Publish license-cleared GenBench datasets

DATA_LICENSES.md

@@ -0,0 +1,40 @@
+# Dataset licenses and attribution
+
+The GenBench software is MIT-licensed, but that license does **not** apply to
+third-party datasets. Each stored dataset retains its upstream license. The
+prepared files are adaptations: GenBench verifies the upstream release, then
+performs the deterministic splitting, normalization, packing, or feature
+selection described in that benchmark's metadata and preparation module.
+
+## Publicly redistributed prepared data
+
+| Directory | Source and attribution | License |
+|---|---|---|
+| `QM9` | Ramakrishnan, Dral, Rupp, and von Lilienfeld, [Data for 133885 GDB-9 molecules](https://api.figshare.com/v2/articles/1057646), including Figshare files 3195389 and 3195404 | [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) |
+| `DeepSTARR` | Bernardo P. de Almeida, [DeepSTARR manuscript data](https://doi.org/10.5281/zenodo.5502060) | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
+| `FSDD` | The [Free Spoken Digit Dataset project and recording contributors](https://github.com/Jakobovski/free-spoken-digit-dataset/tree/26eb9aaf76e81b692f806f9140c2d2777410d7a1) | [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
+| `JetNet30` | Raghav Kansal, Javier Duarte, Su Hao, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, and Dimitrios Gunopulos, [JetNet](https://doi.org/10.5281/zenodo.6975118) | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
+| `MiniBooNE` | Byron Roe, [MiniBooNE particle identification](https://doi.org/10.24432/C5QC87), UCI Machine Learning Repository | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
+| `NavierStokes` | NeuralOperator Team, [Navier-Stokes Dataset](https://doi.org/10.5281/zenodo.12825163) | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
+| `PBMC3k` | 10x Genomics, [3k PBMCs from a Healthy Donor](https://www.10xgenomics.com/datasets/3-k-pbm-cs-from-a-healthy-donor-1-standard-1-1-0) | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
+| `UCIHAR` | Jorge Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto, and Xavier Parra, [Human Activity Recognition Using Smartphones](https://doi.org/10.24432/C54S4K), UCI Machine Learning Repository | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
+
+The prepared FSDD files are distributed under CC BY-SA 4.0. They are a
+separate component of this collection; their ShareAlike condition does not
+relicense the other independently packaged datasets or the GenBench software.
+
+## Source-only datasets
+
+GenBench does not redistribute the following data. The repository contains
+checksum-verified Python preparation code that downloads each release directly
+from its original host for a user's own use.
+
+| Dataset | Why it is source-only |
+|---|---|
+| CIFAR-10 | The official release specifies no dataset license or redistribution grant. |
+| MNIST | The official distribution and the CVDF mirror specify no general redistribution license. |
+| ENZYMES | The TU Dortmund release and its archive README specify no license. |
+| ModelNet10 | Princeton states that original CAD authors retain copyright and that the dataset is provided for academic research only. Users must explicitly acknowledge those terms before GenBench downloads it. |
+
+This file records upstream terms for reproducibility and is not legal advice.
+

DeepSTARR/processed/metadata.json

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+{
+  "dataset": "DeepSTARR enhancer sequences",
+  "zenodo_record": 5502060,
+  "source_url": "https://zenodo.org/records/5502060",
+  "file_md5": {
+    "Sequences_Train.fa": "fe474a31c23db2daf7dc4d1fc49a8e5a",
+    "Sequences_Val.fa": "9d1a0e5c9228aa033378af93074109e6",
+    "Sequences_Test.fa": "c2d831a6e2cfb9345fa5e52de76bb453",
+    "Sequences_activity_Train.txt": "4e65afa021fd426c74dbf24c31b8bbf9",
+    "Sequences_activity_Val.txt": "5f30e863b2239e4f9ade6e7b81b3ff3c",
+    "Sequences_activity_Test.txt": "011690ca80cf967d56fefd0c1386419f",
+    "DeepSTARR.model.json": "9b796f79441e53dc75dd79b911fff872",
+    "DeepSTARR.model.h5": "7e53a9351b2520a4713a5ffdb5f1566c"
+  },
+  "alphabet": "ACGT",
+  "sequence_length": 249,
+  "split_sizes": {
+    "train": 402278,
+    "validation": 40570,
+    "test": 41186
+  },
+  "ambiguous_sequences_removed": {
+    "train": 18,
+    "validation": 0,
+    "test": 0
+  },
+  "activity_columns": [
+    "Dev_log2_enrichment",
+    "Hk_log2_enrichment",
+    "Dev_log2_enrichment_scaled",
+    "Hk_log2_enrichment_scaled",
+    "Dev_log2_enrichment_quantile_normalized",
+    "Hk_log2_enrichment_quantile_normalized"
+  ],
+  "generative_training": "official train split only",
+  "official_activity_predictor": "Keras weights ported exactly to PyTorch at evaluation"
+}

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DeepSTARR/raw/DeepSTARR.model.json

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FSDD/processed/metadata.json

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+  "source_url": "https://github.com/Jakobovski/free-spoken-digit-dataset/archive/26eb9aaf76e81b692f806f9140c2d2777410d7a1.zip",
+  "source_commit": "26eb9aaf76e81b692f806f9140c2d2777410d7a1",
+  "source_doi": "10.5281/zenodo.1342401",
+  "license": "CC BY-SA 4.0",
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+  "split_protocol": "recording indices 0-4 test, 5-49 train",
+  "split_sizes": {
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+    "test": 300
+  },
+  "speakers": [
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+    "nicolas",
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+    "yweweler"
+  ],
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JetNet30/processed/metadata.json

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+    "padding": "all normalized features set to zero where mask is absent"
+  },
+  "split_seed": 42,
+  "split_fractions": [
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+  "split_implementation": "numpy default_rng permutation after g,q,t,w,z concatenation, matching JetNet",
+  "split_sizes": {
+    "train": 616000,
+    "validation": 132000,
+    "test": 132000
+  }
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+  "source_url": "https://archive.ics.uci.edu/static/public/199/miniboone+particle+identification.zip",
+  "source_doi": "10.24432/C5QC87",
+  "license": "CC BY 4.0",
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+    "ENSG00000213246",
+    "ENSG00000120727",
+    "ENSG00000234745",
+    "ENSG00000099783",
+    "ENSG00000132002",
+    "ENSG00000153310",
+    "ENSG00000110700",
+    "ENSG00000183283",
+    "ENSG00000111913",
+    "ENSG00000126759",
+    "ENSG00000130770",
+    "ENSG00000156738",
+    "ENSG00000089280",
+    "ENSG00000120963",
+    "ENSG00000147274",
+    "ENSG00000116473",
+    "ENSG00000161203",
+    "ENSG00000134825",
+    "ENSG00000109861",
+    "ENSG00000075785",
+    "ENSG00000125356",
+    "ENSG00000196329",
+    "ENSG00000131795",
+    "ENSG00000137752",
+    "ENSG00000057608",
+    "ENSG00000155957",
+    "ENSG00000105701",
+    "ENSG00000100450",
+    "ENSG00000126353",
+    "ENSG00000163736",
+    "ENSG00000163737"
+  ],
+  "selected_gene_names": [
+    "LYZ",
+    "S100A9",
+    "HLA-DRA",
+    "CST3",
+    "TYROBP",
+    "S100A8",
+    "NKG7",
+    "LGALS1",
+    "CD74",
+    "HLA-DPB1",
+    "S100A4",
+    "LTB",
+    "CCL5",
+    "HLA-DRB1",
+    "HLA-DPA1",
+    "AIF1",
+    "LST1",
+    "FCER1G",
+    "IL32",
+    "SAT1",
+    "COTL1",
+    "S100A6",
+    "CTSS",
+    "FCN1",
+    "S100A11",
+    "FTL",
+    "CD3D",
+    "JUN",
+    "PSAP",
+    "RPL34",
+    "SRGN",
+    "VIM",
+    "CXCR4",
+    "PTPRCAP",
+    "GPX1",
+    "GNLY",
+    "GSTP1",
+    "LDHB",
+    "S100A10",
+    "TYMP",
+    "JUNB",
+    "ZFP36",
+    "MT-ATP6",
+    "IFITM2",
+    "MT-CO2",
+    "ANXA1",
+    "LY6E",
+    "HLA-DRB5",
+    "CYBA",
+    "IER2",
+    "ISG15",
+    "NEAT1",
+    "NPC2",
+    "KLF6",
+    "ARPC1B",
+    "CD37",
+    "ZFP36L2",
+    "CD3E",
+    "FTH1",
+    "CLIC1",
+    "NFKBIA",
+    "GAPDH",
+    "CALM1",
+    "SH3BGRL3",
+    "LGALS2",
+    "EMP3",
+    "GABARAP",
+    "TSC22D3",
+    "GIMAP7",
+    "IL7R",
+    "IFI6",
+    "DUSP1",
+    "EIF4A1",
+    "HCST",
+    "HINT1",
+    "HSPA8",
+    "CTSW",
+    "LIMD2",
+    "ANXA2",
+    "RPSAP58",
+    "TSPO",
+    "TXNIP",
+    "PLAC8",
+    "UBB",
+    "TMEM66",
+    "IFITM1",
+    "ACTG1",
+    "ARPC3",
+    "EIF4A2",
+    "RBM3",
+    "ALDOA",
+    "RAC1",
+    "FXYD5",
+    "ID2",
+    "CIRBP",
+    "CD99",
+    "SELL",
+    "SRSF5",
+    "KLF2",
+    "RAC2",
+    "PSME1",
+    "HNRNPA2B1",
+    "HMGB1",
+    "RHOA",
+    "CCNI",
+    "ARPC2",
+    "BTG1",
+    "TAGLN2",
+    "PSME2",
+    "CD48",
+    "PNRC1",
+    "NAP1L1",
+    "DDX5",
+    "NPM1",
+    "EIF3H",
+    "C6orf48",
+    "EVL",
+    "GMFG",
+    "RPL36AL",
+    "ISG20",
+    "UCP2",
+    "ATP5L",
+    "OAZ1",
+    "MT-ND2",
+    "MYL6",
+    "SERP1",
+    "AES",
+    "LAPTM5",
+    "SLC25A5",
+    "PSMB9",
+    "YBX1",
+    "MYL12B",
+    "FOS",
+    "CD7",
+    "GLTSCR2",
+    "ENO1",
+    "EIF3K",
+    "ITGB2",
+    "COX7C",
+    "CD53",
+    "ITM2B",
+    "TUBA1A",
+    "PRELID1",
+    "CALM2",
+    "LSP1",
+    "RARRES3",
+    "TPI1",
+    "ATP5E",
+    "SUMO2",
+    "GSTK1",
+    "PSMA7",
+    "ARPC5",
+    "FKBP1A",
+    "COMMD6",
+    "HSP90AA1",
+    "TOMM7",
+    "APRT",
+    "CD52",
+    "GPSM3",
+    "GPX4",
+    "FOSB",
+    "BRK1",
+    "RPL22",
+    "ATP6V0E1",
+    "CD79B",
+    "UBE2D3",
+    "TIMP1",
+    "GZMA",
+    "CNBP",
+    "HNRNPDL",
+    "NOSIP",
+    "NBEAL1",
+    "PYCARD",
+    "LAMTOR4",
+    "CAPZB",
+    "TMA7",
+    "EIF3F",
+    "HLA-DQA1",
+    "CHCHD2",
+    "HMGN1",
+    "ERP29",
+    "IFITM3",
+    "SSR2",
+    "YWHAB",
+    "COX6B1",
+    "RNASET2",
+    "C19orf43",
+    "EDF1",
+    "CORO1A",
+    "CST7",
+    "MYL12A",
+    "SOD1",
+    "ATP5G2",
+    "ATP6V1G1",
+    "SNRPD2",
+    "SLC25A6",
+    "GZMB",
+    "OST4",
+    "CD79A",
+    "PPIA",
+    "PPIB",
+    "UQCR11",
+    "VAMP8",
+    "UBC",
+    "TRAF3IP3",
+    "ATP5D",
+    "PPP1CA",
+    "PPDPF",
+    "DRAP1",
+    "C1orf162",
+    "HLA-DQB1",
+    "NDUFA4",
+    "SRSF7",
+    "SSR4",
+    "CD69",
+    "SLC25A3",
+    "PCBP2",
+    "EEF1B2",
+    "MT-ND5",
+    "TKT",
+    "ARL6IP4",
+    "RPS21",
+    "EEF2",
+    "TALDO1",
+    "GIMAP4",
+    "PABPC1",
+    "HIGD2A",
+    "GTF3A",
+    "SERF2",
+    "UBXN1",
+    "TMBIM6",
+    "EIF3G",
+    "COX5B",
+    "HLA-E",
+    "BST2",
+    "TUBA1B",
+    "TCEB2",
+    "RPL41",
+    "CFD",
+    "SRP14",
+    "LDHA",
+    "PSMB8",
+    "ZFAS1",
+    "EIF3L",
+    "ANAPC16",
+    "FCGR3A",
+    "TPM3",
+    "HNRNPK",
+    "SNX3",
+    "ARL6IP5",
+    "SRSF3",
+    "RPL23",
+    "CD44",
+    "IL2RG",
+    "COX6A1",
+    "CEBPB",
+    "RAN",
+    "GUK1",
+    "SH3BGRL",
+    "HSP90AB1",
+    "RGS10",
+    "VAMP2",
+    "PRR13",
+    "NEDD8",
+    "ATP5O",
+    "C11orf31",
+    "CD63",
+    "AP1S2",
+    "PCBP1",
+    "CCND3",
+    "ARF1",
+    "UQCRH",
+    "PKM",
+    "SKP1",
+    "SAP18",
+    "MT2A",
+    "CRIP1",
+    "PARK7",
+    "HNRNPA0",
+    "TMEM123",
+    "PGK1",
+    "PTPRC",
+    "H2AFZ",
+    "SERPINA1",
+    "HLA-DMA",
+    "TRAPPC1",
+    "PSMB1",
+    "CAP1",
+    "UBE2D2",
+    "NDUFA13",
+    "CSTB",
+    "CUTA",
+    "MCL1",
+    "ANP32B",
+    "RPS27A",
+    "LCK",
+    "RHOG",
+    "AP2S1",
+    "RPL38",
+    "NDUFB9",
+    "ICAM3",
+    "SF3B5",
+    "RPS4Y1",
+    "FYB",
+    "UBL5",
+    "PPP1R15A",
+    "CD2",
+    "COX7A2L",
+    "GLRX",
+    "C10orf54",
+    "EID1",
+    "UXT",
+    "H3F3A",
+    "COX6C",
+    "COPE",
+    "C19orf53",
+    "ATP5A1",
+    "JTB",
+    "ATP5G3",
+    "NDUFA11",
+    "RPL36A",
+    "LGALS3",
+    "PTGES3",
+    "COX4I1",
+    "RPS29",
+    "EIF3D",
+    "SERPINB1",
+    "ATP6V0B",
+    "RAP1B",
+    "ATP5B",
+    "ACTR3",
+    "PNISR",
+    "SRSF2",
+    "BIN2",
+    "CFL1",
+    "LCP1",
+    "U2AF1",
+    "ISCU",
+    "BTG2",
+    "NDUFS5",
+    "PRF1",
+    "RGS2",
+    "RPSA",
+    "SMDT1",
+    "EIF3E",
+    "PEBP1",
+    "NDUFB8",
+    "NDUFB11",
+    "MZT2B",
+    "CIB1",
+    "COX7A2",
+    "COX8A",
+    "HNRNPC",
+    "PFN1",
+    "MORF4L1",
+    "BTF3",
+    "TMEM59",
+    "MT-CYB",
+    "ATP6V1F",
+    "DYNLL1",
+    "MIF",
+    "RPLP0",
+    "TRMT112",
+    "CMPK1",
+    "RPS26",
+    "ARHGDIB",
+    "ST13",
+    "NHP2L1",
+    "SPCS1",
+    "SEPT7",
+    "C11orf58",
+    "STK17A",
+    "DBI",
+    "C9orf16",
+    "CDC42",
+    "PLP2",
+    "CD68",
+    "UBE2L6",
+    "SYF2",
+    "HNRNPF",
+    "PFDN5",
+    "GRN",
+    "PRDX5",
+    "PSMB3",
+    "LAMTOR1",
+    "GABARAPL2",
+    "HCLS1",
+    "CALM3",
+    "CYTIP",
+    "CD247",
+    "PSMB10",
+    "C1QBP",
+    "UFC1",
+    "POLD4",
+    "POLR2L",
+    "ATP5H",
+    "NDUFB2",
+    "H2AFY",
+    "LRRFIP1",
+    "HMGB2",
+    "GHITM",
+    "YWHAZ",
+    "PDIA3",
+    "WDR83OS",
+    "COX5A",
+    "DAD1",
+    "C4orf3",
+    "ARF6",
+    "ATP5F1",
+    "ANXA5",
+    "VPS28",
+    "RBM39",
+    "YPEL5",
+    "SERBP1",
+    "RPS3A",
+    "CEBPD",
+    "LINC00936",
+    "CD164",
+    "CNN2",
+    "CSNK2B",
+    "CCDC85B",
+    "RPS20",
+    "ACAP1",
+    "EVI2B",
+    "UBE2I",
+    "EIF3M",
+    "SNRPB",
+    "GZMM",
+    "HLA-F",
+    "LITAF",
+    "FGFBP2",
+    "GIMAP1",
+    "SSU72",
+    "ANXA6",
+    "UQCR10",
+    "ERH",
+    "CASP4",
+    "NDFIP1",
+    "GSTO1",
+    "DHRS7",
+    "SEPT9",
+    "CCL3",
+    "SEC11A",
+    "CARD16",
+    "SEC61B",
+    "HLA-A",
+    "EIF4G2",
+    "HNRNPA1",
+    "SRP9",
+    "SON",
+    "RSL1D1",
+    "C12orf57",
+    "OSTF1",
+    "RNH1",
+    "ATP5J2",
+    "NOP10",
+    "STXBP2",
+    "SPI1",
+    "XBP1",
+    "PTPN6",
+    "DUSP2",
+    "RPL39",
+    "RPS11",
+    "BLOC1S1",
+    "CCDC109B",
+    "RABAC1",
+    "HNRNPA3",
+    "TBC1D10C",
+    "CHMP4A",
+    "TUBB",
+    "MT-ND1",
+    "PGLS",
+    "NDUFB10",
+    "RPL7",
+    "SF1",
+    "C20orf24",
+    "SUB1",
+    "C14orf166",
+    "PRDX1",
+    "FIS1",
+    "GYPC",
+    "SUPT4H1",
+    "PAIP2",
+    "HLA-B",
+    "HNRNPM",
+    "DNAJB1",
+    "FAM49B",
+    "RPS13",
+    "DAZAP2",
+    "FAM65B",
+    "CFP",
+    "ATPIF1",
+    "MS4A1",
+    "FUS",
+    "ZNF706",
+    "RBMX",
+    "RAP1A",
+    "AP2M1",
+    "TMEM258",
+    "CTSC",
+    "RAB7A",
+    "NDUFA1",
+    "GIMAP5",
+    "RBM8A",
+    "CASP1",
+    "GDI2",
+    "TMBIM4",
+    "FKBP8",
+    "GZMH",
+    "CCR7",
+    "PPBP",
+    "PF4"
+  ],
+  "selected_gene_variances": [
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QM9/METADATA.md

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+# QM9 dataset and TD-jumps split
+
+## Summary
+
+This directory contains the with-hydrogen QM9 molecular geometries and the
+exact train/validation/test split used by the QM9 experiments in the TD-jumps
+paper, *Trans-Dimensional Generative Modeling via Jump Diffusion Models*.
+
+The split was reconstructed from the released jump-diffusion code at commit
+`b2e31007d2c5792dddc1bf03f2e92bf889f42fd8`. The stored split arrays were
+verified byte-for-byte against an independent reconstruction of the TD-jumps
+row arrays.
+
+## Origin and filtering
+
+The molecular data come from the official GDB9/QM9 XYZ archive:
+
+- Archive: <https://ndownloader.figshare.com/files/3195389>
+- Archive SHA-256:
+  `3a63848ac80691bdb8d41834b575afad345b9300d7a2db0c38adb7f6eaa8360c`
+- Official uncharacterized-molecule list:
+  <https://ndownloader.figshare.com/files/3195404>
+- Exclusion-list SHA-256:
+  `3aa5115d540b356de94791d4a74c3bf1ed91c469ecf52a4f5d7cc0506fe02e24`
+
+The original archive contains 133,885 molecules. The same 3,054
+uncharacterized molecules excluded by the released TD-jumps loader were
+removed, leaving 130,831 molecules. `qm9.npz` stores those retained molecules
+in their original archive order after filtering; the split files contain the
+TD-jumps permutation as row indices into that packed file.
+
+## Exact TD-jumps split
+
+The released TD-jumps code applies NumPy's legacy `RandomState`/MT19937
+permutation with seed 0 to the 130,831 retained molecules, then divides that
+permutation as follows:
+
+| File | Molecules | Permutation positions |
+|---|---:|---:|
+| `train_rows.npy` | 100,000 | 0--99,999 |
+| `valid_rows.npy` | 17,748 | 100,000--117,747 |
+| `test_rows.npy` | 13,083 | 117,748--130,830 |
+
+Each split file is a one-dimensional `int64` NumPy array of zero-based rows in
+`qm9.npz`. The three arrays are disjoint, contain every row exactly once, and
+retain the original seed-0 permutation order used by TD-jumps. They are not
+GDB9 molecule IDs; the corresponding one-based GDB9 ID for a packed row is
+available in `qm9.npz` as `source_ids[row]`.
+
+For example:
+
+```python
+import numpy as np
+
+with np.load("qm9.npz", allow_pickle=False) as qm9:
+    offsets = qm9["molecule_offsets"]
+    coordinates = qm9["coords"]
+    atomic_numbers = qm9["atomic_numbers"]
+    train_rows = np.load("train_rows.npy", allow_pickle=False)
+
+    row = int(train_rows[0])
+    left, right = offsets[row : row + 2]
+    first_training_molecule = {
+        "coords": coordinates[left:right],
+        "atomic_numbers": atomic_numbers[left:right],
+    }
+```
+
+## Packed data schema
+
+`qm9.npz` is a compressed NumPy archive containing:
+
+| Array | Shape | Dtype | Meaning |
+|---|---:|---|---|
+| `coords` | `(2_359_210, 3)` | `float32` | Concatenated XYZ coordinates in angstroms |
+| `atomic_numbers` | `(2_359_210,)` | `int16` | Concatenated atomic numbers |
+| `molecule_offsets` | `(130_832,)` | `int64` | Half-open offsets into the atom arrays |
+| `source_ids` | `(130_831,)` | `int64` | One-based IDs in the official GDB9 archive |
+| `vocabulary_atomic_numbers` | `(5,)` | `int16` | Model vocabulary order `[6, 1, 7, 8, 9]` |
+
+The atom order inside each molecule is the order in the official XYZ file; the
+packed dataset does not reorder atoms. In 130,372 molecules all hydrogens follow
+all heavy atoms, 417 molecules have hydrogens intermingled with heavy atoms,
+and 42 molecules contain no hydrogen. The released TD-jumps training
+configuration subsequently randomizes the real-atom ordering on each dataset
+fetch (`shuffle_node_ordering=true`), but that augmentation is not baked into
+these files.
+
+## File checksums
+
+SHA-256 checksums of the files in this directory:
+
+| File | SHA-256 |
+|---|---|
+| `qm9.npz` | `ddba2129feb7f1676ae0137820d08e489555b8d928c2b943f80be6673dc2ef26` |
+| `train_rows.npy` | `aae8ded4c106664ca06029f697bd6f4b9d2ebd12f1ab8b0506950aa79d3613f1` |
+| `valid_rows.npy` | `ac3a8de760f21a278a64375c2d1aa01468ba73a13cf42613a6511882a41871e7` |
+| `test_rows.npy` | `94bd03e0e51002a954030efe7610e8b81a44dbd51f57d1a9a96d50d0f21c4990` |
+
+The reconstruction verified all 130,831 retained molecules against the
+MurrellLab QM9 serialization: element arrays matched exactly and coordinates
+were bitwise identical after conversion to `float32`.

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README.md

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+---
+pretty_name: GenBench prepared benchmark datasets
+license: other
+---
+
+# GenBench prepared datasets
+
+This repository contains the exact prepared arrays used by GenBench for
+datasets whose upstream terms affirmatively allow redistribution. It is a
+mixed-license collection: there is no blanket license covering every file.
+See [`DATA_LICENSES.md`](DATA_LICENSES.md) for attribution, license links, and
+the transformations made by GenBench.
+
+| Directory | Prepared contents | License |
+|---|---|---|
+| `QM9` | Packed characterized molecules and TD-jumps split rows | CC0 1.0 |
+| `DeepSTARR` | Sequence/activity splits and official predictor weights | CC BY 4.0 |
+| `FSDD` | Fixed-window spoken-digit waveform splits | CC BY-SA 4.0 |
+| `JetNet30` | Five-class particle-cloud splits | CC BY 4.0 |
+| `MiniBooNE` | Literature split and normalization | CC BY 4.0 |
+| `NavierStokes` | Fourier-downsampled vorticity-field splits | CC BY 4.0 |
+| `PBMC3k` | Expression splits, PCA, empirical quantiles, and metadata | CC BY 4.0 |
+| `UCIHAR` | Subject-separated inertial-window splits | CC BY 4.0 |
+
+`manifest.json` records the byte size and SHA-256 of every file consumed by
+the GenBench downloader. Original archives are omitted because the prepared
+files are sufficient to train and evaluate the published baselines.
+
+## Source-only benchmarks
+
+CIFAR-10, MNIST, ENZYMES, and ModelNet10 are intentionally not hosted here.
+Their upstream terms do not clearly authorize GenBench to publish another
+copy. The GenBench Python package downloads them directly from their original
+hosts and verifies their checksums. ModelNet10 additionally requires explicit
+acknowledgement of Princeton's academic-research terms.
+
+```bash
+python -m genbench.download_source_data \
+  --output-dir ../datasets \
+  --datasets cifar10 mnist enzymes
+
+python -m genbench.download_source_data \
+  --output-dir ../datasets \
+  --datasets modelnet10 \
+  --accept-modelnet-academic-research-terms
+```
+
+The analytic spiral and checkerboard targets have no stored dataset; GenBench
+generates those distributions directly in Python.
+

UCIHAR/processed/metadata.json

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+{
+  "dataset": "Human Activity Recognition Using Smartphones",
+  "source_url": "https://archive.ics.uci.edu/static/public/240/human+activity+recognition+using+smartphones.zip",
+  "source_doi": "10.24432/C54S4K",
+  "license": "CC BY 4.0",
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+    "body_gyro_z"
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+  "activities": [
+    "walking",
+    "walking_upstairs",
+    "walking_downstairs",
+    "sitting",
+    "standing",
+    "laying"
+  ],
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+    "test": 2947
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+  "window_length": 128,
+  "sampling_rate_hz": 50,
+  "normalization": "per-channel training mean and population standard deviation",
+  "mean": [
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+    0.028755486011505127,
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+    0.10687880963087082,
+    0.4068150520324707,
+    0.3818543255329132,
+    0.255743145942688
+  ]
+}

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manifest.json

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+  "repository": "MurrellLab/GenBench",
+  "files": [
+    {
+      "path": "DATA_LICENSES.md",
+      "bytes": 3355,
+      "sha256": "3daea05dbde5990bf88b08883c8f83a165d8409aecba70e662fa6f069d5ba143"
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