---
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-regression
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- covid-19
- funding
- humanitarian-financial-tracking-service-fts
- dza
pretty_name: "Algeria - Requirements and Funding Data"
dataset_info:
  splits:
    - name: train
      num_examples: 16
    - name: test
      num_examples: 4
---

# Algeria - Requirements and Funding Data

**Publisher:** OCHA Financial Tracking System (FTS) · **Source:** [HDX](https://data.humdata.org/dataset/dza-requirements-and-funding-data) · **License:** `cc-by-igo` · **Updated:** 2026-04-13

---

## 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 time-series observations. Temporal coverage is indicated by the `date`, `firstreporteddate` column(s). Geographic scope: **DZA**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 20 |
| **Columns** | 33 (9 numeric, 19 categorical, 5 datetime) |
| **Train split** | 16 rows |
| **Test split** | 4 rows |
| **Geographic scope** | DZA |
| **Publisher** | OCHA Financial Tracking System (FTS) |
| **HDX last updated** | 2026-04-13 |

---

## Variables

**Geographic** — `budgetyear` (range 2024.0–2026.0), `srcorganizationtypes` (Governments, Multilateral Organizations), `srclocations` (ESP, DEU, HRV), `srcusageyearstart` (range 2023.0–2026.0), `srcusageyearend` (range 2023.0–2026.0) and 11 others.

**Temporal** — `date`, `firstreporteddate`, `decisiondate`, `updatedat`.

**Outcome / Measurement** — `amountusd` (range 0.0–6000000.0), `originalamount` (range 0.0–4000000.0).

**Identifier / Metadata** — `id` (range 295408.0–383347.0), `refcode`, `esa_source`, `esa_processed`.

**Other** — `description` (Enhancing Livelihoods and Promoting Self-reliance among Sahrawi Refugees, Amélioration de l’accès à l’eau potable en qualité et en quantité dans les camps des réfugiés sahraouis de Tindouf - Algérie 2025, Algeria Interim Country Strategic Plan July 2019-December 2024 and Libya Country Strategic Plan 2023-2025), `srcorganization` (Spain, Government of, European Commission's Humanitarian Aid and Civil Protection Department, Germany, Government of), `destorganization` (Danish Refugee Council, World Food Programme, Solidaridad Internacional), `destglobalclusters` (Food Security, Health, Water Sanitation Hygiene), `method` and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-dza-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 |
|---|---|---|---|
| `date` | datetime64[ns] | 0.0% |  |
| `budgetyear` | int64 | 0.0% | 2024.0 – 2026.0 (mean 2025.25) |
| `description` | object | 0.0% | Enhancing Livelihoods and Promoting Self-reliance among Sahrawi Refugees, Amélioration de l’accès à l’eau potable en qualité et en quantité dans les camps des réfugiés sahraouis de Tindouf - Algérie 2025, Algeria Interim Country Strategic Plan July 2019-December 2024 and Libya Country Strategic Plan 2023-2025 |
| `amountusd` | int64 | 0.0% | 0.0 – 6000000.0 (mean 924512.05) |
| `srcorganization` | object | 0.0% | Spain, Government of, European Commission's Humanitarian Aid and Civil Protection Department, Germany, Government of |
| `srcorganizationtypes` | object | 0.0% | Governments, Multilateral Organizations |
| `srclocations` | object | 35.0% | ESP, DEU, HRV |
| `srcusageyearstart` | int64 | 0.0% | 2023.0 – 2026.0 (mean 2024.95) |
| `srcusageyearend` | int64 | 0.0% | 2023.0 – 2026.0 (mean 2025.1) |
| `destorganization` | object | 5.0% | Danish Refugee Council, World Food Programme, Solidaridad Internacional |
| `destorganizationtypes` | object | 5.0% | NGOs, Multilateral Organizations, Red Cross/Red Crescent Organizations |
| `destglobalclusters` | object | 15.0% | Food Security, Health, Water Sanitation Hygiene |
| `destlocations` | object | 0.0% | DZA, DZA,LBY, BFA,CAF,CMR,COD,DZA,ETH,LBY,MLI,NER,NGA,SDN,SSD,TCD |
| `destusageyearstart` | int64 | 0.0% | 2023.0 – 2026.0 (mean 2025.75) |
| `destusageyearend` | int64 | 0.0% | 2026.0 – 2026.0 (mean 2026.0) |
| `contributiontype` | object | 0.0% | financial |
| `flowtype` | object | 0.0% | Standard, Parked |
| `method` | object | 0.0% |  |
| `boundary` | object | 0.0% |  |
| `onboundary` | object | 0.0% |  |
| `status` | object | 0.0% |  |
| `firstreporteddate` | datetime64[ns] | 0.0% |  |
| `decisiondate` | datetime64[ns] | 10.0% |  |
| `keywords` | object | 80.0% |  |
| `originalamount` | float64 | 5.0% | 0.0 – 4000000.0 (mean 586689.6842) |
| `originalcurrency` | object | 5.0% |  |
| `exchangerate` | float64 | 5.0% | 0.851 – 0.948 (mean 0.8849) |
| `id` | int64 | 0.0% | 295408.0 – 383347.0 (mean 353873.65) |
| `refcode` | object | 5.0% |  |
| `createdat` | datetime64[ns] | 0.0% |  |
| `updatedat` | datetime64[ns] | 0.0% |  |
| `esa_source` | object | 0.0% |  |
| `esa_processed` | object | 0.0% |  |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `budgetyear` | 2024.0 | 2026.0 | 2025.25 | 2025.0 |
| `amountusd` | 0.0 | 6000000.0 | 924512.05 | 304907.0 |
| `srcusageyearstart` | 2023.0 | 2026.0 | 2024.95 | 2025.0 |
| `srcusageyearend` | 2023.0 | 2026.0 | 2025.1 | 2025.0 |
| `destusageyearstart` | 2023.0 | 2026.0 | 2025.75 | 2026.0 |
| `destusageyearend` | 2026.0 | 2026.0 | 2026.0 | 2026.0 |
| `originalamount` | 0.0 | 4000000.0 | 586689.6842 | 246575.0 |
| `exchangerate` | 0.851 | 0.948 | 0.8849 | 0.865 |
| `id` | 295408.0 | 383347.0 | 353873.65 | 363643.5 |

---

## 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`. 6 column(s) with >80% missing values were removed: `destplan`, `destplancode`, `destplanid`, `destproject`, `destprojectcode`, `destemergency`. 5 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: `srclocations`, `keywords`.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/dza-requirements-and-funding-data) for the publisher's own methodology notes and caveats.

---

## Citation

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