---
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: 2025-10-12
---


    # 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