electricsheepafrica/africa-interagency-response-plans

---
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
- humanitarian-response-plan-hrp
- hxl
- people-in-need-pin
- afg
- bfa
- bdi
- cmr
- caf
pretty_name: "Interagency Response Plans"
dataset_info:
splits:
- name: train
num_examples: 34
- name: test
num_examples: 8
---

# Interagency Response Plans

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/interagency-response-plans) · **License:** `cc-by` · **Updated:** 2025-08-26

---

## Abstract

The 2024 GHO launched in December 2023 required $46.6 billion to assist 181 million of the 301 million people in need of aid in 73 countries.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-08-26. Geographic scope: **AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 25 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)** | 43 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 34 rows |
| **Test split** | 8 rows |
| **Geographic scope** | AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 25 others |
| **Publisher** | HDX |
| **HDX last updated** | 2025-08-26 |

---

## Variables

**Geographic** — `interagency_response_plans` (#country+name, Afghanistan (RRP), Ukraine), `iso_alpha_3_codes` (#country+code, MLI, VEN), `plan_type` (Humanitarian response plan, Regional response plan, Flash appeal), `data_explorer_pin` (TRUE, FALSE, #meta+included).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-08).

**Other** — `` (range 1.0–42.0), `in_need` (range 0.0–28600000.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-interagency-response-plans")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `` | float64 | 2.3% | 1.0 – 42.0 (mean 21.5) |
| `interagency_response_plans` | object | 0.0% | #country+name, Afghanistan (RRP), Ukraine |
| `iso_alpha_3_codes` | object | 37.2% | #country+code, MLI, VEN |
| `plan_type` | object | 0.0% | Humanitarian response plan, Regional response plan, Flash appeal |
| `in_need` | float64 | 2.3% | 0.0 – 28600000.0 (mean 8283801.1667) |
| `data_explorer_pin` | object | 0.0% | TRUE, FALSE, #meta+included |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `` | 1.0 | 42.0 | 21.5 | 21.5 |
| `in_need` | 0.0 | 28600000.0 | 8283801.1667 | 5100000.0 |

---

## 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) 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 HDX 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: `iso_alpha_3_codes`.
- This dataset spans 33 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/interagency-response-plans) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_interagency_response_plans,
title = {Interagency Response Plans},
author = {HDX},
year = {2025},
url = {https://data.humdata.org/dataset/interagency-response-plans},
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.*