Standardize Electric Sheep Africa dataset card

README.md

@@ -1,219 +1,147 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
+license: cc-by-4.0
 language:
 - en
-license: cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
-- other
-task_ids: []
+- tabular-regression
+multilinguality: monolingual
+size_categories:
+- 1K<n<10K
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- covid-19
-- epidemics-outbreaks
-- health
-- ben
-- civ
-- cod
-- gha
-- ken
-pretty_name: "Perceptions and impact of Coronavirus in Sub-Saharan African countries"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 3189
-    - name: test
-      num_examples: 797
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "humanitarian-development"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "covid-19"
+- "epidemics-outbreaks"
+- "health"
+- "ben"
+- "civ"
+- "cod"
+- "gha"
+- "ken"
+- "food-security"
+pretty_name: "Perceptions and impact of Coronavirus in Sub-Saharan African countries | Africa (original)"
 ---
 
-# Perceptions and impact of Coronavirus in Sub-Saharan African countries
+# Perceptions and impact of Coronavirus in Sub-Saharan African countries | Africa (original)
 
-**Publisher:** Mobile Accord, Inc. (GeoPoll) · **Source:** [HDX](https://data.humdata.org/dataset/covid-19-impacts-africa) · **License:** `cc-by` · **Updated:** 2025-09-26
+**Size category:** `1K<n<10K` - **Formats:** `parquet` - **Sector:** humanitarian_development - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
 
----
+![size](https://img.shields.io/badge/size-1K%3Cn%3C10K-blue)
+![sector](https://img.shields.io/badge/sector-humanitarian_development-green)
+![downloads](https://img.shields.io/badge/HF_downloads-8-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
 
-## Abstract
+## TL;DR
 
-This data and report examine perceptions and the impact of COVID-19 in 12 countries throughout sub-Saharan Africa. Topics covered include greatest concerns surrounding coronavirus, preventative measures being taken, changes in food market operability and food security, consumer behavior changes, and trust in governments to prevent the spread of coronavirus. This dataset includes data from 10 of the markets. Please contact us for access to data from all markets, the questionnaire, and with any other questions.
+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.
 
-Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `survey_date` column(s). Geographic scope: **BEN, CIV, COD, GHA, KEN, MOZ, NGA, RWA, and 4 others**.
+## What This Dataset Covers
 
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
 
----
+Dataset context from the existing Hugging Face card: Perceptions and impact of Coronavirus in Sub-Saharan African countries Publisher: Mobile Accord, Inc. (GeoPoll) · Source: HDX · License: cc-by · Updated: 2025-09-26 Abstract This data and report examine perceptions and the impact of COVID-19 in 12 countries throughout sub-Saharan Africa. Topics covered include greatest concerns surrounding coronavirus, preventative measures being taken, changes in food market operability and food security, consumer behavior changes, and… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-covid-19-impacts-africa.
 
-## Dataset Characteristics
+## Dataset Profile
 
-| | |
+| Field | Value |
 |---|---|
-| **Domain** | Food security and nutrition |
-| **Unit of observation** | First-level administrative unit observations |
-| **Rows (total)** | 3,987 |
-| **Columns** | 64 (9 numeric, 28 categorical, 1 datetime) |
-| **Train split** | 3,189 rows |
-| **Test split** | 797 rows |
-| **Geographic scope** | BEN, CIV, COD, GHA, KEN, MOZ, NGA, RWA, and 4 others |
-| **Publisher** | Mobile Accord, Inc. (GeoPoll) |
-| **HDX last updated** | 2025-09-26 |
-
----
-
-## Variables
-
-**Geographic** — `survey_date`, `country` (Kenya, Nigeria, Ghana), `admin1` (Lusaka, Abidjan, Kigali), `birthyear` (range 1928.0–2004.0), `preventativemeasures_avoiding_public_places` and 7 others.
-
-**Demographic** — `gender_weights` (range 0.7608–1.4586), `age_group_weights` (range 0.619–1.8537), `gender` (Male, Female), `age_group` (15-25, 26-35, 36+), `age` (range 15.0–91.0) and 1 others.
-
-**Outcome / Measurement** — `total_weights` (range 0.1396–6.4719), `foodamount`.
-
-**Identifier / Metadata** — `preventativemeasures_avoiding_public_transport`, `informationsources_newspapers`, `informationsources_other`, `informationsources_radio`, `informationsources_social_media` and 3 others.
-
-**Other** — `adm1_weights` (range 0.2219–3.7881), `sec` (C1, B, A), `awareness` (Yes), `urban_rural` (Urban area, Rural area), `biggestchallenge` (Money, Corona virus, Coronavirus) and 31 others.
-
----
-
-## Quick Start
+| Hugging Face repo | [`electricsheepafrica/africa-covid-19-impacts-africa`](https://huggingface.co/datasets/electricsheepafrica/africa-covid-19-impacts-africa) |
+| Sector | humanitarian_development |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, covid-19, epidemics-outbreaks, health, ben, civ, cod, gha, ken |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `1K<n<10K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-20 08:44:22+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-covid-19-impacts-africa")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
+ds = load_dataset("electricsheepafrica/africa-covid-19-impacts-africa")
+print(ds)
 
-print(train.shape)
-train.head()
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
 
----
+### Convert To Pandas When Tabular
 
-## Schema
-
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `gender_weights` | float64 | 0.0% | 0.7608 – 1.4586 (mean 1.0) |
-| `age_group_weights` | float64 | 0.0% | 0.619 – 1.8537 (mean 1.0001) |
-| `adm1_weights` | float64 | 0.0% | 0.2219 – 3.7881 (mean 0.9987) |
-| `total_weights` | float64 | 0.0% | 0.1396 – 6.4719 (mean 0.9881) |
-| `survey_date` | datetime64[ns] | 0.0% |  |
-| `country` | object | 0.0% | Kenya, Nigeria, Ghana |
-| `gender` | object | 0.0% | Male, Female |
-| `age_group` | object | 0.0% | 15-25, 26-35, 36+ |
-| `admin1` | object | 0.0% | Lusaka, Abidjan, Kigali |
-| `sec` | object | 0.0% | C1, B, A |
-| `age` | int64 | 0.0% | 15.0 – 91.0 (mean 31.2904) |
-| `birthyear` | int64 | 0.0% | 1928.0 – 2004.0 (mean 1987.7096) |
-| `awareness` | object | 0.0% | Yes |
-| `urban_rural` | object | 0.0% | Urban area, Rural area |
-| `biggestchallenge` | object | 49.9% | Money, Corona virus, Coronavirus |
-| `levelconcern` | int64 | 0.0% | 1.0 – 5.0 (mean 4.3128) |
-| `concerns` | object | 0.0% | Contracting the disease, Economic impact, Global infections |
-| `riskawareness` | object | 0.0% | Yes, No |
-| `tested` | object | 0.0% |  |
-| `virusprevention` | object | 0.0% |  |
-| `preventativemeasures_avoiding_public_places` | bool | 0.0% |  |
-| `preventativemeasures_avoiding_public_transport` | bool | 0.0% |  |
-| `preventativemeasures_increasing_hygiene` | bool | 0.0% |  |
-| `preventativemeasures_other` | bool | 0.0% |  |
-| `preventativemeasures_working_from_home` | bool | 0.0% |  |
-| `handwashing` | object | 0.0% |  |
-| `socialdistancing` | object | 0.0% |  |
-| `healthbehavior` | object | 0.0% |  |
-| `economicimpact` | int64 | 0.0% | 1.0 – 5.0 (mean 4.2854) |
-| `marketoperability` | object | 0.0% |  |
-| `foodlocations` | object | 0.0% |  |
-| `foodlocations2` | object | 48.3% |  |
-| `foodshopping` | object | 0.0% |  |
-| `foodamount` | object | 0.0% |  |
-| `foodworry` | object | 0.0% |  |
-| `brandpurchase` | object | 20.3% |  |
-| `nonessentialitems` | object | 0.0% |  |
-| `governmenttrust` | int64 | 0.0% | 1.0 – 5.0 (mean 3.4542) |
-| `commercialtrust_banks` | bool | 0.0% |  |
-| `commercialtrust_brands` | bool | 0.0% |  |
-| `commercialtrust_other` | bool | 0.0% |  |
-| `commercialtrust_retailers` | bool | 0.0% |  |
-| `commercialtrust_telecommuncations` | bool | 0.0% |  |
-| `initiativetaken_limited_store_traffic` | bool | 0.0% |  |
-| `initiativetaken_none` | bool | 0.0% |  |
-| `initiativetaken_sectioned_intercepts` | bool | 0.0% |  |
-| `initiativetaken_staff_in_protective_gear` | bool | 0.0% |  |
-| `groups_brands` | bool | 0.0% |  |
-| `groups_governments` | bool | 0.0% |  |
-| `groups_ngos` | bool | 0.0% |  |
-| `groups_private_sector` | bool | 0.0% |  |
-| `groups_retailers` | bool | 0.0% |  |
-| `informationsources_friends_family` | bool | 0.0% |  |
-| `informationsources_government_messages` | bool | 0.0% |  |
-| `informationsources_newspapers` | bool | 0.0% |  |
-| `informationsources_other` | bool | 0.0% |  |
-| `informationsources_radio` | bool | 0.0% |  |
-| `informationsources_social_media` | bool | 0.0% |  |
-| `informationsources_tv` | bool | 0.0% |  |
-| `socialmedia` | object | 59.3% |  |
-| `mediaconsumption` | object | 0.0% |  |
-| `staypositive` | object | 49.9% |  |
-| `esa_source` | object | 0.0% |  |
-| `esa_processed` | object | 0.0% |  |
-
----
-
-## Numeric Summary
-
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `gender_weights` | 0.7608 | 1.4586 | 1.0 | 1.0 |
-| `age_group_weights` | 0.619 | 1.8537 | 1.0001 | 1.0 |
-| `adm1_weights` | 0.2219 | 3.7881 | 0.9987 | 1.0 |
-| `total_weights` | 0.1396 | 6.4719 | 0.9881 | 1.0 |
-| `age` | 15.0 | 91.0 | 31.2904 | 29.0 |
-| `birthyear` | 1928.0 | 2004.0 | 1987.7096 | 1990.0 |
-| `levelconcern` | 1.0 | 5.0 | 4.3128 | 5.0 |
-| `economicimpact` | 1.0 | 5.0 | 4.2854 | 5.0 |
-| `governmenttrust` | 1.0 | 5.0 | 3.4542 | 4.0 |
+```python
+from datasets import Dataset
 
----
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
 
-## Curation
+## Data Quality Notes
 
-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 exact duplicate rows were removed. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
+- 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
 
-## Limitations
+- **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-covid-19-impacts-africa](https://huggingface.co/datasets/electricsheepafrica/africa-covid-19-impacts-africa)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
 
-- Data originates from Mobile Accord, Inc. (GeoPoll) 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: `biggestchallenge`, `foodlocations2`, `brandpurchase`, `socialmedia`, `staypositive`.
-- This dataset spans 12 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/covid-19-impacts-africa) for the publisher's own methodology notes and caveats.
+## 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
-@dataset{hdx_africa_covid_19_impacts_africa,
-  title     = {Perceptions and impact of Coronavirus in Sub-Saharan African countries},
-  author    = {Mobile Accord, Inc. (GeoPoll)},
-  year      = {2025},
-  url       = {https://data.humdata.org/dataset/covid-19-impacts-africa},
-  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
+@misc{electric_sheep_africa_africa_covid_19_impacts_africa_2026,
+  title        = {Perceptions and impact of Coronavirus in Sub-Saharan African countries | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-covid-19-impacts-africa},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-covid-19-impacts-africa}}
 }
 ```
 
+## 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.
+
 ---
 
-*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.