836
+  }
+ ]
+}

manifests/fd/zenodo__19698954.json

@@ -0,0 +1,93 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "zenodo:19698954",
+ "url": "10.5281/zenodo.19698954",
+ "host": "zenodo.org",
+ "commit": "",
+ "license": "CC-BY-4.0",
+ "license_confirmed_by": "license of the archive's record",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "Readme.md",
+   "sha256": "caa6462c22f5d27b84481420671de806590177829fd6b1aac6c491e46528e91d",
+   "language": "Text",
+   "lines": 62,
+   "truncated": false,
+   "block": 8,
+   "row": 9770
+  },
+  {
+   "path": "figures/Figure _code2.ipynb",
+   "sha256": "50b353e6f5450f5e7402ac57a32b445765acc346e383965fd0bfd51d6e35298d",
+   "language": "Jupyter",
+   "lines": 535,
+   "truncated": false,
+   "block": 6,
+   "row": 720
+  },
+  {
+   "path": "figures/Figure_code1.ipynb",
+   "sha256": "794fa591e5736d45b5786e9e8e806c2b1599acf67f08a7a3afa2d23b69cc5379",
+   "language": "Jupyter",
+   "lines": 854,
+   "truncated": false,
+   "block": 6,
+   "row": 959
+  },
+  {
+   "path": "src/CEGAN.py",
+   "sha256": "33af788b8d4b4565c1eafdc75a3daf84de99045b0797695f28c2c00f6bff92a6",
+   "language": "Python",
+   "lines": 317,
+   "truncated": false,
+   "block": 7,
+   "row": 5027
+  },
+  {
+   "path": "src/DyNex-film.py",
+   "sha256": "57b9c4fe34adb8c73a794bf96a605bc110b43beb4b394563dda86e63438f7dfc",
+   "language": "Python",
+   "lines": 89,
+   "truncated": false,
+   "block": 6,
+   "row": 37835
+  },
+  {
+   "path": "src/RL.py",
+   "sha256": "eefd3550c1111e476eff52f1c0417dea14f6480f8d402781cf1099e18fad720c",
+   "language": "Python",
+   "lines": 320,
+   "truncated": false,
+   "block": 7,
+   "row": 4549
+  },
+  {
+   "path": "src/config.py",
+   "sha256": "322273bad585a629c49fdabe05a5faac6a000b5a434fafd81e08865d74ba2379",
+   "language": "Python",
+   "lines": 63,
+   "truncated": false,
+   "block": 6,
+   "row": 38739
+  },
+  {
+   "path": "src/proevogen.py",
+   "sha256": "670ebb40840617b6b4befa33b51d66d6ee80529f43147f9b8f67fdd0669628d1",
+   "language": "Python",
+   "lines": 538,
+   "truncated": false,
+   "block": 7,
+   "row": 9709
+  },
+  {
+   "path": "src/train.py",
+   "sha256": "d4e01ce6082db6df6050b6fab07391a4870da5a11917abdf942be068354ae8dc",
+   "language": "Python",
+   "lines": 271,
+   "truncated": false,
+   "block": 7,
+   "row": 4372
+  }
+ ]
+}

manifests/fe/github.com__chengjin-git__deepplaque.json

@@ -0,0 +1,57 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/chengjin-git/deepplaque",
+ "url": "https://www.github.com/ChengJin-git/DeepPlaque",
+ "host": "github.com",
+ "commit": "02bcfc5e534b50298e49b93c994f34e56356eef4",
+ "license": "CC-BY-NC-ND-4.0",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "with_conditions",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "f7f28b8c7a1af76b9874ca6d040e8b1eb6768fd1043106c9d315090ea96754e7",
+   "language": "License",
+   "lines": 402,
+   "truncated": false,
+   "block": 6,
+   "row": 2103
+  },
+  {
+   "path": "README.md",
+   "sha256": "8f6d784992fdf0e12fd6e8e82560b6184ed64479bb9fdbbe256664f33d767403",
+   "language": "Text",
+   "lines": 141,
+   "truncated": false,
+   "block": 8,
+   "row": 10534
+  },
+  {
+   "path": "pipeline/02_inference/configs/inference_config.py",
+   "sha256": "67f06fb1b49013cb520a990382dc3254fd584d1a2e9eab3a5cd68a9c6e7b82da",
+   "language": "Python",
+   "lines": 167,
+   "truncated": false,
+   "block": 6,
+   "row": 42331
+  },
+  {
+   "path": "pipeline/02_inference/inference.py",
+   "sha256": "201cc813d0d0a4556443d68eacaa0369efca51d1f5eb0771216eb2a3291a5509",
+   "language": "Python",
+   "lines": 258,
+   "truncated": false,
+   "block": 7,
+   "row": 1917
+  },
+  {
+   "path": "pipeline/02_inference/run_inference.sh",
+   "sha256": "16f51ce4ff1938345f71360ecaa3c92e274495ff02a1e58a8028efc0a0ba346e",
+   "language": "Shell",
+   "lines": 56,
+   "truncated": false,
+   "block": 8,
+   "row": 8128
+  }
+ ]
+}

manifests/fe/github.com__dunni3__flowmol.json

@@ -0,0 +1,489 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/dunni3/flowmol",
+ "url": "https://github.com/dunni3/FlowMol",
+ "host": "github.com",
+ "commit": "77cae22174b7792b0e25e9e0414038420736d841",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "4b27a2aeff430ec871d84ab80b452ab025e4b07a0753341b917299f2d56637c6",
+   "language": "License",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 1785
+  },
+  {
+   "path": "build_env.sh",
+   "sha256": "8b5677a7a9b0656452ad0df5e10f19b73b5924669e8c32ffc469a1d11dea9baf",
+   "language": "Shell",
+   "lines": 17,
+   "truncated": false,
+   "block": 8,
+   "row": 7352
+  },
+  {
+   "path": "dataset_metrics.py",
+   "sha256": "52dc5a5c4e00dbf39a06f19cdc6668805156b1830bea0c36976227cdbdf9b6e3",
+   "language": "Python",
+   "lines": 129,
+   "truncated": false,
+   "block": 6,
+   "row": 41207
+  },
+  {
+   "path": "examples/flowmol_demo.ipynb",
+   "sha256": "e753a6be2ce215b99ab7d4e402e34d9a53790d1633f891e7f3c5bd2cce9e5878",
+   "language": "Jupyter",
+   "lines": 77,
+   "truncated": false,
+   "block": 5,
+   "row": 16411
+  },
+  {
+   "path": "flowmol/__init__.py",
+   "sha256": "85ea2e513033654780cd1ad734411c904490efd0ef7516a5781a74c035bb699a",
+   "language": "Python",
+   "lines": 77,
+   "truncated": false,
+   "block": 6,
+   "row": 34284
+  },
+  {
+   "path": "flowmol/analysis/ff_energy.py",
+   "sha256": "77d86220f04d7f39e84ac00d1d6f367a10787e38d16f76f24fe0ef47e883045f",
+   "language": "Python",
+   "lines": 19,
+   "truncated": false,
+   "block": 6,
+   "row": 22969
+  },
+  {
+   "path": "flowmol/analysis/metrics.py",
+   "sha256": "db8275c3c69d7b83b991fa221183edc16a2f47ad6fef43c5feec2ded7f69f47e",
+   "language": "Python",
+   "lines": 428,
+   "truncated": false,
+   "block": 7,
+   "row": 8022
+  },
+  {
+   "path": "flowmol/analysis/molecule_builder.py",
+   "sha256": "a4de6f4d117703ab414d8b58b80699eb1aa6366e334887a140884bc06bbfd976",
+   "language": "Python",
+   "lines": 333,
+   "truncated": false,
+   "block": 7,
+   "row": 5259
+  },
+  {
+   "path": "flowmol/analysis/reos.py",
+   "sha256": "f819393c5c971fe0a9b34ab4fd618ad030713f87e2786a8f0e06c2ad1cde79df",
+   "language": "Python",
+   "lines": 85,
+   "truncated": false,
+   "block": 6,
+   "row": 34974
+  },
+  {
+   "path": "flowmol/analysis/ring_systems.py",
+   "sha256": "abe601a4527e55221ad2ae298aaf9b35e81f82103c30c9cffa8f28b38a7800d4",
+   "language": "Python",
+   "lines": 64,
+   "truncated": false,
+   "block": 6,
+   "row": 35315
+  },
+  {
+   "path": "flowmol/data_processing/adaptive_sampler.py",
+   "sha256": "7aa972f3ac49241b30d8fdb74cd9d5b1ee7fe127e7c2635d5b3c34011fee190a",
+   "language": "Python",
+   "lines": 71,
+   "truncated": false,
+   "block": 6,
+   "row": 34192
+  },
+  {
+   "path": "flowmol/data_processing/data_module.py",
+   "sha256": "0540d0945e2699d21501795d9047d3ebc2ef71d3b29e6e0ade2f3c57a9d59d78",
+   "language": "Python",
+   "lines": 71,
+   "truncated": false,
+   "block": 6,
+   "row": 33853
+  },
+  {
+   "path": "flowmol/data_processing/dataset.py",
+   "sha256": "5867b8cfa8ab00e1248bd0435a7c696cde43ae72cf8d421e5ac39867afa1e96c",
+   "language": "Python",
+   "lines": 184,
+   "truncated": false,
+   "block": 7,
+   "row": 1232
+  },
+  {
+   "path": "flowmol/data_processing/geom.py",
+   "sha256": "228e8205a3403f07d2309039bc8d40104666e5ff99395bac618fb7556869be5c",
+   "language": "Python",
+   "lines": 246,
+   "truncated": false,
+   "block": 7,
+   "row": 2217
+  },
+  {
+   "path": "flowmol/data_processing/prealign.py",
+   "sha256": "3f1c4199edd7c0e3696015d605c0ad19048c787e34255db802735150340cd7ed",
+   "language": "Python",
+   "lines": 89,
+   "truncated": false,
+   "block": 6,
+   "row": 36263
+  },
+  {
+   "path": "flowmol/data_processing/priors.py",
+   "sha256": "1c1bdb85316036d37fe620225662c7c3f5b476af55c9a42df06ac17c2841a66f",
+   "language": "Python",
+   "lines": 316,
+   "truncated": false,
+   "block": 7,
+   "row": 3145
+  },
+  {
+   "path": "flowmol/data_processing/samplers.py",
+   "sha256": "f5b36da7959ae63784799f29e1158d54ce46562372ed0af4502077e05cd74e09",
+   "language": "Python",
+   "lines": 88,
+   "truncated": false,
+   "block": 6,
+   "row": 37762
+  },
+  {
+   "path": "flowmol/data_processing/utils.py",
+   "sha256": "e1dc91602c930d5d843600d9953a2598994fc83b69108393e6700966c3e27631",
+   "language": "Python",
+   "lines": 46,
+   "truncated": false,
+   "block": 6,
+   "row": 33876
+  },
+  {
+   "path": "flowmol/model_utils/load.py",
+   "sha256": "5be849bc7c769312e85960d3c587887c5c265057a5503290e0f4fe861f55d0f3",
+   "language": "Python",
+   "lines": 89,
+   "truncated": false,
+   "block": 6,
+   "row": 38336
+  },
+  {
+   "path": "flowmol/model_utils/sweep_config.py",
+   "sha256": "416e9cdb3b7f70b1b49a15a260f392a8ca004f8b7fda3551d8b3e4b0e79bccf7",
+   "language": "Python",
+   "lines": 131,
+   "truncated": false,
+   "block": 6,
+   "row": 45045
+  },
+  {
+   "path": "flowmol/models/ctmc_vector_field.py",
+   "sha256": "9dcb41a8e551f8c664f17c068bf49d89fb2d8b18c30cacbea740018052c505b9",
+   "language": "Python",
+   "lines": 510,
+   "truncated": false,
+   "block": 7,
+   "row": 8934
+  },
+  {
+   "path": "flowmol/models/flowmol.py",
+   "sha256": "d7db5bddf2738956bc9b6fa159f7dffcf4f2597accdee8a2dc74074ced49c15d",
+   "language": "Python",
+   "lines": 589,
+   "truncated": false,
+   "block": 7,
+   "row": 10026
+  },
+  {
+   "path": "flowmol/models/gvp.py",
+   "sha256": "b453166d04a0ad05d275d08a4afe07ce2ba2ff0f4b45e841e2acaac5d4fcc327",
+   "language": "Python",
+   "lines": 543,
+   "truncated": false,
+   "block": 7,
+   "row": 9306
+  },
+  {
+   "path": "flowmol/models/interpolant_scheduler.py",
+   "sha256": "4534697820a490f1169c49dfa7ce1caa8ec062b7771b711ceccabe1a0a29c4d9",
+   "language": "Python",
+   "lines": 154,
+   "truncated": false,
+   "block": 6,
+   "row": 43281
+  },
+  {
+   "path": "flowmol/models/lr_scheduler.py",
+   "sha256": "9c75b615d3b8ed14eddddf66227ab0c3b5910f4191786759405af057c6dc8531",
+   "language": "Python",
+   "lines": 75,
+   "truncated": false,
+   "block": 6,
+   "row": 34838
+  },
+  {
+   "path": "flowmol/models/self_conditioning.py",
+   "sha256": "3dfb40819f76e84d6d98f4ee5806523b942b74f3b30a63d02b7fff3e4a79b8be",
+   "language": "Python",
+   "lines": 103,
+   "truncated": false,
+   "block": 6,
+   "row": 37727
+  },
+  {
+   "path": "flowmol/models/vector_field.py",
+   "sha256": "5f304ca86e0362d0ed7ea22574afbb9890773d7a65e80acbaa1584259ceb5cd8",
+   "language": "Python",
+   "lines": 880,
+   "truncated": false,
+   "block": 7,
+   "row": 11476
+  },
+  {
+   "path": "flowmol/utils/ctmc_utils.py",
+   "sha256": "f2a1aa96760a687f15ee7cc556e15b997427c29eb889aea4255b7c24489218c0",
+   "language": "Python",
+   "lines": 35,
+   "truncated": false,
+   "block": 6,
+   "row": 31694
+  },
+  {
+   "path": "flowmol/utils/dataset_stats.py",
+   "sha256": "b341dd95ad74215e58b2c7752682b8003a387c738f1d8307f85fc5d87f31b930",
+   "language": "Python",
+   "lines": 29,
+   "truncated": false,
+   "block": 6,
+   "row": 27792
+  },
+  {
+   "path": "flowmol/utils/dirflow.py",
+   "sha256": "5f7191364d22b64f14151796d8805f331a01794cdb4b4e7ccb9777268ef9ae0d",
+   "language": "Python",
+   "lines": 50,
+   "truncated": false,
+   "block": 6,
+   "row": 31620
+  },
+  {
+   "path": "flowmol/utils/divergences.py",
+   "sha256": "04a12e898bec154f2da875ebecea4498fac0a5bca04345e82b87a0f7c52c7021",
+   "language": "Python",
+   "lines": 30,
+   "truncated": false,
+   "block": 6,
+   "row": 24946
+  },
+  {
+   "path": "flowmol/utils/ema.py",
+   "sha256": "6ef79754d1eedd4fd29498ef447679cc6fd15a921fe248f01b7f176e7b387a5b",
+   "language": "Python",
+   "lines": 55,
+   "truncated": false,
+   "block": 6,
+   "row": 30910
+  },
+  {
+   "path": "flowmol/utils/embedding.py",
+   "sha256": "2c73e0ae3bfa9aee933fc725a5377c4f68e321c90d16e1b5aa11de3f66261605",
+   "language": "Python",
+   "lines": 58,
+   "truncated": false,
+   "block": 6,
+   "row": 32955
+  },
+  {
+   "path": "flowmol/utils/path.py",
+   "sha256": "1950c4d253937595b7f58ba5df8890edeb2affc7026abbadcf5b84dcbac670cb",
+   "language": "Python",
+   "lines": 7,
+   "truncated": false,
+   "block": 6,
+   "row": 21223
+  },
+  {
+   "path": "fm3_evals/ablations/gen_cmds/gen_min_cmds.py",
+   "sha256": "d96a3fc0e0ae92c2ea140e97e06329ad33565e64301e01a723bf5b36e8327cf0",
+   "language": "Python",
+   "lines": 48,
+   "truncated": false,
+   "block": 6,
+   "row": 31582
+  },
+  {
+   "path": "fm3_evals/ablations/gen_cmds/gen_rmsd_cmds.py",
+   "sha256": "4ba15e9700d85b71784da67728ec4268c354456dc255302f26bbdc05e1b31c63",
+   "language": "Python",
+   "lines": 38,
+   "truncated": false,
+   "block": 6,
+   "row": 30576
+  },
+  {
+   "path": "fm3_evals/ablations/gen_cmds/gen_test_cmds.py",
+   "sha256": "89f919b59f11fcf9de04984f1d2dc34332b8bc97c05a4d44ffbb7d3686dec1c0",
+   "language": "Python",
+   "lines": 50,
+   "truncated": false,
+   "block": 6,
+   "row": 29699
+  },
+  {
+   "path": "fm3_evals/baselines/compute_baseline_comparison.py",
+   "sha256": "030cdd93f33de80d2ebf2ae43f89be7c2636f708a2bedd41281481142931792e",
+   "language": "Python",
+   "lines": 117,
+   "truncated": false,
+   "block": 6,
+   "row": 41189
+  },
+  {
+   "path": "fm3_evals/baselines/gen_cmds/gen_baseline_comparison_cmds.py",
+   "sha256": "4d30687d33ec4e3121a9a149306d50e0a54a5b07ab22632000e0079fe48a7d9c",
+   "language": "Python",
+   "lines": 73,
+   "truncated": false,
+   "block": 6,
+   "row": 33960
+  },
+  {
+   "path": "fm3_evals/baselines/gen_cmds/gen_bsmin_cmds.py",
+   "sha256": "24dcbe872cbe92f3d10085dc10022d530bcb1bf8df8de975401e4dc082272b70",
+   "language": "Python",
+   "lines": 44,
+   "truncated": false,
+   "block": 6,
+   "row": 30960
+  },
+  {
+   "path": "fm3_evals/baselines/gen_cmds/gen_rmsd_cmds.py",
+   "sha256": "d6c6c41e2004bc7f5605db7647989c4b95109acd818f1089aded6c2dcbb42d45",
+   "language": "Python",
+   "lines": 50,
+   "truncated": false,
+   "block": 6,
+   "row": 33322
+  },
+  {
+   "path": "fm3_evals/geometry/geom_utils/geom_drugs_valency_table.py",
+   "sha256": "2a2d494998fd718c123af61aeb85beb0f557b19d661624701ad25687e0930bb4",
+   "language": "Python",
+   "lines": 153,
+   "truncated": false,
+   "block": 6,
+   "row": 38595
+  },
+  {
+   "path": "fm3_evals/geometry/geom_utils/molecule_stability.py",
+   "sha256": "65ea8e2bba09dbc94bcec4bff8a71980287c4f3e7364fa43bf75b791c6659c1f",
+   "language": "Python",
+   "lines": 103,
+   "truncated": false,
+   "block": 6,
+   "row": 37540
+  },
+  {
+   "path": "fm3_evals/geometry/geom_utils/pair_geometry.py",
+   "sha256": "393b879dff78f4c603b6f3a42a07e0e6ba9923c2051fe8a8a3670dfdc55e7e13",
+   "language": "Python",
+   "lines": 114,
+   "truncated": false,
+   "block": 6,
+   "row": 40515
+  },
+  {
+   "path": "fm3_evals/geometry/geom_utils/utils.py",
+   "sha256": "631c3b2ddb2445ae43dbc3780641faa2ba4f5488e583bc38fb9a0227b549ecfd",
+   "language": "Python",
+   "lines": 170,
+   "truncated": false,
+   "block": 6,
+   "row": 41653
+  },
+  {
+   "path": "fm3_evals/geometry/rmsd_energy.py",
+   "sha256": "bc0f19c8b6df58b39d4904fb93fb53a891b308f6f40a542071b6f0bf87d99074",
+   "language": "Python",
+   "lines": 144,
+   "truncated": false,
+   "block": 6,
+   "row": 42447
+  },
+  {
+   "path": "fm3_evals/geometry/xtb_optimization.py",
+   "sha256": "a92d83d91f9062f836c26f9c39793640b321c25f2706284733aa846764c4c5ee",
+   "language": "Python",
+   "lines": 176,
+   "truncated": false,
+   "block": 6,
+   "row": 44429
+  },
+  {
+   "path": "get_data_valencies.py",
+   "sha256": "aca3771be4363a4540235a7603b902a5b6c4eb175bc0afeb6e7aea4c9017a415",
+   "language": "Python",
+   "lines": 186,
+   "truncated": false,
+   "block": 6,
+   "row": 43790
+  },
+  {
+   "path": "process_geom.py",
+   "sha256": "73c3118bbd40a8841f01cf469e66ecd61f9ab7ec9796680aca684ede41091e59",
+   "language": "Python",
+   "lines": 378,
+   "truncated": false,
+   "block": 7,
+   "row": 6117
+  },
+  {
+   "path": "process_qm9.py",
+   "sha256": "54d56654ba68bab8bb6e836e5e26d443572f2807321eeb1c54504aaede468ce9",
+   "language": "Python",
+   "lines": 280,
+   "truncated": false,
+   "block": 7,
+   "row": 3535
+  },
+  {
+   "path": "readme.md",
+   "sha256": "6e007b110d309a829edbba2f2f03e9b0ef4a5d00267d51a64a3532edbb79da4a",
+   "language": "Text",
+   "lines": 131,
+   "truncated": false,
+   "block": 8,
+   "row": 10622
+  },
+  {
+   "path": "test.py",
+   "sha256": "98cd6267c1f88839e61a736e0388172de32c377605757c31a96a2f4e64c2d104",
+   "language": "Python",
+   "lines": 261,
+   "truncated": false,
+   "block": 7,
+   "row": 3520
+  },
+  {
+   "path": "train.py",
+   "sha256": "b3c5ea2e1c6bed0f056038969f6f58e87b72c4fe5ff3a2b326cf3c85628852ff",
+   "language": "Python",
+   "lines": 161,
+   "truncated": false,
+   "block": 6,
+   "row": 43487
+  }
+ ]
+}

manifests/fe/github.com__johof__lungmask.json

@@ -0,0 +1,120 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/johof/lungmask",
+ "url": "https://github.com/JoHof/lungmask",
+ "host": "github.com",
+ "commit": "87fc9f2b701458a6d38f5e64d59a5e60bc68c884",
+ "license": "Apache-2.0",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4",
+   "language": "License",
+   "lines": 201,
+   "truncated": false,
+   "block": 1,
+   "row": 2216
+  },
+  {
+   "path": "README.md",
+   "sha256": "f55858cc52d8bbe1e266b0988c2cf437ae6a2a4ecb36920e8ed1981fd31699d0",
+   "language": "Text",
+   "lines": 117,
+   "truncated": false,
+   "block": 8,
+   "row": 10507
+  },
+  {
+   "path": "lungmask/__init__.py",
+   "sha256": "9938a3c621c46dab107ad068bddffcd8888be73c85827fe47c021776229d8b9c",
+   "language": "Python",
+   "lines": 1,
+   "truncated": false,
+   "block": 6,
+   "row": 20213
+  },
+  {
+   "path": "lungmask/__main__.py",
+   "sha256": "17f98f8688d94250ad8c7858bf5f08ea0bd5ca5999050348cb3b3a1f6c2d5719",
+   "language": "Python",
+   "lines": 149,
+   "truncated": false,
+   "block": 6,
+   "row": 40007
+  },
+  {
+   "path": "lungmask/logger.py",
+   "sha256": "090ba9d50fa2633c6dd0439f2669fa40f7cc218369a6146cd282c9877a8a99ae",
+   "language": "Python",
+   "lines": 13,
+   "truncated": false,
+   "block": 6,
+   "row": 22038
+  },
+  {
+   "path": "lungmask/mask.py",
+   "sha256": "7eb005ad2ce4ca312c02be04cd4f2c6be8fdb2ba280b9c04f1b5537a45738daa",
+   "language": "Python",
+   "lines": 279,
+   "truncated": false,
+   "block": 7,
+   "row": 2615
+  },
+  {
+   "path": "lungmask/resunet.py",
+   "sha256": "7d102d8609f77d813ff441c2583e821420ac88780d8a8b78b6d44c3f6173672a",
+   "language": "Python",
+   "lines": 181,
+   "truncated": false,
+   "block": 6,
+   "row": 43837
+  },
+  {
+   "path": "lungmask/utils.py",
+   "sha256": "c1e0bd5e65babcec676a1cabd6679cf38b4a12f8ddc0ba268b3e60317fd1cbb9",
+   "language": "Python",
+   "lines": 423,
+   "truncated": false,
+   "block": 7,
+   "row": 6821
+  },
+  {
+   "path": "setup.py",
+   "sha256": "055835b1a628b6a14068c68738343b2a12043ce6f6fb011efb6be6c6b87b19e6",
+   "language": "Python",
+   "lines": 3,
+   "truncated": false,
+   "block": 6,
+   "row": 20284
+  },
+  {
+   "path": "tests/test_cli.py",
+   "sha256": "ba20f8a72cd98c90e5627ebaae36beb83ed51a1f996cdc57948821218caa6ae5",
+   "language": "Python",
+   "lines": 20,
+   "truncated": false,
+   "block": 6,
+   "row": 23376
+  },
+  {
+   "path": "tests/test_mask.py",
+   "sha256": "faf8fe8f62922f4745bbf4ca61fcf5abc9451c4631e8c16fe794b516b4d7f910",
+   "language": "Python",
+   "lines": 60,
+   "truncated": false,
+   "block": 6,
+   "row": 31221
+  },
+  {
+   "path": "tests/test_utils.py",
+   "sha256": "4987792d271c7d29f8df70ce83aff95e9743337842b0578ec4e9c50c16055299",
+   "language": "Python",
+   "lines": 163,
+   "truncated": false,
+   "block": 6,
+   "row": 41119
+  }
+ ]
+}

manifests/fe/zenodo__19050612.json

@@ -0,0 +1,813 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "zenodo:19050612",
+ "url": "10.5281/zenodo.19050612",
+ "host": "zenodo.org",
+ "commit": "",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "21d08115889f9e962ed37e4140a8ae85deab4d163622c678cf47034730986c02",
+   "language": "License",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 1769
+  },
+  {
+   "path": "README.md",
+   "sha256": "98fb03b0e1a30e8d5b46affd61e103ff0e917b62f119d4a33fcdbc762e9f85c9",
+   "language": "Text",
+   "lines": 136,
+   "truncated": false,
+   "block": 8,
+   "row": 10375
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/data/__init__.py",
+   "sha256": "9fdf8c5be5ecdafab3a297cd16408f094e4b5060d6b07e3f154893c8d48cf281",
+   "language": "Python",
+   "lines": 3,
+   "truncated": false,
+   "block": 6,
+   "row": 20830
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/data/data.py",
+   "sha256": "701a02a16b22ec65dddb72e3f9929dd7d98a1f5c82875b0dbe51634e4256a8a9",
+   "language": "Python",
+   "lines": 244,
+   "truncated": false,
+   "block": 7,
+   "row": 1141
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/data/scaffold.py",
+   "sha256": "97e16d7e7f15aaa9edccfd505dc35f80041676031b444ba91a44afb43aaf58b4",
+   "language": "Python",
+   "lines": 274,
+   "truncated": false,
+   "block": 7,
+   "row": 4451
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/data/scaler.py",
+   "sha256": "ec60d531063a880111deb32a2f1ff4ff9fbf1885ac60399805ceb8cf431d8b9e",
+   "language": "Python",
+   "lines": 65,
+   "truncated": false,
+   "block": 6,
+   "row": 34395
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/data/utils.py",
+   "sha256": "c9ca2fa82161499bddecac177fcd10784319cac66cddc76282ba0a7caeb70be6",
+   "language": "Python",
+   "lines": 400,
+   "truncated": false,
+   "block": 7,
+   "row": 5913
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/features/__init__.py",
+   "sha256": "04bb84f5f4fb6ef0892e0d963138c5b95e5f2769f77929bf2fa29431bc6b706e",
+   "language": "Python",
+   "lines": 3,
+   "truncated": false,
+   "block": 6,
+   "row": 21530
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/features/features_generators.py",
+   "sha256": "22cd55b8266833048cefeceb08f7682225c21f6c1c0761205e7a7dd561e465ef",
+   "language": "Python",
+   "lines": 145,
+   "truncated": false,
+   "block": 6,
+   "row": 41980
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/features/featurization.py",
+   "sha256": "3c0784ae9a498d5b4fbf83a811e04acca05f4c7b52aaf1bf52ac3e950803c40d",
+   "language": "Python",
+   "lines": 458,
+   "truncated": false,
+   "block": 7,
+   "row": 8009
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/features/utils.py",
+   "sha256": "4ddd22c0dadfa7f997b890aacc327edf231a4c122beacddeb99cdd50714571a2",
+   "language": "Python",
+   "lines": 53,
+   "truncated": false,
+   "block": 6,
+   "row": 31430
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/models/__init__.py",
+   "sha256": "8557066efb5ec1123865b812805e4c776d24ce25081b27582e0deaed6f1fc888",
+   "language": "Python",
+   "lines": 2,
+   "truncated": false,
+   "block": 6,
+   "row": 20834
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/models/cmpn.py",
+   "sha256": "fd7a8728eddbff29e6e400292b9ca6daa42375b8420c963c6910664609cd5616",
+   "language": "Python",
+   "lines": 218,
+   "truncated": false,
+   "block": 7,
+   "row": 2259
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/models/loss/loss.py",
+   "sha256": "567c467c20737e9cd9e256ab8598212a4d5a0baaffd23951c605791982499a1a",
+   "language": "Python",
+   "lines": 71,
+   "truncated": false,
+   "block": 6,
+   "row": 33995
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/models/loss/loss_computer.py",
+   "sha256": "6d10c4a3639c3ac2e4e84c6a1d4fc34f0190359664d60104e9c1bf19a11f5ba1",
+   "language": "Python",
+   "lines": 26,
+   "truncated": false,
+   "block": 6,
+   "row": 23916
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/models/model.py",
+   "sha256": "69063a0b6ce6fa97f363da962f2bf2ab7a5de5dff683484d8af55777f0315b57",
+   "language": "Python",
+   "lines": 240,
+   "truncated": false,
+   "block": 7,
+   "row": 1440
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/models/mpn.py",
+   "sha256": "4c2acba77b4a789e1030d72a8102af58d699c0165df9be584dc11f259804393b",
+   "language": "Python",
+   "lines": 158,
+   "truncated": false,
+   "block": 6,
+   "row": 44622
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/nn_utils.py",
+   "sha256": "347987460dcffe6ace7a33ff8f598413fcf8bf921c96c90abd2d2c4c0ff3acef",
+   "language": "Python",
+   "lines": 213,
+   "truncated": false,
+   "block": 7,
+   "row": 577
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/parsing.py",
+   "sha256": "c24aad5ee1e30d270b9aece127fe359c85014133280b9d8cece5f6880eaf7df0",
+   "language": "Python",
+   "lines": 338,
+   "truncated": false,
+   "block": 7,
+   "row": 7494
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/random_forest.py",
+   "sha256": "511d4be1ebf8ac4f9ebf78091145b28d45357b6045f057570caf927fb70b5213",
+   "language": "Python",
+   "lines": 155,
+   "truncated": false,
+   "block": 6,
+   "row": 43382
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/torchlight/__init__.py",
+   "sha256": "3b20e7bc8cb5b0358355c743a142a7c229e126c54d1205d6980a19c07d4bd165",
+   "language": "Python",
+   "lines": 18,
+   "truncated": false,
+   "block": 6,
+   "row": 23635
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/torchlight/logger.py",
+   "sha256": "c645129023edf9226ef1b660bff6441823764f64263c06e9c95d0e5edb71a28d",
+   "language": "Python",
+   "lines": 147,
+   "truncated": false,
+   "block": 6,
+   "row": 40169
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/torchlight/metric.py",
+   "sha256": "99edb7581155107e5d993d5d5407f1f053a92511541826dd32329bd4a3928884",
+   "language": "Python",
+   "lines": 127,
+   "truncated": false,
+   "block": 6,
+   "row": 38519
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/torchlight/module.py",
+   "sha256": "2bcb1625a6785c4c0cbe3d94f612be7ade3082d910a79435b345648a5182357e",
+   "language": "Python",
+   "lines": 133,
+   "truncated": false,
+   "block": 6,
+   "row": 42556
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/torchlight/utils.py",
+   "sha256": "cd348de3592b1b19f5f35c1a60cf7c14d9ac20fa5173f685ca8b1a4bf97487da",
+   "language": "Python",
+   "lines": 173,
+   "truncated": false,
+   "block": 6,
+   "row": 42024
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/torchlight/vocab.py",
+   "sha256": "17d89bffe24d847e70cf93b9496b6377d20faba92a2e15e47566b37961dcf747",
+   "language": "Python",
+   "lines": 137,
+   "truncated": false,
+   "block": 6,
+   "row": 39942
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/train/cross_validate.py",
+   "sha256": "9a043c0887bc4ab1fef3958802f4e049bb37812e39dfd4bee89194bf6850742f",
+   "language": "Python",
+   "lines": 54,
+   "truncated": false,
+   "block": 6,
+   "row": 31886
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/train/evaluate.py",
+   "sha256": "0db5177ce9d4931b1009035e4dd429c2608fbc5d988e77d49ecb416dc6811de2",
+   "language": "Python",
+   "lines": 111,
+   "truncated": false,
+   "block": 6,
+   "row": 38568
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/train/make_predictions.py",
+   "sha256": "e01b2c5cebe1008be6a2dcda8310831639da9a5a2b44beeda346d745b187d5b4",
+   "language": "Python",
+   "lines": 145,
+   "truncated": false,
+   "block": 6,
+   "row": 41793
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/train/predict.py",
+   "sha256": "b5b53260fc3ffe55f15db99d2a4342420f99d0ae32f3271979fb555aaf7d5bf0",
+   "language": "Python",
+   "lines": 102,
+   "truncated": false,
+   "block": 6,
+   "row": 35528
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/train/run_training.py",
+   "sha256": "d307a460aaf1474225960c3740bd799389575a3d8872f92944aadf47746a17e9",
+   "language": "Python",
+   "lines": 225,
+   "truncated": false,
+   "block": 7,
+   "row": 2377
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/train/train.py",
+   "sha256": "a1c52893c256d09c6f3c9b8c765b663b48d1b82caa791c8e052508c510bd42a1",
+   "language": "Python",
+   "lines": 113,
+   "truncated": false,
+   "block": 6,
+   "row": 39256
+  },
+  {
+   "path": "baseline/KANO/chemprop_local/utils.py",
+   "sha256": "0285c6992191ab5e2a8d04f5e97e284b1b10aa70e855ecdcb68a00eb0631adcb",
+   "language": "Python",
+   "lines": 336,
+   "truncated": false,
+   "block": 7,
+   "row": 4209
+  },
+  {
+   "path": "baseline/KANO/initial/get_dict.py",
+   "sha256": "4a6612f07654398354ece209c43b96783104f74ea6e4fb39da653487365d74b4",
+   "language": "Python",
+   "lines": 104,
+   "truncated": false,
+   "block": 6,
+   "row": 36220
+  },
+  {
+   "path": "baseline/KANO/kano_excel_splits.py",
+   "sha256": "ff4d37ec72e13e7195ba831309bab6c5dc91799cf9bd0aaf1226698c89e2f1e1",
+   "language": "Python",
+   "lines": 84,
+   "truncated": false,
+   "block": 6,
+   "row": 33837
+  },
+  {
+   "path": "baseline/KANO/run_stat.py",
+   "sha256": "ce9f1d024f838515a57f59fc94a2252aab666627bd1bbe37ed3a7444fe901bd7",
+   "language": "Python",
+   "lines": 129,
+   "truncated": false,
+   "block": 6,
+   "row": 40297
+  },
+  {
+   "path": "baseline/KANO/train.py",
+   "sha256": "0f76b24b3c513d4b58e81ecd9919ef3119bc2e24a20bb9c49aa4c34175d475d9",
+   "language": "Python",
+   "lines": 67,
+   "truncated": false,
+   "block": 6,
+   "row": 33576
+  },
+  {
+   "path": "baseline/acs/checkpointing.py",
+   "sha256": "ebd6e66e401391d1fda0841b62ed5ec733e150e65860ad28595746cf3bbe8a11",
+   "language": "Python",
+   "lines": 95,
+   "truncated": false,
+   "block": 6,
+   "row": 37253
+  },
+  {
+   "path": "baseline/acs/dataset.py",
+   "sha256": "a424a5524bc36e4f0a1de53e4a8bd4346c0b75ed83d79436981fc9f8f511d75f",
+   "language": "Python",
+   "lines": 390,
+   "truncated": false,
+   "block": 7,
+   "row": 6799
+  },
+  {
+   "path": "baseline/acs/main.py",
+   "sha256": "e12606cd5e65c1b704fa3717a1ef5f391bf6145e72ce7f95945a030172ca0e61",
+   "language": "Python",
+   "lines": 809,
+   "truncated": false,
+   "block": 7,
+   "row": 11249
+  },
+  {
+   "path": "baseline/acs/metrics_and_losses.py",
+   "sha256": "007ad59bd84a519c74a151c05977e491cfef8be1cf28c85e91929b544f8a8606",
+   "language": "Python",
+   "lines": 146,
+   "truncated": false,
+   "block": 6,
+   "row": 41598
+  },
+  {
+   "path": "baseline/acs/model.py",
+   "sha256": "ee0f2c80209530c712fb8ccad50df77aabdd19d27edac5459a302e28c9520882",
+   "language": "Python",
+   "lines": 279,
+   "truncated": false,
+   "block": 7,
+   "row": 2736
+  },
+  {
+   "path": "baseline/acs/performance_analysis.py",
+   "sha256": "191d3a1eba36e80c79c62233f7596cfaab987999075a4bf0af9ebb03990e5b4f",
+   "language": "Python",
+   "lines": 159,
+   "truncated": false,
+   "block": 7,
+   "row": 2249
+  },
+  {
+   "path": "baseline/acs/smiles_utils.py",
+   "sha256": "c9f652e91e9c4d43b86e8020d852cc489da5253975e1b21e79ed1199b90ea1f5",
+   "language": "Python",
+   "lines": 19,
+   "truncated": false,
+   "block": 6,
+   "row": 22627
+  },
+  {
+   "path": "baseline/attentiveFP/afp_model/AttentiveLayers.py",
+   "sha256": "e11327ed370d066b66a34b0cd4c3ae568f0b6d201efc81dd6734bbd87de56c78",
+   "language": "Python",
+   "lines": 143,
+   "truncated": false,
+   "block": 7,
+   "row": 581
+  },
+  {
+   "path": "baseline/attentiveFP/afp_model/AttentiveLayers_viz.py",
+   "sha256": "2456173e9b9d33fd55a97079878b0108e13e0f15560a7357cf9bd518b73b9fbf",
+   "language": "Python",
+   "lines": 165,
+   "truncated": false,
+   "block": 7,
+   "row": 1510
+  },
+  {
+   "path": "baseline/attentiveFP/afp_model/Featurizer.py",
+   "sha256": "233f7ddc5f700c398ef785862de2b762bafbee37c92b3fb0e4a3e3469b143410",
+   "language": "Python",
+   "lines": 111,
+   "truncated": false,
+   "block": 6,
+   "row": 37805
+  },
+  {
+   "path": "baseline/attentiveFP/afp_model/Featurizer_aromaticity_rm.py",
+   "sha256": "9bcaf54d88d290d1a38e425a297c622ca8e19f8c8c52b924efa760ca1b2e2e56",
+   "language": "Python",
+   "lines": 100,
+   "truncated": false,
+   "block": 6,
+   "row": 37197
+  },
+  {
+   "path": "baseline/attentiveFP/afp_model/getFeatures.py",
+   "sha256": "4e5dffcd190ee2011541ecc4251f1c9e5ce39267eb33500e2589223ff89cd81c",
+   "language": "Python",
+   "lines": 220,
+   "truncated": false,
+   "block": 7,
+   "row": 2741
+  },
+  {
+   "path": "baseline/attentiveFP/afp_model/getFeatures_aromaticity_rm.py",
+   "sha256": "f9bb2a6267ddffb877ef35c49e27b651ba6c28d22806be32614bae23de6c4159",
+   "language": "Python",
+   "lines": 567,
+   "truncated": false,
+   "block": 7,
+   "row": 8779
+  },
+  {
+   "path": "baseline/attentiveFP/afp_with_saved_splits.py",
+   "sha256": "4079012d8c81cf7d90cced418ec533fd634dd4b3329443bcf18b15cdd40ca571",
+   "language": "Python",
+   "lines": 422,
+   "truncated": false,
+   "block": 7,
+   "row": 7807
+  },
+  {
+   "path": "baseline/catboost_kfold_baseline_all.py",
+   "sha256": "46668648c5bfbfe1e6a4d29bfb6650efd8117d3b7e357b17c9a6d27f49aab3c8",
+   "language": "Python",
+   "lines": 445,
+   "truncated": false,
+   "block": 7,
+   "row": 7654
+  },
+  {
+   "path": "baseline/chemprop_with_saved_splits.py",
+   "sha256": "302ad69ad38b2a4bdb8fdd648e238b3d765f2fa14dec661b2c08ca1757784b73",
+   "language": "Python",
+   "lines": 155,
+   "truncated": false,
+   "block": 6,
+   "row": 43097
+  },
+  {
+   "path": "baseline/ml_qspr_build_uob3.py",
+   "sha256": "5266f0af59eb3fc8e3c5a981c86ce13ad77691ffa8ac2a5328ddfdd1dbe90064",
+   "language": "Python",
+   "lines": 341,
+   "truncated": false,
+   "block": 7,
+   "row": 4868
+  },
+  {
+   "path": "baseline/ml_qspr_with_saved_splits.py",
+   "sha256": "68d9c558346b5497b67d9a5568e73f8315a1b4dfe3c66fbc96ebb435e00117a3",
+   "language": "Python",
+   "lines": 307,
+   "truncated": false,
+   "block": 7,
+   "row": 6068
+  },
+  {
+   "path": "baseline/mlp_kfold_baseline_all.py",
+   "sha256": "ccb4f9af11e809cfdbe136fa0cc8b9636532fe0b7748916015686ab963ae5731",
+   "language": "Python",
+   "lines": 417,
+   "truncated": false,
+   "block": 7,
+   "row": 6582
+  },
+  {
+   "path": "baseline/molclr/dataset/dataset.py",
+   "sha256": "bf2bdc67d6c47a851519579cea38a48b0a6f17fc8dfac4488791eb19e911c633",
+   "language": "Python",
+   "lines": 185,
+   "truncated": false,
+   "block": 6,
+   "row": 45104
+  },
+  {
+   "path": "baseline/molclr/dataset/dataset_excel_splits_finetune.py",
+   "sha256": "95f7b40dc05d17e3cb5976c4859e4291acef25e27d2b51f19f0190617b5f7df1",
+   "language": "Python",
+   "lines": 149,
+   "truncated": false,
+   "block": 6,
+   "row": 42852
+  },
+  {
+   "path": "baseline/molclr/dataset/dataset_mix.py",
+   "sha256": "7701dd8911658cd93b8e9b2d037ff361c09da56908efac554afc0a902c322194",
+   "language": "Python",
+   "lines": 258,
+   "truncated": false,
+   "block": 7,
+   "row": 2605
+  },
+  {
+   "path": "baseline/molclr/dataset/dataset_subgraph.py",
+   "sha256": "249aae9fb4aa271367fbb35c77af3a55c2c245b6af702dbc147f246375bf6ab2",
+   "language": "Python",
+   "lines": 231,
+   "truncated": false,
+   "block": 7,
+   "row": 797
+  },
+  {
+   "path": "baseline/molclr/dataset/dataset_test.py",
+   "sha256": "55c2b39418971405c93bb05aa946e4a786e4a63ecd7c6aa19ea0484f0aafbf73",
+   "language": "Python",
+   "lines": 224,
+   "truncated": false,
+   "block": 7,
+   "row": 1111
+  },
+  {
+   "path": "baseline/molclr/finetune.py",
+   "sha256": "6ddfe8a4affce12e6490fabf238fb9b99c2adee5f719479ff038d605fd62af01",
+   "language": "Python",
+   "lines": 261,
+   "truncated": false,
+   "block": 7,
+   "row": 3780
+  },
+  {
+   "path": "baseline/molclr/models/gcn_finetune.py",
+   "sha256": "fba420a51b79eb194cb5e113cbeab7b6e5a9b7bdaaa0ad50fd0c91a025826ea8",
+   "language": "Python",
+   "lines": 179,
+   "truncated": false,
+   "block": 6,
+   "row": 43717
+  },
+  {
+   "path": "baseline/molclr/models/gcn_molclr.py",
+   "sha256": "fd4c07e54e9e47291dcfe70a73e3e28dbba8c17ff7fb766fbd986a16a565e881",
+   "language": "Python",
+   "lines": 163,
+   "truncated": false,
+   "block": 6,
+   "row": 42412
+  },
+  {
+   "path": "baseline/molclr/models/ginet_finetune.py",
+   "sha256": "8fb1df60cad7e2de341ec8e0c09cee78d0158287512461f6c657c2113db5400a",
+   "language": "Python",
+   "lines": 158,
+   "truncated": false,
+   "block": 6,
+   "row": 42442
+  },
+  {
+   "path": "baseline/molclr/models/ginet_molclr.py",
+   "sha256": "5acb1a02aef6042d71104985e427d2a91813273f2c94c4f2fe3a36da895cd26f",
+   "language": "Python",
+   "lines": 117,
+   "truncated": false,
+   "block": 6,
+   "row": 39144
+  },
+  {
+   "path": "baseline/molclr/utils/nt_xent.py",
+   "sha256": "0853c57f5999eb849a02de50f03105614dc019281c12b0a5e3dbce5887aab8b3",
+   "language": "Python",
+   "lines": 65,
+   "truncated": false,
+   "block": 6,
+   "row": 33958
+  },
+  {
+   "path": "baseline/svr_kfold_baseline_all.py",
+   "sha256": "e434ea7810cf9637f534f86de3a8b22da2626ad16c961468c787365d7465118d",
+   "language": "Python",
+   "lines": 307,
+   "truncated": false,
+   "block": 7,
+   "row": 3466
+  },
+  {
+   "path": "baseline/transformer_kfold_baseline_all.py",
+   "sha256": "df1769ce492cfc90698611416017d6234f1444678392d59116df11123bd98ed3",
+   "language": "Python",
+   "lines": 452,
+   "truncated": false,
+   "block": 7,
+   "row": 7317
+  },
+  {
+   "path": "dataset/molDataset.py",
+   "sha256": "be9c5dba1a5d537ef5ef99eb37bf651fa05e6129fc226d463b926d941f1f668f",
+   "language": "Python",
+   "lines": 13,
+   "truncated": false,
+   "block": 6,
+   "row": 21802
+  },
+  {
+   "path": "dataset/testDataset.py",
+   "sha256": "82c416e8105eaabc2aa4b512bef71909323b4ba7db0aa9f7c77e8b5349d89c34",
+   "language": "Python",
+   "lines": 18,
+   "truncated": false,
+   "block": 6,
+   "row": 22755
+  },
+  {
+   "path": "descriptors_group/feature_fusion_util.py",
+   "sha256": "e189f9de6b7a3542eb50e7d92aaa4317ffe9cc2cdffd1e64cb3a745e1acb2d66",
+   "language": "Python",
+   "lines": 62,
+   "truncated": false,
+   "block": 6,
+   "row": 30106
+  },
+  {
+   "path": "load_model/kfold_predict.py",
+   "sha256": "26b6fa7954c381a8af4823c8befe6ba187292c0ed4e0346f80c3ce9786a536b6",
+   "language": "Python",
+   "lines": 276,
+   "truncated": false,
+   "block": 7,
+   "row": 3498
+  },
+  {
+   "path": "load_model/kfold_review.py",
+   "sha256": "eee3dfb80f4e00d96920d0ce04dcf648361a85a5706c203f7c45d4b9f692722f",
+   "language": "Python",
+   "lines": 197,
+   "truncated": false,
+   "block": 6,
+   "row": 44212
+  },
+  {
+   "path": "load_model/load_model_utils.py",
+   "sha256": "9c663e3ce9303b9bc77b737e58f566cacf5ecb843c5fea5195bee2fe4cfa36a3",
+   "language": "Python",
+   "lines": 169,
+   "truncated": false,
+   "block": 6,
+   "row": 40508
+  },
+  {
+   "path": "load_model/save_kfold_fused_feature.py",
+   "sha256": "0311cad6c4379ee0680018717409957d9cd1eefeb0fb38a1dfc8fa6181e7c634",
+   "language": "Python",
+   "lines": 329,
+   "truncated": false,
+   "block": 7,
+   "row": 2244
+  },
+  {
+   "path": "model/embedding_net.py",
+   "sha256": "ed834a1df412284d2a7f217321e56fa1ae0a39ff337bf1858501f4dfb10b1262",
+   "language": "Python",
+   "lines": 93,
+   "truncated": false,
+   "block": 6,
+   "row": 33174
+  },
+  {
+   "path": "model/transformerModel.py",
+   "sha256": "120b9f1cda1b7a01b95bc33007aa1a36f97f9e60c73cb18ae4e4a0ff4407de7c",
+   "language": "Python",
+   "lines": 140,
+   "truncated": false,
+   "block": 6,
+   "row": 41276
+  },
+  {
+   "path": "predict.sh",
+   "sha256": "dfff2bf57ee24e391614735d2bc8c1cbcb953f2ece392c8708399e381d2ee47b",
+   "language": "Shell",
+   "lines": 9,
+   "truncated": false,
+   "block": 8,
+   "row": 6929
+  },
+  {
+   "path": "prepare/prepare_moleculeNet_desc.py",
+   "sha256": "3024799a5a0f6174145a085fa7f733492fce15b456e72961524f6302f5a6f981",
+   "language": "Python",
+   "lines": 147,
+   "truncated": false,
+   "block": 6,
+   "row": 40501
+  },
+  {
+   "path": "prepare/prepare_our_descriptors.py",
+   "sha256": "c60bf5317ac0495f8833a8cf08e0e313789076cb279322c2a2f1b6d453fa7343",
+   "language": "Python",
+   "lines": 196,
+   "truncated": false,
+   "block": 6,
+   "row": 41238
+  },
+  {
+   "path": "reproduce.sh",
+   "sha256": "f9a87636dc2ac110bb07a904ecc0d87a8f9258c23f55f3c6d716c314a6f5012d",
+   "language": "Shell",
+   "lines": 11,
+   "truncated": false,
+   "block": 8,
+   "row": 6548
+  },
+  {
+   "path": "train.sh",
+   "sha256": "aa7441eb7404f232ba6b93b44434ebf7e3f02c42a0573b38a6961c2c36b427f7",
+   "language": "Shell",
+   "lines": 15,
+   "truncated": false,
+   "block": 8,
+   "row": 6914
+  },
+  {
+   "path": "train_kfold/kfold_prepare.py",
+   "sha256": "5f14b9291fd6a85c26f0d8a8548867ab8841c575ced5fce8ceabef552be0118a",
+   "language": "Python",
+   "lines": 40,
+   "truncated": false,
+   "block": 6,
+   "row": 28288
+  },
+  {
+   "path": "train_kfold/kfold_scaler.py",
+   "sha256": "19e16a85778f55b10347a08ee897ddb8b8b3bc86ed2f78843144263f77cda072",
+   "language": "Python",
+   "lines": 78,
+   "truncated": false,
+   "block": 6,
+   "row": 34990
+  },
+  {
+   "path": "train_kfold/train.py",
+   "sha256": "f320aac17c47a552a5e78326ef708ed7ee8794ee81479b366205f4febb114694",
+   "language": "Python",
+   "lines": 371,
+   "truncated": false,
+   "block": 7,
+   "row": 7356
+  },
+  {
+   "path": "train_kfold/train_domain.py",
+   "sha256": "9384b16ae2f91797034e0c75cb398ace0a6515f99df7da07b497f9a8725b78e2",
+   "language": "Python",
+   "lines": 564,
+   "truncated": false,
+   "block": 7,
+   "row": 8860
+  },
+  {
+   "path": "train_moleculeNet/train_moleculeNet.py",
+   "sha256": "80a47266467cec05c84808d111efa082e30b18b1971b1f19d01fe90c95e8e175",
+   "language": "Python",
+   "lines": 317,
+   "truncated": false,
+   "block": 7,
+   "row": 4017
+  },
+  {
+   "path": "train_moleculeNet/train_moleculeNet.sh",
+   "sha256": "36154782bb98c6aa478872f66c3dbcd862c6bbb85d9fbc09ca5f8ecfb17edd62",
+   "language": "Shell",
+   "lines": 10,
+   "truncated": false,
+   "block": 8,
+   "row": 6795
+  }
+ ]
+}

manifests/fe/zenodo__19229995.json

@@ -0,0 +1,102 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "zenodo:19229995",
+ "url": "10.5281/zenodo.19229995",
+ "host": "zenodo.org",
+ "commit": "",
+ "license": "CC-BY-4.0",
+ "license_confirmed_by": "license of the archive's record",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "Readme.md",
+   "sha256": "86fb8183f95e128293463375bae29844eec91999e0f0edf8fd1a4b18e6573c93",
+   "language": "Text",
+   "lines": 8,
+   "truncated": false,
+   "block": 8,
+   "row": 9570
+  },
+  {
+   "path": "Spatial signaling score/AnalysisSpatialNetworks.R",
+   "sha256": "7c61063bd64bd8f9193540e160a914271fcf88f31b5507ca61a3a332b167e906",
+   "language": "R",
+   "lines": 65,
+   "truncated": false,
+   "block": 8,
+   "row": 2671
+  },
+  {
+   "path": "Spatial signaling score/CalculateSpatialSignalingScore.R",
+   "sha256": "9533d04a012402afc77b07c28c6bede7848ae6074a9975d8473ba7fbc78319c9",
+   "language": "R",
+   "lines": 167,
+   "truncated": false,
+   "block": 8,
+   "row": 3851
+  },
+  {
+   "path": "Spatial signaling score/DistToDataframe.R",
+   "sha256": "4f82a01f12ce76cba39df3a8be6bb8e7dbf89fdbcbd0e5c55746ddc0440151de",
+   "language": "R",
+   "lines": 14,
+   "truncated": false,
+   "block": 8,
+   "row": 1453
+  },
+  {
+   "path": "Spatial signaling score/FindDifferentialSpatialSignalingScore.R",
+   "sha256": "28dde4f10b9e64a60cf7efb281c40cb07d1a2bfdef98bbd21ce9cc752ddb9ab0",
+   "language": "R",
+   "lines": 126,
+   "truncated": false,
+   "block": 8,
+   "row": 3598
+  },
+  {
+   "path": "Spatial signaling score/LoadAllMetrics.R",
+   "sha256": "a09b7dd8c24574671c676ca1617823c1965689dc748c7be86d86b08851b6e78a",
+   "language": "R",
+   "lines": 58,
+   "truncated": false,
+   "block": 8,
+   "row": 2866
+  },
+  {
+   "path": "Spatial signaling score/Spatialsignalingscore_main.R",
+   "sha256": "1f7df602893648e5406dc255c86e79c075ec109b399053216df5b7bfeddc825f",
+   "language": "R",
+   "lines": 685,
+   "truncated": false,
+   "block": 8,
+   "row": 5083
+  },
+  {
+   "path": "Spatial signaling score/VisualizeS3Dotplot.R",
+   "sha256": "a6905bf21771443d17b2a16796ea3413768f1be5b0da21dc4fdfaebe5602abed",
+   "language": "R",
+   "lines": 224,
+   "truncated": false,
+   "block": 8,
+   "row": 4362
+  },
+  {
+   "path": "Spatial signaling score/create_spatial_network_for_touched_cells.py",
+   "sha256": "1e0923d3b9bbdcd248a9b677d475ec9d7eac0305f599b9c93f9de36263e4dbad",
+   "language": "Python",
+   "lines": 79,
+   "truncated": false,
+   "block": 6,
+   "row": 36038
+  },
+  {
+   "path": "Spot assigment/Assign_spots_EDT.py",
+   "sha256": "cf51bd9702a7789639ba7db7d6fc23d2ece4e05521fc1c72acb39341f2181358",
+   "language": "Python",
+   "lines": 388,
+   "truncated": false,
+   "block": 7,
+   "row": 8076
+  }
+ ]
+}

manifests/fe/zenodo__19666107.json

@@ -0,0 +1,21 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "zenodo:19666107",
+ "url": "https://doi.org/10.5281/zenodo.19666107",
+ "host": "zenodo.org",
+ "commit": "",
+ "license": "CC-BY-4.0",
+ "license_confirmed_by": "license of the archive's record",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "Torord_SRR_CC_VC_ITER_e_misura.m",
+   "sha256": "2649c4821078c961c5ef5bbab121f9e153730c6ff7b8212eed4440fa3eb7abe5",
+   "language": "MATLAB",
+   "lines": 163,
+   "truncated": false,
+   "block": 6,
+   "row": 13190
+  }
+ ]
+}

manifests/ff/github.com__biox-nku__scbond.json

@@ -0,0 +1,228 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/biox-nku/scbond",
+ "url": "https://github.com/BioX-NKU/scBOND",
+ "host": "github.com",
+ "commit": "22a21923897186ce3ca55e893a9693ae05583d1d",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "17647d15e09a4765abfd25aa7ccdc5c8bc491d57b487c7e3286d5ae50a9d7c0f",
+   "language": "License",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 1394
+  },
+  {
+   "path": "README.md",
+   "sha256": "ca2938309f9f1a3c61514cf39f031ebb2370701f2df64341e0f5acc634925fd0",
+   "language": "Text",
+   "lines": 165,
+   "truncated": false,
+   "block": 8,
+   "row": 10860
+  },
+  {
+   "path": "docs/source/Tutorial/RNA_DNAm_paired_basic/.ipynb_checkpoints/scBOND_usage-checkpoint.ipynb",
+   "sha256": "43978b95e86eb9a6da7da49f7aa83d586ac65efe6cf6110885d7e39be65dd590",
+   "language": "Jupyter",
+   "lines": 137,
+   "truncated": false,
+   "block": 5,
+   "row": 16755
+  },
+  {
+   "path": "docs/source/Tutorial/RNA_DNAm_paired_basic/scBOND_usage.ipynb",
+   "sha256": "43978b95e86eb9a6da7da49f7aa83d586ac65efe6cf6110885d7e39be65dd590",
+   "language": "Jupyter",
+   "lines": 137,
+   "truncated": false,
+   "block": 5,
+   "row": 16755
+  },
+  {
+   "path": "docs/source/Tutorial/RNA_DNAm_variants/.ipynb_checkpoints/scBOND_aug_usage-checkpoint.ipynb",
+   "sha256": "185cc221af847e919cd4ca5dce38b7d126fc6b51fa273f2def3224867bd6714c",
+   "language": "Jupyter",
+   "lines": 112,
+   "truncated": false,
+   "block": 5,
+   "row": 16565
+  },
+  {
+   "path": "docs/source/Tutorial/RNA_DNAm_variants/.ipynb_checkpoints/scBOND_for_single_modality-checkpoint.ipynb",
+   "sha256": "b64db49126677bb4d74419148cd49e5058a66b9405c51c64bf717bf0f0bda101",
+   "language": "Jupyter",
+   "lines": 123,
+   "truncated": false,
+   "block": 5,
+   "row": 16687
+  },
+  {
+   "path": "docs/source/Tutorial/RNA_DNAm_variants/scBOND_aug_usage.ipynb",
+   "sha256": "185cc221af847e919cd4ca5dce38b7d126fc6b51fa273f2def3224867bd6714c",
+   "language": "Jupyter",
+   "lines": 112,
+   "truncated": false,
+   "block": 5,
+   "row": 16565
+  },
+  {
+   "path": "docs/source/Tutorial/RNA_DNAm_variants/scBOND_for_single_modality.ipynb",
+   "sha256": "b64db49126677bb4d74419148cd49e5058a66b9405c51c64bf717bf0f0bda101",
+   "language": "Jupyter",
+   "lines": 123,
+   "truncated": false,
+   "block": 5,
+   "row": 16687
+  },
+  {
+   "path": "docs/source/conf.py",
+   "sha256": "658ee7e031f8b42322842aaace73175d44b02ac657a23eedbf17b6a18e6ff510",
+   "language": "Python",
+   "lines": 95,
+   "truncated": false,
+   "block": 6,
+   "row": 36443
+  },
+  {
+   "path": "examples/scBOND_aug_usage.ipynb",
+   "sha256": "185cc221af847e919cd4ca5dce38b7d126fc6b51fa273f2def3224867bd6714c",
+   "language": "Jupyter",
+   "lines": 112,
+   "truncated": false,
+   "block": 5,
+   "row": 16565
+  },
+  {
+   "path": "examples/scBOND_for_single_modality.ipynb",
+   "sha256": "b64db49126677bb4d74419148cd49e5058a66b9405c51c64bf717bf0f0bda101",
+   "language": "Jupyter",
+   "lines": 123,
+   "truncated": false,
+   "block": 5,
+   "row": 16687
+  },
+  {
+   "path": "examples/scBOND_usage.ipynb",
+   "sha256": "43978b95e86eb9a6da7da49f7aa83d586ac65efe6cf6110885d7e39be65dd590",
+   "language": "Jupyter",
+   "lines": 137,
+   "truncated": false,
+   "block": 5,
+   "row": 16755
+  },
+  {
+   "path": "scBond/__init__.py",
+   "sha256": "650f2b5496aa21d9624b19bc4eea93ba89085f9b05246265f5e084490fc01a21",
+   "language": "Python",
+   "lines": 6,
+   "truncated": false,
+   "block": 6,
+   "row": 20993
+  },
+  {
+   "path": "scBond/bond.py",
+   "sha256": "ba981bd9dc4fe0267805f62fcf3a665c71274d6345788916db2ace373f2ad341",
+   "language": "Python",
+   "lines": 786,
+   "truncated": false,
+   "block": 7,
+   "row": 10243
+  },
+  {
+   "path": "scBond/calculate_cluster.py",
+   "sha256": "2eb13d12048c76f949d222137c9fb531a7f75e5967edfa2badf376471f38083d",
+   "language": "Python",
+   "lines": 42,
+   "truncated": false,
+   "block": 6,
+   "row": 29064
+  },
+  {
+   "path": "scBond/data_processing.py",
+   "sha256": "9e01ff06de25fa656c2f2efa5a49b4854ad0f83ec0f4204fa6161102725f2ff7",
+   "language": "Python",
+   "lines": 297,
+   "truncated": false,
+   "block": 7,
+   "row": 3816
+  },
+  {
+   "path": "scBond/draw_cluster.py",
+   "sha256": "861965f590a27061c4f02a0e4c135f64ea6306a0e5a22f9bd85d070abfd625a3",
+   "language": "Python",
+   "lines": 132,
+   "truncated": false,
+   "block": 6,
+   "row": 38794
+  },
+  {
+   "path": "scBond/logger.py",
+   "sha256": "7711d52290e8a96f2f96434270095dc1d30161f086351670026fc9dce51ccb26",
+   "language": "Python",
+   "lines": 50,
+   "truncated": false,
+   "block": 6,
+   "row": 27361
+  },
+  {
+   "path": "scBond/model_component.py",
+   "sha256": "7183197677929041df751d4c84ed249d9494ca26809c310e08f0d5a2604f18ca",
+   "language": "Python",
+   "lines": 830,
+   "truncated": false,
+   "block": 7,
+   "row": 10853
+  },
+  {
+   "path": "scBond/model_utlis.py",
+   "sha256": "c3a404b217d077b0cb82f864083df9c2cf1cdd80aa07bc8d2b06623fa62152f9",
+   "language": "Python",
+   "lines": 350,
+   "truncated": false,
+   "block": 7,
+   "row": 2264
+  },
+  {
+   "path": "scBond/split_datasets.py",
+   "sha256": "ae2d88fa9b997f3234480de128f5b9c8e1bb5d92d907868e10a9ae0bc0e0e00b",
+   "language": "Python",
+   "lines": 107,
+   "truncated": false,
+   "block": 6,
+   "row": 34886
+  },
+  {
+   "path": "scBond/train_model.py",
+   "sha256": "4b962111e968f966b4baf25ba5957fd498bdfc86134f67f0638fbea34baa33bc",
+   "language": "Python",
+   "lines": 1230,
+   "truncated": false,
+   "block": 8,
+   "row": 467
+  },
+  {
+   "path": "scBond/version.py",
+   "sha256": "90ce79aaa852adce1bf92b4d4ee374dd2ae0d24c1f8351b805c9022a1532b5a1",
+   "language": "Python",
+   "lines": 1,
+   "truncated": false,
+   "block": 1,
+   "row": 4964
+  },
+  {
+   "path": "setup.py",
+   "sha256": "df6aea1de9f4df7789c5f02c96e77646da8d87c3e2f4c50d78ddbb843f3acea2",
+   "language": "Python",
+   "lines": 43,
+   "truncated": false,
+   "block": 6,
+   "row": 30725
+  }
+ ]
+}

manifests/ff/github.com__eightmm__ba-pred.json

@@ -0,0 +1,129 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/eightmm/ba-pred",
+ "url": "https://github.com/eightmm/BA-Pred",
+ "host": "github.com",
+ "commit": "869c60c21d896cfdc5e23c76510e5b59a41bece0",
+ "license": "Apache-2.0",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "b26336e513d158e8e51dc6418492d0b89fd98625e389319e79b0181f30a4f71d",
+   "language": "License",
+   "lines": 199,
+   "truncated": false,
+   "block": 6,
+   "row": 2047
+  },
+  {
+   "path": "README.md",
+   "sha256": "8cab4a871a26773253f84cb629f19fb0978be5d6d35c395a0d9fa8e60186b491",
+   "language": "Text",
+   "lines": 172,
+   "truncated": false,
+   "block": 8,
+   "row": 10427
+  },
+  {
+   "path": "example/run.sh",
+   "sha256": "ae8192ac7668d4651f9d25c0848a3b820028c455120b084774f64ffe3ac9d8b0",
+   "language": "Shell",
+   "lines": 21,
+   "truncated": false,
+   "block": 8,
+   "row": 6921
+  },
+  {
+   "path": "src/bapred/__init__.py",
+   "sha256": "4af01bf83ac2fd0d432e46c5667c0863912e7fc19f05398399cfa6c220e52cfa",
+   "language": "Python",
+   "lines": 3,
+   "truncated": false,
+   "block": 6,
+   "row": 20279
+  },
+  {
+   "path": "src/bapred/data/atom_feature.py",
+   "sha256": "6dde8f7574855aca0b12b65ac0b2bd7865dc76909cedb682fb71f4c794d011c2",
+   "language": "Python",
+   "lines": 171,
+   "truncated": false,
+   "block": 7,
+   "row": 755
+  },
+  {
+   "path": "src/bapred/data/data.py",
+   "sha256": "59ce463f49fb6886e052d7aa42ae760b2a1455543847a618c5daa86fa4f5f350",
+   "language": "Python",
+   "lines": 321,
+   "truncated": false,
+   "block": 7,
+   "row": 4417
+  },
+  {
+   "path": "src/bapred/data/utils.py",
+   "sha256": "b9264d3146599e1e275500c462079608476ae2e01cb515947bbfb4d3c3d1a077",
+   "language": "Python",
+   "lines": 22,
+   "truncated": false,
+   "block": 6,
+   "row": 23289
+  },
+  {
+   "path": "src/bapred/inference.py",
+   "sha256": "0300e81507ca2694dce8400f8c7851abc2933cd8a1ff5f55c23a614a35724881",
+   "language": "Python",
+   "lines": 103,
+   "truncated": false,
+   "block": 6,
+   "row": 38731
+  },
+  {
+   "path": "src/bapred/model/GatedGCNLSPE.py",
+   "sha256": "26999a3c24b68065a8e98c45d3618c49b807d49b2e06007f7e3f64c4d305b237",
+   "language": "Python",
+   "lines": 114,
+   "truncated": false,
+   "block": 6,
+   "row": 38037
+  },
+  {
+   "path": "src/bapred/model/GraphGPS.py",
+   "sha256": "f06d7928abca0b1c4b33af4622bb6cf97b2d90c4c1935f241f4ac88e47b68505",
+   "language": "Python",
+   "lines": 70,
+   "truncated": false,
+   "block": 6,
+   "row": 30597
+  },
+  {
+   "path": "src/bapred/model/MHA.py",
+   "sha256": "fac5ec8935a2b430d4c00567a32728418a2d6bc7b145792f510f8c99543835e1",
+   "language": "Python",
+   "lines": 42,
+   "truncated": false,
+   "block": 6,
+   "row": 28708
+  },
+  {
+   "path": "src/bapred/model/model.py",
+   "sha256": "7d89e31d69978a7ce54faefa4472e740c496f4246a8200725f22ae680a2e6fd3",
+   "language": "Python",
+   "lines": 128,
+   "truncated": false,
+   "block": 6,
+   "row": 40446
+  },
+  {
+   "path": "tests/test_inference.py",
+   "sha256": "2fa56a8442c633700d9f9bbc65ca973f9739fdcae4e34c344ac78b3973b58437",
+   "language": "Python",
+   "lines": 45,
+   "truncated": false,
+   "block": 6,
+   "row": 30173
+  }
+ ]
+}

manifests/ff/github.com__petersaj__ap_histology.json

@@ -0,0 +1,309 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/petersaj/ap_histology",
+ "url": "https://github.com/petersaj/AP_histology",
+ "host": "github.com",
+ "commit": "67d71af75657dbd14bd97604514337f4822b2a98",
+ "license": "GPL-3.0",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "AP_histology.m",
+   "sha256": "2ad4479107a1761f8e7431f62cdd84daf04e07652cc3c5f8c7eb0dc5469e3e0a",
+   "language": "MATLAB",
+   "lines": 702,
+   "truncated": false,
+   "block": 6,
+   "row": 18938
+  },
+  {
+   "path": "LICENSE",
+   "sha256": "3972dc9744f6499f0f9b2dbf76696f2ae7ad8af9b23dde66d6af86c9dfb36986",
+   "language": "License",
+   "lines": 674,
+   "truncated": false,
+   "block": 1,
+   "row": 2235
+  },
+  {
+   "path": "README.md",
+   "sha256": "513131d5d2e2b3f1e1ddfb556f2ef74b76dc486c44432bfbd39d69fe50f10117",
+   "language": "Text",
+   "lines": 7,
+   "truncated": false,
+   "block": 8,
+   "row": 9581
+  },
+  {
+   "path": "analysis_functions/+ap_histology/histology_volume.m",
+   "sha256": "15df0e82bb32dc154eaa2561af6993f016c877192307519dc4d3c03f72006570",
+   "language": "MATLAB",
+   "lines": 81,
+   "truncated": false,
+   "block": 6,
+   "row": 13263
+  },
+  {
+   "path": "gui_functions/+ap_histology/align_auto_histology_atlas.m",
+   "sha256": "45987a60a963f3f2106c12a4e1e7c7387b7162b39f1b20d47558892ba0887d8b",
+   "language": "MATLAB",
+   "lines": 193,
+   "truncated": false,
+   "block": 6,
+   "row": 16328
+  },
+  {
+   "path": "gui_functions/+ap_histology/align_manual_histology_atlas.m",
+   "sha256": "61c9d79b8d76b40310ba4ddec365a1d740553570c40da708339128a5030d00c2",
+   "language": "MATLAB",
+   "lines": 369,
+   "truncated": false,
+   "block": 6,
+   "row": 17920
+  },
+  {
+   "path": "gui_functions/+ap_histology/align_manual_histology_atlas_v2.m",
+   "sha256": "842e1c563810a58ae7042ebe7a9754b2434a1948afa6cf2384ebeff0e4cd1f56",
+   "language": "MATLAB",
+   "lines": 229,
+   "truncated": false,
+   "block": 6,
+   "row": 16891
+  },
+  {
+   "path": "gui_functions/+ap_histology/annotation2ccf.m",
+   "sha256": "96b92dc76bf9251a3918283dc550428e41c3f02b1b3312b6c5cd6ee5e0642627",
+   "language": "MATLAB",
+   "lines": 98,
+   "truncated": false,
+   "block": 6,
+   "row": 14122
+  },
+  {
+   "path": "gui_functions/+ap_histology/annotator.m",
+   "sha256": "02b9ea88da38d1ec67528051f71702cb5c16d005d1edd8e389c5fc046872e9b7",
+   "language": "MATLAB",
+   "lines": 227,
+   "truncated": false,
+   "block": 6,
+   "row": 17009
+  },
+  {
+   "path": "gui_functions/+ap_histology/choose_histology_atlas.m",
+   "sha256": "22270f359dc831c6f6ef70b6cb3061d0b2676012a07a118a57e623273bbac0c1",
+   "language": "MATLAB",
+   "lines": 539,
+   "truncated": false,
+   "block": 6,
+   "row": 18511
+  },
+  {
+   "path": "gui_functions/+ap_histology/export_annotated_histology_vector.m",
+   "sha256": "c3c41d6e78148c005b965277a7fdbbcfb828a03855451fb0de240b632bd959ea",
+   "language": "MATLAB",
+   "lines": 3,
+   "truncated": false,
+   "block": 6,
+   "row": 2275
+  },
+  {
+   "path": "gui_functions/+ap_histology/flip_slices.m",
+   "sha256": "565d6e2847406de06dce8be5ee8246d56aff8ddf70638dd8397a891980e05863",
+   "language": "MATLAB",
+   "lines": 52,
+   "truncated": false,
+   "block": 6,
+   "row": 9135
+  },
+  {
+   "path": "gui_functions/+ap_histology/grab_atlas_slice.m",
+   "sha256": "a5493e65f1607dea9c65785950792f8a866c6e15a78a14a0f73d670168d88f88",
+   "language": "MATLAB",
+   "lines": 107,
+   "truncated": false,
+   "block": 6,
+   "row": 12540
+  },
+  {
+   "path": "gui_functions/+ap_histology/reorder_slices.m",
+   "sha256": "6ba8fb76557830f8cbe5af516e27f632ee5d9bf6c110cf81c1b8395889ee8ea4",
+   "language": "MATLAB",
+   "lines": 113,
+   "truncated": false,
+   "block": 6,
+   "row": 12472
+  },
+  {
+   "path": "gui_functions/+ap_histology/rigid_transform.m",
+   "sha256": "5def9f26e62b19385577af07d093a7c3ae270bec9d0f66e58cfacba1b039711b",
+   "language": "MATLAB",
+   "lines": 64,
+   "truncated": false,
+   "block": 6,
+   "row": 9777
+  },
+  {
+   "path": "gui_functions/+ap_histology/rotate_center_slices.m",
+   "sha256": "653d266f3079ab6d0c46599dbcf9932763189f3b4371fe745459d85ed7f11a33",
+   "language": "MATLAB",
+   "lines": 59,
+   "truncated": false,
+   "block": 6,
+   "row": 10301
+  },
+  {
+   "path": "gui_functions/+ap_histology/set_channel_properties.m",
+   "sha256": "8f85b6306a25c99e5e7094678efbb958e5b132ba93d1faf5bfbd59f2c0bec91a",
+   "language": "MATLAB",
+   "lines": 72,
+   "truncated": false,
+   "block": 6,
+   "row": 11112
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/AP_align_probe_histology.m",
+   "sha256": "08596d1a3c50785b7a9d25bb091382cf78fb5b1ccf98ad90d69171e556a3c534",
+   "language": "MATLAB",
+   "lines": 195,
+   "truncated": false,
+   "block": 6,
+   "row": 15944
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/AP_grab_fullsize_histology_slices.m",
+   "sha256": "ede5b80d603b8130ecc3287cab6cf21703c05bb4314ec40670f5b8bb24b6918f",
+   "language": "MATLAB",
+   "lines": 64,
+   "truncated": false,
+   "block": 6,
+   "row": 10431
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/AP_histology2ccf.m",
+   "sha256": "3a5977c78a6ffc9d49cb11872abb43560621191ff28d7721e12d60f97c4b37a1",
+   "language": "MATLAB",
+   "lines": 53,
+   "truncated": false,
+   "block": 6,
+   "row": 10333
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/AP_view_aligned_histology_volume.m",
+   "sha256": "fbfc916fd03332493f83ef457d093d2baa732933502b3f41c678a83f9ac8c9a9",
+   "language": "MATLAB",
+   "lines": 166,
+   "truncated": false,
+   "block": 6,
+   "row": 14944
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/annotate_neuropixels.m",
+   "sha256": "ec8bbb5c4203a50cb083590189f3f2178959c6d7c01fcead2bd8c559ff6ffc03",
+   "language": "MATLAB",
+   "lines": 404,
+   "truncated": false,
+   "block": 6,
+   "row": 18330
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/annotate_probes.m",
+   "sha256": "16a1682b2c89fc4d246f5edcda7967923828fb4ec29e20781bb9a4404743eb97",
+   "language": "MATLAB",
+   "lines": 91,
+   "truncated": false,
+   "block": 6,
+   "row": 12800
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/annotate_volume.m",
+   "sha256": "6bf43531eef51b8b915aea90c58a83e95121a88ea760c2f04351efacba292fe2",
+   "language": "MATLAB",
+   "lines": 90,
+   "truncated": false,
+   "block": 6,
+   "row": 12893
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/annotation2ccf.m",
+   "sha256": "b85d149528ff5c8549c1b066594d9b843ebef018d9b4131f60adb97a9d9ec232",
+   "language": "MATLAB",
+   "lines": 124,
+   "truncated": false,
+   "block": 6,
+   "row": 14081
+  },
+  {
+   "path": "unused_functions/+ap_histology_unused/demo_histology_pipeline.m",
+   "sha256": "af6347523286c4ad4c68697153ed2c910e6591c38833840a350c21f373b3b968",
+   "language": "MATLAB",
+   "lines": 92,
+   "truncated": false,
+   "block": 6,
+   "row": 12294
+  },
+  {
+   "path": "utility_functions/+ap_histology/export_ibl_probe.m",
+   "sha256": "0d0e656e584abc5de4e98828da7f3380358292a4a0ffaf4012aafcc12a6c5884",
+   "language": "MATLAB",
+   "lines": 37,
+   "truncated": false,
+   "block": 6,
+   "row": 9025
+  },
+  {
+   "path": "utility_functions/+ap_histology/fit_probe_line.m",
+   "sha256": "61f342d9afe3ef54e297633738d71b33d4b45007094554d9d6c2c1f1f11e33f9",
+   "language": "MATLAB",
+   "lines": 81,
+   "truncated": false,
+   "block": 6,
+   "row": 12602
+  },
+  {
+   "path": "utility_functions/+ap_histology/loadStructureTree.m",
+   "sha256": "edb97561c7a9d542385804145723183f89cae041d3ede6d7cf34cbf6d2c6f7bf",
+   "language": "MATLAB",
+   "lines": 51,
+   "truncated": false,
+   "block": 6,
+   "row": 9866
+  },
+  {
+   "path": "utility_functions/+ap_histology/load_ccf.m",
+   "sha256": "c27b382b335f5e43fa78369c70d5ca4662b7c3583055a372c96aa2ac693083a2",
+   "language": "MATLAB",
+   "lines": 21,
+   "truncated": false,
+   "block": 6,
+   "row": 5571
+  },
+  {
+   "path": "utility_functions/+ap_histology/natsort.m",
+   "sha256": "11287983340128da4ce0284ed4c1f40c99d98bc3f4c5396d6ba6b07c2a19df82",
+   "language": "MATLAB",
+   "lines": 330,
+   "truncated": false,
+   "block": 6,
+   "row": 18042
+  },
+  {
+   "path": "utility_functions/+ap_histology/natsortfiles.m",
+   "sha256": "ccf88b5f6d410b0de3f7d822b73438e9bc8beffcf0e3d52d472121552d11b265",
+   "language": "MATLAB",
+   "lines": 169,
+   "truncated": false,
+   "block": 6,
+   "row": 15624
+  },
+  {
+   "path": "utility_functions/+ap_histology/read_image_metadata.m",
+   "sha256": "bd4d59e80443244caa5d87ced70a701e1b938d0b50fea45dba8b4cb7c113074b",
+   "language": "MATLAB",
+   "lines": 23,
+   "truncated": false,
+   "block": 6,
+   "row": 6309
+  }
+ ]
+}

manifests/ff/github.com__teichlab__bbknn.json

@@ -0,0 +1,120 @@
+{
+ "format": "oscr-script-manifest/1",
+ "repository": "github.com/teichlab/bbknn",
+ "url": "https://github.com/Teichlab/bbknn",
+ "host": "github.com",
+ "commit": "95ce34b8905cbde307704a77436c354938ba0367",
+ "license": "MIT",
+ "license_confirmed_by": "license file LICENSE",
+ "redistribution": "yes",
+ "files": [
+  {
+   "path": "LICENSE",
+   "sha256": "d9b9bdb8569b419dd891bfd269253fb946c08f6fa1b5ce96250992ffae6a0f4d",
+   "language": "License",
+   "lines": 19,
+   "truncated": false,
+   "block": 6,
+   "row": 1709
+  },
+  {
+   "path": "README.md",
+   "sha256": "ef860b34d34810c259cda346a52343c5bdfb3a24d142390dd2717c680f0c80d3",
+   "language": "Text",
+   "lines": 106,
+   "truncated": false,
+   "block": 8,
+   "row": 10590
+  },
+  {
+   "path": "bbknn/__init__.py",
+   "sha256": "a61d1ffdb2619967e528dac50626a624ab508edb8ece2f073b7d7d5b8d1aae84",
+   "language": "Python",
+   "lines": 229,
+   "truncated": false,
+   "block": 7,
+   "row": 4239
+  },
+  {
+   "path": "bbknn/matrix.py",
+   "sha256": "eebcc5ec28f172db5ca84f1888a8ffe9da0eaf97a57077e6aa0ed229b21fb19f",
+   "language": "Python",
+   "lines": 389,
+   "truncated": false,
+   "block": 7,
+   "row": 6843
+  },
+  {
+   "path": "docs/conf.py",
+   "sha256": "da7d077d08ac78150eb1c948a730a30ba6f0340a64909eb72a44332cd1930535",
+   "language": "Python",
+   "lines": 161,
+   "truncated": false,
+   "block": 6,
+   "row": 41192
+  },
+  {
+   "path": "examples/benchmark.ipynb",
+   "sha256": "5020d69e15b267d6e8edee5508bdd71a9155e75de20a22d20ce79f3c0c16baff",
+   "language": "Jupyter",
+   "lines": 207,
+   "truncated": false,
+   "block": 5,
+   "row": 17458
+  },
+  {
+   "path": "examples/benchmark2.ipynb",
+   "sha256": "1d7028d190adb9e2453303906d434f5665cd76108ab6508f9731cb450712504c",
+   "language": "Jupyter",
+   "lines": 64,
+   "truncated": false,
+   "block": 5,
+   "row": 16253
+  },
+  {
+   "path": "examples/benchmark3-new-R-methods.ipynb",
+   "sha256": "f1067f4a59626e48d12c55911bbed1eb101bd0b4d700b5e819a6fd497c1465d6",
+   "language": "Jupyter",
+   "lines": 70,
+   "truncated": false,
+   "block": 5,
+   "row": 16306
+  },
+  {
+   "path": "examples/demo.ipynb",
+   "sha256": "4db67bafc35bf933da0349f73da7a675c34f61d5c4fc155399f8ebadb5fa428b",
+   "language": "Jupyter",
+   "lines": 64,
+   "truncated": false,
+   "block": 5,
+   "row": 16555
+  },
+  {
+   "path": "examples/mouse-harmony.ipynb",
+   "sha256": "12f9c6eca21c310c8a75e06b419aec8c69513291c1f93eb73a458289ac635dac",
+   "language": "Jupyter",
+   "lines": 50,
+   "truncated": false,
+   "block": 5,
+   "row": 16056
+  },
+  {
+   "path": "examples/mouse.ipynb",
+   "sha256": "509a43853995223eb5f076be095ad5ca262ed1a4790a1f37738f33c783eef515",
+   "language": "Jupyter",
+   "lines": 112,
+   "truncated": false,
+   "block": 5,
+   "row": 16284
+  },
+  {
+   "path": "examples/simulation.ipynb",
+   "sha256": "c8e1b22ae8676913fd7b28bfcdf1a42a1c30cff375e566404297e41c1eb2e522",
+   "language": "Jupyter",
+   "lines": 86,
+   "truncated": false,
+   "block": 5,
+   "row": 16435
+  }
+ ]
+}