Datasetpublic
OpenScientificCodeRegistry/Database

Open Scientific Code Registry (OSCR): the authors' scripts The code published by the authors of open-access neuroscience papers, as found and verified by Open Scientific Code Registry (OSCR). Each file is here exactly as it is at the source, at the verified commit, under the license of its repository. 421,275 unique files (3,782 MB of text) from 8,849 repositories, in 24 Parquet block(s). Only files whose repository's license allows redistribution, confirmed by the repository's… See the full description on the dataset page: https://huggingface.co/datasets/OpenScientificCodeRegistry/Database.
sourceHugging Faceotherupdated 2d agoView on Hugging Face
0likes5.6kdownloads
rootmanifests06github.com__goekelab__m6anet.json
github.com__goekelab__m6anet.json291 linesDownload Raw Back to 06
1{2 "format": "oscr-script-manifest/1",3 "repository": "github.com/goekelab/m6anet",4 "url": "https://github.com/GoekeLab/m6anet",5 "host": "github.com",6 "commit": "590ec277cb48d61774f0872395099e466022e810",7 "license": "MIT",8 "license_confirmed_by": "license file LICENSE",9 "redistribution": "yes",10 "files": [11  {12   "path": "LICENSE",13   "sha256": "ca50d26b9487cd839659e2a080ce69037edf6edd0cd5d48f95c71cb04c488646",14   "language": "License",15   "lines": 21,16   "truncated": false,17   "block": 10,18   "row": 604319  },20  {21   "path": "README.md",22   "sha256": "1fd0aac76b84e5c8c259bd2a81bae4d08bc39b99bae6ef7bc08f7309b2c5b1b5",23   "language": "Text",24   "lines": 304,25   "truncated": false,26   "block": 10,27   "row": 2001328  },29  {30   "path": "docs/source/conf.py",31   "sha256": "7fa1c9c5f26bc5bfcc715a7e366ddd16bc9f8ddd6c8038576095689c6a754517",32   "language": "Python",33   "lines": 58,34   "truncated": false,35   "block": 10,36   "row": 1184437  },38  {39   "path": "m6anet/__init__.py",40   "sha256": "7a247948904f38547171881615fac2faede8bc67d3ea5c9d65f0043545a71cc3",41   "language": "Python",42   "lines": 30,43   "truncated": false,44   "block": 10,45   "row": 1073846  },47  {48   "path": "m6anet/deprecated/compute_norm_factors.py",49   "sha256": "278e292c0030ccb54b4f8b39af725d972e03021c36693bacf619af9abb07868d",50   "language": "Python",51   "lines": 51,52   "truncated": false,53   "block": 10,54   "row": 1172055  },56  {57   "path": "m6anet/deprecated/dataprep.py",58   "sha256": "7fa9209ff3d4ddb5e4195a55de3c3f5f0d47dd21d1422240e5da63f9b9c182dc",59   "language": "Python",60   "lines": 72,61   "truncated": false,62   "block": 10,63   "row": 1250364  },65  {66   "path": "m6anet/deprecated/inference.py",67   "sha256": "bb8002867ecaad16fe669a6916f582e37d4593af4f23875832f8aca98b028946",68   "language": "Python",69   "lines": 99,70   "truncated": false,71   "block": 10,72   "row": 1336773  },74  {75   "path": "m6anet/deprecated/train.py",76   "sha256": "46d49c4accc29bef3dca00b5cd4855c3036d733bf3c30a97f4f79aee3e1d1225",77   "language": "Python",78   "lines": 139,79   "truncated": false,80   "block": 10,81   "row": 1413882  },83  {84   "path": "m6anet/model/model.py",85   "sha256": "61ee774c1f55c6bead60f140446a29ab6b76e776c973d2571eae66e677b0fac4",86   "language": "Python",87   "lines": 164,88   "truncated": false,89   "block": 10,90   "row": 1464191  },92  {93   "path": "m6anet/model/model_blocks/__init__.py",94   "sha256": "81393020933c33c63ff5a241555ee99c3e886c6c2c68b29381b6ec8932e78ab9",95   "language": "Python",96   "lines": 11,97   "truncated": false,98   "block": 10,99   "row": 10581100  },101  {102   "path": "m6anet/model/model_blocks/blocks.py",103   "sha256": "a2f5e7da0c397edf16f58c6a451f254181ff5c6b35909afdbe5fa0e6edc39f53",104   "language": "Python",105   "lines": 266,106   "truncated": false,107   "block": 10,108   "row": 15354109  },110  {111   "path": "m6anet/model/model_blocks/pooling_blocks.py",112   "sha256": "f2912b2d3127997eb8cf279f2ee32c7e67a81b1e2fc52e38507d4b741793ee6c",113   "language": "Python",114   "lines": 412,115   "truncated": false,116   "block": 10,117   "row": 16302118  },119  {120   "path": "m6anet/scripts/compute_norm_factors.py",121   "sha256": "e7c8bf1578821e1a9aa7da7ed521e1f042fc86b6089735a6597f3e97e077cd3e",122   "language": "Python",123   "lines": 43,124   "truncated": false,125   "block": 10,126   "row": 11445127  },128  {129   "path": "m6anet/scripts/convert.py",130   "sha256": "d145dc796b2906a2088bf63c2f633b548414ac57489e44f7f709d0752f576692",131   "language": "Python",132   "lines": 34,133   "truncated": false,134   "block": 10,135   "row": 10874136  },137  {138   "path": "m6anet/scripts/dataprep.py",139   "sha256": "8df4a78cacba2d512106ebb75a44621ff845568019a343ab93c14162d78bd827",140   "language": "Python",141   "lines": 70,142   "truncated": false,143   "block": 10,144   "row": 12470145  },146  {147   "path": "m6anet/scripts/inference.py",148   "sha256": "44cbe4c80bfce0b5207b30f6ff2788b87d94d6023b62573970b9ac6b4c769caa",149   "language": "Python",150   "lines": 106,151   "truncated": false,152   "block": 10,153   "row": 13608154  },155  {156   "path": "m6anet/scripts/train.py",157   "sha256": "51fd1c2bc51f2412b2cea4d6a6725970782b0c410eef2c3ee450be27e1d350b1",158   "language": "Python",159   "lines": 131,160   "truncated": false,161   "block": 10,162   "row": 14016163  },164  {165   "path": "m6anet/tests/conftest.py",166   "sha256": "62639ac9d1a7f075fdbfc5e35d0e5e6e2d44acc4f8e11a714e7dbb8c42085f26",167   "language": "Python",168   "lines": 121,169   "truncated": false,170   "block": 10,171   "row": 13090172  },173  {174   "path": "m6anet/tests/test_dataprep.py",175   "sha256": "7d37646d969c2baaaeb5293ac2dac55499d95db13cb7c33246e40f239cb9ba12",176   "language": "Python",177   "lines": 58,178   "truncated": false,179   "block": 10,180   "row": 12120181  },182  {183   "path": "m6anet/tests/test_inference.py",184   "sha256": "7a3a93a54af3877a343305c36f982a77c4f4bc250b0d636961271753b10a5a2e",185   "language": "Python",186   "lines": 82,187   "truncated": false,188   "block": 10,189   "row": 13512190  },191  {192   "path": "m6anet/utils/builder.py",193   "sha256": "98c98b4c5bfa06598178d47e14ca2240db8123b3144155caadb19fe7fc91074b",194   "language": "Python",195   "lines": 110,196   "truncated": false,197   "block": 10,198   "row": 13784199  },200  {201   "path": "m6anet/utils/constants.py",202   "sha256": "5c757472a254923aea151e22e962a3a333b16ab83107858d1fbe92a6f29688da",203   "language": "Python",204   "lines": 37,205   "truncated": false,206   "block": 10,207   "row": 12116208  },209  {210   "path": "m6anet/utils/data_utils.py",211   "sha256": "b929f3b7cedb42820df6daba5f2a280f05d55e924742fb68ba4547704099efd3",212   "language": "Python",213   "lines": 511,214   "truncated": false,215   "block": 10,216   "row": 17089217  },218  {219   "path": "m6anet/utils/dataprep_utils.py",220   "sha256": "e96f34681b1e3519cb6df5611041c1170d87809615004a5181e2770fc5ca0fb3",221   "language": "Python",222   "lines": 488,223   "truncated": false,224   "block": 10,225   "row": 17041226  },227  {228   "path": "m6anet/utils/helper.py",229   "sha256": "c19c576a3ba5023985a245dc36c48e54c031ba9209144c48978a49e5e99b6c3e",230   "language": "Python",231   "lines": 104,232   "truncated": false,233   "block": 10,234   "row": 12797235  },236  {237   "path": "m6anet/utils/inference_utils.py",238   "sha256": "44a3e3158788f2958c47aba232b5d16422d157711e1529c3d991477f82069ebc",239   "language": "Python",240   "lines": 140,241   "truncated": false,242   "block": 10,243   "row": 14846244  },245  {246   "path": "m6anet/utils/loss_functions/loss_functions.py",247   "sha256": "06e0b807c00f9a8313cc71c37a8f0ee03ea9cca619c881fb2dac3a841f24cc05",248   "language": "Python",249   "lines": 39,250   "truncated": false,251   "block": 10,252   "row": 11322253  },254  {255   "path": "m6anet/utils/norm_utils.py",256   "sha256": "a3027517c972ef60b3e0cd48c5a9c11c3f83268662942f1bef78132f1bdfc76d",257   "language": "Python",258   "lines": 180,259   "truncated": false,260   "block": 10,261   "row": 15156262  },263  {264   "path": "m6anet/utils/sampler_utils.py",265   "sha256": "901fb2c0da32d8faa0dcb5876fb43f196b006623b401031a7391aadcb76474ad",266   "language": "Python",267   "lines": 261,268   "truncated": false,269   "block": 10,270   "row": 15644271  },272  {273   "path": "m6anet/utils/training_utils.py",274   "sha256": "7367f5ba9492ff7dc165d69659a2a3880cd53e04b84dfc369bea0694698a7028",275   "language": "Python",276   "lines": 268,277   "truncated": false,278   "block": 10,279   "row": 15779280  },281  {282   "path": "setup.py",283   "sha256": "3d15639897619d8c421795a29c6cb04c18cc7b601659a286ec58c0039a2a2571",284   "language": "Python",285   "lines": 66,286   "truncated": false,287   "block": 10,288   "row": 12459289  }290 ]291}