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
zenodo__16809359.json255 linesDownload Raw Back to fe
1{2 "format": "oscr-script-manifest/1",3 "repository": "zenodo:16809359",4 "url": "https://doi.org/10.5281/zenodo.16809359",5 "host": "zenodo.org",6 "commit": "",7 "license": "CC-BY-4.0",8 "license_confirmed_by": "license of the archive's record",9 "redistribution": "yes",10 "files": [11  {12   "path": "README.md",13   "sha256": "534d731401ba8efffd215ce90b0eb057d888d50fdd0eb1d2c395ae57bdcf8faf",14   "language": "Text",15   "lines": 57,16   "truncated": false,17   "block": 4,18   "row": 239019  },20  {21   "path": "SimplePolynomial.ipynb",22   "sha256": "b2e089e09d4fce10b450e45403c7295ea682f01471974ea2429de1e7df4fb5c4",23   "language": "Jupyter",24   "lines": 349,25   "truncated": false,26   "block": 3,27   "row": 764128  },29  {30   "path": "gcn_task/STONEDanalysis.ipynb",31   "sha256": "60011db547123a80640cc8f13d4a3d8024904710c9700b99105d7f5536a972bb",32   "language": "Jupyter",33   "lines": 538,34   "truncated": false,35   "block": 3,36   "row": 771037  },38  {39   "path": "gcn_task/adversarial_eval.py",40   "sha256": "c036781f911cc77d86ff0a993b2d92f7312b4ee2d4a3fbca140b64d60fe18b35",41   "language": "Python",42   "lines": 411,43   "truncated": false,44   "block": 3,45   "row": 2646646  },47  {48   "path": "gcn_task/dglgcn.py",49   "sha256": "8cf3fd7c2c8bcb8c9e613768972996922e061b6e1419a9647b7eb93c61f02138",50   "language": "Python",51   "lines": 1122,52   "truncated": false,53   "block": 3,54   "row": 2789955  },56  {57   "path": "gcn_task/gcn_results/postprocess_stuff/adversarial_postprocess.ipynb",58   "sha256": "82e2c3c3ed850aa4c419465da6813c52a069430bf26457d1ad6e6cc468135cb4",59   "language": "Jupyter",60   "lines": 359,61   "truncated": false,62   "block": 3,63   "row": 764364  },65  {66   "path": "gcn_task/gcn_results/postprocess_stuff/adversarial_training_curves.py",67   "sha256": "acdf505c6209e0932c1ffa3522c8ff2791e90957e5f996fd533fe9e734ab9da5",68   "language": "Python",69   "lines": 118,70   "truncated": false,71   "block": 3,72   "row": 2247173  },74  {75   "path": "gcn_task/gcn_results/postprocess_stuff/correlation_tables.ipynb",76   "sha256": "d5e7dff53cc86382b92e1b899a41d81690f4d2ec60e6f0c9c64c0dc5888d01b4",77   "language": "Jupyter",78   "lines": 157,79   "truncated": false,80   "block": 3,81   "row": 734482  },83  {84   "path": "gcn_task/gcn_results/postprocess_stuff/main_plots.ipynb",85   "sha256": "22af51de8189f5c032fca4a7ff1265f1b8925bfc072e192b2d1e6df6f6230beb",86   "language": "Jupyter",87   "lines": 472,88   "truncated": false,89   "block": 3,90   "row": 772991  },92  {93   "path": "gcn_task/gcn_results/postprocess_stuff/statistical_tests.ipynb",94   "sha256": "1619bae706053b48560c935a8795989f6984cefd544c6a86c88fc0caec8edce4",95   "language": "Jupyter",96   "lines": 383,97   "truncated": false,98   "block": 3,99   "row": 7623100  },101  {102   "path": "gcn_task/gcn_results/postprocess_stuff/tradeoff_analysis.ipynb",103   "sha256": "f713ac08c6b7dad16a391e26736b75753732c3a7a5be85e99dae69f390936b20",104   "language": "Jupyter",105   "lines": 295,106   "truncated": false,107   "block": 3,108   "row": 7593109  },110  {111   "path": "gcn_task/gcn_results/postprocess_stuff/training_curves_n_parity_plots.ipynb",112   "sha256": "dededb3215a427022113d82f9c3793d63695fdc27709210a2d87d93545ed123c",113   "language": "Jupyter",114   "lines": 209,115   "truncated": false,116   "block": 3,117   "row": 7487118  },119  {120   "path": "gcn_task/omission_gcn.ipynb",121   "sha256": "b66dee65bb2c8c787597049f6a0296f60bbc8482faa3b392b3cd99a0892de3a3",122   "language": "Jupyter",123   "lines": 198,124   "truncated": false,125   "block": 3,126   "row": 7423127  },128  {129   "path": "gcn_task/papermill_run.py",130   "sha256": "6a45c6f905b7076c219155581fb1e54f605d6343f8a4c2887eec6f1ea897112a",131   "language": "Python",132   "lines": 52,133   "truncated": false,134   "block": 3,135   "row": 20294136  },137  {138   "path": "gcn_task/xnoise_gcn.ipynb",139   "sha256": "4fdaa4a349d25a0b1119a545079ba1bda88496832deba569ad02d7cbe540f521",140   "language": "Jupyter",141   "lines": 229,142   "truncated": false,143   "block": 3,144   "row": 7457145  },146  {147   "path": "gcn_task/ynoise_gcn.ipynb",148   "sha256": "18ed6c30e736be74fb65653e0ef6c91a9e1cd693acd46693249a49b78554be4f",149   "language": "Jupyter",150   "lines": 201,151   "truncated": false,152   "block": 3,153   "row": 7435154  },155  {156   "path": "mlp_task/censor_methods.py",157   "sha256": "2cf949e6067f88384b46c836c8a5f028f22149bfcdae625b04fde38344ed26d6",158   "language": "Python",159   "lines": 174,160   "truncated": false,161   "block": 3,162   "row": 23970163  },164  {165   "path": "mlp_task/mlp_fxns.py",166   "sha256": "35edf10bf48a95d48ca4684616c2c69b476f4e1bdf31ab9d3082bd116f6fc8bf",167   "language": "Python",168   "lines": 379,169   "truncated": false,170   "block": 3,171   "row": 26159172  },173  {174   "path": "mlp_task/mlp_quick_comparison_run.ipynb",175   "sha256": "08105eacba4d450de70bddf7d8f2e4d263bb338bc749a5353f494bf87676c49e",176   "language": "Jupyter",177   "lines": 587,178   "truncated": false,179   "block": 3,180   "row": 7747181  },182  {183   "path": "mlp_task/mlp_results/mlp_correlation_tables.ipynb",184   "sha256": "aeb7f9d5b17df940fb62e13092b7879f5f67bd4ac672000b74a17b6b07878dd1",185   "language": "Jupyter",186   "lines": 158,187   "truncated": false,188   "block": 3,189   "row": 7357190  },191  {192   "path": "mlp_task/mlp_results/mlp_main_plots.ipynb",193   "sha256": "1825b51a82ca467878ee3639c6ff5949d5ee17ea4fb77a48c6d8b2b413fbc5a0",194   "language": "Jupyter",195   "lines": 549,196   "truncated": false,197   "block": 3,198   "row": 7753199  },200  {201   "path": "mlp_task/mlp_results/statistical_tests.ipynb",202   "sha256": "7b3d1046c95c1db8aaeec91f7e179262bb61f80c52015910502ddc894da0dfc7",203   "language": "Jupyter",204   "lines": 460,205   "truncated": false,206   "block": 3,207   "row": 7681208  },209  {210   "path": "mlp_task/omit_mlp.ipynb",211   "sha256": "d97f1f40f24a9015b9fd32b970a146fd11333c27b934748bbccbc3af26467460",212   "language": "Jupyter",213   "lines": 248,214   "truncated": false,215   "block": 3,216   "row": 7522217  },218  {219   "path": "mlp_task/papermill_run.py",220   "sha256": "79382a79544a4b92b5311c4065df275e9152752be828e796cf60839c7ee39b78",221   "language": "Python",222   "lines": 44,223   "truncated": false,224   "block": 3,225   "row": 19905226  },227  {228   "path": "mlp_task/plot_fxns.py",229   "sha256": "dd37bee91dc6e7cb9e3275dc29e96d58eb32de8bb7c37ce71c120d16958ce675",230   "language": "Python",231   "lines": 80,232   "truncated": false,233   "block": 3,234   "row": 21440235  },236  {237   "path": "mlp_task/xnoise_mlp.ipynb",238   "sha256": "4db8e67cef58a1b0d50dff2b0b65be7a7cf9fb3050e960a847b0f36bc9d0ddfb",239   "language": "Jupyter",240   "lines": 202,241   "truncated": false,242   "block": 3,243   "row": 7426244  },245  {246   "path": "mlp_task/ynoise_mlp.ipynb",247   "sha256": "076e388b37e919dbc999bd177599e01d6ca6335b69808a406c25e3db817e57e2",248   "language": "Jupyter",249   "lines": 196,250   "truncated": false,251   "block": 3,252   "row": 7421253  }254 ]255}