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