{
 "id": "h2o64/samplingdb",
 "owner": "h2o64",
 "name": "samplingdb",
 "downloads": 505,
 "likes": 0,
 "last_modified": "2026-10-29T13:15:29.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [],
 "citation": "The samples are released under the MIT License, like samplingdb. They are draws from densities\nthat others defined, so please also credit the sources of the densities you use:\n\n- DW-4, LJ-13 and LJ-55: the potentials of Köhler et al. 2020, as implemented in bgflow.\n- The logistic regressions: the Ionosphere, Sonar, Statlog (German Credit) and Breast Cancer\n  Wisconsin (Diagnostic) datasets of the UCI Machine Learning Repository, under CC BY 4.0. Seeds:\n  the data of Crowder 1978. Brownian: the model of inference_gym. LGCP: the Finnish pines data of\n  Møller et al. 1998.\n- The peptides: the systems of the Sequential Boltzmann Generators data (Tan et al. 2025), with the\n  Amber ff99SB-ILDN and ff14SB force fields and OBC implicit solvent, evaluated with OpenMM.\n- The ternary glass: the swap-friendly Kob-Andersen mixture of Jung et al. 2023, after Kob and\n  Andersen 1995.\n- The binary glass: the two-dimensional 80:20 Kob-Andersen mixture of Cheng et al. 2026, after Kob\n  and Andersen 1995.",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "00f65f1a7f50a099b93b8411f2a3f9cdc0c285d6",
 "stated_evidence": "https://huggingface.co/datasets/h2o64/samplingdb/blob/00f65f1a7f50a099b93b8411f2a3f9cdc0c285d6/README.md",
 "file_evidence": "https://huggingface.co/datasets/h2o64/samplingdb/tree/00f65f1a7f50a099b93b8411f2a3f9cdc0c285d6",
 "card_nc": false
}