# Dataset Storage IQR Outlier Flagging and String Normalisation

> Dataset Storage IQR Outlier Flagging and String Normalisation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Normalises string fields and flags IQR-based outliers in dataset storage records (used_mb, row_count) for up to 200 rows, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-032?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-storage-iqr-outlier-flagging-and-string-normalisation-d939d43b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_hisMsfMyKBeyVjFEJ2RwB

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 dataset-storage-iqr-outlier-flagging-and-string-normalisation-d939d43b -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and string normalisation on my dataset storage records — I have about 50 rows with fields like row_id, dataset_group, date, used_mb, and row_count, and I want to flag the outliers in used_mb?

## When to prefer this

Choose this endpoint when you need both string normalisation and IQR-based outlier flagging on dataset storage records (used_mb, row_count) in a single low-cost call. It is best suited for batch quality checks on up to 200 rows of storage metrics without requiring external data, live predictions, or professional assessments. Prefer alternatives if you need correlation analysis, trend fitting, or comparisons against benchmark datasets.

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- All numeric values identical — IQR is zero, no outliers flagged
- Null values in selected numeric field — may reduce outlier detection accuracy

## How this service works

Dataset storage — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied dataset storage records (used_mb, row_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

Two independent analytical reports per call: one covering string normalisation of dataset_group and related string fields, and one flagging rows whose selected numeric field (used_mb or row_count) falls beyond 1.5× the interquartile range. Results are computed locally with no external lookup, covering up to 200 input rows.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-storage-iqr-outlier-flagging-and-string-normalisation-d939d43b/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)