electricsheepafrica/africa-mrt-requirements-and-funding-data

---
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
- covid-19
- funding
- humanitarian-financial-tracking-service-fts
- mrt
pretty_name: "Mauritania - Requirements and Funding Data"
dataset_info:
splits:
- name: train
num_examples: 32
- name: test
num_examples: 8
---

# Mauritania - Requirements and Funding Data

**Publisher:** OCHA Financial Tracking System (FTS) · **Source:** [HDX](https://data.humdata.org/dataset/mrt-requirements-and-funding-data) · **License:** `cc-by-igo` · **Updated:** 2026-04-06

---

## Abstract

FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's [Financial Tracking Service](https://fts.unocha.org/) and is encoded as utf-8.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `startdate`, `enddate` 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)** | 40 |
| **Columns** | 14 (6 numeric, 6 categorical, 2 datetime) |
| **Train split** | 32 rows |
| **Test split** | 8 rows |
| **Geographic scope** | MRT |
| **Publisher** | OCHA Financial Tracking System (FTS) |
| **HDX last updated** | 2026-04-06 |

---

## Variables

**Geographic** — `countrycode` (MRT), `typeid` (range 4.0–2071.0), `typename` (Regional response plan, Humanitarian response plan, Strategic response plan), `year` (range 2001.0–2028.0).

**Temporal** — `startdate`, `enddate`.

**Outcome / Measurement** — `percentfunded` (range 23.0–88.0).

**Identifier / Metadata** — `id` (range 190.0–661.0), `name` (Not specified, Mauritania Plan de Travail Humanitaire 2018, Mauritanie Plan de Réponse Humanitaire 2017), `code` (OMRT18, HMRT17, HMRT16), `esa_source` (HDX), `esa_processed` (2026-04-06).

**Other** — `requirements` (range 9004053.0–116000000.0), `funding` (range 12303.0–127317413.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mrt-requirements-and-funding-data")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `countrycode` | object | 0.0% | MRT |
| `id` | float64 | 70.0% | 190.0 – 661.0 (mean 391.0833) |
| `name` | object | 0.0% | Not specified, Mauritania Plan de Travail Humanitaire 2018, Mauritanie Plan de Réponse Humanitaire 2017 |
| `code` | object | 70.0% | OMRT18, HMRT17, HMRT16 |
| `typeid` | float64 | 70.0% | 4.0 – 2071.0 (mean 410.9167) |
| `typename` | object | 70.0% | Regional response plan, Humanitarian response plan, Strategic response plan |
| `startdate` | datetime64[ns] | 70.0% | |
| `enddate` | datetime64[ns] | 70.0% | |
| `year` | int64 | 0.0% | 2001.0 – 2028.0 (mean 2013.775) |
| `requirements` | float64 | 70.0% | 9004053.0 – 116000000.0 (mean 65068275.25) |
| `funding` | int64 | 0.0% | 12303.0 – 127317413.0 (mean 29071550.45) |
| `percentfunded` | float64 | 70.0% | 23.0 – 88.0 (mean 61.25) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `id` | 190.0 | 661.0 | 391.0833 | 400.0 |
| `typeid` | 4.0 | 2071.0 | 410.9167 | 111.0 |
| `year` | 2001.0 | 2028.0 | 2013.775 | 2013.5 |
| `requirements` | 9004053.0 | 116000000.0 | 65068275.25 | 81854760.5 |
| `funding` | 12303.0 | 127317413.0 | 29071550.45 | 20309607.0 |
| `percentfunded` | 23.0 | 88.0 | 61.25 | 65.5 |

---

## 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`. 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 OCHA Financial Tracking System (FTS) 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: `id`, `code`, `typeid`, `typename`, `startdate`, `enddate`, `requirements`, `percentfunded`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/mrt-requirements-and-funding-data) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_mrt_requirements_and_funding_data,
title = {Mauritania - Requirements and Funding Data},
author = {OCHA Financial Tracking System (FTS)},
year = {2026},
url = {https://data.humdata.org/dataset/mrt-requirements-and-funding-data},
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.*