Standardize Electric Sheep Africa dataset card

README.md

@@ -5,80 +5,109 @@ language:
 task_categories:
 - tabular-classification
 - tabular-regression
-multilinguality: monolingual
+multilinguality: multilingual
 size_categories:
 - n<1K
 tags:
-- tabular
-- csv
-- africa
-- tunisia
-- official-statistics
-- open-data
-- agriculture
-pretty_name: "Liste des films subventionnés par le CNCI pendant 2022 et 2023 | Africa (Tunisia official open data)"
+- "tabular"
+- "africa"
+- "open-data"
+- "official-statistics"
+- "tunisia"
+- "minstere-des-affaires-culturelles"
+- "tunisia-open-data"
+- "demographics"
+- "culture"
+- "2022"
+- "2023"
+- "court-metrage"
+- "films"
+- "long-metrage"
+- "realisateur"
+- "societe-de-production"
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-00000-of-00001.parquet
+pretty_name: "Liste Des Films Subventionnes Par Le Cnci Pendant 2022 Et | Africa (Tunisia Open Data)"
 ---
 
-# Liste des films subventionnés par le CNCI pendant 2022 et 2023 | Africa (Tunisia official open data)
+# Liste Des Films Subventionnes Par Le Cnci Pendant 2022 Et | Africa (Tunisia Open Data)
 
-99 rows - 1 Africa country - 2022-2023 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
+**99 rows** - **1 Africa country/area** - **2022-2023** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
 ![rows](https://img.shields.io/badge/rows-99-blue)
 ![countries](https://img.shields.io/badge/countries-1-green)
-![years](https://img.shields.io/badge/years-2022-2023-orange)
+![period](https://img.shields.io/badge/period-2022--2023-orange)
 ![indicators](https://img.shields.io/badge/indicators-0-purple)
 ![license](https://img.shields.io/badge/license-other-lightgrey)
 
 ## TL;DR
 
-This dataset packages one official `CSV` resource from **Tunisia** 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 **99 rows** from **Tunisia Open Data**, covering **Liste Des Films Subventionnes Par Le Cnci Pendant 2022 Et**. 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:** [Liste des films subventionnés par le CNCI pendant 2022 et 2023](https://catalog.data.gov.tn/dataset/liste-des-films-subventionnes-par-le-cnci-2022-2023)
-- **Publisher:** Minstère des affaires culturelles
-- **Resource:** [Liste des films subventionnés par le CNCI](http://www.openculture.gov.tn/dataset/eb31ec5d-73d5-4ca7-852d-556b1cef60ae/resource/0c40a024-8e6d-4207-8033-ced8f5a8b18d/download/liste-des-films-subventionnes-par-le-cnci-2022-2023.csv)
-- **Format:** `CSV`
-- **License:** [Other open license]()
-- **Packaging mode:** `tabular_resource`
+Demographic datasets help analysts understand population structure, household conditions, migration, gender, age, and settlement patterns.
+
+Source-provided context: Liste des films subventionnés par le CNCI pour les années 2022 et 2023
+
+## How To Read This Dataset
+
+- **One row means:** one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
+- **Primary geography column:** `country_iso3`.
+- **Best time column:** `not detected`.
+- **Time coverage basis:** source metadata.
+- **Recommended join keys:** `country_iso3` where available plus source-specific keys.
+
+## Coverage
+
+| Dimension | Value |
+|---|---:|
+| Rows | 99 |
+| Countries/areas | 1 |
+| First period | 2022 |
+| Last period | 2023 |
+| Indicators | 0 |
+| Columns | 19 |
+| 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 |
+|------|-----:|-----------:|----------:|------|
 | `TUN` | 99 | 2022 | 2023 | `Tunisia` |
 
-## Indicators or Resource Contents
+## Indicators, Variables, Or Resource Contents
 
-- This source file is packaged as a normalized tabular resource.
+- This repo preserves one source tabular resource with its usable columns kept together.
 
 ## Schema
 
 | Column | Type | Description | Example |
 |--------|------|-------------|---------|
-| `source_record_id` | `string` | Stable row identifier for tabular resources. | `ed3497f7-609d-4c10-a930-895f402dc4f9:0` |
-| `country_iso3` | `string` | ISO3 country code. | `TUN` |
-| `country_name` | `string` | Country name. | `Tunisia` |
-| `annee` | `int64` | Source column. | `2022` |
-| `non_du_film` | `string` | Source column. | `Motherhood` |
-| `genre_du_film` | `string` | Source column. | `روائي طويل` |
-| `societe_de_production` | `string` | Source column. | `Instinct Bleu` |
-| `realisateur` | `string` | Source column. | `مريم جوبر` |
-| `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2022` |
-| `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2023` |
-| `source_period_label` | `string` | Human-readable period inferred from source resource metadata. | `2022-2023` |
+| `source_record_id` | `string` | Stable row identifier assigned during Electric Sheep Africa engineering. | `ed3497f7-609d-4c10-a930-895f402dc4f9:0` |
+| `country_iso3` | `string` | ISO3 country or area code. | `TUN` |
+| `country_name` | `string` | Country or area name. | `Tunisia` |
+| `annee` | `int64` | Source column from the original resource. | `2022` |
+| `non_du_film` | `string` | Source column from the original resource. | `Motherhood` |
+| `genre_du_film` | `string` | Source column from the original resource. | `روائي طويل` |
+| `societe_de_production` | `string` | Source column from the original resource. | `Instinct Bleu` |
+| `realisateur` | `string` | Source column from the original resource. | `مريم جوبر` |
+| `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2022` |
+| `source_period_end_year` | `int64` | End year inferred from source metadata. | `2023` |
+| `source_period_label` | `string` | Source column from the original resource. | `2022-2023` |
 | `source_provider` | `string` | Publishing organization. | `Minstère des affaires culturelles` |
-| `source_dataset` | `string` | Source package title. | `Liste des films subventionnés par le CNCI pendant 2022 et 2023` |
-| `source_resource` | `string` | Source resource title. | `Liste des films subventionnés par le CNCI` |
-| `source_package_id` | `string` | CKAN package UUID. | `64823c1f-19b1-4856-83b3-52b62b8c0434` |
-| `source_resource_id` | `string` | CKAN resource UUID. | `ed3497f7-609d-4c10-a930-895f402dc4f9` |
-| `source_url` | `string` | Original source resource URL. | `http://www.openculture.gov.tn/dataset/eb31ec5d-73d5-4ca7-852d-556b1cef60` |
+| `source_dataset` | `string` | Source dataset or package title. | `Liste des films subventionnés par le CNCI pendant 2022 et 2023` |
+| `source_resource` | `string` | Source resource title, table name, or file name. | `Liste des films subventionnés par le CNCI` |
+| `source_package_id` | `string` | Source package identifier. | `64823c1f-19b1-4856-83b3-52b62b8c0434` |
+| `source_resource_id` | `string` | Source resource identifier. | `ed3497f7-609d-4c10-a930-895f402dc4f9` |
+| `source_url` | `string` | Original source URL or download URL. | `http://www.openculture.gov.tn/dataset/eb31ec5d-73d5-4ca7-852d-556b1ce...` |
 | `license_id` | `string` | Source license identifier. | `other-open` |
-| `retrieved_at` | `string` | UTC retrieval timestamp. | `2026-07-18T23:08:36Z` |
+| `retrieved_at` | `string` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-07-18T23:08:36Z` |
 
 ## Usage
 
@@ -90,51 +119,92 @@ df = ds["train"].to_pandas()
 print(df.head())
 ```
 
-### Filter to one country
+### Inspect Columns
 
 ```python
-sample_country = df[df["country_iso3"] == "TUN"]
+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"] == "TUN"]
 ```
 
+### 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
+
+- No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
+- 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:** [Tunisia Open Data](https://catalog.data.gov.tn/dataset/liste-des-films-subventionnes-par-le-cnci-2022-2023)
+- **Publisher:** Minstère des affaires culturelles
+- **Portal:** [https://catalog.data.gov.tn](https://catalog.data.gov.tn)
+- **Resource:** [Liste des films subventionnés par le CNCI](http://www.openculture.gov.tn/dataset/eb31ec5d-73d5-4ca7-852d-556b1cef60ae/resource/0c40a024-8e6d-4207-8033-ced8f5a8b18d/download/liste-des-films-subventionnes-par-le-cnci-2022-2023.csv)
+- **License:** other-open
+- **Retrieved/generated:** `2026-07-18T23:13:23Z`
+- **Hugging Face repo:** [electricsheepafrica/africa-tunisia-liste-des-films-subventionnes-par-le-cnci-pendant-2022-et-c3c35dc3](https://huggingface.co/datasets/electricsheepafrica/africa-tunisia-liste-des-films-subventionnes-par-le-cnci-pendant-2022-et-c3c35dc3)
+
+## 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
+
+- Build demographic profiles
+- Normalize indicators per capita
+- Join with service-delivery datasets
+- Check missingness before modeling
+- Use `country_iso3` as the safest geography join key when present
+
 ## Citation
 
 ```bibtex
 @misc{electric_sheep_africa_africa_tunisia_liste_des_films_subventionnes_par_le_cnci_pendant_2022_et_c3c35dc_2023,
-  title        = {Liste des films subventionnés par le CNCI pendant 2022 et 2023 | Africa (Tunisia official open data)},
+  title        = {Liste Des Films Subventionnes Par Le Cnci Pendant 2022 Et | Africa (Tunisia Open Data)},
   author       = {Minstère des affaires culturelles},
   year         = {2023},
   url          = {https://catalog.data.gov.tn/dataset/liste-des-films-subventionnes-par-le-cnci-2022-2023},
-  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-tunisia-liste-des-films-subventionnes-par-le-cnci-pendant-2022-et-c3c35dc3}}
 }
 ```
 
 ## License
 
-Released under [Other open license]().
-
-Original data (c) Minstère des affaires culturelles. When using this dataset, please cite both the
-original source above and the Electric Sheep Africa repackaging.
+Released under other-open.
 
-## About Electric Sheep
+Original data is published by Minstère des affaires culturelles. 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 2026-07-19 via the Electric Sheep pipeline. Source URL:
-http://www.openculture.gov.tn/dataset/eb31ec5d-73d5-4ca7-852d-556b1cef60ae/resource/0c40a024-8e6d-4207-8033-ced8f5a8b18d/download/liste-des-films-subventionnes-par-le-cnci-2022-2023.csv
+Provenance: README standardized 2026-08-12 by the Electric Sheep Africa README system. Source URL: https://catalog.data.gov.tn/dataset/liste-des-films-subventionnes-par-le-cnci-2022-2023