Datasets:

electricsheepafrica
/

africa-civ-climate-trace

@@ -1,160 +1,144 @@
 ---
-annotations_creators:
-- no-annotation
-language_creators:
-- found
 language:
 - en
-license: cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
 task_categories:
 - tabular-classification
 - tabular-regression
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- climate-weather
-- environment
-- points-of-interest-poi
-- civ
-pretty_name: "Côte d'Ivoire: Greenhouse Gas and Air Pollutant Emissions"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 10278
-    - name: test
-      num_examples: 2569
 ---
-# Côte d'Ivoire: Greenhouse Gas and Air Pollutant Emissions
-**Publisher:** Climate TRACE · **Source:** [HDX](https://data.humdata.org/dataset/civ-climate-trace) · **License:** `cc-by` · **Updated:** 2026-03-30
----
-## Abstract
-Climate TRACE is a non-profit coalition of organizations building a timely, open, and accessible inventory of exactly where greenhouse gas emissions are coming from. Climate TRACE estimates greenhouse gas (GHG) and air pollutant emissions for over 2.7 million sources (from over 744 million assets), and every single country globally.
-The Climate TRACE emissions inventory includes:
-- Annual country-level emissions by sub-sector and by gas beginning in 2015
-- Monthly source-level emissions by sub-sector and gas beginning in 2021 and confidence
-- Emissions source ownership where and when available.
-Each row in this dataset represents time-series observations. Data was last updated on HDX on 2026-03-30. Geographic scope: **CIV**.
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
----
-## Dataset Characteristics
-| | |
 |---|---|
-| **Domain** | Climate and environment |
-| **Unit of observation** | Time-series observations |
-| **Rows (total)** | 12,848 |
-| **Columns** | 13 (4 numeric, 9 categorical, 0 datetime) |
-| **Train split** | 10,278 rows |
-| **Test split** | 2,569 rows |
-| **Geographic scope** | CIV |
-| **Publisher** | Climate TRACE |
-| **HDX last updated** | 2026-03-30 |
----
-## Variables
-**Geographic** — `year` (range 2024.0–2026.0), `emissionsquantity` (range 0.0–12601.4197).
-**Temporal** — `month` (range 1.0–12.0).
-**Identifier / Metadata** — `full_name` (Côte d'Ivoire, Lagunes District, CIV, Bas-Sassandra District, CIV), `id` (CIV, CIV.7_1, CIV.2_1), `level_0_id` (CIV), `level_1_id` (CIV.7_1, CIV.2_1, CIV.10_1), `name` (Côte d'Ivoire, Lagunes District, Bas-Sassandra District) and 2 others.
-**Other** — `level` (range 0.0–1.0), `sector` (agriculture, manufacturing, transportation), `gas` (ch4).
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-civ-climate-trace")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `full_name` | object | 0.0% | Côte d'Ivoire, Lagunes District, CIV, Bas-Sassandra District, CIV |
-| `id` | object | 0.0% | CIV, CIV.7_1, CIV.2_1 |
-| `level` | int64 | 0.0% | 0.0 – 1.0 (mean 0.9222) |
-| `level_0_id` | object | 0.0% | CIV |
-| `level_1_id` | object | 7.8% | CIV.7_1, CIV.2_1, CIV.10_1 |
-| `name` | object | 0.0% | Côte d'Ivoire, Lagunes District, Bas-Sassandra District |
-| `year` | int64 | 0.0% | 2024.0 – 2026.0 (mean 2024.6104) |
-| `month` | int64 | 0.0% | 1.0 – 12.0 (mean 6.6885) |
-| `sector` | object | 0.0% | agriculture, manufacturing, transportation |
-| `gas` | object | 0.0% | ch4 |
-| `emissionsquantity` | float64 | 0.0% | 0.0 – 12601.4197 (mean 218.8097) |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-04 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `level` | 0.0 | 1.0 | 0.9222 | 1.0 |
-| `year` | 2024.0 | 2026.0 | 2024.6104 | 2025.0 |
-| `month` | 1.0 | 12.0 | 6.6885 | 7.0 |
-| `emissionsquantity` | 0.0 | 12601.4197 | 218.8097 | 0.1883 |
----
-## 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: `level_2_id`. 12,682 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.
----
-## Limitations
-- Data originates from Climate TRACE and has not been independently validated by ESA.
-- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/civ-climate-trace) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_civ_climate_trace,
-  title     = {Côte d'Ivoire: Greenhouse Gas and Air Pollutant Emissions},
-  author    = {Climate TRACE},
-  year      = {2026},
-  url       = {https://data.humdata.org/dataset/civ-climate-trace},
-  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:
+- 10K<n<100K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "climate-environment"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "climate-weather"
+- "environment"
+- "points-of-interest-poi"
+- "civ"
+- "climate"
+- "weather"
+pretty_name: "Côte d'Ivoire: Greenhouse Gas and Air Pollutant Emissions | Africa (original)"
 ---
+# Côte d'Ivoire: Greenhouse Gas and Air Pollutant Emissions | Africa (original)
+**Size category:** `10K<n<100K` - **Formats:** `parquet` - **Sector:** climate_environment - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-climate_environment-green)
+![downloads](https://img.shields.io/badge/HF_downloads-17-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: Côte d'Ivoire: Greenhouse Gas and Air Pollutant Emissions Publisher: Climate TRACE · Source: HDX · License: cc-by · Updated: 2026-03-30 Abstract Climate TRACE is a non-profit coalition of organizations building a timely, open, and accessible inventory of exactly where greenhouse gas emissions are coming from. Climate TRACE estimates greenhouse gas (GHG) and air pollutant emissions for over 2.7 million sources (from over 744 million assets), and every single country… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-civ-climate-trace.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-civ-climate-trace`](https://huggingface.co/datasets/electricsheepafrica/africa-civ-climate-trace) |
+| Sector | climate_environment |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, climate-weather, environment, points-of-interest-poi, civ |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `10K<n<100K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-04 15:49:38+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-civ-climate-trace")
+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-civ-climate-trace](https://huggingface.co/datasets/electricsheepafrica/africa-civ-climate-trace)
+- **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_civ_climate_trace_2026,
+  title        = {Côte d'Ivoire: Greenhouse Gas and Air Pollutant Emissions | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-civ-climate-trace},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-civ-climate-trace}}
 }
 ```
+## 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`.