How do I open a .res (Microsoft Access database), CSV exports file?

Upload it to Matflow (free tier) and it is parsed into a structured, validated dataset automatically — or parse it yourself in Python with the open-source options listed on this page.

Can I convert .res (Microsoft Access database), CSV exports to CSV?

Yes. Matflow normalises the file into a governed dataset you can export as CSV with all metadata preserved. The DIY Python route on this page also converts to CSV.

Is my data kept private?

Files are stored scoped to your account, never used to train models for other users, and fully exportable. Original raw files are retained as provenance.

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Arbin cycler files (.res / CSV), parsed and analyzed — free

What an Arbin .res file is (a Microsoft Access database), how to export and analyze Arbin cycler data — upload to Matflow free, or parse it yourself in Python.

Formats: .res (Microsoft Access database), CSV exports · For: Battery & materials labs (MITS Pro)

The format

What the format actually is

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

Arbin MITS Pro writes test results to .res files that are, surprisingly, full Microsoft Access databases — you can open them with MS Access, or read them with MDB tools from Python.

The canonical table is Channel_Normal_Table (with Channel_Charge_Energy / Channel_Discharge_Energy and W, Wh columns) plus auxiliary channels in Channel_Normal_Table_W1/W2…, joined via Test_ID and Data_Point indexes.

Statistical tables (Channel_Normal_Statistical) hold per-step aggregates. Firmware differences shift column names — scripts that match on exact names break across instruments.

Watch out

Vendor quirks that break naive parsers

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

Test names live in file names, not columns — a folder of cells is only traceable by naming discipline.

Multiple channels interleaved per file; you must filter by Channel_Index.

Cycle_Index semantics: rests and CV holds can inflate cycle counts.

Open source

Do it yourself in Python

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

Instruments & parsers →

pandas_access or mdbtools to dump the Access tables to CSV/DataFrames.

Join the auxiliary channel tables on (Test_ID, Data_Point); recompute per-cycle capacity from dQ = ∫I dt over discharge segments only.

Assert column presence per firmware with a schema check at load time — Arbin is the most firmware-variable of the big four.

Upload 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 .res file to Matflow and it lands as a governed, validated dataset — original file preserved as provenance, every column documented. Free tier, no card.

Keep reading

Other formats we parse

This guide covers one of 10 instrument formats.

.mpr .nda / .ndax .xye / .xy .jdx / .dx Agilent .D directory with .ms / .dpi files, Thermo .raw, Shimadzu .qgd, JCAMP-DX exports Bruker .0 Agilent/Varian .ads/.dsw, Shimadzu .spc/.uvd, Thermo .usd, CSV/Excel exports Bruker fid/ser + acqus/blocks, JEOL .jdf, Varian/Agilent fid + procpar, JCAMP-DX exports TA Instruments TRIOS exports, Anton Paar