Team Ai
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
github.com__yenlin-chen__geometric_tm-archive.json345 linesDownload Raw Back to 25
1{2 "format": "oscr-script-manifest/1",3 "repository": "github.com/yenlin-chen/geometric_tm-archive",4 "url": "https://github.com/yenlin-chen/Geometric_Tm-archive/tree/1.0.0",5 "host": "github.com",6 "commit": "8826fe69f472138a30af78b1fd93f81b86fef251",7 "license": "MIT",8 "license_confirmed_by": "license file LICENSE",9 "redistribution": "yes",10 "files": [11  {12   "path": "LICENSE",13   "sha256": "737c5714eb069e70d594aaa8a99782677156e549f6ea3bf2b9c3e6311ea4bd9b",14   "language": "License",15   "lines": 21,16   "truncated": false,17   "block": 6,18   "row": 155619  },20  {21   "path": "README.md",22   "sha256": "a3d816a3655743346cd48a3b01780fa8b21a1594a2ac6df63b71c9201f52ebdf",23   "language": "Text",24   "lines": 43,25   "truncated": false,26   "block": 8,27   "row": 1005728  },29  {30   "path": "experiments/M1/backbone_O-contact_12-codir_X-coord_X-deform_X/test_distr-DeepSTABp.py",31   "sha256": "5e489471e5ef47fc89b1427f2436a55140b275002196b3967ed2dd787caa9ed1",32   "language": "Python",33   "lines": 447,34   "truncated": false,35   "block": 7,36   "row": 621937  },38  {39   "path": "experiments/M1/backbone_O-contact_12-codir_X-coord_X-deform_X/train-10fold.py",40   "sha256": "2f04d83eb02b65497076fdd5c04cde4e6eaf892dca61ee8982355abdb1e18259",41   "language": "Python",42   "lines": 576,43   "truncated": false,44   "block": 7,45   "row": 878346  },47  {48   "path": "experiments/M2/backbone_O-contact_12-codir_X-coord_X-deform_X/test_distr-DeepSTABp.py",49   "sha256": "ca0de75c5c2890b3ef97f5326476c1003bb88f69a73c877c744a679a0721d6f6",50   "language": "Python",51   "lines": 447,52   "truncated": false,53   "block": 7,54   "row": 621855  },56  {57   "path": "experiments/M2/backbone_O-contact_12-codir_X-coord_X-deform_X/train-10fold.py",58   "sha256": "2f3b58aa43d5809701c466374179d5f39c17aa39d6295dfbea5d8c745eb61391",59   "language": "Python",60   "lines": 576,61   "truncated": false,62   "block": 7,63   "row": 878064  },65  {66   "path": "experiments/M2/backbone_X-contact_12-codir_1CONT-coord_1CONT-deform_1CONT/test_distr-DeepSTABp.py",67   "sha256": "9a440ddb461ba9591772e7215c345e8c74fafb66b2c717fab88933b72b4d22be",68   "language": "Python",69   "lines": 447,70   "truncated": false,71   "block": 7,72   "row": 622773  },74  {75   "path": "experiments/M2/backbone_X-contact_12-codir_1CONT-coord_1CONT-deform_1CONT/train-10fold.py",76   "sha256": "da9323809cca6c2777c6a7461a2ad3cd755f557200992305ce00c1c5e7de0110",77   "language": "Python",78   "lines": 576,79   "truncated": false,80   "block": 7,81   "row": 878882  },83  {84   "path": "experiments/S1/backbone_X-contact_12-codir_X-coord_X-deform_X/test_distr-DeepSTABp.py",85   "sha256": "e1f51833569dabd1257da7705adf4d6fc25c50f65fd6d76a7fa2b6f97bb91f19",86   "language": "Python",87   "lines": 447,88   "truncated": false,89   "block": 7,90   "row": 621091  },92  {93   "path": "experiments/S1/backbone_X-contact_12-codir_X-coord_X-deform_X/train-10fold.py",94   "sha256": "6cc77306c43d37e4159ae7c80c403712c025c3d9b1125263b5608973e1ba24f9",95   "language": "Python",96   "lines": 576,97   "truncated": false,98   "block": 7,99   "row": 8775100  },101  {102   "path": "experiments/S1/backbone_X-contact_X-codir_20N-coord_X-deform_X/test_distr-DeepSTABp.py",103   "sha256": "fc38d342077bac521d69d7d9a63ab2df1762257571e31a8b8252187b38602eb2",104   "language": "Python",105   "lines": 447,106   "truncated": false,107   "block": 7,108   "row": 6206109  },110  {111   "path": "experiments/S1/backbone_X-contact_X-codir_20N-coord_X-deform_X/train-10fold.py",112   "sha256": "19368859e99818a30f3ab901738b537d829fd61f2aba319d29acaf419cd2ca26",113   "language": "Python",114   "lines": 576,115   "truncated": false,116   "block": 7,117   "row": 8772118  },119  {120   "path": "experiments/S1/backbone_X-contact_X-codir_X-coord_1DCONT-deform_X/test_distr-DeepSTABp.py",121   "sha256": "ae0253dae848ef9fefc95e1c71184906fbb431ddb55e83401bd3d1ccdcd29c1a",122   "language": "Python",123   "lines": 447,124   "truncated": false,125   "block": 7,126   "row": 6208127  },128  {129   "path": "experiments/S1/backbone_X-contact_X-codir_X-coord_1DCONT-deform_X/train-10fold.py",130   "sha256": "acb039c4d24be011938b643383fbc0a8fa3af299ff39f859d2b744a55365397d",131   "language": "Python",132   "lines": 576,133   "truncated": false,134   "block": 7,135   "row": 8774136  },137  {138   "path": "experiments/S1/backbone_X-contact_X-codir_X-coord_X-deform_2DSIGMA/test_distr-DeepSTABp.py",139   "sha256": "8ddd75637c17f1ad12658447e3560ac8cb9406ddcfc8d06c26beee6b33a5e085",140   "language": "Python",141   "lines": 447,142   "truncated": false,143   "block": 7,144   "row": 6211145  },146  {147   "path": "experiments/S1/backbone_X-contact_X-codir_X-coord_X-deform_2DSIGMA/train-10fold.py",148   "sha256": "6769788292ea8e8a5cf2ebf6533d12b0c4e338926d0b9af504ecab030af6c987",149   "language": "Python",150   "lines": 576,151   "truncated": false,152   "block": 7,153   "row": 8776154  },155  {156   "path": "experiments/build folds/build folds.ipynb",157   "sha256": "08b62cd6db90522595175c935bfd08661eb45af6758267c18513fd15c380f78e",158   "language": "Jupyter",159   "lines": 85,160   "truncated": false,161   "block": 5,162   "row": 16232163  },164  {165   "path": "notebooks (analysis and plots)/20250721-2 critical residue analysis for best performing protein/SCR-1.retreive uniprotkb info.ipynb",166   "sha256": "9c5d04edbd4e74ffb2e49c2f2e4f7317cab34113f556a2c476182fa999677919",167   "language": "Jupyter",168   "lines": 140,169   "truncated": false,170   "block": 5,171   "row": 16602172  },173  {174   "path": "notebooks (analysis and plots)/20250721-2 critical residue analysis for best performing protein/SCR-2.critical residue analysis.ipynb",175   "sha256": "047e4f15a986bee9c68de5d3b61e52684fddf15cf8c635e45d451062aba59e76",176   "language": "Jupyter",177   "lines": 268,178   "truncated": false,179   "block": 5,180   "row": 17344181  },182  {183   "path": "notebooks (analysis and plots)/20250721-2 critical residue analysis for best performing protein/SCR-3.plot against sequence.ipynb",184   "sha256": "b19f1d3b008143ed9ecabf921d23b21bcb4c64741cb93218a473f7bd9e5f03a3",185   "language": "Jupyter",186   "lines": 551,187   "truncated": false,188   "block": 6,189   "row": 332190  },191  {192   "path": "notebooks (analysis and plots)/20250721-2 critical residue analysis for best performing protein/SCR-4.plot on structures.ipynb",193   "sha256": "d561654033454357f4259053ae14e799979a4b0ff7f628c38e4785ba08e540cb",194   "language": "Jupyter",195   "lines": 631,196   "truncated": false,197   "block": 6,198   "row": 622199  },200  {201   "path": "notebooks (analysis and plots)/20250916-1 analysis of best performing proteins/analysis.ipynb",202   "sha256": "8a60099f6be7c7e9c44c1a7ac157b84368b8703e19498e2cbc4910b68f56ff67",203   "language": "Jupyter",204   "lines": 676,205   "truncated": false,206   "block": 6,207   "row": 450208  },209  {210   "path": "notebooks (analysis and plots)/20250916-1 analysis of best performing proteins/edge count distribution.ipynb",211   "sha256": "30eafc355d9603398b90bb27cdfb5f325bc7a4da0b0ec9049e85b904c282264c",212   "language": "Jupyter",213   "lines": 312,214   "truncated": false,215   "block": 5,216   "row": 17475217  },218  {219   "path": "src/data_collation/collate DeepSTABp - 20241121 - list of files inferred from deepstabp code.ipynb",220   "sha256": "dfcfe9484c648e0a8ba6d88e4553d8bc68fd2834568e39c3db707f9bebe22ce3",221   "language": "Jupyter",222   "lines": 484,223   "truncated": false,224   "block": 6,225   "row": 328226  },227  {228   "path": "src/data_collation/project_directories.py",229   "sha256": "d840619ba3e0bc356abebcc2fa6a32dda7f52ff2d7d0ba9ee32dee68d58d7fc6",230   "language": "Python",231   "lines": 15,232   "truncated": false,233   "block": 6,234   "row": 22240235  },236  {237   "path": "src/ml_modules/__init__.py",238   "sha256": "dd88bf12e86704c31147813f308daa54138e53589c9b1764bc0888b8854a04b0",239   "language": "Python",240   "lines": 6,241   "truncated": false,242   "block": 6,243   "row": 20600244  },245  {246   "path": "src/ml_modules/data/__init__.py",247   "sha256": "1376793ecca28c98c3f363a03e7646db680282c32682561adc61bf3ddf0b7a37",248   "language": "Python",249   "lines": 41,250   "truncated": false,251   "block": 6,252   "row": 24267253  },254  {255   "path": "src/ml_modules/data/datasets.py",256   "sha256": "e77c9f0a5f8b96d03a646b97cff1c6b33a90ad2fc9f83385d12eaaff1ab217bf",257   "language": "Python",258   "lines": 689,259   "truncated": false,260   "block": 7,261   "row": 9899262  },263  {264   "path": "src/ml_modules/data/encoders.py",265   "sha256": "5b7a2011c400403290368697fdc7263c1e58dbfcd95ba65797a6d086638cb00e",266   "language": "Python",267   "lines": 56,268   "truncated": false,269   "block": 6,270   "row": 29776271  },272  {273   "path": "src/ml_modules/data/enm.py",274   "sha256": "62b8120a6c9fe40b54eb2c9158b36acfb16523e33bedd90c8d05c820446f5550",275   "language": "Python",276   "lines": 421,277   "truncated": false,278   "block": 7,279   "row": 5805280  },281  {282   "path": "src/ml_modules/data/retrievers.py",283   "sha256": "67f096125935d1052344bcdcdd24eec222ec694bfc726d98e5b0e7b2bb775a91",284   "language": "Python",285   "lines": 402,286   "truncated": false,287   "block": 7,288   "row": 6106289  },290  {291   "path": "src/ml_modules/data/summarize_couplings.py",292   "sha256": "f9c47a5decedc50083106f4acfc845ffae74e65c4de77ff1bfb6b3dcbfb8b6af",293   "language": "Python",294   "lines": 249,295   "truncated": false,296   "block": 7,297   "row": 823298  },299  {300   "path": "src/ml_modules/data/transforms.py",301   "sha256": "51810a0194253900b54017623d0b8d5e414818f04a00f2324d434560303b5183",302   "language": "Python",303   "lines": 39,304   "truncated": false,305   "block": 6,306   "row": 25027307  },308  {309   "path": "src/ml_modules/training/mGCNConv.py",310   "sha256": "30caa7c28eb814c03a119619ef3fec77371b850ae70ffa693653192289c84eea",311   "language": "Python",312   "lines": 143,313   "truncated": false,314   "block": 6,315   "row": 40921316  },317  {318   "path": "src/ml_modules/training/metrics.py",319   "sha256": "3bd176523c71b5d3d6d7550478f00e5410816485a62d8bf4635fa0f11a8a52f9",320   "language": "Python",321   "lines": 102,322   "truncated": false,323   "block": 6,324   "row": 35324325  },326  {327   "path": "src/ml_modules/training/model_arch.py",328   "sha256": "21ef053b49f59070f7d894e74a37cb57efb4a85923ac8034bb81a65d7589e7dc",329   "language": "Python",330   "lines": 1051,331   "truncated": false,332   "block": 7,333   "row": 11179334  },335  {336   "path": "src/ml_modules/training/trainer.py",337   "sha256": "91972bec4f5c0dbca25e8b56e7a5e45be261e2027205d58558ab023dc1e6b026",338   "language": "Python",339   "lines": 249,340   "truncated": false,341   "block": 7,342   "row": 1129343  }344 ]345}