# Dataset Storage Analytics — String Normalisation + Two-Measure Correlation

> Dataset Storage Analytics — String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Runs string normalisation and Pearson/rank correlation analysis between used_mb and row_count fields on up to 200 dataset storage records, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-storage-analytics-string-normalisation-two-measure-correlation-c70502f2
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_QD9HehKbrG3hnNkjmcTSQ

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-analytics-string-normalisation-two-measure-correlation-c70502f2 -d '<json body>'
```

Example prompt: Can you normalize the dataset group names and run a two-measure correlation between used_mb and row_count for my storage records? I have about 150 rows with fields like row_id, dataset_group, date, used_mb, and row_count — here's the data.

## When to prefer this

Choose this endpoint when you need to simultaneously clean/normalise categorical string fields (dataset_group names) and compute a statistical correlation between two numeric storage measures (used_mb and row_count) on up to 200 rows of dataset storage records. Prefer this over generic analytics APIs when your data specifically involves storage metrics with the exact fields: row_id, dataset_group, date, used_mb, row_count. This is a local, offline computation — no data leaves to third-party prediction or lookup services.

## Known failure modes

- Fewer than 1 row submitted — minimum 1 row required
- More than 200 rows submitted — maxItems limit exceeded
- Missing required fields (row_id, dataset_group, date, used_mb, or row_count) causing validation error
- Invalid date format (must match YYYY-MM-DD pattern)
- Null values in both numeric measures may reduce correlation reliability
- row_id or dataset_group strings exceeding 80 characters
- Insufficient numeric variance in used_mb or row_count to compute meaningful correlation

## How this service works

Dataset storage — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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

Returns two analytical reports: (1) a string normalisation report cleaning and standardising the dataset_group text fields, and (2) a two-measure correlation report quantifying the statistical relationship between used_mb and row_count across the submitted rows. No external lookups or predictions are made — all analysis is performed locally on the supplied data.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-storage-analytics-string-normalisation-two-measure-correlation-c70502f2/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)