Add dataset card

README.md

@@ -1,56 +1,166 @@
 ---
-dataset_info:
-  features:
-  - name: ref_area
-    dtype: string
-  - name: ref_area.label
-    dtype: string
-  - name: source
-    dtype: string
-  - name: source.label
-    dtype: string
-  - name: indicator
-    dtype: string
-  - name: indicator.label
-    dtype: string
-  - name: sex
-    dtype: string
-  - name: sex.label
-    dtype: string
-  - name: classif1
-    dtype: string
-  - name: classif1.label
-    dtype: string
-  - name: time
-    dtype: int64
-  - name: obs_value
-    dtype: float64
-  - name: obs_status
-    dtype: string
-  - name: obs_status.label
-    dtype: string
-  - name: note_indicator
-    dtype: string
-  - name: note_indicator.label
-    dtype: string
-  - name: note_source
-    dtype: string
-  - name: note_source.label
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 302583
-    num_examples: 800
-  - name: test
-    num_bytes: 70383
-    num_examples: 201
-  download_size: 26701
-  dataset_size: 372966
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+- time-series-forecasting
+multilinguality: monolingual
+size_categories:
+- 1K<n<10K
+tags:
+- tabular
+- asia
+- ilostat
+- informal-economy
+- ilo
+- labour
+- employment
+pretty_name: "Informal employment rate by sex and economic activity -- 19th ICLS (%) | Asia (ILOSTAT)"
 ---
+
+# Informal employment rate by sex and economic activity -- 19th ICLS (%) | Asia (ILOSTAT)
+
+🌏 **1,001 observations** · **18 Asia countries** · **2014–2025** · *Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)*
+
+![rows](https://img.shields.io/badge/rows-1,001-blue) ![countries](https://img.shields.io/badge/countries-18-green) ![years](https://img.shields.io/badge/years-2014–2025-orange) ![indicators](https://img.shields.io/badge/indicators-1-purple) ![license](https://img.shields.io/badge/license-cc-by-4.0-lightgrey)
+
+## TL;DR
+
+This dataset contains **1,001 observations** of `Informal economy` data across **18 Asia countries**, spanning **2014–2025**, covering **1 distinct indicators**.
+
+## About the source
+
+**ILOSTAT** is the ILO's central statistics database, the leading global source for labour statistics. It compiles indicators across employment, unemployment, wages, working time, child labour, informal economy, social protection, occupational injuries, and SDG decent work targets — drawing on national labour force surveys, household income surveys, establishment surveys, and administrative records. Coverage spans 200+ economies, with the ILO's Department of Statistics responsible for harmonisation.
+
+- **Source:** [ILOSTAT](https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_5NIF_SEX_ECO_RT)
+- **Publisher:** International Labour Organization (ILO)
+- **License:** [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Topic:** Informal economy
+
+## Methodology
+
+Data pulled directly from the ILOSTAT REST API at `https://rplumber.ilo.org/data/indicator?id=EMP_5NIF_SEX_ECO_RT` and filtered to Asia ISO3 country codes. ILOSTAT harmonises raw survey microdata using ICLS (International Conference of Labour Statisticians) definitions; sources are flagged in the `source.label` column for traceability.
+
+## Geographic coverage
+
+18 Asia countries · top rows shown below, sorted by row count:
+
+| Country | Rows | First year | Last year |
+|---------|-----:|-----------:|----------:|
+| `CYP` | 154 | 2014 | 2024 |
+| `JOR` | 127 | 2017 | 2024 |
+| `ARM` | 105 | 2018 | 2024 |
+| `MNG` | 90 | 2019 | 2024 |
+| `VNM` | 81 | 2020 | 2024 |
+| `GEO` | 76 | 2020 | 2024 |
+| `BGD` | 60 | 2022 | 2024 |
+| `MMR` | 60 | 2017 | 2020 |
+| `BRN` | 60 | 2014 | 2024 |
+| `MDV` | 36 | 2016 | 2019 |
+| `LAO` | 35 | 2017 | 2022 |
+| `AFG` | 18 | 2021 | 2021 |
+| `LBN` | 18 | 2019 | 2019 |
+| `KOR` | 18 | 2019 | 2019 |
+| `PAK` | 18 | 2025 | 2025 |
+| ... | _3 more countries_ | | |
+
+## Indicators (sample)
+
+- `EMP_5NIF_SEX_ECO_RT` — Informal employment rate by sex and economic activity -- 19th ICLS (%)
+
+## Schema
+
+| Column | Type | Description | Example |
+|--------|------|-------------|---------|
+| `ref_area` | `string` | ISO 3166-1 alpha-3 country code | `AFG` |
+| `ref_area.label` | `string` | Country name in English | `Afghanistan` |
+| `source` | `string` | ILOSTAT source code (e.g. labour force survey) | `BA:15715` |
+| `source.label` | `string` | Source name in English | `LFS - Labour Force Survey` |
+| `indicator` | `string` | ILOSTAT indicator code | `EMP_5NIF_SEX_ECO_RT` |
+| `indicator.label` | `string` | Indicator name in English | `Informal employment rate by sex and e…` |
+| `sex` | `string` | Disaggregation by sex (SEX_T = total, SEX_M = male, SEX_F = female) | `SEX_T` |
+| `sex.label` | `string` | — | `Total` |
+| `classif1` | `string` | First classification variable (age, education, status, etc.) | `ECO_SECTOR_TOTAL` |
+| `classif1.label` | `string` | — | `Economic activity (Broad sector): Total` |
+| `time` | `int64` | Observation year | `2021` |
+| `obs_value` | `float64` | Observed indicator value (unit varies — see indicator definition) | `78.882` |
+| `obs_status` | `string` | Observation status flag (e.g. provisional, unreliable) | `B` |
+| `obs_status.label` | `string` | — | `Break in series` |
+| `note_indicator` | `string` | — | `I11:264` |
+| `note_indicator.label` | `string` | — | `Break in series: Methodology revised` |
+| `note_source` | `string` | — | `R1:3513_S3:8` |
+| `note_source.label` | `string` | — | `Repository: ILO-STATISTICS - Micro da…` |
+
+## Disaggregation dimensions
+
+The following columns provide disaggregation dimensions:
+- **`sex`** (4 unique values): `SEX_T`, `SEX_M`, `SEX_F`, `SEX_O`
+
+## Data quality & caveats
+
+- Data is annual frequency. Some indicators also publish monthly or quarterly series — those are not included here.
+- When an indicator has multiple sources for the same country×year, the ILO-selected 'best source' is used.
+- Disaggregation columns (`sex`, `classif1`, `classif2`) are non-null only when the indicator publishes that breakdown.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("electricsheepasia/asia-ilo-emp-5nif-sex-eco-rt-informal-employment-rate-by-sex-and-economic-activ")
+df = ds["train"].to_pandas()
+print(df.head())
+```
+
+### Filter to one country
+
+```python
+indonesia = df[df["ref_area"] == "IDN"]
+```
+
+### Time-series for a single indicator
+
+```python
+sample = (df[df["indicator"] == "EMP_5NIF_SEX_ECO_RT"]
+          .sort_values("time"))
+sample.plot(x="time", y="obs_value", title="EMP_5NIF_SEX_ECO_RT")
+```
+
+### Pivot to country × year matrix
+
+```python
+matrix = (df[df["indicator"] == "EMP_5NIF_SEX_ECO_RT"]
+          .pivot_table(index="time", columns="ref_area", values="obs_value"))
+print(matrix.tail())
+```
+
+## Citation
+
+```bibtex
+@misc{asia_ilo_emp_5nif_sex_eco_rt_informal_employment_rate_by_sex_and_economic_activ_2025,
+  title        = {Informal employment rate by sex and economic activity -- 19th ICLS (%) | Asia (ILOSTAT)},
+  author       = {International Labour Organization (ILO)},
+  year         = {2025},
+  url          = {https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_5NIF_SEX_ECO_RT},
+  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-ilo-emp-5nif-sex-eco-rt-informal-employment-rate-by-sex-and-economic-activ}}
+}
+```
+
+## License
+
+Released under [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/).
+
+Original data © International Labour Organization (ILO). When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.
+
+## About Electric Sheep
+
+Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia on HuggingFace. 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/electricsheepasia](https://huggingface.co/electricsheepasia)
+
+---
+
+_Provenance: ingested 2026-05-28 via the Electric Sheep pipeline. Source URL: https://www.ilo.org/shinyapps/bulkexplorer/?id=EMP_5NIF_SEX_ECO_RT_