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.

Free instrument-data tool

Neware .nda / .ndax files, parsed and analyzed — free

Q: How do I open a .nda / .ndax (binary; .ndax is a zip container), CSV/XLSX via desktop export 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.

Q: Can I convert .nda / .ndax (binary; .ndax is a zip container), CSV/XLSX via desktop export 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.

What Neware .nda and .ndax files contain, why CSV exports reset their time columns, and how to convert/analyze Neware battery-cycler data — upload to Matflow free, or parse in Python.

Formats: .nda / .ndax (binary; .ndax is a zip container), CSV/XLSX via desktop export · For: Battery labs (BTS / CT-series cyclers)

The format

What the format actually is

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

Neware BTS and CT-series cyclers record to proprietary .nda (or newer .ndax, a zip-compressed container) files holding raw records, test info, and channel metadata.

The desktop software exports CSV/XLSX, but exports are record-frequency-limited — if "record every 1 s" was set, fast pulses can be thinned below what the cell actually experienced.

Exports commonly reset the time column at every step change and report capacity per step rather than per cycle — pivot tables on cycle number silently double-count rest steps.

Watch out

Vendor quirks that break naive parsers

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

Multiple record types coexist in one file; naive loaders mix metadata rows with measurement rows.

Voltage may be channel-referenced; current sign conventions differ from Bio-Logic/Arbin.

Older .nda versions lack the zip structure — two binary layouts in the wild.

Open source

Do it yourself in Python

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

Instruments & parsers →

NewareNDA (community package) reads .nda/.ndax directly into pandas DataFrames.

After loading: recompute monotonic timestamps per channel, group records by cycle with the rest-step convention you document, and normalise capacity units immediately.

Keep the original binary next to the parsed parquet — the export thinning is only discoverable later with provenance.

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 .nda / .ndax 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 .res .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