Free instrument-data tool

UV-Vis data files, read and analyzed — free

How UV-Vis spectrophotometer data is stored (Agilent/Varian, Shimadzu, Thermo .usd), how to read/convert absorbance spectra — upload to Matflow free, or parse in Python.

Formats: Agilent/Varian .ads/.dsw, Shimadzu .spc/.uvd, Thermo .usd, CSV/Excel exports · For: Chemistry, biochemistry & materials labs

The format

What the format actually is

Where the data hides, and what a naive parser loses.

UV-Vis exports are usually readable text or CSV — which is exactly why labs have thousands of them and zero structure: one CSV per measurement, naming conventions drifting per student.

Kinetics time-courses, wavelength scans, and photometric repeats get exported in different layouts from the same instrument, so the "parse the folder" script grows one special case per student.

Quantitative workflows (Beer-Lambert calibrations, band-gap Tauc plots, kinetics fits) need the metadata — path length, slit width, scan rate — that CSV exports often omit.

Watch out

Vendor quirks that break naive parsers

Each of these has produced a silently wrong number in a real workflow.

Shimadzu binary .spc files predate their CSV exports — legacy data may only be readable with the galactic/universal SPC readers.
Excel exports merge headers across cells; pandas needs explicit header handling.
Baseline drift between sessions breaks naive absorbance comparisons without a recorded blank.
Open source

Do it yourself in Python

We would rather you succeed with or without us. The open-source path:

spc (community package) reads Thermo Galactic .spc files; plain CSVs need only pandas.
Tauc analysis: (αhν)^n vs hν linear extrapolation — trivial in numpy once absorbance + film thickness are structured.
Kinetics: fit absorbance-vs-time with scipy curve_fit; keep the raw trace, not just the fit.
Upload path

What happens if you use Matflow

The parser is only step one — the value is the review and the provenance that follow.

01

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.

02

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.

03

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 Agilent/Varian .ads/.dsw, Shimadzu .spc/.uvd, Thermo .usd, CSV/Excel exports file to Matflow and it lands as a governed, validated dataset — original file preserved as provenance, every column documented. Free tier, no card.