Datasets:

electricsheepafrica
/

africa-cod-ibtracs-tropical-storm-tracks

@@ -1,163 +1,142 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
 language:
 - en
-license: cc-by-4.0
-multilinguality:
-- monolingual
 size_categories:
 - 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- other
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- cyclones-hurricanes-typhoons
-- hxl
-- cod
-pretty_name: "Democratic Republic of the Congo: IBTrACS Storm Tracks"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 29692
-    - name: test
-      num_examples: 7423
 ---
-# Democratic Republic of the Congo: IBTrACS Storm Tracks
-**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/cod-ibtracs-tropical-storm-tracks) · **License:** `cc-by-igo` · **Updated:** 2026-02-24
----
-## Abstract
-The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.
- Fields available:
- SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.
- ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)
- BASIN: Basin of the current storm position
- SUBBASIN: Sub-basin of the current storm position
- NATURE: Type of storm (a combination of the various types from the available sources)
- NUMBER: Number of the storm for the year (restarts at 1 for each year
- LAT: Mean position - latitude (a combination of the available positions)
- LON: Mean position - longitude (a combination of the available positions)
- WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency
- WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.
-Each row in this dataset represents geolocated point observations. Temporal coverage is indicated by the `iso_time` column(s). Geographic scope: **COD**.
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
----
-## Dataset Characteristics
-| | |
 |---|---|
-| **Domain** | Climate and environment |
-| **Unit of observation** | Geolocated point observations |
-| **Rows (total)** | 37,116 |
-| **Columns** | 12 (3 numeric, 8 categorical, 1 datetime) |
-| **Train split** | 29,692 rows |
-| **Test split** | 7,423 rows |
-| **Geographic scope** | COD |
-| **Publisher** | HDX |
-| **HDX last updated** | 2026-02-24 |
----
-## Variables
-**Geographic** — `iso_time`, `lat` (range -43.7–19.0), `lon` (range 11.3–119.1).
-**Outcome / Measurement** — `number` (range 1.0–145.0).
-**Identifier / Metadata** — `sid` (2023036S12117, 2000032S11116, 1951008S07074), `esa_source` (HDX), `esa_processed` (2026-04-05).
-**Other** — `basin` (South Indian, North India,  ), `subbasin` (Missing, Arabian Sea, Western Australia), `nature` (Tropical, Not reported, Mixture(contradicting report from different agencies)), `wmo_wind` ( , 25, 20), `wmo_pres` ( , 1000, 1002).
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-cod-ibtracs-tropical-storm-tracks")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `sid` | object | 0.0% | 2023036S12117, 2000032S11116, 1951008S07074 |
-| `number` | float64 | 0.0% | 1.0 – 145.0 (mean 29.692) |
-| `basin` | object | 0.0% | South Indian, North India,   |
-| `subbasin` | object | 0.0% | Missing, Arabian Sea, Western Australia |
-| `iso_time` | datetime64[ns] | 0.0% |  |
-| `nature` | object | 0.0% | Tropical, Not reported, Mixture(contradicting report from different agencies) |
-| `lat` | float64 | 0.0% | -43.7 – 19.0 (mean -17.7825) |
-| `lon` | float64 | 0.0% | 11.3 – 119.1 (mean 50.6742) |
-| `wmo_wind` | object | 0.0% |  , 25, 20 |
-| `wmo_pres` | object | 0.0% |  , 1000, 1002 |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-05 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `number` | 1.0 | 145.0 | 29.692 | 13.0 |
-| `lat` | -43.7 | 19.0 | -17.7825 | -17.7 |
-| `lon` | 11.3 | 119.1 | 50.6742 | 48.7 |
----
-## Curation
-Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 4 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
----
-## Limitations
-- Data originates from HDX and has not been independently validated by ESA.
-- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/cod-ibtracs-tropical-storm-tracks) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_cod_ibtracs_tropical_storm_tracks,
-  title     = {Democratic Republic of the Congo: IBTrACS Storm Tracks},
-  author    = {HDX},
-  year      = {2026},
-  url       = {https://data.humdata.org/dataset/cod-ibtracs-tropical-storm-tracks},
-  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
 }
 ```
 ---
-*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*

 ---
+license: cc-by-4.0
 language:
 - en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
 size_categories:
 - 10K<n<100K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "climate-environment"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "cyclones-hurricanes-typhoons"
+- "hxl"
+- "cod"
+- "climate"
+pretty_name: "Democratic Republic of the Congo: IBTrACS Storm Tracks | Africa (original)"
 ---
+# Democratic Republic of the Congo: IBTrACS Storm Tracks | Africa (original)
+**Size category:** `10K<n<100K` - **Formats:** `parquet` - **Sector:** climate_environment - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-climate_environment-green)
+![downloads](https://img.shields.io/badge/HF_downloads-38-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-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: Democratic Republic of the Congo: IBTrACS Storm Tracks Publisher: HDX · Source: HDX · License: cc-by-igo · Updated: 2026-02-24 Abstract The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-cod-ibtracs-tropical-storm-tracks.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-cod-ibtracs-tropical-storm-tracks`](https://huggingface.co/datasets/electricsheepafrica/africa-cod-ibtracs-tropical-storm-tracks) |
+| Sector | climate_environment |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, cyclones-hurricanes-typhoons, hxl, cod |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `10K<n<100K` |
+| Countries | Democratic Republic of the Congo |
+| ISO3 coverage | `COD` |
+| Last modified on HF | `2026-04-05 21:40:22+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/africa-cod-ibtracs-tropical-storm-tracks")
+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:** original
+- **Publisher/source attribution:** original
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-cod-ibtracs-tropical-storm-tracks](https://huggingface.co/datasets/electricsheepafrica/africa-cod-ibtracs-tropical-storm-tracks)
+- **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_africa_cod_ibtracs_tropical_storm_tracks_2026,
+  title        = {Democratic Republic of the Congo: IBTrACS Storm Tracks | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-cod-ibtracs-tropical-storm-tracks},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-cod-ibtracs-tropical-storm-tracks}}
 }
 ```
+## License
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+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`.