metadata
license: gpl
dataset_name: nigerian_transport_and_logistics_blockchain_logistics
pretty_name: Nigeria Transport & Logistics – Blockchain Logistics Records
size_categories:
- 10K<n<1M
task_categories:
- time-series-forecasting
- tabular-regression
- tabular-classification
- other
tags:
- nigeria
- transport
- logistics
- mobility
- fleet
- supply-chain
language:
- en
created: 2025-10-12T00:00:00.000Z
# Nigeria Transport & Logistics – Blockchain Logistics Records
On-chain logistics events with actors, custody, network, and validity.
- **[category]** Emerging & Advanced
- **[rows]** ~120,000
- **[formats]** CSV + Parquet (snappy)
- **[geography]** Nigeria (major cities, corridors, ports, airports)
## Schema
| column | dtype |
|---|---| | timestamp | object | | tx_hash | object | | shipment_id | object | | action | object | | actor_wallet | object | | chain | object | | custody | object | | valid | bool | | note | object |
## Usage
```python
import pandas as pd
df = pd.read_parquet('data/nigerian_transport_and_logistics_blockchain_logistics/nigerian_transport_and_logistics_blockchain_logistics.parquet')
df.head()
```
```python
from datasets import load_dataset
ds = load_dataset('electricsheepafrica/nigerian_transport_and_logistics_blockchain_logistics')
ds
```
## Notes
- Nigeria-specific parameters (fleets, roads, traffic, fuel prices)
- Time-of-day traffic effects and seasonal impacts where applicable
- Physical plausibility checks embedded during generation