AI app for science and research · no coding needed
Research contributor agreement tracker
Discuss contribution expectations early with a traceable record.
Made for: Multi-institution research coordinators

What it does for you
The problem
Contribution expectations are unclear until publication approaches.
What it gives you
Team-approved contribution record
What you give it
Team-approved contribution statementsproject milestones
How it works, step by step
- Capture agreed roles
- Link completed contributions
- Flag unresolved expectations
- Draft review agendas
- Record team decisions
- Export contribution summaries
What you see on screen
- Contribution plan
- Update evidence
- Team review
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 contributor agreement tracker 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 contributor agreement tracker 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 build3 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 criteria12 KB
- demo/index.htmlThe working demo on sample data197 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 multi-institution research coordinators, turn team-approved contribution statements and project milestones into team-approved contribution record. Address this specific problem: contribution expectations are unclear until publication approaches. The aim: discuss contribution expectations early with a traceable record. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.
The buyer creates a project, supplies team-approved contribution statements and project milestones, and confirms scope and access. Users correct extracted facts, resolve flagged uncertainties and approve the final team-approved contribution record before use. Retain source links and a version history for the next cycle.
How the AI works
Summarize declared contributions without judging scientific worth. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Administrative record; no automated authorship entitlement decisions. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.
What to build first
Costed pilot: Administrative record; no automated authorship entitlement decisions. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: capture agreed roles; link completed contributions. Support the third task through an assisted review queue: flag unresolved expectations. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of team-approved contribution record. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.
What it can connect to
Authorized datasets, papers, protocols, code and research records. Calendars, email, task managers and relevant business records. Use draft actions and supervised handoffs first, then enable only specifically authorized writes. Begin with uploads and exports of team-approved contribution statements and project milestones. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
The screens in detail
Use a queue or timeline as the opening view, with clear owners, dates and current states. Each case opens into its source context, proposed actions and discussion. Give external participants a limited form or status page. Make the next required action visible without opening every record. Open with contribution plan; move into update evidence for the detailed task; finish in team review for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.




