01
Why I built it
A research question can require public-data collection, preparation, analysis, visualization, and evidence documentation across several tools.
I built Collabysis as an AI data scientist for researchers. The workspace accepts a plain-language question, searches public sources, analyzes data, creates visualizations, and records the evidence behind the result.
02
What it does
Collect public-web data
Collabysis searches public sites and datasets within the access those sources provide and the terms that govern them. It records each source URL, retrieval time, and collection method with the data.
Ask a research question
Researchers state questions in plain language. The assistant proposes a suitable analysis and requests clarification when a choice changes the answer.
Analyze and visualize
The app runs analyses, creates charts and dashboards, and connects results to the data and methodology.
Export the evidence
Users export results, source records, methods, parameters, and supporting files for review or reproduction.
03
How it is built
- Local analysis
- A Python engine runs the available statistical methods locally. A browser interface prepares and explores data.
- Reproducibility and visualization
- Charts, dashboards, methods, parameters, sources, and exports link each result to its evidence.
04
Important to know
Collabysis assists with public-web collection and data analysis. Researchers assess rights, quality, study design, and interpretation for each result.
Available methods and connectors cover a defined set of research questions and sources. Public datasets have access, license, quality, and coverage limits.
Complex studies require statistical expertise.