Update Croissant metadata with author attribution for arXiv preprint release

croissant.json

@@ -0,0 +1,261 @@
+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "cr": "http://mlcommons.org/croissant/",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "data": {
+      "@id": "cr:data",
+      "@type": "@json"
+    },
+    "dataType": {
+      "@id": "cr:dataType",
+      "@type": "@vocab"
+    },
+    "dct": "http://purl.org/dc/terms/",
+    "examples": {
+      "@id": "cr:examples",
+      "@type": "@json"
+    },
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileProperty": "cr:fileProperty",
+    "fileObject": "cr:fileObject",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "replace": "cr:replace",
+    "samplingRate": "cr:samplingRate",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "name": "PROTAC-Bench",
+  "description": "Cold-target evaluation benchmark for PROTAC degradation prediction. 10,748 entries across 173 protein targets with 65 Leave-One-Target-Out (LOTO) folds. Merged from PROTAC-DB 3.0, Ribes et al., and DegradeMaster with canonical SMILES standardization.",
+  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "license": "https://creativecommons.org/licenses/by/4.0/",
+  "url": "https://huggingface.co/datasets/ThorKl/protac-bench",
+  "version": "1.0.0",
+  "citeAs": "@misc{klamt2026protacbench, title={Decomposing the Generalization Gap in PROTAC Activity Prediction: Variance Attribution and the Inter-Laboratory Ceiling}, author={Klamt, Thor and Nejdl, Wolfgang and Tang, Ming}, year={2026}, eprint={arXiv:XXXX.XXXXX}, archivePrefix={arXiv}, primaryClass={cs.LG}}",
+  "datePublished": "2026-05-02",
+  "creator": [
+  {
+    "@type": "sc:Person",
+    "name": "Thor Klamt",
+    "affiliation": "L3S Research Center, Leibniz Universitaet Hannover",
+    "url": "https://orcid.org/0009-0005-6168-3655"
+  },
+  {
+    "@type": "sc:Person",
+    "name": "Wolfgang Nejdl",
+    "affiliation": "L3S Research Center, Leibniz Universitaet Hannover",
+    "url": "https://orcid.org/0000-0003-3374-2193"
+  },
+  {
+    "@type": "sc:Person",
+    "name": "Ming Tang",
+    "affiliation": "L3S Research Center, Leibniz Universitaet Hannover",
+    "url": "https://orcid.org/0000-0002-5993-5906"
+  }
+],
+  "keywords": [
+    "PROTAC",
+    "protein degradation",
+    "drug discovery",
+    "benchmark",
+    "cold-target evaluation",
+    "binary classification"
+  ],
+  "rai:dataCollection": "PROTAC-Bench aggregates 10,748 PROTAC-target pairs from three publicly released sources: PROTAC-DB 3.0 (Weng et al., 2023; Nucleic Acids Research), the Ribes et al. (2024) curated benchmark, and DegradeMaster (Liu et al., 2024). Records were de-duplicated on canonical SMILES + UniProt accession pairs. SMILES were standardised with RDKit canonicalisation; targets were mapped to UniProt accessions via UniProt REST API queries on HGNC/UniProt-name strings supplied in source databases.",
+  "rai:dataCollectionType": "Aggregation of pre-existing publicly published datasets; no primary experimental data collection.",
+  "rai:dataCollectionTimeframe": "Source databases span PROTAC publications 2001-2024 (PROTAC-DB 3.0 release plus subsequent literature); merged corpus frozen 2025-Q4. Temporal split: pre-2023 entries used for training (1,866 entries), 2024 entries held out for prospective evaluation (132 entries); 2023 entries excluded as a temporal gap year.",
+  "rai:dataCollectionRawData": "Processed: SMILES are canonicalised, targets are normalised to UniProt accessions, activity labels are binarised (DC50<1 uM OR Dmax>50% -> 1). Raw DC50 / Dmax values are preserved in dc50_nm / dmax_pct columns for users who prefer custom thresholds.",
+  "rai:dataCollectionMissingData": "~38% of entries report only Dmax or only DC50, not both. The binary label is computed from whichever potency endpoint is available. Cell line, assay format, and time-point metadata are NOT included; users needing assay-context-aware modelling should consult the original source publications.",
+  "rai:dataAnnotationProtocol": "Activity labels are inherited from the source databases' published binarisation rules. Each source's primary literature was hand-curated by that source's authors; PROTAC-Bench performs no additional re-annotation. The cross-source label-agreement rate on the 1,247 entries appearing in two or more source DBs is 98.4% (kappa=0.96).",
+  "rai:dataAnnotationPlatform": "No platform - labels propagated from upstream curated databases (PROTAC-DB 3.0 web portal exports, Ribes et al. 2024 supplementary tables, DegradeMaster 2024 release).",
+  "rai:dataAnnotationAnalysis": "Inter-source agreement was measured on the 1,247 entries shared by >=2 source databases: raw agreement 98.4%, Cohen's kappa 0.96. Disagreements (12 cases) were retained as separate rows flagged with source_conflict=true rather than resolved by majority vote, to preserve the upstream signal.",
+  "rai:dataAnnotationPerItemTime": "Not applicable - no per-item human annotation was performed by the PROTAC-Bench authors. Upstream curators do not report per-record annotation timing.",
+  "rai:dataAnnotationDemographics": "Not applicable: labels derive from biochemical assay readouts in source publications, not from human-judgement annotation.",
+  "rai:dataAnnotationTools": "No annotation tools were used - upstream labels were ingested verbatim. Standardisation tooling (not annotation): RDKit 2024.03 for SMILES canonicalisation; UniProt REST API (https://rest.uniprot.org/uniprotkb) for target-name to accession resolution.",
+  "rai:dataPreprocessingProtocol": "SMILES canonicalisation: RDKit MolToSmiles(mol, canonical=True) after MolFromSmiles round-trip with sanitisation. Stereochemistry preserved. Target normalisation: UniProt accessions resolved via the UniProt REST API; entries that fail to resolve to a single canonical accession are dropped (1,043 records, 8.8% of pre-merge total).",
+  "rai:dataPreprocessingImputation": "None. Missing potency values are kept as null; the binary label is computed from whatever potency value is available. Entries with neither DC50 nor Dmax are excluded from the benchmark.",
+  "rai:dataPreprocessingManipulation": "De-duplication on (canonical SMILES, UniProt) tuples; cross-source conflict resolution by majority vote (3 sources) or, if 2 sources conflict (12 cases), retained as separate entries flagged with source_conflict=true.",
+  "rai:dataUseCases": "(1) Benchmarking PROTAC degradation prediction models under cold-target evaluation (held-out UniProt accessions). (2) Studying generalisation decay as molecular similarity to training set decreases. (3) Measuring E3-ligase scaffold transferability (VHL <-> CRBN). (4) Few-shot transfer experiments for low-data targets. NOT INTENDED for direct clinical candidate selection - predictions are research-stage and have not been validated against held-out wet-lab assays beyond the source databases.",
+  "rai:dataLimitation": "(1) E3-ligase imbalance: VHL and CRBN account for 87% of records; performance on rare E3 ligases (RNF114, IAP, MDM2, ...) is data-limited. (2) Target-class imbalance: kinases dominate (47% of entries) due to PROTAC literature focus. (3) Activity-label binarisation discards potency gradient - models cannot learn DC50 ranking. (4) Assay heterogeneity is not encoded - the same compound assayed by different labs at different time-points may receive divergent labels. (5) Publication-positivity bias: inactive PROTACs are systematically under-reported in the literature.",
+  "rai:dataBiases": "Documented biases: (a) chemotype bias toward CRBN/VHL warhead families documented in the cheminformatics literature; (b) target bias toward oncology targets (BCR-ABL, BTK, AR, EGFR, BRD4 are over-represented); (c) lab-of-origin confounding - three labs contribute >40% of records, introducing potential lab-specific assay-condition signatures that models can latch onto (see task14_within_target_cross_lab.json and the 'lab-confound' analysis in the paper).",
+  "rai:dataSocialImpact": "Positive: lowers the entry barrier for ML-driven PROTAC design, enables reproducible benchmarking and reduces wasted wet-lab effort on poorly-generalising models. Negative / dual-use: PROTAC technology in principle enables targeted degradation of arbitrary proteins; however, this dataset contains only published research-stage compounds and provides no novel uplift for misuse beyond what is already in the primary literature. No human-subject data; no privacy concerns.",
+  "rai:personalSensitiveInformation": "None. The dataset contains chemical structures (SMILES), protein identifiers (UniProt accessions), and biochemical activity labels. No human-subject data, no PII, no patient-derived material.",
+  "rai:dataReleaseMaintenancePlan": "Distributed under CC-BY-4.0 via HuggingFace Datasets. Maintained by the PROTAC-Bench authors; versioned releases tagged in the HF repo and the source repository's RELEASE_MANIFEST.md. Issues / corrections accepted via GitHub issues; merged updates tagged as semver minor releases. No deprecation date - long-term maintenance is committed for at least the duration of the NeurIPS 2026 reproducibility window (2026-2028).",
+  "distribution": [
+    {
+      "@type": "cr:FileObject",
+      "@id": "protac_bench.csv",
+      "name": "protac_bench.csv",
+      "description": "Main dataset: 10,748 PROTAC entries with SMILES, target, E3 ligase type, and binary activity label.",
+      "contentUrl": "data/protac_bench.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "6d273d9fbfb1921f5b2da9ba94a74d46fe787ffd3f6a766058c8d1c3c76d30fa"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "loto_folds.json",
+      "name": "loto_folds.json",
+      "description": "65 pre-computed Leave-One-Target-Out fold assignments with test indices, entry counts, and activity rates.",
+      "contentUrl": "data/loto_folds.json",
+      "encodingFormat": "application/json",
+      "sha256": "61564e68683db7c46424a5b6b58fe25d7cab319ee3dc7a300f86b7611bb4de3c"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "lofo_folds.json",
+      "name": "lofo_folds.json",
+      "description": "Leave-One-Family-Out fold assignments. 22 protein families covering 61 LOFO-eligible targets (LOTO-eligible targets that are mapped to a named family in robustness/lofo.py FAMILY_MAP). Family-level holdout: each fold's test_indices are all rows whose target_uniprot belongs to the family's target set.",
+      "contentUrl": "data/lofo_folds.json",
+      "encodingFormat": "application/json",
+      "sha256": "16d4d2fabe2ac84c0f7528128a49f94832eef92db096edd4f3bf8ee2ed32c8ee"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "cross_lab_folds.json",
+      "name": "cross_lab_folds.json",
+      "description": "Within-target cross-lab fold assignments. 36 targets with >=20 entries, >=3 publications (DOIs), and both classes present. For each (target, paper) holdout where the paper has >=5 entries and both classes, test_indices are the rows matching that target and DOI; train is everything else. Used by the lab-confound analysis (results/task14_within_target_cross_lab.json).",
+      "contentUrl": "data/cross_lab_folds.json",
+      "encodingFormat": "application/json",
+      "sha256": "3beb321d4a2e5e2365501b48a38d885cffc3fecf5b82a99c0c5b268bfc8bd964"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "temporal_prospective_folds.json",
+      "name": "temporal_prospective_folds.json",
+      "description": "Temporal prospective split. Train: rows with publication year < 2023 (1,866 entries). Test: rows with publication year == 2024 (132 entries). 2023 entries are excluded as a temporal gap year. pub_year derived from DOI mapping; rows without resolved pub_year are excluded from the split.",
+      "contentUrl": "data/temporal_prospective_folds.json",
+      "encodingFormat": "application/json",
+      "sha256": "a68950af03b1be29df7ddb47b373e234cd72190fb2101433b2f818f8aa6cc67b"
+    },
+    {
+      "@type": "cr:FileObject",
+      "@id": "admet_scores.csv",
+      "name": "admet_scores.csv",
+      "description": "7-property ADMET cascade scores for all 10,748 entries.",
+      "contentUrl": "data/admet_scores.csv",
+      "encodingFormat": "text/csv",
+      "sha256": "db5c33175ec6c1f1f9608cc230f655b2dd823b1e745df36e34d0616ec056d7e0"
+    }
+  ],
+  "recordSet": [
+    {
+      "@type": "cr:RecordSet",
+      "@id": "protac_entries",
+      "name": "protac_entries",
+      "description": "Individual PROTAC degradation entries.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/smiles",
+          "name": "smiles",
+          "description": "Canonical SMILES representation of the PROTAC molecule.",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "smiles"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/target_uniprot",
+          "name": "target_uniprot",
+          "description": "UniProt accession ID of the target protein.",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "target_uniprot"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/e3_type",
+          "name": "e3_type",
+          "description": "E3 ubiquitin ligase type (VHL, CRBN, or Other).",
+          "dataType": "sc:Text",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "e3_type"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/label",
+          "name": "label",
+          "description": "Binary activity label (1 = active: DC50 < 1 uM OR Dmax > 50%, 0 = inactive).",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "label"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/dc50_nm",
+          "name": "dc50_nm",
+          "description": "Half-maximal degradation concentration in nanomolar (when available).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "dc50_nm"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "protac_entries/dmax_pct",
+          "name": "dmax_pct",
+          "description": "Maximum degradation percentage (when available).",
+          "dataType": "sc:Float",
+          "source": {
+            "fileObject": {
+              "@id": "protac_bench.csv"
+            },
+            "extract": {
+              "column": "dmax_pct"
+            }
+          }
+        }
+      ]
+    }
+  ]
+}