Datasets:

electricsheepafrica
/

nigerian_transport_and_logistics_drone_delivery

@@ -1,58 +1,142 @@
 ---
-license: gpl
-dataset_name: nigerian_transport_and_logistics_drone_delivery
-pretty_name: Nigeria Transport & Logistics – Drone Delivery
-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 – Drone Delivery
-    Drone missions with payload, distance/duration, battery usage, outcomes and reasons.
-    - **[category]** Emerging & Advanced
-    - **[rows]** ~140,000
-    - **[formats]** CSV + Parquet (snappy)
-    - **[geography]** Nigeria (major cities, corridors, ports, airports)
-    ## Schema
-    | column | dtype |
 |---|---|
-| mission_id | object |
-| launch_time | object |
-| city | object |
-| launch_lat | float64 |
-| launch_lon | float64 |
-| payload_kg | float64 |
-| distance_km | float64 |
-| duration_min | float64 |
-| battery_start_pct | float64 |
-| battery_end_pct | float64 |
-| outcome | object |
-| failure_reason | object |
-    ## Usage
-    ```python
-    import pandas as pd
-    df = pd.read_parquet('data/nigerian_transport_and_logistics_drone_delivery/nigerian_transport_and_logistics_drone_delivery.parquet')
-    df.head()
-    ```
-    ```python
-    from datasets import load_dataset
-    ds = load_dataset('electricsheepafrica/nigerian_transport_and_logistics_drone_delivery')
-    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

 ---
+license: other
+language:
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- 100K<n<1M
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "infrastructure-transport"
+- "parquet"
+- "tabular"
+- "text"
+- "nigeria"
+- "transport"
+- "logistics"
+- "mobility"
+- "fleet"
+- "supply-chain"
+pretty_name: "Nigeria Transport & Logistics – Drone Delivery | Africa (Electric Sheep Africa metadata inventory)"
 ---
+# Nigeria Transport & Logistics – Drone Delivery | Africa (Electric Sheep Africa metadata inventory)
+**Size category:** `100K<n<1M` - **Formats:** `parquet` - **Sector:** infrastructure_transport - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-100K%3Cn%3C1M-blue)
+![sector](https://img.shields.io/badge/sector-infrastructure_transport-green)
+![downloads](https://img.shields.io/badge/HF_downloads-58-orange)
+![license](https://img.shields.io/badge/license-other-lightgrey)
+## TL;DR
+This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
+## What This Dataset Covers
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
+Dataset context from the existing Hugging Face card: # Nigeria Transport & Logistics – Drone Delivery Drone missions with payload, distance/duration, battery usage, outcomes and reasons. - **[category]** Emerging & Advanced - **[rows]** ~140,000 - **[formats]** CSV + Parquet (snappy) - **[geography]** Nigeria (major cities, corridors, ports, airports) ## Schema | column | dtype | |---|---| | mission_id | object | | launch_time | object | | city | object | | launch_lat | float64 | | launch_lon | float64 | | payload_kg | float64 | |… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/nigerian_transport_and_logistics_drone_delivery.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/nigerian_transport_and_logistics_drone_delivery`](https://huggingface.co/datasets/electricsheepafrica/nigerian_transport_and_logistics_drone_delivery) |
+| Sector | infrastructure_transport |
+| Topic tags | nigeria, transport, logistics, mobility, fleet, supply-chain |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `100K<n<1M` |
+| Countries | Nigeria |
+| ISO3 coverage | `NGA` |
+| Last modified on HF | `2025-10-12 15:54:22+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+## How To Read This Dataset
+- Start from the repository files and the dataset viewer when available.
+- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
+- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
+- Preserve missing values until you have a defensible imputation rule.
+## Usage
+```python
+from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/nigerian_transport_and_logistics_drone_delivery")
+print(ds)
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
+### Convert To Pandas When Tabular
+```python
+from datasets import Dataset
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
+## Data Quality Notes
+- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
+- Exact schema, row counts, and source files should be inspected in the repository data files.
+- Metadata gaps from the inventory: upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **Source context:** Electric Sheep Africa metadata inventory
+- **Publisher/source attribution:** Public dataset metadata
+- **License:** gpl
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/nigerian_transport_and_logistics_drone_delivery](https://huggingface.co/datasets/electricsheepafrica/nigerian_transport_and_logistics_drone_delivery)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+## Suggested Analyses
+- Inspect schema and missingness before modeling.
+- Profile variables by geography, time, and subgroup columns where present.
+- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
+- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.
+## Citation
+```bibtex
+@misc{electric_sheep_africa_nigerian_transport_and_logistics_drone_delivery_2026,
+  title        = {Nigeria Transport & Logistics – Drone Delivery | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/nigerian_transport_and_logistics_drone_delivery},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/nigerian_transport_and_logistics_drone_delivery}}
+}
+```
+## License
+Released under gpl.
+Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.
+## About Electric Sheep Africa
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
+---
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.