Datasets:

electricsheepafrica
/

africa-unicef-esaro-regional-db-31-october-2017

@@ -1,144 +1,144 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
 language:
 - en
-license: cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- n<1K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
 - tabular-regression
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- eastern-africa
-- funding
-- ago
-- bdi
-- eri
-- eth
-- ken
-pretty_name: "UNICEF ESARO Regional db 31 March 2018"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 14
-    - name: test
-      num_examples: 3
 ---
-# UNICEF ESARO Regional db 31 March 2018
-**Publisher:** UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) · **Source:** [HDX](https://data.humdata.org/dataset/unicef-esaro-regional-db-31-october-2017) · **License:** `cc-by` · **Updated:** 2024-08-30
----
-## Abstract
-UNICEF Eastern and Southern Africa database - Target, Response and Funding as of 31 March 2018
-Each row in this dataset represents tabular records. Temporal coverage is indicated by the `unnamed_1` column(s). Geographic scope: **AGO, BDI, ERI, ETH, KEN, MDG, SOM, SSD, and 1 others**.
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
----
-## Dataset Characteristics
-| | |
 |---|---|
-| **Domain** | Humanitarian and development data |
-| **Unit of observation** | Tabular records |
-| **Rows (total)** | 18 |
-| **Columns** | 4 (0 numeric, 3 categorical, 1 datetime) |
-| **Train split** | 14 rows |
-| **Test split** | 3 rows |
-| **Geographic scope** | AGO, BDI, ERI, ETH, KEN, MDG, SOM, SSD, and 1 others |
-| **Publisher** | UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) |
-| **HDX last updated** | 2024-08-30 |
----
-## Variables
-**Identifier / Metadata** — `unnamed_1`, `esa_source` (HDX), `esa_processed` (2026-04-06).
-**Other** — `instructions` (1. The sheets to be updated are individual country tabs (Som etc.) and the "funding details" tabs. The "situation" tab updated to reflect any changes in context or situational data., South Sudan, Madagascar).
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `instructions` | object | 5.6% | 1. The sheets to be updated are individual country tabs (Som etc.) and the "funding details" tabs. The "situation" tab updated to reflect any changes in context or situational data., South Sudan, Madagascar |
-| `unnamed_1` | datetime64[ns] | 55.6% |  |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-06 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-_No numeric columns._
----
-## Curation
-Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 1 column(s) with >80% missing values were removed: `unnamed_2`. 1 exact duplicate rows were removed. 1 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
----
-## Limitations
-- Data originates from UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) and has not been independently validated by ESA.
-- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
-- The following columns have >20% missing values and should be treated with caution in modelling: `unnamed_1`.
-- This dataset spans 9 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/unicef-esaro-regional-db-31-october-2017) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_unicef_esaro_regional_db_31_october_2017,
-  title     = {UNICEF ESARO Regional db 31 March 2018},
-  author    = {UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive)},
-  year      = {2024},
-  url       = {https://data.humdata.org/dataset/unicef-esaro-regional-db-31-october-2017},
-  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
 }
 ```
 ---
-*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*

 ---
+license: cc-by-4.0
 language:
 - en
 task_categories:
 - tabular-classification
 - tabular-regression
+multilinguality: monolingual
+size_categories:
+- n<1K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "humanitarian-development"
+- "parquet"
+- "text"
+- "humanitarian"
+- "hdx"
+- "eastern-africa"
+- "funding"
+- "ago"
+- "bdi"
+- "eri"
+- "eth"
+- "ken"
+pretty_name: "UNICEF ESARO Regional db 31 March 2018 | Africa (original)"
 ---
+# UNICEF ESARO Regional db 31 March 2018 | Africa (original)
+**Size category:** `n<1K` - **Formats:** `parquet` - **Sector:** humanitarian_development - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-n%3C1K-blue)
+![sector](https://img.shields.io/badge/sector-humanitarian_development-green)
+![downloads](https://img.shields.io/badge/HF_downloads-12-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
+## TL;DR
+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.
+## What This Dataset Covers
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
+Dataset context from the existing Hugging Face card: UNICEF ESARO Regional db 31 March 2018 Publisher: UNICEF Eastern and Southern Africa Regional Office (ESARO) (inactive) · Source: HDX · License: cc-by · Updated: 2024-08-30 Abstract UNICEF Eastern and Southern Africa database - Target, Response and Funding as of 31 March 2018 Each row in this dataset represents tabular records. Temporal coverage is indicated by the unnamed_1 column(s). Geographic scope: AGO, BDI, ERI, ETH, KEN, MDG, SOM, SSD, and 1 others. Curated into… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017`](https://huggingface.co/datasets/electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017) |
+| Sector | humanitarian_development |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, eastern-africa, funding, ago, bdi, eri, eth, ken |
+| Modalities | `text` |
+| Formats | `parquet` |
+| Size category | `n<1K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-06 14:19:28+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+## How To Read This Dataset
+- 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.
+## Usage
 ```python
 from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017")
+print(ds)
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
+### Convert To Pandas When Tabular
+```python
+from datasets import Dataset
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
+## 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
+- **Source context:** original
+- **Publisher/source attribution:** original
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017](https://huggingface.co/datasets/electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+## Suggested Analyses
+- 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
+@misc{electric_sheep_africa_africa_unicef_esaro_regional_db_31_october_2017_2026,
+  title        = {UNICEF ESARO Regional db 31 March 2018 | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-unicef-esaro-regional-db-31-october-2017}}
 }
 ```
+## License
+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`.