Datasets:
BS stringclasses 923
values | Energy float64 0.75 100 | load float64 0 0.99 | ESMode1 float64 0 1 | ESMode2 float64 0 1 | ESMode3 float64 0 0.15 | ESMode5 float64 0 0.77 | ESMode6 float64 0 0.93 | RUType stringclasses 12
values | Mode stringclasses 2
values | Frequency float64 156 980 | Bandwidth int64 2 20 | Antennas int64 1 64 | TXpower float64 5.38 8.38 | load_Cell1 float64 0.01 0.89 ⌀ | ESMode1_Cell1 float64 0 1 ⌀ | ESMode2_Cell1 float64 0 1 ⌀ | ESMode3_Cell1 float64 0 0.06 ⌀ | ESMode6_Cell1 float64 0 0.93 ⌀ | day int32 1 8 | weekday_number int32 0 6 | hour int32 0 23 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
B_595 | 44.843049 | 0.63944 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | 0.105615 | 0 | 0 | 0 | 0.763506 | 2 | 0 | 10 |
B_21 | 20.478326 | 0.01364 | 0.943056 | 0.942222 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 6 |
B_495 | 13.751868 | 0.07902 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 1 | 6.427504 | null | null | null | null | null | 3 | 1 | 0 |
B_728 | 57.99701 | 0.605117 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 9 |
B_298 | 37.967115 | 0.01999 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | 0.0186 | 0 | 0 | 0 | 0 | 1 | 6 | 4 |
B_277 | 36.173393 | 0.700152 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 2 | 0 | 12 |
B_34 | 56.053812 | 0.71705 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | 0.094923 | 0 | 0 | 0 | 0.807132 | 2 | 0 | 20 |
B_802 | 8.221226 | 0 | 0 | 0 | 0 | 0 | 0 | Type5 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 1 | 6 | 13 |
B_494 | 12.107623 | 0.01593 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 1 | 6.875934 | null | null | null | null | null | 4 | 2 | 13 |
B_742 | 59.192825 | 0.1936 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 4 | 6.875934 | null | null | null | null | null | 5 | 3 | 21 |
B_488 | 13.153961 | 0.01544 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 1 | 6.427504 | null | null | null | null | null | 6 | 4 | 6 |
B_366 | 31.240658 | 0.2169 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 4 | 2 | 10 |
B_575 | 18.38565 | 0.05961 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 2 | 0 | 13 |
B_195 | 17.339312 | 0.02882 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 5 | 3 | 5 |
B_543 | 11.80867 | 0.0156 | 0 | 0 | 0 | 0 | 0 | Type2 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 6 |
B_79 | 68.609865 | 0.463043 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 4 | 6.875934 | null | null | null | null | null | 7 | 5 | 23 |
B_549 | 31.091181 | 0.31565 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 8 |
B_257 | 20.627803 | 0.10274 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 2 | 0 | 22 |
B_94 | 21.375187 | 0.16234 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 7 | 5 | 7 |
B_112 | 62.630792 | 0.45465 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 4 | 6.875934 | null | null | null | null | null | 3 | 1 | 2 |
B_57 | 42.600897 | 0.62832 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 22 |
B_522 | 19.880419 | 0.24192 | 0 | 0 | 0 | 0 | 0 | Type3 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 2 | 0 | 21 |
B_589 | 28.101644 | 0.101319 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 2 | 0 | 4 |
B_1006 | 10.762332 | 0.045923 | 0 | 0 | 0 | 0 | 0.924341 | Type5 | Mode2 | 426.98 | 2 | 2 | 7.319432 | null | null | null | null | null | 2 | 0 | 22 |
B_336 | 25.261584 | 0.59635 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 10 |
B_530 | 14.947683 | 0.06802 | 0 | 0 | 0 | 0 | 0 | Type5 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 1 | 6 | 4 |
B_781 | 52.316891 | 0.36566 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 11 |
B_437 | 63.976084 | 0.91565 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 21 |
B_374 | 27.055306 | 0.20393 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 2 | 0 | 12 |
B_215 | 19.581465 | 0.2724 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 3 | 1 | 12 |
B_753 | 41.405082 | 0.92347 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 19 |
B_419 | 12.855007 | 0.01211 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 1 | 6.875934 | null | null | null | null | null | 4 | 2 | 15 |
B_359 | 8.221226 | 0.02612 | 0.731944 | 0.735833 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 4 | 2 | 3 |
B_716 | 20.627803 | 0.25974 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 5 | 3 | 9 |
B_306 | 10.463378 | 0.00985 | 0.969444 | 0.970556 | 0 | 0 | 0 | Type2 | Mode2 | 532 | 20 | 4 | 6.875934 | null | null | null | null | null | 5 | 3 | 2 |
B_207 | 16.292975 | 0.01318 | 0 | 0.000278 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 7 |
B_441 | 42.750374 | 0.9847 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 19 |
B_562 | 18.834081 | 0.06352 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 6 | 4 | 6 |
B_251 | 20.627803 | 0.13338 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 20 |
B_578 | 34.828102 | 0.608628 | 0 | 0 | 0 | 0 | 0 | Type5 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 2 | 0 | 15 |
B_597 | 43.796712 | 0.44958 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | 0.089808 | 0 | 0 | 0 | 0.800526 | 2 | 0 | 11 |
B_254 | 26.158445 | 0.20933 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 5 | 3 | 21 |
B_764 | 26.158445 | 0.084152 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 1 | 6 | 7 |
B_623 | 19.431988 | 0.12756 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 1 | 6 | 20 |
B_679 | 13.602392 | 0.01616 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 1 | 6.427504 | null | null | null | null | null | 6 | 4 | 3 |
B_398 | 14.947683 | 0.15041 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 1 | 6.875934 | null | null | null | null | null | 5 | 3 | 11 |
B_325 | 26.606876 | 0.32373 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 2 | 0 | 10 |
B_437 | 53.512706 | 0.8218 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 1 |
B_65 | 25.261584 | 0.26587 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 15 |
B_397 | 18.684604 | 0.27533 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 1 | 6.875934 | null | null | null | null | null | 5 | 3 | 9 |
B_585 | 27.204783 | 0.07828 | 0 | 0 | 0 | 0 | 0 | Type3 | Mode2 | 155.6 | 10 | 2 | 6.427504 | 0.22802 | 0 | 0 | 0 | 0 | 2 | 0 | 0 |
B_406 | 18.236173 | 0.09564 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 18 |
B_183 | 31.988042 | 0.39285 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 5 | 3 | 18 |
B_734 | 38.415546 | 0.10058 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 7 | 5 | 5 |
B_822 | 25.261584 | 0.115429 | 0.415278 | 0 | 0 | 0 | 0.229831 | Type8 | Mode1 | 697.002 | 10 | 8 | 8.375336 | null | null | null | null | null | 2 | 0 | 0 |
B_714 | 21.973094 | 0.03344 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 4 | 2 | 15 |
B_148 | 18.086697 | 0.04992 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 6 | 4 | 1 |
B_622 | 8.071749 | 0.0103 | 0.888889 | 0.890833 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 1 | 6.875934 | null | null | null | null | null | 3 | 1 | 0 |
B_97 | 60.089686 | 0.628862 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 4 | 2 | 18 |
B_270 | 49.925262 | 0.58717 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 18 |
B_329 | 19.880419 | 0.05546 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 5 | 3 | 8 |
B_810 | 18.834081 | 0.01583 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 4 | 2 | 4 |
B_89 | 47.384155 | 0.333181 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 1 |
B_597 | 33.183857 | 0.11029 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 7 | 5 | 6 |
B_792 | 19.282511 | 0.23642 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 1 | 6 | 20 |
B_335 | 23.617339 | 0.23962 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 10 |
B_613 | 33.48281 | 0.67727 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 19 |
B_321 | 20.179372 | 0.05667 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 10 |
B_285 | 29.596413 | 0.61407 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 2 | 0 | 23 |
B_235 | 50.672646 | 0.629872 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.427504 | null | null | null | null | null | 3 | 1 | 10 |
B_259 | 22.869955 | 0.04866 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 1 | 6.875934 | null | null | null | null | null | 5 | 3 | 2 |
B_450 | 13.303438 | 0.15592 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 5 | 1 | 5.979073 | null | null | null | null | null | 4 | 2 | 23 |
B_325 | 26.457399 | 0.4866 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 2 | 0 | 9 |
B_531 | 26.008969 | 0.17098 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 1 | 6.875934 | null | null | null | null | null | 2 | 0 | 12 |
B_776 | 19.282511 | 0.06762 | 0.001389 | 0.000833 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 7 |
B_479 | 24.962631 | 0.03864 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 3 |
B_594 | 18.535127 | 0.04354 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 4 |
B_287 | 22.421525 | 0.20732 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 2 | 0 | 11 |
B_782 | 50.224215 | 0.704989 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 2 | 0 | 13 |
B_287 | 22.421525 | 0.17664 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 4 | 2 | 10 |
B_14 | 15.54559 | 0.02506 | 0 | 0 | 0 | 0 | 0 | Type5 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 3 | 1 | 4 |
B_335 | 30.19432 | 0.4024 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 0 |
B_287 | 22.272048 | 0.31712 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 1 | 6 | 22 |
B_12 | 8.370703 | 0.00842 | 0.979167 | 0.979722 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 4 | 2 | 2 |
B_669 | 8.071749 | 0.00862 | 1 | 1 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 5 |
B_735 | 40.508221 | 0.28691 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | null | null | null | null | null | 5 | 3 | 18 |
B_176 | 29.297459 | 0.38834 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 19 |
B_606 | 9.715994 | 0.00826 | 1 | 1 | 0 | 0 | 0 | Type2 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 3 | 1 | 4 |
B_730 | 17.339312 | 0.08744 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 6 | 4 | 5 |
B_219 | 12.107623 | 0.03906 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 12 |
B_62 | 70.852018 | 0.57183 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 4 | 6.875934 | null | null | null | null | null | 5 | 3 | 18 |
B_24 | 19.880419 | 0.0649 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 5 | 3 | 6 |
B_411 | 19.133034 | 0.08942 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 1 | 6 | 12 |
B_233 | 8.96861 | 0.01291 | 0.852778 | 0.847778 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 4 | 2 | 5 |
B_648 | 44.394619 | 0.84815 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.980568 | null | null | null | null | null | 2 | 0 | 18 |
B_200 | 24.962631 | 0.43282 | 0 | 0 | 0 | 0 | 0 | Type4 | Mode2 | 532 | 20 | 2 | 6.875934 | null | null | null | null | null | 1 | 6 | 23 |
B_726 | 20.478326 | 0.04414 | 0 | 0 | 0 | 0 | 0 | Type6 | Mode2 | 189 | 10 | 2 | 6.427504 | null | null | null | null | null | 2 | 0 | 7 |
B_387 | 59.342302 | 0.77375 | 0 | 0 | 0 | 0 | 0 | Type2 | Mode2 | 532 | 20 | 4 | 6.875934 | null | null | null | null | null | 6 | 4 | 17 |
B_689 | 38.266069 | 0.63297 | 0 | 0 | 0 | 0 | 0 | Type7 | Mode2 | 365 | 20 | 2 | 6.726457 | null | null | null | null | null | 1 | 6 | 10 |
B_573 | 31.240658 | 0.070904 | 0 | 0 | 0 | 0 | 0 | Type1 | Mode2 | 365 | 20 | 2 | 6.875934 | 0.059077 | 0 | 0 | 0 | 0.878871 | 2 | 0 | 1 |
BeyondArena Datasets
Datasets from BeyondArena, a unified, holistic benchmark for tabular data that supports diverse task types (IID, temporal, grouped), across sample size and feature dimensionality scales, with diverse feature types (with text, with high cardinality) from a broad range of disciplines.
We introduce BeyondArena and its datasets in: [PLACEHOLDER]
Click for BibTeX!
PLACEHOLDER
More details:
- Project page and leaderboard: http://tabarena.ai/
- Code / Benchmark repository: https://tabarena.ai/code
Quickstart
We recommend using the datasets via Data Foundry, which resolves a curated container (table + dtypes + task metadata + outer CV splits) by name and caches it locally:
pip install data-foundry
from data_foundry.collections import BEYOND_ARENA
container = BEYOND_ARENA.get_dataset("airfoil_self_noise")
print(container.describe()) # full identity + dtypes + task + splits
print(container.dataset.shape) # the actual DataFrame
print(container.task_metadata.split_regime) # "iid", "temporal_non_iid", or "grouped_non_iid"
df = container.dataset
target = container.task_metadata.target_column_name
for repeat_id, folds in container.experiment_metadata.splits.items():
for fold_id, (train_idx, test_idx) in folds.items():
X_train, y_train = df.iloc[train_idx].drop(columns=target), df.iloc[train_idx][target]
X_test, y_test = df.iloc[test_idx].drop(columns=target), df.iloc[test_idx][target]
# ... fit, evaluate ...
To pre-download the entire collection in a single network round-trip:
from data_foundry.collections import BEYOND_ARENA
BEYOND_ARENA.prefetch() # warms the cache once
for container in BEYOND_ARENA.iter_containers(): # now hits disk only
print(container.dataset_metadata.unique_name, container.dataset.shape)
See Data Foundry's examples for a full benchmarking walkthrough, the three split regimes (IID / temporal / grouped), and the curation flow.
Datasets
BeyondArena comes with 142 datasets. BeyondArena covers tabular classification and regression tasks. And the following types of datasets:
- IID tabular data
- Non-IID temporal tabular data
- Non-IID grouped tabular data
- IID and non-IID tabular data with text features
- Tabular data with high-cardinality categoricals
Dataset Selection Overview
We build on top of the dataset curation protocol of TabArena-v0.1 (https://arxiv.org/abs/2506.16791) and curate 142 tiny to large-sized, tabular IID and non-IID tasks. For details, see the paper.
Dataset Dashboard
We curated a diverse set of datasets. We share the dataset sizes (w.r.t. rows, columns, and cells), their age distribution, the distribution of feature types per dataset, and the share of datasets from a specific problem type, task type, dataset source, or application domain.
Per-Dataset Index
Per-dataset metadata for the BeyondArena benchmark, sorted by number of rows (N).
Click for expand all 142 Datasets!
Columns. N = rows · d = columns (before preprocessing) · C = classes (regression: —) · Prob. = problem type (Binary classification / Multiclass / Regression) · Task = task type (IID / Temporal / Grouped) · Age = years since publication at release time.
Domain abbreviations. M & H = Medical & Healthcare · B & M = Business & Marketing · B & L = Biology & Life Sciences · T & I = Technology & Internet · I & M = Industry & Manufacturing · C & M = Chemistry & Material Science · E & C = Environmental Science & Climate · P & A = Physics & Astronomy.
Each dataset has an academic_reference_bibtex_key in its dataset_metadata.dataset-mold-v1.json; the matching BibTeX entries are collected in dataset_references.bib. The BibKey(s) column below lists the keys to look up in that file (some datasets cite multiple sources).
| Dataset | Domain | Source | Year | Age | N | d | C | Prob. | Task | BibKey(s) |
|---|---|---|---|---|---|---|---|---|---|---|
| hepatitis_survival_prediction | M & H | UCI | 1981 | 45 | 155 | 19 | 2 | Binary | IID | efron1981statistical |
| cirrhosis_patient_survival_prediction | M & H | UCI | 1984 | 42 | 161 | 17 | — | Reg | IID | dickson1989prognosis |
| clock_protein_toxicity | B & L | UCI | 2021 | 5 | 171 | 1,117 | 2 | Binary | IID | gul2021structure |
| pancreatic_cancer_mouse_detection | M & H | Other | 2003 | 23 | 181 | 6,771 | 2 | Binary | Grouped | hingorani2003preinvasive |
| lung_cancer_epithelial_genexp | M & H | GOV Website | 2006 | 20 | 187 | 22,215 | 2 | Binary | IID | spira2007airway |
| parkinsons_biomedical_voice_measurements | M & H | UCI | 2007 | 19 | 195 | 23 | 2 | Binary | Grouped | little2007exploiting |
| lung_cancer | M & H | Other | 2001 | 25 | 197 | 12,600 | 4 | Multi | IID | bhattacharjee2001classification |
| audiology_diagnosis | M & H | UCI | 1987 | 39 | 199 | 68 | 3 | Multi | IID | bareiss1990protos |
| heart_disease_va_long_beach | M & H | UCI | 1989 | 37 | 200 | 13 | 2 | Binary | IID | detrano1989international |
| forensic_glass_identification | C & M | UCI | 1987 | 39 | 214 | 9 | 6 | Multi | IID | German1987glass |
| early_stage_diabetes_risk_prediction | M & H | UCI | 2019 | 7 | 251 | 16 | 2 | Binary | IID | islam2019likelihood |
| body_density_prediction | M & H | Kaggle | 1985 | 41 | 252 | 13 | — | Reg | IID | penrose1985generalized |
| ljubljana_breast_cancer | M & H | UCI | 1988 | 38 | 286 | 9 | 2 | Binary | IID | Zwitter1988BreastCancer |
| heart_disease_hungary | M & H | UCI | 1989 | 37 | 294 | 13 | 2 | Binary | IID | detrano1989international |
| heart_failure_followup_survival | M & H | UCI | 2020 | 6 | 299 | 12 | 2 | Binary | IID | chicco2020machine |
| ljubljana_primary_tumor | M & H | UCI | 1987 | 39 | 302 | 17 | 11 | Multi | IID | Zwitter1987primarytumor |
| heart_disease_cleveland | M & H | UCI | 1989 | 37 | 303 | 13 | 2 | Binary | IID | detrano1989international |
| biomechanical_orthopaedic_prediction | M & H | UCI | 2011 | 15 | 310 | 6 | 3 | Multi | IID | Barreto2005Vertebral |
| gallstone_disease | M & H | UCI | 2023 | 3 | 319 | 38 | 2 | Binary | IID | esen2024early |
| prostate_cancer_detection | M & H | Other | 2002 | 24 | 322 | 15,154 | 2 | Binary | IID | petricoin2002serum |
| ecoli_proteins | B & L | UCI | 1996 | 30 | 327 | 6 | 5 | Multi | IID | horton1996probabilistic |
| horse_colic_survival | B & L | UCI | 1989 | 37 | 344 | 20 | 3 | Multi | IID | McLeish1989HorseColic |
| blood_tests_drink_prediction | M & H | UCI | 1996 | 30 | 345 | 5 | — | Reg | IID | UCILiverDisorders2016 |
| eryhemato_squamous_disease | M & H | UCI | 1997 | 29 | 366 | 34 | 6 | Multi | IID | guvenir1998learning |
| dementia_prediction | M & H | Other | 2010 | 16 | 370 | 8 | 3 | Multi | Grouped | marcus2010open |
| south_africa_coronary_heart_disease | M & H | Kaggle | 1983 | 43 | 462 | 9 | 2 | Binary | IID | rossouw1983coronary |
| obesity_estimation | M & H | UCI | 2019 | 7 | 498 | 14 | — | Reg | IID | palechor2019dataset |
| telemonitoring_parkinsons_biomedical_voice_measurements | M & H | UCI | 2007 | 19 | 502 | 19 | — | Reg | Grouped | tsanas2009accurate |
| forest_fires | E & C | UCI | 2008 | 18 | 517 | 12 | — | Reg | IID | cortez2007data |
| qsar_aquatic_toxicity | B & L | UCI | 2014 | 12 | 546 | 8 | — | Reg | IID | cassotti2014prediction |
| micro_mass | B & L | UCI | 2013 | 13 | 571 | 1,082 | 20 | Multi | Grouped | mahe2014automatic |
| indian_liver_patient_dataset | M & H | UCI | 2012 | 14 | 583 | 10 | 2 | Binary | IID | ramana2012critical |
| drug_induced_autoimmunity_prediction | M & H | UCI | 2025 | 1 | 597 | 177 | 2 | Binary | IID | huang2025interdia |
| hepatitis_c_prediction | M & H | UCI | 2018 | 8 | 608 | 12 | 4 | Multi | IID | hoffmann2018using |
| biogeographical_ancestry_prediction | B & L | GitHub | 2025 | 1 | 635 | 104 | 10 | Multi | IID | heinzel2025advancing, ruiz2023development, xavier2020development |
| student_portuguese_performance | Education | UCI | 2008 | 18 | 649 | 30 | — | Reg | IID | silva2008using |
| credit_approval | Finance | UCI | 1987 | 39 | 690 | 15 | 2 | Binary | IID | quinlan1987simplifying |
| blood_transfusion | M & H | UCI | 2008 | 18 | 748 | 4 | 2 | Binary | IID | yeh2009knowledge |
| regensburg_pediatric_appendicitis | M & H | Other | 2021 | 5 | 763 | 51 | 2 | Binary | IID | marcinkevivcs2024interpretable |
| mutual_funds_india | Finance | Kaggle | 2023 | 3 | 793 | 12 | — | Reg | IID | Barnawal2022MutualFundsIndiaDetailed |
| qsar_fish_toxicity | B & L | UCI | 2015 | 11 | 908 | 6 | — | Reg | IID | cassotti2015similarity |
| tour_travels_churn | B & M | Kaggle | 2021 | 5 | 954 | 6 | 2 | Binary | IID | Tejashvi2023TourTravelsCustomerChurnPrediction |
| credit_g | Finance | UCI | 1994 | 32 | 1,000 | 20 | 2 | Binary | IID | hofmann1994statlog |
| maternal_health_risk | M & H | UCI | 2020 | 6 | 1,014 | 6 | 3 | Multi | IID | ahmed2020review |
| concrete_compressive_strength | C & M | UCI | 1998 | 28 | 1,030 | 8 | — | Reg | IID | yeh1998modeling |
| qsar_biodeg | B & L | UCI | 2013 | 13 | 1,054 | 41 | 2 | Binary | IID | mansouri2013quantitative |
| mice_protein_trisomy_discriminant | B & L | UCI | 2015 | 11 | 1,080 | 76 | 8 | Multi | Grouped | higuera2015self |
| garments_worker_productivity | I & M | UCI | 2020 | 6 | 1,197 | 15 | — | Reg | Temporal | imran2021mining |
| asp_potassco_classification | T & I | ASlib | 2014 | 12 | 1,212 | 136 | 11 | Multi | Grouped | hoos2014claspfolio, bischl_aslib_2016 |
| wine_world_cost | B & M | Kaggle | 2023 | 3 | 1,279 | 14 | — | Reg | IID | Rustamov2023WineDataset |
| healthcare_insurance_expenses | M & H | Kaggle | 2023 | 3 | 1,338 | 6 | — | Reg | IID | arunjangir2452023insurance |
| website_phishing | T & I | UCI | 2014 | 12 | 1,353 | 9 | 3 | Multi | IID | abdelhamid2014phishing |
| fitness_club | B & M | Kaggle | 2023 | 3 | 1,500 | 6 | 2 | Binary | IID | ddosad2023fitness |
| airfoil_self_noise | P & A | UCI | 2014 | 12 | 1,503 | 5 | — | Reg | IID | brooks1989airfoil |
| fiat_500 | T & I | Kaggle | 2020 | 6 | 1,538 | 7 | — | Reg | IID | paolocons2020fiat |
| mic | M & H | UCI | 2020 | 6 | 1,699 | 111 | 8 | Multi | IID | golovenkin2020trajectories |
| bad_customer_detection | B & M | Kaggle | 2020 | 6 | 1,723 | 13 | 2 | Binary | IID | Podsyp2020IsThisAGoodCustomer |
| cardiotocography | M & H | UCI | 2010 | 16 | 2,126 | 22 | 3 | Multi | Grouped | campos2010cardiotocography |
| marketing_campaign | B & M | Kaggle | 2020 | 6 | 2,240 | 25 | 2 | Binary | IID | saldanha2020marketing |
| coffee_rating_prediction | B & M | Kaggle | 2023 | 3 | 2,369 | 12 | — | Reg | Temporal | AlIrsyad2023CoffeeDataCoffeeReview |
| hazelnut_spread_contaminant_detection | B & L | OpenML | 2020 | 6 | 2,400 | 30 | 2 | Binary | IID | ricci2021machine |
| seismic_bumps | E & C | UCI | 2013 | 13 | 2,584 | 15 | 2 | Binary | IID | sikora2010application |
| iranian_churn | B & M | UCI | 2011 | 15 | 2,850 | 13 | 2 | Binary | IID | keramati2011churn |
| sat11_hand_algo_runtime | T & I | ASlib | 2011 | 15 | 2,960 | 169 | — | Reg | Grouped | xu-sat12a, sat12, bischl_aslib_2016 |
| splice | B & L | UCI | 1991 | 35 | 3,190 | 60 | 3 | Multi | IID | towell1994knowledge |
| thyroid_discordant | M & H | UCI | 1986 | 40 | 3,711 | 26 | 2 | Binary | IID | quinlan1987simplifying |
| bioresponse | B & L | Kaggle | 2012 | 14 | 3,751 | 1,776 | 2 | Binary | IID | bioresponse2012hamner |
| hiva_agnostic | C & M | Other | 2007 | 19 | 3,845 | 1,518 | 2 | Binary | IID | guyon2007agnostic |
| mercedes_benz_greener_manufacturing | I & M | Kaggle | 2017 | 9 | 4,204 | 371 | — | Reg | Temporal | Novy2017MercedesBenzGreenerManufacturing |
| predict_students_dropout_and_academic_success | Education | UCI | 2021 | 5 | 4,424 | 36 | 3 | Multi | IID | martins2021early |
| santander_transaction_value | Finance | Kaggle | 2018 | 8 | 4,447 | 540 | — | Reg | IID | McDonald2018SantanderValuePredictionChallenge |
| churn | T & I | OpenML | 2005 | 21 | 5,000 | 19 | 2 | Binary | IID | marcoulides2005churn |
| homeq_default_prediction | B & M | Other | 2016 | 10 | 5,708 | 12 | 2 | Binary | IID | baesens2016credit |
| qsar_tid_11 | C & M | OpenML | 2015 | 11 | 5,741 | 1,024 | — | Reg | IID | olier2018meta |
| polish_companies_bankruptcy | Finance | UCI | 2010 | 16 | 5,790 | 64 | 2 | Binary | IID | zikeba2016ensemble |
| wine_quality | C & M | UCI | 2009 | 17 | 6,497 | 12 | — | Reg | IID | cortez2009modeling |
| musk | C & M | UCI | 1994 | 32 | 6,598 | 166 | 2 | Binary | Grouped | dietterich1993comparison |
| taiwanese_bankruptcy_prediction | Finance | UCI | 2009 | 17 | 6,819 | 92 | 2 | Binary | IID | liang2016financial |
| naticusdroid_android_permissions_dataset | T & I | UCI | 2021 | 5 | 7,491 | 85 | 2 | Binary | IID | mathur2021naticusdroid |
| coil_2000 | B & M | UCI | 2000 | 26 | 9,822 | 85 | 2 | Binary | IID | van2000coil |
| bank_customer_churn | B & M | Kaggle | 2020 | 6 | 10,000 | 10 | 2 | Binary | IID | Topre2022BankCustomerChurn |
| immoscout_german_house_prices | B & M | Kaggle | 2019 | 7 | 10,317 | 23 | — | Reg | IID | Shritech2019GermanHousingPricePrediction, OpenML43342Dataset |
| heloc | Finance | Kaggle | 2021 | 5 | 10,459 | 23 | 2 | Binary | IID | averkiyoliabev2021heloc |
| jm1 | T & I | OpenML | 2004 | 22 | 10,885 | 21 | 2 | Binary | IID | menzies2004good |
| ghanas_indigenous_intel | E & C | Zindi | 2025 | 1 | 10,928 | 10 | 4 | Multi | Temporal | zindi_ghana_indigenous_intel_2025 |
| ecommerce_shipping | B & M | Kaggle | 2021 | 5 | 10,999 | 10 | 2 | Binary | IID | gopalani2021ecommerce |
| video_game_fps_prediction | T & I | OpenML | 2020 | 6 | 12,288 | 38 | — | Reg | Grouped | peeters2021performance |
| online_shoppers_purchasing_intention_dataset | B & M | UCI | 2017 | 9 | 12,330 | 17 | 2 | Binary | IID | sakar2019real |
| in_vehicle_coupon_recommendation | B & M | UCI | 2017 | 9 | 12,684 | 24 | 2 | Binary | IID | wang2017bayesian |
| miami_housing | Finance | Kaggle | 2016 | 10 | 13,776 | 15 | — | Reg | IID | bourassa2021big |
| emscad | B & M | Other | 2014 | 12 | 17,460 | 17 | 2 | Binary | IID | vidros2017automatic |
| early_learning_predictors | Education | Other | 2023 | 3 | 18,874 | 743 | — | Reg | Grouped | DataDrive2030_2024_elom_thrivebyfive |
| hr_analytics | B & M | Kaggle | 2021 | 5 | 19,158 | 12 | 2 | Binary | IID | arashnic2021hr |
| houses | B & M | Other | 1990 | 36 | 19,675 | 8 | — | Reg | IID | pace1997sparse |
| superconductivity | P & A | UCI | 2018 | 8 | 21,263 | 81 | — | Reg | IID | hamidieh2018data |
| sberbank_housing_market_forecasting | B & M | Kaggle | 2017 | 9 | 27,195 | 386 | — | Reg | Temporal | Herman2024HomeCreditCreditRiskModelStability |
| credit_card_clients_default | Finance | UCI | 2009 | 17 | 30,000 | 23 | 2 | Binary | IID | yeh2009comparisons |
| amazon_employee_access | B & M | Kaggle | 2010 | 16 | 32,769 | 9 | 2 | Binary | IID | hamner2013amazon |
| california_house_prices_2020 | B & M | Kaggle | 2021 | 5 | 41,528 | 41 | — | Reg | Temporal | d2lcourse2021california_house_prices |
| bank_marketing | Finance | UCI | 2012 | 14 | 45,211 | 13 | 2 | Binary | IID | moro2014bank-marketing |
| food_delivery_time | B & M | Kaggle | 2023 | 3 | 45,451 | 9 | — | Reg | IID | rajatkumar302023food |
| physiochemical_protein | C & M | UCI | 2013 | 13 | 45,730 | 9 | — | Reg | IID | rana2013protein |
| anes_voting_2026 | Social Science | Other | 2026 | 0 | 48,587 | 318 | 2 | Binary | Temporal | anes2026timeseries |
| kdd_cup_09_appetency | B & M | Other | 2008 | 18 | 50,000 | 212 | 2 | Binary | IID | guyon2009analysis |
| diamonds | B & M | Other | 2015 | 11 | 53,940 | 9 | — | Reg | IID | wickham2016data |
| otto_group_product_classification_challenge | B & M | Kaggle | 2015 | 11 | 61,878 | 93 | 9 | Multi | IID | Bossan2015OttoGroupProductClassificationChallenge |
| labour_inspection_compliance | I & M | Other | 2019 | 7 | 63,634 | 376 | 2 | Binary | IID | flogard2022dataset |
| video_transcoding_time_prediction | T & I | UCI | 2015 | 11 | 68,784 | 18 | — | Reg | Grouped | deneke2014video |
| santander_customer_satisfaction | B & M | Kaggle | 2016 | 10 | 71,080 | 307 | 2 | Binary | IID | Jimenez2016SantanderCustomerSatisfaction |
| diabetes_130_us | M & H | UCI | 2014 | 12 | 71,518 | 44 | 2 | Binary | IID | strack2014impact |
| kick | B & M | Kaggle | 2011 | 15 | 72,983 | 32 | 2 | Binary | Temporal | DontGetKicked |
| aps_failure | I & M | UCI | 2016 | 10 | 76,000 | 170 | 2 | Binary | IID | ida2016challenge |
| sdss_17 | P & A | Kaggle | 2022 | 4 | 78,053 | 11 | 3 | Multi | IID | accetta2022seventeenth |
| hotel_booking_demand | B & M | Other | 2019 | 7 | 81,418 | 31 | 2 | Binary | Temporal | antonio2019hotel |
| 5g_energy_consumption | T & I | HuggingFace | 2023 | 3 | 92,629 | 20 | — | Reg | Grouped | huawei_netop_5g_energy_consumption |
| sepsis_survival_minimal_clinical_records | M & H | UCI | 2020 | 6 | 110,204 | 3 | 2 | Binary | IID | chicco2020survival |
| sf_permit_time | B & M | GOV Website | 2025 | 1 | 116,954 | 37 | — | Reg | Temporal | SanFrancisco2026BuildingPermits |
| wids_diabetes_mellitus | M & H | Kaggle | 2021 | 5 | 127,358 | 181 | 2 | Binary | IID | Matthys2021WiDSDatathon2021 |
| customer_satisfaction_in_airline | B & L | Kaggle | 2023 | 3 | 129,880 | 21 | 2 | Binary | IID | yakhyojon2023airlinesatisfaction |
| pva_revenue_prediction_kddcup98 | B & M | Other | 1997 | 29 | 144,095 | 477 | 2 | Binary | IID | Parsa1998KDDCup1998 |
| give_me_some_credit | Finance | Kaggle | 2011 | 15 | 150,000 | 10 | 2 | Binary | IID | cukierski2011credit |
| acquire_valued_shoppers_challenge | B & M | Kaggle | 2014 | 12 | 160,057 | 111 | 2 | Binary | Temporal | DMDave2014AcquireValuedShoppersChallenge |
| kickstarter | B & M | Other | 2025 | 1 | 187,118 | 15 | 2 | Binary | Temporal | webrobots2026kickstarter |
| allstate_claims_severity | Insurance | Kaggle | 2016 | 10 | 188,317 | 130 | — | Reg | IID | Ferguson2016AllstateClaimsSeverity |
| santander_customer_transaction_prediction | Finance | Kaggle | 2019 | 7 | 200,000 | 600 | 2 | Binary | IID | Piedra2019SantanderCustomerTransactionPrediction |
| homesite_quote_conversion | Insurance | Kaggle | 2015 | 11 | 260,753 | 295 | 2 | Binary | IID | Darrel2015HomesiteQuoteConversion |
| home_credit_default_risk | Finance | Kaggle | 2018 | 8 | 307,507 | 504 | 2 | Binary | IID | Montoya2018HomeCreditDefaultRisk |
| covertype | E & C | UCI | 1998 | 28 | 512,625 | 13 | 3 | Multi | Grouped | blackard1999comparative |
| ieee_fraud_detection | Finance | Kaggle | 2019 | 7 | 590,540 | 435 | 2 | Binary | Temporal | ieee-fraud-detection |
| porto_seguro | Insurance | Kaggle | 2017 | 9 | 595,206 | 37 | 2 | Binary | IID | Howard2017PortoSegurosSafeDriverPrediction |
| rossmann_store_sales | B & M | Kaggle | 2015 | 11 | 844,392 | 15 | — | Reg | Temporal | kaggle_rossmann_store_sales |
| lending_club_1m | Finance | Kaggle | 2018 | 8 | 1,064,751 | 96 | 2 | Binary | Temporal | sanz2025credit |
| home_credit_default_stability_1m | Finance | Kaggle | 2024 | 2 | 1,224,927 | 711 | 2 | Binary | Temporal | Herman2024HomeCreditCreditRiskModelStability |
| consumer_complaints_1m | Finance | GOV Website | 2025 | 1 | 1,226,140 | 12 | 3 | Multi | Temporal | cfpb2025ConsumerComplaintDatabase |
| sepsis_prediction_1m | M & H | Other | 2019 | 7 | 1,228,686 | 42 | 2 | Binary | Grouped | reyna2020early |
| amex_non_iid_1m | Finance | Kaggle | 2022 | 4 | 1,249,605 | 189 | 2 | Binary | Grouped | howard2022amex |
| delivery_eta_1m | I & M | Kaggle | 2024 | 2 | 1,250,000 | 225 | — | Reg | Temporal | rubachev2025tabred |
| cooking_time_1m | I & M | Kaggle | 2024 | 2 | 1,250,000 | 196 | — | Reg | Temporal | rubachev2025tabred |
| climate_model_weather_forecasting_1m | E & C | Kaggle | 2024 | 2 | 1,250,000 | 100 | — | Reg | Temporal | rubachev2025tabred |
| maps_router_eta_1m | I & M | Kaggle | 2024 | 2 | 1,250,000 | 988 | — | Reg | Temporal | rubachev2025tabred |
| mercari_price_suggestion_1m | B & M | Kaggle | 2018 | 8 | 1,250,000 | 6 | — | Reg | IID | Howard2017MercariPriceSuggestionChallenge |
| electric_motor_temperature_prediction | I & M | Kaggle | 2021 | 5 | 1,296,316 | 109 | — | Reg | Grouped | kirchgassner2020estimating |
Dataset Structure
The release ships as a flat bundle of 142 datasets. Each dataset lives in its own top-level directory named by unique_name, with a UUID-named version subdirectory holding all artifacts. Two layout variants exist:
<dataset_name>/<uuid>/... # default (132 datasets)
<dataset_name>/versions/<uuid>/... # versioned wrapper (10 large non-IID datasets)
Each directory contains exactly six files:
<uuid>/
├── dataset.parquet # the table (rows × columns)
├── dtypes.json # column name → pandas dtype
├── container_metadata.json # uuid + sha256 checksum
├── dataset_metadata.dataset-mold-v1.json # provenance & curation notes
├── task_metadata.predictive-ml-task-mold-v1.json # target, problem type, metric, split keys
└── experiment_metadata.predictive-ml-splits-mold-v1.json # CV fold indices
For details on files and the metadata structure, checkout DataFoundry!
Loading a single dataset directly
Each per-dataset config in this card's frontmatter routes only dataset.parquet, which is enough to get
the table but not the sibling metadata files (dtypes.json, task_metadata.*, experiment_metadata.*
with the CV folds, dataset_metadata.*, container_metadata.json). Because the benchmark protocol depends
on those files, the recommended path is to download the whole dataset folder with huggingface_hub:
from huggingface_hub import snapshot_download
local_dir = snapshot_download(
repo_id="TabArena/BeyondArena",
repo_type="dataset",
allow_patterns=["churn/**"], # one or more <dataset_name>/** globs
)
# local_dir/<dataset_name>/<uuid>/ now contains all six files for that dataset.
For the 10 datasets that use the versions/ wrapper (see Dataset Structure), the layout
is <dataset_name>/versions/<uuid>/... — the <dataset_name>/** glob already covers both layouts.
If you only need the table (no folds, no metadata), the datasets library shortcut works:
from datasets import load_dataset
ds = load_dataset("<org>/BeyondArena", name="churn") # any per-dataset config_name
Downloading the full bundle
from huggingface_hub import snapshot_download
local_dir = snapshot_download(
repo_id="<org>/BeyondArena",
repo_type="dataset",
)
Licensing
This collection is released under the terms in LICENSE (copyright-at-original-authors).
Individual datasets retain their original licenses; see each dataset metadata for their source-specific terms.
Citation
If you use BeyondArena, please cite:
PLACEHOLDER
BibTeX:
PLACEHOLDER
Per-Dataset References
If you use individual datasets, please also cite their original authors. BibTeX for every dataset in the benchmark is shipped alongside this card in dataset_references.bib (one entry per unique academic_reference_bibtex_key referenced by the dataset metadata files).
Changelog
- [27th May 2026] — Initial release: 142 curated IID and non-IID tasks.
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