AI app for science and research · no coding needed
Research data cleaning
Reversible, documented transformations with researcher-approved rules.
Made for: Research groups with messy observational datasets
What it does for you
The problem
Undocumented cleaning steps undermine subsequent analysis.
What it gives you
Cleaned dataset and transformation script
What you give it
Authorized datasetsdata dictionariescleaning rules
How it works, step by step
- Profile missing values
- Detect inconsistent coding
- Propose transformations
- Preserve raw data
- Generate reproducible scripts
- Validate reviewed outputs
What you see on screen
- Data profile
- transformation preview
- provenance log
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Research data cleaning with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Research data cleaning with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links1 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare23 KB
- prompt-vps.mdThe same build on your own server (Docker)23 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data198 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
For research groups with messy observational datasets, turn authorized datasets, data dictionaries and cleaning rules into cleaned dataset and transformation script. Address the recurring problem: undocumented cleaning steps undermine subsequent analysis. The pilot measures verified transformations and reproducibility against the buyer's current method, before the larger build.
Scope one technical task, inspect authorized material, propose an implementation, build in a controlled environment, run relevant checks, obtain the required change approval, deliver with recovery instructions, and monitor the agreed operating scope. Start with authorized datasets, data dictionaries and cleaning rules and finish with cleaned dataset and transformation script.
How the AI works
Explain code or configuration, draft transformations and propose technical changes. Execute deterministic validation and meaningful tests. Engineers review correctness, access handling and failure behavior before deployment.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
What to build first
Begin with research groups with messy observational datasets and one recurring use case. Build the first two modules: profile missing values; detect inconsistent coding. Provide operator assistance for the third module: propose transformations. Deliver cleaned dataset and transformation script through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
What it can connect to
Authorized datasets, papers, protocols, code and research records. Approved repositories, application APIs, execution platforms and monitoring systems. Validate current API access and behavior during discovery before promising compatibility. These are candidate integration categories, not verified supported connectors.
The screens in detail
Show a work backlog, proposed changes and verification results. Link each item to its source configuration, code or data mapping. Provide execution logs and an owner-facing health view. Keep environments and approval states clearly separated so a draft cannot be mistaken for a live change. In this product, the first view is data profile, followed by transformation preview and provenance log.
