Datasets:

dsadd4
/

UltraMS-MSnLib-Benchmark

The frozen, formula-disjoint 80/10/10 train / validation / test split of the complete MSnLib tandem mass spectral library (560,084 spectra, all adducts). This directory is also shipped as split_A_full_msnlib/ inside ultrams_msnlib_benchmark_v1/.

	train	validation	test	total
molecular formulas	16,885	2,110	2,112	21,107
spectra	446,667	55,980	57,437	560,084

Every spectrum of a molecular formula lies in exactly one fold; no formula occurs in two folds. The frozen assignment is the fold column of MSnLib.csv and the split is replayed exactly by build_split_a.py.

Files

file	rows	content
`formula_split.csv(.gz)`	21,107	`formula, fold, n_spectra` — one row per molecular formula
`row_formula_fold.csv(.gz)`	560,084	`row_index, formula, fold` — authoritative row → formula → fold map for all spectra
`test_spectra.csv(.gz)`	57,437	`row_index, smiles, precursor_mz, adduct, collision_energy, fold, canonical_isomeric_smiles`
`val_spectra.csv(.gz)`	55,980	same schema for the validation fold
`msnlib_rt.csv.gz`	560,084	`row_index, rt` — compact per-spectrum retention time (seconds); gzipped only
`test_molecules_voting.csv(.gz)`	5,002	official multiple-spectrum voting units (Figure 3d / 3a voting): the test spectra grouped by the MSnLib source SMILES string, group size ≥ 2 (57,436 spectra); reproduces the published voting task's `n_molecules = 5002` grouping member-for-member
`test_molecules_voting_canonical_view.csv(.gz)`	4,888	alternative canonical view (not the official unit): the same test spectra grouped by RDKit canonical isomeric SMILES, group size ≥ 2 (57,436 spectra)
`REPORT.json`		machine-readable report: counts, expected values, hashes, cross-checks, anomaly record
`VERIFY.md`		full verification record (inputs, counts, cross-checks, replay transcript)
`build_split_a.py`		replay from `MSnLib.csv` and SHA-256 check against the frozen tables

row_index is the 0-based data row of MSnLib.csv (560,084 rows, 752,707,651 bytes, SHA-256 be54b33039cf18675751ea3c6354b592177c899a0cd5df303ae616330133ec07; physical line = data row + 2). Every .csv has a .csv.gz twin that decompresses to the identical bytes.

Official voting units (Figure 3d / Figure 3a voting)

The official multiple-spectrum voting unit is the MSnLib source SMILES string. The 57,437 test spectra contain 5,003 distinct source strings; one of them occurs in only one spectrum, so test_molecules_voting.csv has 5,002 groups covering 57,436 spectra. This is the grouping of the published multiple-spectrum voting task and it is verified member-for-member against the repository voting table (n_molecules = 5002).

For a chemically deduplicated view, test_molecules_voting_canonical_view.csv groups the same spectra by RDKit canonical isomeric SMILES: 4,889 distinct molecules, of which 4,888 groups cover the same 57,436 spectra. 114 source-string groups merge into chemically identical molecules, and the single excluded molecule is CC(C)(C)OC(=O)NCC1(CC(=O)O)CCCCC1. The canonical view is an alternative, not the official voting unit. See VERIFY.md §7.

Retention times (RT)

msnlib_rt.csv.gz is a compact companion table (1.4 MB gzipped, no 756 MB full CSV): row_index is the 0-based MSnLib.csv data row (0 … 560,083) and rt is the retention time in seconds, kept in the source text representation. It was exported from MSnLib_with_rt.csv (755,984,910 bytes, SHA-256 a5b7c38228287c5c1de401050445319b331ee9a5f17993d6e104e9a734fe2964, columns mzs,intensities,smiles,precursor_mz,adduct,collision_energy,fold,rt) after verifying row-by-row that its first seven columns are identical to MSnLib.csv in the same order (560,084/560,084 rows, 0 mismatches). RT is source metadata from the MSnLib records (CC BY 4.0; see NOTICE.md / the parent package NOTICE.md).

import gzip
import pandas as pd

test = pd.read_csv("test_spectra.csv")
rt = pd.read_csv("msnlib_rt.csv.gz")           # row_index, rt
test_rt = test.merge(rt, on="row_index", how="left")
assert test_rt["rt"].notna().all()

The table is needed by the label-blind grouping / multiple-spectrum voting analyses (Figure 3d), which use RT as spectrum metadata.

Quick start

import pandas as pd

formulas = pd.read_csv("formula_split.csv")
test = pd.read_csv("test_spectra.csv")
rows = pd.read_csv("row_formula_fold.csv")          # 560,084 rows

# formula-level disjointness: one fold per formula
assert formulas.groupby("formula")["fold"].nunique().max() == 1
assert formulas["n_spectra"].sum() == 560_084

Rebuild and verify

# read-only replay + hash check (needs MSnLib.csv and RDKit 2026.03.5):
python3 build_split_a.py --csv /path/to/datasets/MSnLib/MSnLib.csv

# write the tables (byte-identical with the frozen copies under RDKit 2026.03.5):
python3 build_split_a.py --csv /path/to/MSnLib.csv --output-dir replay_split_a

The split is formula-disjoint, not molecule-disjoint: different molecules can share a formula and therefore a fold. The molecule-disjoint benchmark is split B (strict-adduct 70/15/15) of ultrams_msnlib_benchmark_v1/; the two splits must never be combined into a single global-disjointness claim. See figures_mapping.md in the parent package for which manuscript panels use which split, and NOTICE.md for the MSnLib attribution (Brungs et al., Zenodo 10.5281/zenodo.11163380, CC BY 4.0).