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Scientific Dataset Records — Missingness Co-occurrence
x402
Scientific Dataset Records — Missingness Co-occurrence is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.020338/call, status unknown (last checked 2026-10-15).
Counts joint absence (co-occurrence of missingness) of source, method, and unit declarations across public or synthetic scientific dataset metadata records.
Price / call$0.020338
Success—
StatusUnknown
How this service works
Count joint absence of source, method and unit declarations in public/synthetic dataset metadata. No raw specimen data, imputation or scientific validity determination. Offline typed metadata only. Retained-result recovery: 24 hours; minimal receipt metadata: 90 days.
Output
Returns counts of joint absence (co-occurrence of missingness) for combinations of source, method, and unit declarations across the submitted metadata records, covering up to 32 records per call. Results are retained for 24 hours with minimal receipt metadata kept for 90 days.
Example use cases
Research data quality audit
“I have 20 synthetic dataset metadata records and I need to know how frequently source and unit declarations are both missing simultaneously — can you run a missingness co-occurrence count on them?”
Public dataset metadata compliance check
“Audit these 15 public scientific dataset records and tell me how often the method and source fields are jointly absent — I'm trying to identify systemic gaps in our metadata declarations.”
Pre-publication metadata gap analysis
“Before we publish this batch of 8 synthetic dataset records, can you count how many have co-occurring missing source, method, or unit declarations so we can prioritize what to fix?”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/intel/scientific-dataset-records/missingness-cooccurrence?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to quantify how often multiple metadata declaration fields (source, method, unit) are simultaneously absent in public or synthetic scientific dataset records — specifically for offline typed metadata auditing without any imputation, raw specimen data, or scientific validity assessment. Prefer it over single-field missingness checks when the joint co-occurrence pattern across multiple declaration types is what matters.
Side effects
- Submission of more than 32 records returns a validation error
- nonpersonalMetadata must be set to true; calls with false or absent value will be rejected
- dataClass must be exactly 'public' or 'synthetic'; other values cause schema validation failure
- Empty records array (fewer than 1 item) triggers a minimum-items validation error
- Submitting records containing actual specimen data rather than typed metadata may cause rejection
- Result unavailability after 24-hour retention window returns not-found or expired error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| records | array | |
| dataClass | string | |
| nonpersonalMetadata | boolean |
Tags
x402
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