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Synthetic inventory reconciliation QA: 5,000 events + 300 expected balances, CSV

Fully synthetic inventory movements; fictional timestamps from January 1, 2031. · Updated Nov 10, 2026

Fixed price

Sample structure: Not assessed

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Seller: Alan

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Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License: Internal testing and demos (personal/commercial); modification allowed. No redistribution, resale or sublicensing of files/generated records. No warranty, updates or support SLA. See README.
Source / creator: Alan - AI-assisted synthetic fixture generation
Collection method: AI-assisted deterministic Python generator, seed 20261110. Entirely synthetic labels and records; no third-party source data. Integer-unit inventory model.
Coverage / snapshot: Fully synthetic inventory movements; fictional timestamps from January 1, 2031.
Data last updated: Not documented
Update schedule: Fixed snapshot v1.0. No future updates included or promised.

QA fixture only. Negative stock and zero adjustments intentional. Transfers paired but not atomic. No valuation, reservations, concurrency or production compatibility. No real business observations.

Sample structure score: Not assessed

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About this data

Replay 5,000 artificial inventory events and compare your final stock balances against 300 supplied expected results. AI-assisted QA fixture for CSV ingestion, running balances and paired warehouse-transfer checks, not real business inventory. ZIP includes inventory_movements.csv (5,000 ordered rows, 9 columns), expected_balances.csv (300 SKU/warehouse keys), schema, deterministic Python standard-library generator, independent replay validator and 12 integrity/corruption tests. 100 fictional SKU labels and 3 warehouse labels. Includes 701 paired transfers. Clean ZIP passes tests; a separate SQLite SUM matches all 300 final balances. Suggested checks: import event rows in supplied order, apply signed integer-unit deltas, verify each transfer pair conserves units across warehouses, then compare final balances by SKU/warehouse. Negative stock and zero adjustments are intentional. Dates are fixed fictional 2031 dates, not observed transactions. Limits: no prices, inventory valuation, reservations, concurrent transactions, forecasting or statistical fidelity. Transfers are paired but not atomic. Not a production inventory system or validated training benchmark. No source customer records. Internal lawful testing/demos only; modification allowed. No redistribution, resale or sublicensing of files/generated records. No warranty, future updates, support SLA or production compatibility. Full terms in README.txt. Automatic public samples remain disabled; full files are paid.

Retrieve with your agent or Python

Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.

Download the Python example

python3 retrieve-dataset.py 7d0336e2-7a73-47a2-84e7-b8ab6cfa425f --output dataset.bin

Full supplier documentation

AI-assisted synthetic fixture for CSV ingestion, running inventory balances and warehouse-transfer QA. 5,000 ordered events, 9 columns, 100 fictional SKU labels and 3 fictional warehouse labels. Includes 300 expected final balances and 701 paired transfers. ZIP contains movement CSV, expected-balance CSV, deterministic Python standard-library generator (seed 20261110), schema, independent replay validator, 12 corruption tests and README. All 300 final balances match an additional independent SQLite SUM(delta) check; clean ZIP extraction tests pass. Integer-unit model only. Negative stock is intentional: 85 final key balances are negative. Zero-delta adjustments and zero opening quantities are valid. Transfers are paired adjacent events one minute apart, not atomic. Fictional timestamps begin January 1, 2031. No real people, contact details, business records or source datasets used. Not representative data or a forecasting/training benchmark. No inventory valuation, reservations, serial/lot tracking, concurrency model, tax/accounting assurance or production compatibility. Internal personal/commercial testing and demos permitted; modification allowed. No resale, redistribution or sublicensing of files or generated records. No warranty, future updates or support SLA. The generator overwrites its output CSVs/schema in its own directory; run in a copy if modified. Automatic sample generation disabled; no full-file public preview.

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Sample Data

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For AI Agents

Via MCP Server

# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
  "mcpServers": {
    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
  }
}

# 2. Your agent can then call:
search_datasets({ query: "Synthetic inventory reconcilia" })
// Found: 7d0336e2-7a73-47a2-84e7-b8ab6cfa425f
buy_now({ dataset_id: "7d0336e2-7a73-47a2-84e7-b8ab6cfa425f" })  // uses your saved card

Via REST API

# Authenticate with your API key:
curl https://api.databazaar.io/datasets/7d0336e2-7a73-47a2-84e7-b8ab6cfa425f \
  -H "Authorization: Bearer YOUR_API_KEY"

# Purchase:
curl -X POST https://api.databazaar.io/datasets/7d0336e2-7a73-47a2-84e7-b8ab6cfa425f/buy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"dataset_id": "7d0336e2-7a73-47a2-84e7-b8ab6cfa425f"}'