Solution Database / Science and Research
Research consent scope registry
Researchers cannot easily tell which data uses participants approved. Keep dataset use restrictions tied to consent versions.
01The offer
For university research data stewards, turn authorized consent templates and steward-approved dataset metadata into steward-approved consent scope register. Address this specific problem: researchers cannot easily tell which data uses participants approved. The aim: keep dataset use restrictions tied to consent versions. The pilot tests whether that benefit holds up against reviewer effort and real operating costs.
- For
- University research data stewards
- Takes in
- Authorized consent templates and steward-approved dataset metadata
- Delivers
- Steward-approved consent scope register
- Message
- Keep dataset use restrictions tied to consent versions. Demonstrate the result with map one synthetic study dataset for university research data stewards. Use a concrete before-and-after example without promising unmeasured savings.
- Lead magnet
- Map one synthetic study dataset
02How it works
- Extract stated use limits
- Link consent versions
- Flag ambiguous scope
- Track withdrawal procedures
- Record steward decisions
- Export permission summaries
Workflow
The buyer creates a project, supplies authorized consent templates and steward-approved dataset metadata, and confirms scope and access. The working sequence is: 1. Extract stated use limits. 2. Link consent versions. 3. Flag ambiguous scope. 4. Track withdrawal procedures. 5. Record steward decisions. 6. Export permission summaries. Users correct extracted facts, resolve flagged uncertainties and approve the final steward-approved consent scope register before use. Retain source links and a version history for the next cycle.
AI and people
Extract terms without deciding research authorization. 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.
Screens
Key screens: Dataset register, Use permissions, Steward review. Use a searchable table or visual gallery with filters for the domain’s important attributes. Open each item into a detail drawer containing source records, ownership and history. Put proposed merges and field changes in a separate review queue. Provide a preview before any bulk export. Open with dataset register; move into use permissions for the detailed task; finish in steward review for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.
Admin
Record ownership, access permissions, change proposals, original-value retention, version history, review dates, bulk import/export and duplicate resolution. Include organization-scoped access, named project owners, review queues, usage limits, export history and retention settings. Never reuse private customer material for other accounts without permission.
03Market gap
Alternatives buyers use today
Spreadsheets, shared folders, existing asset or information management systems and manual data cleanup. Position this concept around keep dataset use restrictions tied to consent versions. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.
Where this wins
A useful niche taxonomy, customer-approved mappings and accumulated correction history that improve retrieval and reduce repeated cleanup. For this concept, accumulate permissioned examples and reviewer corrections around keep dataset use restrictions tied to consent versions. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.
04Why now
Science and Research teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. The buyer already feels the problem: researchers cannot easily tell which data uses participants approved.
05Proof & signals
Channels where buyers gather: Research data offices and university libraries. Metrics that prove it works: Unclear use permissions and review time.
Paid pilot
Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run map one synthetic study dataset and deliver steward-approved consent scope register. Compare unclear use permissions and review time with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.
06Execution plan
MVP
Costed pilot: Metadata only; no independent ethics or legal clearance. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract stated use limits; link consent versions. Support the third task through an assisted review queue: flag ambiguous scope. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of steward-approved consent scope register. 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.
First 30 days
Week 1: interview five prospective buyers from university research data stewards and inspect how they handle researchers cannot easily tell which data uses participants approved. Week 2: prepare map one synthetic study dataset using authorized or synthetic material. Week 3: share the demonstration through research data offices and university libraries and seek one bounded paid pilot. Week 4: measure unclear use permissions and review time, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.
After the pilot
After paying customers repeatedly accept steward-approved consent scope register, automate track withdrawal procedures; record steward decisions; export permission summaries. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. Metadata only; no independent ethics or legal clearance.
Retention
Build repeat use around steward-approved consent scope register. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unclear use permissions and review time. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.
Integrations
Authorized datasets, papers, protocols, code and research records. Source systems, catalog exports and cloud file storage. Start with reversible CSV or file imports and validate identifiers before any direct writes. Begin with uploads and exports of authorized consent templates and steward-approved dataset metadata. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case; first modules: extract stated use limits; link consent versions. Manual review in the loop. | 5 days | $14,500 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 6 days | $14,500 |
| Full product | Self-serve onboarding, billing, monitoring and the wider integration set. | 2 weeks | $20,500 |
| Total | $49,500 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $30–$60 | $50–$100 | $80–$160 |
| Full product (about 50 customers) | $110–$210 | $350–$700 | $460–$910 |
Revenue model to test
Test USD 500-2,500 for one collection cleanup and launch, followed by USD 100-500 monthly for maintenance within agreed record limits. Larger migrations and complex rights management are separately scoped. Prices are hypotheses. For this buyer, package the first sale around map one synthetic study dataset and the defined steward-approved consent scope register. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.
Cost drivers
Import cleanup, extraction, storage, indexing, steward review, duplicate investigation and recurring source updates. Initial validation additionally budgets for ethics and data-steward review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.
Safeguards
Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Metadata only; no independent ethics or legal clearance. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.
Take it further
Newly authored additional batch of 210 concepts, dated 2026-09-22, for later import. Checked against the existing 413 catalog for exact title and ID duplication, with editorial review of overlap. Demand, differentiation, pricing, build hours, setup costs and integration feasibility are unvalidated planning hypotheses. Category inspiration links are inherited taxonomy references, not evidence that these concepts were covered there.