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Study Recruitment — String Normalisation + Equal-Width Histogram
x402
Study Recruitment — String Normalisation + Equal-Width Histogram is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Runs two local analytical reports on study recruitment data: string normalisation of course group labels and an equal-width histogram over a chosen numeric field (eligible or enrolled candidates).
Price / call$0.01
Success—
StatusUnknown
How this service works
Study recruitment — String normalisation + Equal-width histogram: String normalisation + Equal-width histogram for supplied study recruitment records (eligible_candidates, enrolled_candidates). 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 two analytical reports: (1) a string normalisation report on the course_group field, detailing cleaned/standardised label variants across up to 200 rows; and (2) an equal-width histogram report for the selected numeric field (eligible_candidates or enrolled_candidates), showing bin edges, bin counts, and frequency distribution across the specified number of bins (1–30).
Example use cases
Candidate distribution audit for course groups
“Here are 150 rows of recruitment records for our clinical study — each has a course_group, date, eligible_candidates, and enrolled_candidates. Please normalize the course group labels and give me a 10-bin equal-width histogram of eligible_candidates so I can see how applicant volume is spread.”
Enrollment funnel frequency analysis
“I need to profile how enrolled_candidates are distributed across our study cohorts. Can you run string normalization on the group names and produce a 5-bin histogram of enrolled_candidates for these 80 recruitment records?”
Data quality check before recruitment report
“Before I write up our recruitment summary, can you normalize the course_group strings in my 200-row dataset and show me an equal-width histogram with 7 bins for eligible_candidates so I can spot any anomalies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-education-09-031?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two complementary analytical passes on study recruitment tabular data in a single call: string normalisation to clean categorical group labels, and an equal-width histogram to understand numeric distribution of candidate counts. It is ideal for offline, supplied-data analysis (no live lookup or external data) with up to 200 rows, and is cost-efficient at $0.01 USDC for both reports together. Prefer this over generic analytics tools when your data is specifically structured around course groups, eligible candidates, and enrolled candidates.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Invalid date format (not YYYY-MM-DD) causes row validation failure
- bins value outside 1–30 range returns a validation error
- Missing required fields (row_id, date, eligible_candidates, enrolled_candidates) causes row rejection
- Null numeric values in the chosen field may produce sparse or incomplete histogram bins
- field enum value not matching 'eligible_candidates' or 'enrolled_candidates' causes parameter error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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