electricsheepafrica/africa-aid-flows-mauritania

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- funding
- who-is-doing-what-and-where-3w-4w-5w
- mrt
pretty_name: "Mauritania - Current IATI Aid Activities"
dataset_info:
splits:
- name: train
num_examples: 3236
- name: test
num_examples: 809
---

# Mauritania - Current IATI Aid Activities

**Publisher:** International Aid Transparency Initiative · **Source:** [HDX](https://data.humdata.org/dataset/iati-mrt) · **License:** `hdx-other` · **Updated:** 2026-04-24

---

## Abstract

List of active aid activities shared via the International Aid Transparency Initiative (IATI). Includes both humanitarian and development activities. More information on each activity (including financial data) is available from [http://www.d-portal.org](http://www.d-portal.org)

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `day_start`, `day_end` column(s). Geographic scope: **MRT**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 4,045 |
| **Columns** | 27 (12 numeric, 13 categorical, 2 datetime) |
| **Train split** | 3,236 rows |
| **Test split** | 809 rows |
| **Geographic scope** | MRT |
| **Publisher** | International Aid Transparency Initiative |
| **HDX last updated** | 2026-04-24 |

---

## Variables

**Geographic** — `day_start`, `day_end`, `day_length` (range 0.0–28853.0), `country_code`, `country_percent` (range 0.0–100.0).

**Outcome / Measurement** — `sector_percent` (range 0.0–100.0).

**Identifier / Metadata** — `aid` (http://d-portal.org/q.html?aid=US-GOV-1-720202451839, http://d-portal.org/q.html?aid=US-GOV-1-720201851084, http://d-portal.org/q.html?aid=US-GOV-1-720202557453), `reporting_ref` (US-GOV-1, XM-DAC-928, FR-3), `funder_ref` (US-GOV-1, FR, XM-DAC-928), `title` (USAID redacted this field in accordance with the exceptions outlined in the Foreign Aid Transparency and Accountability Act of 2016., USAID Travel and Transportation, USAID Pay and Benefits), `status_code` (Finalisation, Implementation, Closed) and 3 others.

**Other** — `reporting` (U.S. Agency for International Development, World Health Organization, AFD), `slug` (who-mrt, unicef-mauritania, ec-intpa-mr), `description` (This program supports the Mission's goal for an independent, prosperous, and healthy Ukraine united around core European values in one or more of the following sectors: strengthening of democratic governance, and more inclusive, sustainable, market-driven economic growth., Administrative costs and operating expenses of USAID contributing to Travel and Transportation., Action A: Man made crisis and natural disasters,Action C: DIPECHO), `commitment` (range -96179504.0–1601730000.0), `spend` (range -677999.1–1354471200.0) and 8 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-aid-flows-mauritania")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `aid` | object | 0.0% | http://d-portal.org/q.html?aid=US-GOV-1-720202451839, http://d-portal.org/q.html?aid=US-GOV-1-720201851084, http://d-portal.org/q.html?aid=US-GOV-1-720202557453 |
| `reporting` | object | 0.0% | U.S. Agency for International Development, World Health Organization, AFD |
| `reporting_ref` | object | 0.0% | US-GOV-1, XM-DAC-928, FR-3 |
| `funder_ref` | object | 0.0% | US-GOV-1, FR, XM-DAC-928 |
| `title` | object | 0.0% | USAID redacted this field in accordance with the exceptions outlined in the Foreign Aid Transparency and Accountability Act of 2016., USAID Travel and Transportation, USAID Pay and Benefits |
| `slug` | object | 0.0% | who-mrt, unicef-mauritania, ec-intpa-mr |
| `status_code` | object | 0.0% | Finalisation, Implementation, Closed |
| `day_start` | datetime64[ns] | 0.0% | |
| `day_end` | datetime64[ns] | 0.0% | |
| `day_length` | float64 | 0.0% | 0.0 – 28853.0 (mean 1264.7799) |
| `description` | object | 0.5% | This program supports the Mission's goal for an independent, prosperous, and healthy Ukraine united around core European values in one or more of the following sectors: strengthening of democratic governance, and more inclusive, sustainable, market-driven economic growth., Administrative costs and operating expenses of USAID contributing to Travel and Transportation., Action A: Man made crisis and natural disasters,Action C: DIPECHO |
| `commitment` | float64 | 0.0% | -96179504.0 – 1601730000.0 (mean 17994847.752) |
| `spend` | float64 | 0.0% | -677999.1 – 1354471200.0 (mean 15985016.462) |
| `commitment_eur` | float64 | 0.0% | -89066320.0 – 1415677200.0 (mean 15933849.8222) |
| `spend_eur` | float64 | 0.0% | -544056.56 – 1240414800.0 (mean 14210362.3796) |
| `commitment_gbp` | float64 | 0.0% | -76572510.0 – 1260278300.0 (mean 13756851.6472) |
| `spend_gbp` | float64 | 0.0% | -482020.84 – 1054165630.0 (mean 12253129.908) |
| `commitment_cad` | float64 | 0.0% | -129152656.0 – 2161499400.0 (mean 23446959.298) |
| `spend_cad` | float64 | 0.0% | -826319.75 – 1837040300.0 (mean 21028923.2993) |
| `flags` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `sector_group` | object | 1.9% | Government & Civil Society-general, Emergency Response, Basic Health |
| `sector_code` | object | 5.3% | Material relief assistance and services, Administrative costs (non-sector allocable), Basic health care |
| `sector_percent` | float64 | 1.8% | 0.0 – 100.0 (mean 64.1184) |
| `country_code` | object | 0.0% | |
| `country_percent` | float64 | 0.0% | 0.0 – 100.0 (mean 62.2288) |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `day_length` | 0.0 | 28853.0 | 1264.7799 | 730.0 |
| `commitment` | -96179504.0 | 1601730000.0 | 17994847.752 | 296508.75 |
| `spend` | -677999.1 | 1354471200.0 | 15985016.462 | 328857.0 |
| `commitment_eur` | -89066320.0 | 1415677200.0 | 15933849.8222 | 264240.34 |
| `spend_eur` | -544056.56 | 1240414800.0 | 14210362.3796 | 296721.94 |
| `commitment_gbp` | -76572510.0 | 1260278300.0 | 13756851.6472 | 224172.03 |
| `spend_gbp` | -482020.84 | 1054165630.0 | 12253129.908 | 255446.75 |
| `commitment_cad` | -129152656.0 | 2161499400.0 | 23446959.298 | 396914.4 |
| `spend_cad` | -826319.75 | 1837040300.0 | 21028923.2993 | 438242.6 |
| `flags` | 0.0 | 0.0 | 0.0 | 0.0 |
| `sector_percent` | 0.0 | 100.0 | 64.1184 | 100.0 |
| `country_percent` | 0.0 | 100.0 | 62.2288 | 100.0 |

---

## 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`. 6 exact duplicate rows were removed. 2 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 International Aid Transparency Initiative 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/iati-mrt) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_aid_flows_mauritania,
title = {Mauritania - Current IATI Aid Activities},
author = {International Aid Transparency Initiative},
year = {2026},
url = {https://data.humdata.org/dataset/iati-mrt},
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.*