| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - tabular-regression |
| - time-series-forecasting |
| multilinguality: monolingual |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - tabular |
| - csv |
| - africa |
| - madagascar |
| - official-statistics |
| - open-data |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-00000-of-00001.parquet |
| pretty_name: "GDACS RSS Information | Africa (Madagascar official open data)" |
| --- |
| |
| # GDACS RSS Information | Africa (Madagascar official open data) |
|
|
| 1,584 rows - 1 Africa country - 2026 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica) |
|
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|
|
| ## TL;DR |
|
|
| This dataset packages one official `CSV` resource from **Madagascar** as |
| ML-ready Parquet. The source file is the provenance boundary; all usable |
| indicators or tabular columns from the resource stay together in this repo. |
|
|
| ## About the source |
|
|
| - **Source:** [GDACS RSS Information](https://data.humdata.org/dataset/gdacs-rss-information) |
| - **Publisher:** Global Disaster Alert and Coordination System |
| - **Resource:** [gdacs_rss_information.csv](https://data.humdata.org/dataset/a87f96f8-16e6-4d51-872c-cfa54a8251ec/resource/4ef001d1-7888-4f5d-98ce-0ca8006787f7/download/gdacs_rss_information.csv) |
| - **Format:** `CSV` |
| - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) |
| - **Packaging mode:** `indicator_long` |
|
|
| ## Geographic coverage |
|
|
| 1 Africa country: |
|
|
| | Country | Rows | First year | Last year | Name | |
| |---------|-----:|-----------:|----------:|------| |
| | `MDG` | 1,584 | 2026 | 2026 | `Madagascar` | |
|
|
| ## Indicators or Resource Contents |
|
|
| - `gdacs-rss-information-severity-value-1ad1460f` - GDACS RSS Information - severity value |
| - `gdacs-rss-information-geo-lat-cb0e3100` - GDACS RSS Information - geo lat |
| - `gdacs-rss-information-geo-long-b19a19b7` - GDACS RSS Information - geo long |
|
|
| ## Schema |
|
|
| | Column | Type | Description | Example | |
| |--------|------|-------------|---------| |
| | `indicator_id` | `string` | Stable indicator identifier. | `gdacs-rss-information-severity-value-1ad1460f` | |
| | `indicator_name` | `string` | Human-readable indicator name. | `GDACS RSS Information - severity value` | |
| | `country_iso3` | `string` | ISO3 country code. | `MDG` | |
| | `country_name` | `string` | Country name. | `Madagascar` | |
| | `date` | `string` | Observation date. | `2026-08-28` | |
| | `year` | `Int64` | Observation year. | `2026` | |
| | `value` | `float64` | Numeric observation value. | `5.0` | |
| | `unit` | `string` | Measurement unit, when available. | `source_units_unspecified` | |
| | `dimension_id` | `string` | Source dimension. | `EQ1562260` | |
| | `dimension_iso3` | `string` | Source dimension. | `CHN` | |
| | `dimension_country` | `string` | Source dimension. | `China` | |
| | `dimension_title` | `string` | Source dimension. | `Orange earthquake (Magnitude 5M, Depth:10km) in China 28/08/2026 05:13 U` | |
| | `dimension_summary` | `string` | Source dimension. | `On 8/28/2026 5:13:35 AM, an earthquake occurred in China potentially aff` | |
| | `dimension_event_type` | `string` | Source dimension. | `Earthquake` | |
| | `dimension_severity_unit` | `string` | Source dimension. | `M` | |
| | `dimension_source` | `string` | Source dimension. | `Joint Research Center of the European Commission` | |
| | `dimension_from_date` | `string` | Source dimension. | `Fri, 28 Aug 2026 05:13:35 GMT` | |
| | `dimension_link` | `string` | Source dimension. | `https://www.gdacs.org/report.aspx?eventtype=EQ&eventid=1562260` | |
| | `dimension_gdacs_bbox` | `string` | Source dimension. | `101.229 109.229 25.2752 33.2752` | |
| | `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `` | |
| | `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `` | |
| | `source_period_label` | `string` | Human-readable period inferred from source resource metadata. | `` | |
| | `source_provider` | `category` | Publishing organization. | `Global Disaster Alert and Coordination System` | |
| | `source_dataset` | `category` | Source package title. | `GDACS RSS Information` | |
| | `source_resource` | `category` | Source resource title. | `gdacs_rss_information.csv` | |
| | `source_package_id` | `category` | CKAN package UUID. | `a87f96f8-16e6-4d51-872c-cfa54a8251ec` | |
| | `source_resource_id` | `category` | CKAN resource UUID. | `4ef001d1-7888-4f5d-98ce-0ca8006787f7` | |
| | `source_url` | `category` | Original source resource URL. | `https://data.humdata.org/dataset/a87f96f8-16e6-4d51-872c-cfa54a8251ec/re` | |
| | `license_id` | `category` | Source license identifier. | `cc-by` | |
| | `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-30T05:38:32Z` | |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("electricsheepafrica/africa-madagascar-gdacs-rss-information-26c68d4b") |
| df = ds["train"].to_pandas() |
| print(df.head()) |
| ``` |
|
|
| ### Filter to one country |
|
|
| ```python |
| sample_country = df[df["country_iso3"] == "MDG"] |
| ``` |
|
|
| ### Work with indicators |
|
|
| ```python |
| if "indicator_id" in df.columns: |
| print(df["indicator_id"].value_counts().head()) |
| sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns]) |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{electric_sheep_africa_africa_madagascar_gdacs_rss_information_26c68d4b_2026, |
| title = {GDACS RSS Information | Africa (Madagascar official open data)}, |
| author = {Global Disaster Alert and Coordination System}, |
| year = {2026}, |
| url = {https://data.humdata.org/dataset/gdacs-rss-information}, |
| publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa}, |
| howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-madagascar-gdacs-rss-information-26c68d4b}} |
| } |
| ``` |
|
|
| ## License |
|
|
| Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |
|
|
| Original data (c) Global Disaster Alert and Coordination System. When using this dataset, please cite both the |
| original source above and the Electric Sheep Africa repackaging. |
|
|
| ## About Electric Sheep |
|
|
| Electric Sheep Africa is part of the Electric Sheep mission: a unified, |
| ML-ready data layer for Africa on Hugging Face. We pull data from authoritative |
| open sources, normalize the schemas, package as Parquet, and publish with |
| consistent dataset cards so researchers and developers can use `load_dataset()` |
| to start working in seconds. |
|
|
| Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica) |
|
|
| --- |
|
|
| Provenance: ingested 2026-08-30 via the Electric Sheep pipeline. Source URL: |
| https://data.humdata.org/dataset/a87f96f8-16e6-4d51-872c-cfa54a8251ec/resource/4ef001d1-7888-4f5d-98ce-0ca8006787f7/download/gdacs_rss_information.csv |
|
|
