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Lab Observations — String Normalisation + Exact Row Deduplication
x402
Lab Observations — String Normalisation + Exact Row Deduplication is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).
Normalises strings and removes exact duplicate rows from lab observation records containing measurement and replicate_count fields, returning 2 independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Lab observations — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication for supplied lab observations records (measurement, replicate_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns 2 independent analytical reports covering: (1) string normalisation results showing standardised values for string fields (course_group, date, row_id), and (2) exact row deduplication results identifying and removing fully duplicate records from the supplied lab observation dataset of up to 200 rows.
Example use cases
Deduplicating student lab experiment records
“I have 120 lab observation rows from student experiments, each with a row_id, course_group, date, measurement, and replicate_count — can you normalise the string fields and strip out any exact duplicate rows before I run my analysis?”
Cleaning measurement data before statistical reporting
“Before I hand off my lab data to the stats team, can you run string normalisation and exact deduplication on these 80 observation records? They have measurement values and replicate counts that might have duplicate entries from data entry errors.”
Auditing course group lab records for data quality
“I've got lab observations from multiple course groups with about 60 rows — can you normalise the course_group strings and remove any completely duplicate rows so the dataset is clean?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-06-020?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have lab observation data (with measurement and replicate_count fields) that needs both string normalisation and exact duplicate removal in a single pass, and your dataset is 200 rows or fewer. It is suited for educational or scientific lab data pipelines where course group labels and row identifiers may have inconsistent capitalisation or whitespace, and where exact duplicate records need to be identified and removed prior to downstream analysis. It performs no external lookups, predictions, or live data access — ideal when you want a deterministic, local data-cleaning step.
Side effects
- More than 200 rows submitted — request exceeds maxItems limit and will be rejected
- Missing required fields (row_id, course_group, date, measurement, replicate_count) on any row causes validation failure
- Invalid date format (not YYYY-MM-DD) triggers schema validation error
- measurement or replicate_count values outside [-1000000000000, 1000000000000] range rejected
- Empty rows array (minItems: 1 not met) causes rejection
- row_id exceeding 80 characters or empty string causes validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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