# Survey Participation Data Quality Pipeline: Record Audit + String Normalisation + Missing-Value Fill

> Survey Participation Data Quality Pipeline: Record Audit + String Normalisation + Missing-Value Fill 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).

Runs three data-quality reports on survey participation records (respondents/invitations): record quality audit, string normalisation, and explicit missing-value fill using zero/mean/median imputation — up to 200 rows per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-10-208?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/survey-participation-data-quality-pipeline-record-audit-string-ebb6ec91
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_-52HG3ET8bUgtdc8b9Oq8

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability survey-participation-data-quality-pipeline-record-audit-string-ebb6ec91 -d '<json body>'
```

Example prompt: Can you audit the quality of my survey participation records for Q4, normalise the course_group strings, and fill any missing respondent counts using the median — I have about 50 rows across groups like 'group-a' and 'group-b' with dates in October 2026?

## When to prefer this

Choose this endpoint when you have survey participation data (respondents and invitations keyed by course group and date) that needs data quality preparation before analysis — specifically when you need all three of record auditing, string normalisation, and missing-value imputation in a single cheap call. Ideal for education or research pipelines doing pre-analysis cleaning on datasets up to 200 rows. Not suitable for live prediction, external data enrichment, or datasets larger than 200 rows per call.

## Known failure modes

- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes validation errors on affected rows
- Missing required fields (row_id, course_group, date) causes row-level failures
- Invalid enum value for field (must be 'respondents' or 'invitations') or method (must be 'zero', 'mean', or 'median') causes request rejection
- Empty rows array returns an error
- Payment failure (insufficient USDC balance) blocks the request

## How this service works

Survey participation — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied survey participation records (respondents, invitations). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports per call: (1) a record quality audit identifying issues such as malformed dates, out-of-range values, or structural problems; (2) string normalisation results for text fields like course_group; and (3) explicit missing-value fill results for the chosen numeric field (respondents or invitations) using the selected method (zero, mean, or median). All processing uses local analytical engines with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "course_group",
     "date",
     "respondents",
     "invitations"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "invitations": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "respondents": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "course_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "invitations": 1,
     "respondents": 10,
     "course_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "invitations": 2,
     "respondents": 20,
     "course_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "invitations": 3,
     "respondents": 15,
     "course_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "invitations": 4,
     "respondents": 30,
     "course_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "respondents",
    "invitations"
   ],
   "type": "string",
   "default": "respondents"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/survey-participation-data-quality-pipeline-record-audit-string-ebb6ec91/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)