AI app for science and research · no coding needed
Multi-source research dataset discovery and stewardship console
Reduce dataset search and preparation time while keeping provenance and review visible.
Made for: Research teams, data stewards and analysts working across many external dataset collections
What it does for you
The problem
Datasets from many sources sit in separate catalogs with inconsistent metadata, so finding, comparing and reusing them takes manual effort and repeated exports.
What it gives you
A reviewed, queryable dataset library with exportable knowledge artifacts
What you give it
Licensed dataset catalogssource metadatausage termsteam queries
Build your own version of DataDepot, Lium AI and more
One app with what these 4 AI tools do, yours to keep and change: DataDepot, Lium AI, Datashake Hub, Coldpress AI.
Everything these tools do, in one app
- Dataset catalog access Provides a centralized collection of datasets from multiple sources that users can browse and access.Found in DataDepot, Lium AI, Datashake Hub and 1 more
- Natural language querying Lets users ask questions in plain English to find information within datasets.Found in DataDepot, Lium AI
- Automated data aggregation Collects and organizes large volumes of data from various sources without manual effort.Found in Datashake Hub
- No-code interface Enables users to work with data without needing programming skills.Found in Datashake Hub
- Customizable workspace Allows users to personalize their view by adding or removing datasets as needed.Found in DataDepot
- Insight extraction Automatically pulls out and displays key insights from data to keep users informed.Found in DataDepot
- Knowledge artifact generation Creates reusable outputs that teams can inspect and build upon for future analyses.Found in Lium AI
- Reusable workflows Turns one-time analyses into collaborative processes that can be repeated and extended.Found in Lium AI
- Domain packs Offers pre-built tools and data connections for specific fields like weather and climate.Found in Lium AI
- Export options Allows data to be sent to other tools like BI platforms, CSV files, or cloud storage.Found in Datashake Hub
- Continuous updates Keeps exported data current by regularly refreshing it.Found in Datashake Hub
- Uniform metadata Provides consistent descriptions for datasets to make discovery and comparison easier.Found in Coldpress AI
- Manually vetted datasets Ensures datasets are reviewed and pre-labelled for quality and usability.Found in Coldpress AI
- Community-driven requests Allows users to request specific datasets and see them added based on demand.Found in Coldpress AI
- Guardrails for accuracy Prevents hallucinations by prompting the system to state uncertainty when appropriate.Found in Lium AI
- Automated location discovery Finds all company locations and their associated review profiles automatically.Found in Datashake Hub
- Integration with existing subscriptions Incorporates users' current research subscriptions and datasets for a unified experience.Found in DataDepot
- Upcoming API and CLI Planned features to allow programmatic access and command-line interaction with datasets.Found in Coldpress AI
How it works, step by step
- Ingest licensed dataset catalogs and source metadata
- Normalize descriptions into uniform metadata fields
- Answer plain-language questions over indexed datasets
- Aggregate records from multiple sources into one workspace
- Provide a no-code query and filter interface
- Let users add or remove datasets in a personal workspace
- Extract and display key insights from selected data
- Generate reusable knowledge artifacts from analyses
- Save one-time analyses as repeatable workflows
- Offer domain packs for fields such as weather and climate
- Export to BI platforms, CSV files and cloud storage
- Refresh exported data on a schedule
- Mark manually vetted and pre-labelled datasets
- Accept community requests for new datasets
- State uncertainty instead of inventing values
- Discover organization locations and linked review profiles
- Connect existing research subscriptions into one view
- Expose planned API and CLI access for programmatic use
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed, queryable dataset library with source references and unresolved questions
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 Multi-source research dataset discovery and stewardship console 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 Multi-source research dataset discovery and stewardship console 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 links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data199 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
Reduce dataset search and preparation time while keeping provenance and review visible. For research teams, data stewards and analysts working across many external dataset collections, convert licensed dataset catalogs, source metadata, usage terms and team queries into a reviewed, queryable dataset library with exportable knowledge artifacts. The benefit is a testable hypothesis, measured through accepted dataset retrievals per steward hour and reuse of approved artifacts; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect licensed dataset catalogs, source metadata, usage terms and team queries, then follow this sequence: 1. Ingest licensed dataset catalogs and source metadata. 2. Normalize descriptions into uniform metadata fields. 3. Answer plain-language questions over indexed datasets. 4. Aggregate records from multiple sources into one workspace. 5. Extract and display key insights from selected data. 6. Generate reusable knowledge artifacts from analyses. Resolve uncertain cases with qualified reviewers, approve a reviewed, queryable dataset library with exportable knowledge artifacts, and measure accepted dataset retrievals per steward hour and reuse of approved artifacts against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final data interpretation, licensing decisions and publication scope remain with qualified reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, dataset licensing, usage permissions and uncertainty statements. Named reviewers approve substantive interpretations and publication scope. One approved source format and a bounded representative dataset set; final data interpretation and licensing checks remain with qualified reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One approved source format and a bounded representative dataset set; final data interpretation and licensing checks remain with qualified reviewers. Implement one approved input format, a bounded representative case set and the first three task modules: ingest licensed dataset catalogs and source metadata; normalize descriptions into uniform metadata fields; answer plain-language questions over indexed datasets. Support later modules with operator review: aggregate records from multiple sources into one workspace; extract and display key insights from selected data; generate reusable knowledge artifacts from analyses. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Authorized dataset catalogs, permitted research subscriptions and approved source APIs. Cloud storage, BI platforms, CSV export destinations and refresh schedulers. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Source and rights intake, Searchable dataset library, Query and artifact workspace, Export and refresh console. Use a filterable catalog list, a large central query and preview canvas, and a right-hand panel for metadata, provenance, usage terms and comments. Let users compare datasets and artifact versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant dataset or artifact. Make the task-specific outcome a reviewed, queryable dataset library with exportable knowledge artifacts visible beside its evidence, review state and value baseline.
