FTIR data files, read and analyzed — free
How FTIR spectra are stored (Bruker OPUS, Nicolet .RAW, JCAMP-DX), how to read/convert them — upload to Matflow free, or parse in Python.
Formats: Bruker .0 (OPUS), Thermo/Nicolet .RAW/.SPS, JCAMP-DX .jdx, CSV exports · For: Chemistry & materials labs
What the format actually is
Where the data hides, and what a naive parser loses.
FTIR vendors each ship a container: Bruker OPUS (binary blocks with instrument/sample metadata), Nicolet OMNIC .RAW, and the open JCAMP-DX as the portable option.
OPUS files are block-structured (instrument status, sample params, data arrays) — the community brukeropus reader maps them, and the metadata blocks are where units and resolution hide.
Most labs then do the same five operations: baseline correct, smooth, normalise, pick peaks, compare against a library — each currently living in a different vendor tool.
Vendor quirks that break naive parsers
Each of these has produced a silently wrong number in a real workflow.
Do it yourself in Python
We would rather you succeed with or without us. The open-source path:
What happens if you use Matflow
The parser is only step one — the value is the review and the provenance that follow.
Upload on the Ingestion page
Excel, CSV, PDF, SDS images and instrument files are parsed into evidence-tagged rows — the original file is retained as provenance.
Review before promotion
Column roles and types are proposed for a reason; correcting a mislabeled column here is the highest-leverage five minutes in the workflow.
Promote to a governed dataset
The accepted rows become a versioned dataset any stage can use — every row keeps its source and extraction status.
Docs: ingestion & extraction · column roles · instrument parsers
Stop parsing by hand
Upload your Bruker .0 file to Matflow and it lands as a governed, validated dataset — original file preserved as provenance, every column documented. Free tier, no card.
Other formats we parse
This guide covers one of 10 instrument formats.