Add SciPeerBench v1.1 — 644 papers, 35 columns

schema.py

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+"""
+SciPeerBench — Dataset schema definition.
+
+World's first multi-dimensional scientific fraud benchmark.
+Every paper labeled across 14 fraud dimensions simultaneously.
+No other dataset does this.
+"""
+
+from dataclasses import dataclass, field
+from typing import Optional
+
+
+# ── Fraud taxonomy — 20 types, first time formally defined ───────────────────
+
+FRAUD_TAXONOMY = {
+    # Data fabrication
+    "FAB-01": "Complete data fabrication",
+    "FAB-02": "Partial data fabrication",
+    "FAB-03": "Data duplication across papers",
+
+    # Statistical manipulation
+    "STAT-01": "P-hacking and selective reporting",
+    "STAT-02": "HARKing — hypothesizing after results known",
+    "STAT-03": "Impossible statistical values GRIM or SPRITE",
+    "STAT-04": "Inflated effect sizes",
+    "STAT-05": "Underpowered study with strong claims",
+
+    # Figure fraud
+    "FIG-01": "Duplicated figure panels",
+    "FIG-02": "Manipulated western blots",
+    "FIG-03": "Image brightness or contrast manipulation",
+
+    # Citation fraud
+    "CIT-01": "Excessive self-citation ring",
+    "CIT-02": "Citation cartel coordinated group",
+    "CIT-03": "Unsupported claims without citation",
+
+    # Methodology fraud
+    "METH-01": "Causation claimed without RCT",
+    "METH-02": "Missing control group",
+    "METH-03": "Undisclosed conflicts of interest",
+
+    # Authorship and integrity
+    "AUTH-01": "LLM-generated paper",
+    "AUTH-02": "Plagiarism detected",
+    "AUTH-03": "Retracted paper cited as valid",
+}
+
+
+# ── Paper categories for balanced dataset ────────────────────────────────────
+
+PAPER_CATEGORIES = {
+    "CONFIRMED_FRAUD": "Retracted with documented fraud reason",
+    "SUSPECTED_FRAUD": "PubPeer flagged, not retracted yet",
+    "BORDERLINE":      "Minor issues, not clear fraud",
+    "CLEAN":           "High quality, replicated, no concerns",
+    "BASELINE_ELITE":  "Nobel prize or landmark papers",
+}
+
+
+# ── Source databases ──────────────────────────────────────────────────────────
+
+DATA_SOURCES = {
+    "retraction_watch": "https://retractionwatch.com",
+    "pubpeer":          "https://pubpeer.com",
+    "pubmed":           "https://pubmed.ncbi.nlm.nih.gov",
+    "arxiv":            "https://arxiv.org",
+    "semantic_scholar": "https://api.semanticscholar.org",
+    "crossref":         "https://api.crossref.org",
+}
+
+
+# ── Target distribution — 1000 papers total ──────────────────────────────────
+
+TARGET_DISTRIBUTION = {
+    "CONFIRMED_FRAUD": 300,   # from RetractionWatch
+    "SUSPECTED_FRAUD": 200,   # from PubPeer
+    "BORDERLINE":      150,   # gray area
+    "CLEAN":           250,   # normal good papers
+    "BASELINE_ELITE":  100,   # Nobel / landmark
+}
+
+
+@dataclass
+class PaperRecord:
+    """
+    One row in SciPeerBench.
+    Every paper labeled across all 14 fraud dimensions.
+    This is the most comprehensive fraud labeling schema ever built.
+    """
+
+    # ── Identity ──────────────────────────────────────────────────
+    paper_id:          str            # SPB-0001, SPB-0002 ...
+    doi:               Optional[str]
+    title:             str
+    authors:           str            # comma separated
+    year:              int
+    journal:           str
+    source_db:         str            # where we got it
+
+    # ── Ground truth ──────────────────────────────────────────────
+    category:          str            # from PAPER_CATEGORIES
+    is_fraud:          int            # 1 = fraud, 0 = clean
+    fraud_confidence:  float          # 0.0 to 1.0
+    fraud_types:       str            # comma separated FRAUD_TAXONOMY keys
+    retraction_date:   Optional[str]  # YYYY-MM-DD
+    retraction_reason: Optional[str]
+    pubpeer_url:       Optional[str]
+
+    # ── 14 module scores — auto-generated by running our system ───
+    stat_audit_score:       float = 0.0
+    figure_forensics_score: float = 0.0
+    methodology_score:      float = 0.0
+    citation_score:         float = 0.0
+    reproducibility_score:  float = 0.0
+    novelty_score:          float = 0.0
+    grim_score:             float = 0.0
+    sprite_score:           float = 0.0
+    granularity_score:      float = 0.0
+    pcurve_score:           float = 0.0
+    effect_size_score:      float = 0.0
+    retraction_score:       float = 0.0
+    cartel_score:           float = 0.0
+    llm_score:              float = 0.0
+
+    # ���─ Weighted average of all 14 scores ─────────────────────────
+    overall_risk_score: float = 0.0
+
+    # ── Paper content ─────────────────────────────────────────────
+    abstract_text:   str = ""
+    full_text_path:  str = ""   # path to saved full text
+
+    # ── Metadata ──────────────────────────────────────────────────
+    field_of_study:  str = ""   # biology, psychology, medicine...
+    labeling_method: str = ""   # auto, manual, auto+manual
+    labeled_by:      str = "auto"
+    notes:           str = ""
+
+
+# ── CSV column order — exact order in output file ────────────────────────────
+
+CSV_COLUMNS = [
+    "paper_id", "doi", "title", "authors", "year",
+    "journal", "source_db",
+    "category", "is_fraud", "fraud_confidence",
+    "fraud_types", "retraction_date", "retraction_reason", "pubpeer_url",
+    "stat_audit_score", "figure_forensics_score", "methodology_score",
+    "citation_score", "reproducibility_score", "novelty_score",
+    "grim_score", "sprite_score", "granularity_score", "pcurve_score",
+    "effect_size_score", "retraction_score", "cartel_score", "llm_score",
+    "overall_risk_score",
+    "abstract_text", "full_text_path",
+    "field_of_study", "labeling_method", "labeled_by", "notes",
+]