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Scientific Dataset Records - Identifier Crosswalk Audit
x402
Scientific Dataset Records - Identifier Crosswalk Audit is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.035298/call, status unknown (last checked 2026-10-20).
Checks one-to-one alias mappings against typed identifier pins in scientific dataset records, auditing crosswalk consistency without certifying ownership or authenticity.
Price / call$0.035298
Success—
StatusUnknown
How this service works
Check one-to-one aliases against every typed pin; identifier mapping does not establish ownership, rights or authenticity for repository/dataset/schema/method/revision-pinned manifest Supplied-record analysis, not independent certification. Full contract at the free describe URL.
Output
Returns an audit of each alias-to-pin mapping in the supplied rows and mappings, reporting which one-to-one aliases match or conflict with their typed identifier pins, without asserting ownership, rights, or authenticity over the records.
Example use cases
Research dataset alias validation
“I have 15 rows from our public research dataset and a list of 15 identifier mappings — can you check the crosswalk and flag any aliases that don't match their typed pins? All metadata is nonpersonal.”
Synthetic data identifier audit
“We generated a synthetic scientific dataset and I need to verify that the identifier crosswalk is consistent — here are 20 record rows and 20 alias mappings, data class is synthetic and all metadata is nonpersonal.”
Pre-publication dataset identifier check
“Before we publish this dataset, can you audit the identifier crosswalk for our 8 dataset records against their declared alias mappings to catch any pin mismatches? Data class is public and nonpersonal metadata only.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/intel/scientific-dataset-records/identifier-crosswalk-audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to audit one-to-one identifier alias crosswalks in scientific dataset records against typed pins, specifically for public or synthetic (non-personal) data. It is appropriate when you need systematic crosswalk consistency checking rather than ownership verification or full certification. Prefer this over general data validation tools when working with scientific dataset identifiers and alias mapping tables.
Side effects
- More than 32 rows or mappings supplied — request rejected due to maxItems constraint
- nonpersonalMetadata not set to true — validation fails
- dataClass not set to 'public' or 'synthetic' — enum validation error
- Mismatched row and mapping counts leading to incomplete audit coverage
- Malformed identifier formats in rows or mappings causing parse errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| mappings | array | |
| dataClass | string | |
| nonpersonalMetadata | boolean |
Tags
x402
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