THE RESEARCH WORKSPACE FOR MANUFACTURING
ResearchFlow.
From raw data to
research-ready insights.
Catch errors. Explore experiments. Document every decision. One streamlined workspace for manufacturing research.
✓ No account needed✓ Works offline✓ Your data stays local
● ● ●EXPERIMENT MONITOR● LIVE PREVIEW
RESEARCH RUNS121Active dataset
DATA QUALITY—Items to review
Process speed vs. defect rateEXPERIMENTAL
Lower speedHigher speed
BUILT FOR THE RESEARCH WORKFLOWImport→Validate→Analyze→Document
YOUR WORKSPACE
Everything you need to move forward.
Choose a tool below. Every tab works with the same editable dataset.
INTERACTIVE DEMO
See the impact of a single change.
Try a hypothetical experiment before uploading your own dataset.
CALCULATED DEFECT RATE4.00%
GOOD UNITS192
DEFECTIVE UNITS8
4 out of every 100 inspected units are defective in this example.
WHY RESEARCHFLOW
Less spreadsheet work. More research.
Designed for repeatable manufacturing experiments and transparent data decisions.
✦Catch costly mistakesFlag suspicious records before they influence your conclusions.
◈Explore process settingsCompare runs and investigate relationships without rebuilding charts.
▣Make results traceableKeep a record of cleaning actions so others can understand your process.
YOUR CURRENT DATASET
Experiment snapshot
These results update when you edit or upload experimental data.
Quality vs. speed
Defect rate by machine speed
Individual runsTrend line
Data health
Issues to review
YOUR WORKSPACE · STEP 1
Start with your data.
Upload your experiment file or try the example. Then click any number to change it.
Your source data stays unchanged when you apply cleaning choices.
STEP 2
Does anything need fixing?
ResearchFlow finds potential issues. You decide what to keep or change.
Here's what we found
These are warnings, not proof that an experiment is wrong.
Choose how to clean
We've selected conservative defaults. Change only what you need.
Physically impossible resultsFor example, more defects than inspected units.
More cleaning options (optional)
Missing defect and inspection counts are never estimated.
Statistical outliers are flagged for review, never removed automatically.
Original data is preserved.
STEP 3
What affects product defects?
Explore patterns across your manufacturing experiments. Each dot represents one run.
Explore your experiments
Choose a machine setting to compare against defect rate.
Purple dots: experimental runs · Orange line: overall trend. A relationship does not prove cause and effect.
Compare machines
Which machines had lower defect rates in this dataset?
Change the setting above to explore a different relationship.
STEP 4
Your results are ready.
Download your research data and a record of what changed.
↧Cleaned experiment data
Download your updated CSV with your cleaning choices applied.
≡Change history
Download a JSON audit report documenting your edits, checks, and cleaning choices.
See what's included in your audit report
This is an exploratory research prototype. Keep original source files and validate methods before scientific publication.