Standardize Electric Sheep Africa dataset card

README.md

@@ -1,143 +1,146 @@
 ---
 license: cc-by-4.0
-task_categories:
-  - tabular-classification
 language:
-  - en
-tags:
-  - healthcare
-  - preterm-birth
-  - kangaroo-mother-care
-  - neonatal
-  - low-birth-weight
-  - kmc
-  - sub-saharan-africa
-  - lmic
-pretty_name: "Preterm Birth & Kangaroo Mother Care (GA, BW, KMC, Respiratory Support, Outcomes)"
+- en
+task_categories:
+- tabular-classification
+- tabular-regression
+multilinguality: monolingual
 size_categories:
-  - 10K<n<100K
-configs:
-  - config_name: nicu
-    data_files: data/preterm_nicu.csv
-  - config_name: special_care_nursery
-    data_files: data/preterm_special_care_nursery.csv
-    default: true
-  - config_name: postnatal_ward
-    data_files: data/preterm_postnatal_ward.csv
+- 10K<n<100K
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "csv"
+- "tabular"
+- "text"
+- "healthcare"
+- "preterm-birth"
+- "kangaroo-mother-care"
+- "neonatal"
+- "low-birth-weight"
+- "kmc"
+- "sub-saharan-africa"
+- "lmic"
+- "mortality"
+- "maternal"
+pretty_name: "Preterm Birth & Kangaroo Mother Care (GA, BW, KMC, Respiratory Support, Outcomes) | Africa (Electric Sheep Africa metadata inventory)"
 ---
 
-# Preterm Birth & Kangaroo Mother Care Dataset
-
-## Abstract
+# Preterm Birth & Kangaroo Mother Care (GA, BW, KMC, Respiratory Support, Outcomes) | Africa (Electric Sheep Africa metadata inventory)
 
-This dataset provides **30,000 simulated preterm neonate records** (10,000 per scenario) from sub-Saharan Africa. Each record contains 50+ variables including maternal factors, gestational age, birth weight, KMC practice, respiratory support (CPAP, surfactant), complications (RDS, sepsis, NEC), and neonatal outcomes. Three settings: NICU (8% mortality), special care nursery (14%), and postnatal ward (33%).
+**Size category:** `10K<n<100K` - **Formats:** `csv` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
-## 1. Introduction
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-31-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
 
-Preterm birth is the leading cause of death in children under 5 worldwide. In SSA, neonatal mortality remains high with limited access to NICU care. KMC — skin-to-skin contact — reduces mortality by 25-40% (NEJM 2021). WHO recommends immediate KMC for LBW infants, starting within 24 hours and provided 8+ hours daily. CPAP and surfactant are scarce outside tertiary centres. Antenatal steroids reduce RDS but coverage remains low.
+## TL;DR
 
-**This dataset is entirely simulated. It must not be used for clinical decision-making.**
+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.
 
-## 2. Methodology
+## What This Dataset Covers
 
-### 2.1 Parameterization
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
 
-| Parameter | Value | Source |
-| --- | --- | --- |
-| KMC mortality reduction | 25-40% | NEJM 2021 |
-| KMC timing | Within 24h, 8+ hrs/day | WHO 2024 |
-| CPAP failure (<1200g) | ~40% | PubMed 2023 |
-| RDS (GA <32) | ~40% | Arch PH 2024 |
-| Antenatal steroids effect | 30-40% RDS reduction | WHO 2024 |
-| Neonatal sepsis | ~15% | PubMed 2024 |
+Dataset context from the existing Hugging Face card: Preterm Birth & Kangaroo Mother Care Dataset Abstract This dataset provides 30,000 simulated preterm neonate records (10,000 per scenario) from sub-Saharan Africa. Each record contains 50+ variables including maternal factors, gestational age, birth weight, KMC practice, respiratory support (CPAP, surfactant), complications (RDS, sepsis, NEC), and neonatal outcomes. Three settings: NICU (8% mortality), special care nursery (14%), and postnatal ward (33%). 1.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/preterm-birth-kmc.
 
-### 2.2 Scenario Design
+## Dataset Profile
 
-| Scenario | CPAP | Surfactant | KMC Unit | Mortality |
-| --- | --- | --- | --- | --- |
-| NICU | Yes | Yes | Yes | 8% |
-| Special care nursery | No | No | Yes | 14% |
-| Postnatal ward | No | No | No | 33% |
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/preterm-birth-kmc`](https://huggingface.co/datasets/electricsheepafrica/preterm-birth-kmc) |
+| Sector | health |
+| Topic tags | healthcare, preterm-birth, kangaroo-mother-care, neonatal, low-birth-weight, kmc, sub-saharan-africa, lmic |
+| Modalities | `tabular`, `text` |
+| Formats | `csv` |
+| Size category | `10K<n<100K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-02-10 21:41:47+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
 
-## 3. Schema
+## How To Read This Dataset
 
-| Column | Type | Description |
-| --- | --- | --- |
-| id | int | Unique identifier |
-| maternal_age | int | Maternal age |
-| gestational_age_weeks | int | GA (24-36 weeks) |
-| birth_weight_g | int | Birth weight |
-| very_low_birth_weight | binary | <1500g |
-| extremely_low_birth_weight | binary | <1000g |
-| apgar_1min / apgar_5min | int | Apgar scores |
-| hypothermia | binary | Temp <36.5C |
-| rds | binary | Respiratory distress syndrome |
-| neonatal_sepsis | binary | Neonatal sepsis |
-| nec | binary | Necrotising enterocolitis |
-| ivh | binary | Intraventricular haemorrhage |
-| kmc_initiated | binary | KMC started |
-| kmc_within_24h | binary | KMC within 24 hours |
-| kmc_hours_per_day | int | Daily KMC hours |
-| exclusive_breastfeeding | binary | Exclusive breastfeeding |
-| cpap_given | binary | CPAP |
-| surfactant_given | binary | Surfactant |
-| antenatal_steroids | binary | Antenatal steroids |
-| neonatal_death | binary | Neonatal death |
-| hospital_days | int | Hospital stay |
+- 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.
 
-## 4. Validation
+## Usage
 
-<p align="center">
-  <img src="validation_report.png" alt="Validation Report" width="100%">
-</p>
+```python
+from datasets import load_dataset
 
-Key validation checks:
+ds = load_dataset("electricsheepafrica/preterm-birth-kmc")
+print(ds)
 
-- **Mortality gradient**: 8% → 14% → 33% ✓
-- **KMC reduces mortality**: ~35% reduction ✓
-- **GA-mortality relationship**: <28wk highest ✓
-- **KMC coverage**: 70% (NICU/SCN) → 15% (postnatal) ✓
-- **Antenatal steroids reduce RDS** ✓
-- **VLBW**: ~40% of preterm cohort ✓
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
 
-## 5. Usage
+### Convert To Pandas When Tabular
 
 ```python
-from datasets import load_dataset
-dataset = load_dataset("electricsheepafrica/preterm-birth-kmc", "special_care_nursery")
-df = dataset["train"].to_pandas()
+from datasets import Dataset
+
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
 ```
 
-## 6. Limitations
+## 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: country, upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+
+## Source And Provenance
 
-- **Simulated**: Not from real neonatal registries.
-- **No follow-up**: No neurodevelopmental outcomes.
-- **No continuous monitoring**: No SpO2 or HR traces.
-- **Simplified**: No detailed ventilation parameters.
-- **No twin-specific**: Multiple pregnancy simplified.
+- **Source context:** Electric Sheep Africa metadata inventory
+- **Publisher/source attribution:** Public dataset metadata
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/preterm-birth-kmc](https://huggingface.co/datasets/electricsheepafrica/preterm-birth-kmc)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-## 7. References
+## Suggested Analyses
 
-1. NEJM (2021). Immediate KMC and survival of LBW infants.
-2. PMC (2024). KMC effectiveness in SSA.
-3. PubMed (2023). KMC systematic review.
-4. PubMed (2023). CPAP failure in resource-limited settings.
-5. Arch Public Health (2024). Preterm neonatal mortality Ethiopia.
-6. WHO Africa (2024). Maternal and newborn mortality.
-7. WHO (2024). KMC guidelines.
+- 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
-@dataset{esa_preterm_kmc_2025,
-  title={Preterm Birth and Kangaroo Mother Care Dataset},
-  author={Electric Sheep Africa},
-  year={2025},
-  publisher={Hugging Face},
-  url={https://huggingface.co/datasets/electricsheepafrica/preterm-birth-kmc}
+@misc{electric_sheep_africa_preterm_birth_kmc_2026,
+  title        = {Preterm Birth & Kangaroo Mother Care (GA, BW, KMC, Respiratory Support, Outcomes) | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/preterm-birth-kmc},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/preterm-birth-kmc}}
 }
 ```
 
 ## License
 
-[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
+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`.