00:00 elapsed
Monday. 9:47 AM.
The data arrived overnight. Four instruments, three formats, two archives, and a README that says "see attached" with nothing attached.
Bruker .dpt files. A Thermo .spc. An Excel spreadsheet with 600 rows. A .npz that nobody remembers who exported. A .zip that might contain the same files already sitting in the folder. Or might not.
This is what spectroscopy research actually looks like.
scroll
10:15 AM. Two hours in.
You have written a script for the .dpt files. It does not work on the .spc. The Excel file has 14 metadata columns mixed in with the wavenumber data. You do not know which columns are spectral and which are patient IDs.
.dpt script v2
.spc no parser
.xlsx 14 cols?
.npz who made this?
.zip duplicate?
README.txt see attached
Time spent on data wrangling: 2h 15m   |   Time spent on science: 0m
Your colleague asks if you have results yet. You do not.
11:30 AM. Lab meeting in 30 minutes.
Multiply this by every student, every postdoc, every instrument, every dataset.
200h
wasted per researcher per year on format conversion
15+
different file formats in a typical spectroscopy project
0
shared output standard across instruments
A PhD student loses a month to data wrangling before their first model runs. A QC lab delays batch release because nobody can agree on a format. A multi-center study stalls because every site exports differently.
The science is ready. The data is not.
Same data. Same folder. Two paths.
00:00
shared timeline
Manual 00:00
Waiting to start...
eQCX 00:00
Waiting to start...
What if it took 6 seconds instead of 6 hours?
Point eQCX at the folder. Press one button.
collect
discover
classify
resample
enrich
export
Archives extracted. Duplicates removed. Metadata mined from READMEs. Patient IDs preserved. Axis ranges separated. Every decision logged.
No configuration. No scripts. No manual column sorting.
6.4 seconds later.
The same data. Clean. Linked. Ready.
metadata.csv
sample_idsample_00000
data_typeRaman
source_fileDATASET.xlsx
histopathologydcis
patient_idS001_M1_R1
verification6/6 PASS
spectra.csv
axiswavenumber_cm_minus_1
samples641
axis points1252
NaN fraction0.0000
data typefloat64
formatML-ready
SHA-256: a3f8e7c2...4e9b  |  Operator: Dr. Smith  |  Audit trail: written
Before eQC
6 hours
Writing scripts. Testing parsers. Sorting columns by hand. Checking which files are duplicates. Losing metadata. Starting over.
Time on data: 85%  |  Time on analysis: 15%
With eQCX
6 seconds
Point at folder. Press Export. Get metadata.csv + spectra.csv. All metadata preserved. Full audit trail. Ready to train.
Time on data: 0.3%  |  Time on analysis: 99.7%
What changes when data is no longer the bottleneck?
You run experiments in the morning and analyze them by lunch. You compare modalities without reformatting. You iterate on models faster. You publish sooner. You move to the next question.
data wrangling actual research
That is the difference.

Get your time back.

eQCX. One folder in. Two clean files out. Every time.
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Duviloper CORE AI LAB  |  NVIDIA Inception Member