Standardize Electric Sheep Africa dataset card

README.md

@@ -5,86 +5,106 @@ language:
 task_categories:
 - tabular-regression
 - time-series-forecasting
-multilinguality: monolingual
+multilinguality: multilingual
 size_categories:
 - n<1K
 tags:
-- tabular
-- csv
-- africa
-- mauritius
-- official-statistics
-- open-data
-- health
+- "tabular"
+- "africa"
+- "open-data"
+- "official-statistics"
+- "mauritius"
+- "mdpa"
+- "health"
+- "health-and-sports"
+- "disease"
+- "pneumonia"
+- "asthma"
+- "government-hospitals"
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-00000-of-00001.parquet
-pretty_name: "Admissions due to certain respiratory diseases by sex in government general hospitals | Africa (Mauritius official open data)"
+pretty_name: "Admissions Due to Certain Respiratory Diseases by Sex in G | Africa (MDPA)"
 ---
 
-# Admissions due to certain respiratory diseases by sex in government general hospitals | Africa (Mauritius official open data)
+# Admissions Due to Certain Respiratory Diseases by Sex in G | Africa (MDPA)
 
-60 rows - 1 Africa country - 2015-2021 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
+**60 rows** - **1 Africa country/area** - **2015-2021** - **1 indicator** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
 ![rows](https://img.shields.io/badge/rows-60-blue)
 ![countries](https://img.shields.io/badge/countries-1-green)
-![years](https://img.shields.io/badge/years-2015-2021-orange)
+![period](https://img.shields.io/badge/period-2015--2021-orange)
 ![indicators](https://img.shields.io/badge/indicators-1-purple)
-![license](https://img.shields.io/badge/license-cc-by-sa-4.0-lightgrey)
+![license](https://img.shields.io/badge/license-cc--by--sa--4.0-lightgrey)
 
 ## TL;DR
 
-This dataset packages one official `CSV` resource from **Mauritius** 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.
+This dataset contains **60 rows** from **MDPA**, covering **Admissions Due to Certain Respiratory Diseases by Sex in G**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.
 
-## About the source
+## What This Dataset Measures
 
-- **Source:** [Admissions due to certain respiratory diseases by sex in government general hospitals](https://data.govmu.org/dataset/admissions-due-certain-respiratory-diseases-sex-government-general-hospitals)
-- **Publisher:** MDPA
-- **Resource:** [Dataset_6.csv](https://data.govmu.org/dataset/9796ea43-36c3-49c0-81e6-ab33b55fed1c/resource/775ef1bd-4858-4d1c-a72d-54af2be52702/download/dataset_6.csv)
-- **Format:** `CSV`
-- **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
-- **Packaging mode:** `indicator_long`
+Health datasets help analysts monitor disease burden, service delivery, population health outcomes, and public-health program performance.
+
+Source-provided context: The data shows admissions due to certain respiratory diseases by sex in government general hospitals for the year 2015 to 2021
+
+## How To Read This Dataset
+
+- **One row means:** one indicator observation for one geography, time period, and optional source dimensions.
+- **Primary geography column:** `country_iso3`.
+- **Best time column:** `year`.
+- **Time coverage basis:** year.
+- **Recommended join keys:** `country_iso3`, `year`, `indicator_id`.
+
+## Coverage
+
+| Dimension | Value |
+|---|---:|
+| Rows | 60 |
+| Countries/areas | 1 |
+| First period | 2015 |
+| Last period | 2021 |
+| Indicators | 1 |
+| Columns | 20 |
+| Source format | CSV |
 
-## Geographic coverage
+## Geographic Coverage
 
-1 Africa country:
+Top areas shown below, sorted by row count when available:
 
-| Country | Rows | First year | Last year | Name |
-|---------|-----:|-----------:|----------:|------|
+| Area | Rows | First year | Last year | Name |
+|------|-----:|-----------:|----------:|------|
 | `MU` | 60 | 2015 | 2021 | `Mauritius` |
 
-## Indicators or Resource Contents
+## Indicators, Variables, Or Resource Contents
 
-- `admissions-due-to-certain-respiratory-diseases-by-sex-in-government-gene-05a69bfa` - Admissions due to certain respiratory diseases by sex in government general hospitals
+- `admissions-due-to-certain-respiratory-diseases-by-sex-in-government-gene-05a69bfa` - Admissions due to certain respiratory diseases by sex in government general hospitals(source_units_unspecified)
 
 ## Schema
 
 | Column | Type | Description | Example |
 |--------|------|-------------|---------|
-| `indicator_id` | `string` | Stable indicator identifier. | `admissions-due-to-certain-respiratory-diseases-by-sex-in-government-gene` |
-| `indicator_name` | `string` | Human-readable indicator name. | `Admissions due to certain respiratory diseases by sex in government gene` |
-| `country_iso3` | `string` | ISO3 country code. | `MU` |
-| `country_name` | `string` | Country name. | `Mauritius` |
-| `year` | `Int64` | Observation year. | `2015` |
-| `value` | `float64` | Numeric observation value. | `2918.0` |
-| `unit` | `string` | Measurement unit, when available. | `source_units_unspecified` |
-| `dimension_disease` | `string` | Source dimension. | `Acute upper respiratory infections` |
-| `dimension_gender` | `string` | Source dimension. | `Male` |
-| `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2015` |
-| `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2021` |
-| `source_period_label` | `category` | Human-readable period inferred from source resource metadata. | `2015-2021` |
-| `source_provider` | `category` | Publishing organization. | `MDPA` |
-| `source_dataset` | `category` | Source package title. | `Admissions due to certain respiratory diseases by sex in government gene` |
-| `source_resource` | `category` | Source resource title. | `Dataset_6.csv` |
-| `source_package_id` | `category` | CKAN package UUID. | `9796ea43-36c3-49c0-81e6-ab33b55fed1c` |
-| `source_resource_id` | `category` | CKAN resource UUID. | `775ef1bd-4858-4d1c-a72d-54af2be52702` |
-| `source_url` | `category` | Original source resource URL. | `https://data.govmu.org/dataset/9796ea43-36c3-49c0-81e6-ab33b55fed1c/reso` |
-| `license_id` | `category` | Source license identifier. | `CC-BY-SA-4.0` |
-| `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-08T16:26:20Z` |
+| `indicator_id` | `string` | Stable source or Electric Sheep Africa indicator identifier. | `admissions-due-to-certain-respiratory-diseases-by-sex-in-government-g...` |
+| `indicator_name` | `string` | Human-readable indicator name. | `Admissions due to certain respiratory diseases by sex in government g...` |
+| `country_iso3` | `string` | ISO3 country or area code. | `MU` |
+| `country_name` | `string` | Country or area name. | `Mauritius` |
+| `year` | `int64` | Observation year. | `2015` |
+| `value` | `double` | Numeric observation value. | `2918.0` |
+| `unit` | `string` | Measurement unit, when supplied by the source. | `source_units_unspecified` |
+| `dimension_disease` | `string` | Source dimension retained during long-form normalization. | `Acute upper respiratory infections` |
+| `dimension_gender` | `string` | Source dimension retained during long-form normalization. | `Male` |
+| `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2015` |
+| `source_period_end_year` | `int64` | End year inferred from source metadata. | `2021` |
+| `source_period_label` | `dictionary<values=string, indices=int8, ordered=0>` | Source column from the original resource. | `2015-2021` |
+| `source_provider` | `dictionary<values=string, indices=int8, ordered=0>` | Publishing organization. | `MDPA` |
+| `source_dataset` | `dictionary<values=string, indices=int8, ordered=0>` | Source dataset or package title. | `Admissions due to certain respiratory diseases by sex in government g...` |
+| `source_resource` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource title, table name, or file name. | `Dataset_6.csv` |
+| `source_package_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source package identifier. | `9796ea43-36c3-49c0-81e6-ab33b55fed1c` |
+| `source_resource_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource identifier. | `775ef1bd-4858-4d1c-a72d-54af2be52702` |
+| `source_url` | `dictionary<values=string, indices=int8, ordered=0>` | Original source URL or download URL. | `https://data.govmu.org/dataset/9796ea43-36c3-49c0-81e6-ab33b55fed1c/r...` |
+| `license_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source license identifier. | `CC-BY-SA-4.0` |
+| `retrieved_at` | `dictionary<values=string, indices=int8, ordered=0>` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-08-08T16:26:20Z` |
 
 ## Usage
 
@@ -96,29 +116,78 @@ df = ds["train"].to_pandas()
 print(df.head())
 ```
 
-### Filter to one country
+### Inspect Columns
 
 ```python
-sample_country = df[df["country_iso3"] == "MU"]
+print(df.info())
+print(df.head())
 ```
 
-### Work with indicators
+### Filter By Geography
 
 ```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])
+if "country_iso3" in df.columns:
+    sample = df[df["country_iso3"] == "MU"]
 ```
 
+### Time-Series Pattern
+
+```python
+if "value" in df.columns and "year" in df.columns:
+    trend = df.sort_values("year")
+```
+
+### Pivot For Analysis
+
+```python
+if {"indicator_id", "year", "value"}.issubset(df.columns):
+    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
+    print(matrix.tail())
+```
+
+## Data Quality Notes
+
+- Canonical time field: `year`.
+- Missing values are preserved rather than silently imputed.
+- Column names are standardized for machine use; source meanings are preserved where known.
+- Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.
+
+## Source And Provenance
+
+- **Source:** [MDPA](https://data.govmu.org/dataset/admissions-due-certain-respiratory-diseases-sex-government-general-hospitals)
+- **Publisher:** MDPA
+- **Portal:** [https://data.govmu.org](https://data.govmu.org)
+- **Resource:** [Dataset_6.csv](https://data.govmu.org/dataset/9796ea43-36c3-49c0-81e6-ab33b55fed1c/resource/775ef1bd-4858-4d1c-a72d-54af2be52702/download/dataset_6.csv)
+- **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+- **Retrieved/generated:** `2026-08-08T16:41:31Z`
+- **Hugging Face repo:** [electricsheepafrica/africa-mauritius-admissions-due-to-certain-respiratory-diseases-by-sex-in-g-6bf75d42](https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-admissions-due-to-certain-respiratory-diseases-by-sex-in-g-6bf75d42)
+
+## Transformations Applied
+
+- Converted the source table to Parquet for efficient analytics and ML workflows.
+- Added or preserved source provenance columns where available.
+- Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
+- Preserved source-reported values without analytical imputation.
+
+## Suggested Analyses
+
+- Compare health outcomes across geographies
+- Track changes over time
+- Join with population or facility data
+- Build time-series views and period-over-period comparisons
+- Pivot to geography x period or indicator x period matrices
+- Check missingness before modeling
+- Use `country_iso3` as the safest geography join key when present
+
 ## Citation
 
 ```bibtex
 @misc{electric_sheep_africa_africa_mauritius_admissions_due_to_certain_respiratory_diseases_by_sex_in_g_6bf7_2021,
-  title        = {Admissions due to certain respiratory diseases by sex in government general hospitals | Africa (Mauritius official open data)},
+  title        = {Admissions Due to Certain Respiratory Diseases by Sex in G | Africa (MDPA)},
   author       = {MDPA},
   year         = {2021},
   url          = {https://data.govmu.org/dataset/admissions-due-certain-respiratory-diseases-sex-government-general-hospitals},
-  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
   howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-admissions-due-to-certain-respiratory-diseases-by-sex-in-g-6bf75d42}}
 }
 ```
@@ -127,20 +196,14 @@ if "indicator_id" in df.columns:
 
 Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
 
-Original data (c) MDPA. When using this dataset, please cite both the
-original source above and the Electric Sheep Africa repackaging.
-
-## About Electric Sheep
+Original data is published by MDPA. Electric Sheep Africa
+engineering standardizes the data for discovery, loading, and analysis on
+Hugging Face. Cite both the original source and this ML-ready dataset when used.
 
-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.
+## About Electric Sheep Africa
 
-Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica)
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
 
 ---
 
-Provenance: ingested 2026-08-08 via the Electric Sheep pipeline. Source URL:
-https://data.govmu.org/dataset/9796ea43-36c3-49c0-81e6-ab33b55fed1c/resource/775ef1bd-4858-4d1c-a72d-54af2be52702/download/dataset_6.csv
+Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.govmu.org/dataset/admissions-due-certain-respiratory-diseases-sex-government-general-hospitals