Datasets:

ilkhamfy
/

IRexp

language:
  - en
license:
  - cc-by-4.0
  - cc-by-sa-4.0
tags:
  - chemistry
  - spectroscopy
  - infrared
  - nmr
  - structure-elucidation
  - cheminformatics
size_categories:
  - 100K<n<1M
pretty_name: IRexp
configs:
  - config_name: commercial
    data_files: data/irexp_commercial.jsonl.gz
  - config_name: resolved
    data_files: data/irexp_resolved.jsonl.gz
  - config_name: train_no_bench
    data_files: data/train_no_bench.jsonl.gz
  - config_name: train_no_bench_nmr
    data_files: data/train_no_bench_nmr.jsonl.gz
  - config_name: pretrain_ir
    data_files: data/pretrain_ir.jsonl.gz
  - config_name: all
    data_files: data/irexp.jsonl.gz
  - config_name: non_commercial
    data_files: data/irexp_non_commercial.jsonl.gz
  - config_name: sharealike
    data_files: data/irexp_sharealike.jsonl.gz

IRexp — experimental IR band lists from open-access literature

Paper: IRexp and IRSpectra-Bench: redistributable experimental IR band lists, a blind peak-list benchmark, and a recall-bound diagnosis of LLM elucidation (manuscript in preparation, 2026)

IRexp is the largest openly redistributable collection of experimental infrared band lists mined from open-access chemistry papers, often with co-reported ¹H/¹³C shift lists and resolved structures.

Important: IRexp contains band lists (peak positions in cm⁻¹), not digitised absorbance traces. This is the form reported in publication text — the regime IRSpectra-Bench evaluates — and is not directly comparable to SDBS or NIST full spectra.

Dataset summary

Split / file	Records	Description
`irexp_commercial.jsonl.gz`	87,617	Primary redistributable — CC-BY + CC0 (`license_pool=commercial`)
`irexp.jsonl.gz`	121,233	Full corpus (multi-licence; every row stamped)
`irexp_non_commercial.jsonl.gz`	20,938	CC-BY-NC* held aside
`irexp_sharealike.jsonl.gz`	1,897	Chemotion CC-BY-SA-4.0 + rare PMC SA
`irexp_empty_unknown.jsonl.gz`	10,781	Empty/unknown — excluded from commercial Zenodo
`irexp_resolved.jsonl.gz`	43,060	Structure-linked (100%; multi-licence — filter by `license_pool`)
… full IR + ¹H + ¹³C + structure	33,201	Multimodal quadruples
`train_no_bench.jsonl.gz`	42,808	Recommended for training — `irexp_resolved` minus all IRSpectra-Bench InChIKey-14
`train_no_bench_nmr.jsonl.gz`	32,949	Same, requiring both ¹H and ¹³C

Provenance & licensing: 119,345 PMC-sourced + 1,888 Chemotion/RADAR4Chem. Per-article Europe PMC join stamps license / license_pool on every row (scripts/join_pmc_licences.py). Commercial training / Zenodo primary = commercial config (87,617). Do not treat the full all split as uniformly CC-BY. See NOTICE and LICENCE_REMEDIATION.md.

Companion benchmark: IRSpectra-Bench — 194 blind elucidation problems built from IRexp; score submissions with scripts/score_submission.py.

Load in three lines

from datasets import load_dataset

# Structure-linked corpus (43,060 records; filter license_pool for commercial use)
ds = load_dataset("ilkhamfy/IRexp", "resolved", split="train")

# Preferred redistributable commercial pool
comm = load_dataset("ilkhamfy/IRexp", "commercial", split="train")

row = ds[0]
print(row["ir_bands_cm-1"][:5], row["smiles"][:40])

For fine-tuning without benchmark leakage, use the train_no_bench config:

ds = load_dataset("ilkhamfy/IRexp", "train_no_bench", split="train")

Or load a file path directly:

ds = load_dataset("ilkhamfy/IRexp", data_files="data/train_no_bench.jsonl.gz", split="train")

Record schema

Each JSONL row:

{
  "id": "AJCQUIFRMABSOZ-UHFFFAOYSA-N",
  "inchikey": "AJCQUIFRMABSOZ-UHFFFAOYSA-N",
  "smiles": "Cc1ccccc1NC(=O)Cn1cc...",
  "selfies": "[C][C][=C]...",
  "ir_bands_cm-1": [3318.0, 3146.0, 1704.0],
  "h_nmr": "9.79 (s, 1H, NH-amide), ...",
  "c_nmr": "164.87, 161.57, ...",
  "ir_source": "experimental",
  "source_doi": "PMC:13234927",
  "pmcid": "PMC13234927",
  "license": "CC-BY",
  "license_pool": "commercial",
  "license_source": "europepmc"
}

Training vs benchmarking

Use case	File	Benchmark overlap
Pretrain IR encoder	`pretrain_ir.jsonl.gz` or all `ir_bands_cm-1`	N/A (mostly unlabeled)
Supervised IR→structure	`train_no_bench.jsonl.gz`	None (248 IK-14 held out)
Evaluate elucidation	IRSpectra-Bench	—
⚠️ Legacy split	`irexp_release/train.jsonl.gz`	117/200 IK-14 overlap — do not use for benchmark evaluation

Rebuild the held-out training pool:

python scripts/build_train_no_bench.py              # 42,808 rows
python scripts/build_train_no_bench.py --require-nmr  # 32,949 rows (H+C required)

Limitations (read before citing)

Band lists, not spectra — median 9 bands (PMC) vs 39 (Chemotion peak-picked).
Literature-transcribed — heterogeneous labs/instruments; not raw .jdx files.
Structure resolution 35% of all records; use irexp_resolved for supervised tasks.
Extraction recall of IR strings per paper not yet human-audited (transcription fidelity audited: 560/560 bands on n=60).

Citation

@article{yabbarov2026irspectra,
  title   = {{IRexp} and {IRSpectra-Bench}: redistributable experimental {IR} band lists,
             a blind peak-list benchmark, and a recall-bound diagnosis of {LLM} elucidation},
  author  = {Yabbarov, Ilkham and Sondhi, Rudra and Vargas-Hern{\'a}ndez, Rodrigo A.},
  year    = {2026},
  note    = {Manuscript in preparation; target J. Chem. Inf. Model.}
}