cross/README.md

Cross-DB Connection Layer — Structure Sample

This directory shows the shape of the cross-DB connection artifacts that bridge Fragrantica and Parfumo. Files here are structural samples — they demonstrate column layouts, formats, and content style without trying to be join-able with the 10+10 perfume samples in ../fragrantica/ and ../parfumo/.

For the full cross-walk dataset (80,968 perfume pairs + 6,522 brand pairs + complete overlap stats), see cross-source-full-2026-05-28.zip from the $400 / Annual / Lifetime tier.

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Files in this sample

File	Content	Full release equivalent
matched_pairs_sample.csv	First 30 of 80,968 F.pid ↔ P.pid pairs	matched_pairs.csv
brand_matches_sample.csv	First 30 of 6,522 brand pairs	brand_matches.csv
field_equivalence_map.csv	Full schema-level F↔P column equivalence (206 rows)	same — small enough to ship whole
accord_overlap.json	15 matched accord pairs + F/P-only counts	same
notes_overlap.json	1,690 direct + 7,977 species-of summary	same
perfumers_overlap.json	1,669 perfumer pairs + Jaccard	same
dictionary_overlap_stats.json	Aggregate stats across notes/accords/perfumers/brands	same
overlap_stats.json	Top-level Jaccard summary	same

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File schemas

matched_pairs_sample.csv — F ↔ P perfume cross-walk

Columns: frag_pid | parf_pid | match_pass | brand_relation | confidence

frag_pid|parf_pid|match_pass|brand_relation|confidence
30|728|exact|same-brand|exact
762|727|exact|same-brand|exact

• match_pass: how this pair was matched (exact, brand_fuzzy, token-subset, etc.)
• brand_relation: same-brand / cross-brand / unknown
• confidence: exact / confident / low — based on G1 stratified audit
• Method: Phase 2B Fellegi-Sunter probabilistic record linkage
• Precision in full release: ~99% (G1 audit)

brand_matches_sample.csv — F ↔ P brand cross-walk

Same shape as matched_pairs but at the brand level.

field_equivalence_map.csv — schema-level column equivalence

Columns: f_field | f_file | p_field | p_file | relation | note

relation values:

• equivalent_same_format — drop-in interchangeable (21 rows)
• equivalent_diff_format — same concept, different encoding (31 rows)
• partial_overlap — overlaps for some uses, diverges for others (7 rows)
• f_only — F has it, P doesn't (59 rows)
• p_only — P has it, F doesn't (88 rows)

Used to know "does F column X have a P counterpart, and if so what's the encoding relation?" — see also field_equivalence_map.md in the full release for the long-form reference.

*_overlap.json files

Each describes one entity type's cross-DB overlap:

• counts (matched, F-only, P-only)
• Jaccard index
• method (Phase 2B / Phase 2.5 / species-of taxonomy)
• snapshot caveats (e.g. perfumers_overlap was computed on F=2,893 snapshot before v5.4 added 75 perfumers)

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How to use this for a join (example)

import pandas as pd

# Load the cross-walk
mp = pd.read_csv("cross/matched_pairs_sample.csv", sep="|")
print(f"Pairs in sample: {len(mp)}")  # 30

# Filter by confidence
exact = mp[mp.confidence == "exact"]
brand_fuzzy = mp[mp.match_pass == "brand_fuzzy"]

# In the FULL release, this scales to:
#   80,968 total pairs · ~99% precision (G1 audit)
#   Use frag_pid / parf_pid to join the per-DB CSVs

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What this sample does NOT include

• Full matched_pairs.csv (80,968 rows / 3.2 MB) — in full release
• Full brand_matches.csv (6,522 rows / 323 KB) — in full release
• field_equivalence_map.md (27 KB, human-readable long-form) — in full release
• Per-row audit decisions or scoring features — in full release

Cross-walk methodology details are in cross/README.md of the full bundle.

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Notes on the F + P sample picks

The 10 F perfumes and 10 P perfumes in ../fragrantica/ and ../parfumo/ were chosen independently — each side picked the most populated rows on its DB, top by rating votes. They don't necessarily join through matched_pairs.csv (the cross-walk shown above is a structural sample, not curated to the 10+10).

For a cross-walk demo that links F and P at the perfume level, see the full release.