---
license: cc-by-4.0
pretty_name: OSAA Metrics — Economic & Trade Indicators
language:
  - en
size_categories:
  - 1M<n<10M
task_categories:
  - tabular-classification
  - tabular-regression
  - feature-extraction
tags:
  - economics
  - development
  - world-bank
  - unesco
  - indicators
  - africa
  - sdg
  - bge-m3
  - embeddings
  - osaa
configs:
  - config_name: master
    data_files: master.parquet
  - config_name: meta
    data_files: meta.parquet
---

# OSAA Metrics

A tidy "One Big Table" of country-year development indicators from the World Bank and UNESCO, paired with a per-indicator metadata table that includes BGE-M3 semantic embeddings for natural-language indicator search.

Built and maintained by **Mirian Lima** ([github.com/mirianlima/osaa-metrics](https://github.com/mirianlima/osaa-metrics)) for the UN Office of the Special Adviser on Africa (OSAA). The companion `osaa-metrics` Python package consumes this dataset directly via DuckDB's `httpfs` extension and exposes it through an MCP server, a CLI, and a marimo notebook.

### `master.parquet` — long-format indicator values

One row per *(indicator × country × year)*. ~9.6 M rows, 6 055 indicators, 215 countries, years 1970–2025.

| Column | Type | Description |
|---|---|---|
| `source` | string | Data provider (`wb`, `uis`) |
| `database` | string | Source database (`wdi`, `sdg`, `opri`) |
| `indicator_code` | string | Provider-native code (e.g. `DT.DOD.DPPG.CD`) — unique across the whole table |
| `indicator_name` | string | Human-readable indicator name |
| `indicator_description` | string | Long-form description |
| `year` | int64 | Calendar year |
| `value` | float64 | Indicator value (units vary per indicator — see description) |
| `iso3` | string | ISO 3166-1 alpha-3 country code |
| `iso2` | string | ISO 3166-1 alpha-2 country code |
| `m49` | int64 | UN M49 numeric area code |
| `country` | string | Country / area name |
| `region` | string | UN M49 region |
| `subregion` | string | UN M49 sub-region |
| `intermediate_region` | string | UN M49 intermediate region (where applicable) |
| `is_ldc` | bool | Least Developed Country flag |
| `is_lldc` | bool | Landlocked Developing Country flag |
| `is_sids` | bool | Small Island Developing State flag |
| `income_group` | string | World Bank income classification |

### `meta.parquet` — per-indicator metadata + embeddings

One row per indicator (6 055 rows). Used for indicator discovery and semantic search.

| Column | Type | Description |
|---|---|---|
| `source`, `database`, `indicator_code` | string | Joins to `master.parquet` |
| `meta_indicator_name` | string | Indicator name |
| `meta_indicator_description` | string | Indicator description |
| `meta_indicator_type` | string | Provider-assigned indicator type |
| `cover_world_pct` | float64 | % of world countries with at least one observation |
| `cover_region_africa` / `_americas` / `_asia` / `_europe` / `_oceania` | float64 | Per-region coverage % |
| `trend_over_years` | float64[] | Yearly observation counts as an array (sparkline-ready) |
| `year_start` / `year_end` | int64 | First / last year with data |
| `stat_cagr_pct` | float64 | Compound annual growth rate of the global mean |
| `indicator_text` | string | Concatenated text used to compute the embedding |
| `embedding` | float32[1024] | BGE-M3 dense embedding of `indicator_text` |

**Note:** the `embedding` column is a 1024-dim float array, so rows look very wide in Data Studio. That's expected.

## Coverage by source

| Source | Database | Indicators |
|---|---|---:|
| UNESCO Institute for Statistics | SDG | 2 506 |
| UNESCO Institute for Statistics | OPRI | 2 042 |
| World Bank | WDI | 1 507 |

## Quick start

### From DuckDB (no auth needed)

```sql
INSTALL httpfs; LOAD httpfs;
SELECT * FROM read_parquet('hf://datasets/spencerlima/osaa-metrics/master.parquet') LIMIT 10;
SELECT * FROM read_parquet('hf://datasets/spencerlima/osaa-metrics/meta.parquet')   LIMIT 10;
```

### From Python via the `osaa-metrics` package

```bash
git clone https://github.com/mirianlima/osaa-metrics
cd osaa-metrics
uv sync --extra mcp --extra viz
uv run osaa-mcp   # MCP server for Claude Desktop
# or
uv run marimo edit notebook/main.py   # interactive notebook
```

Defaults to this dataset (`hf://spencerlima/osaa-metrics`) — no `HF_TOKEN` required for read access.

### From the `datasets` library

```python
from datasets import load_dataset
master = load_dataset("spencerlima/osaa-metrics", "master", split="train")
meta   = load_dataset("spencerlima/osaa-metrics", "meta",   split="train")
```

## Semantic indicator search

The `embedding` column in `meta.parquet` is a normalized BGE-M3 dense vector of `indicator_text`. To find indicators by natural-language description:

```python
import polars as pl, numpy as np
from sentence_transformers import SentenceTransformer

meta = pl.read_parquet("hf://datasets/spencerlima/osaa-metrics/meta.parquet")
embs = np.stack(meta["embedding"].to_list())

model = SentenceTransformer("BAAI/bge-m3")
q = model.encode(["youth unemployment in sub-Saharan Africa"], normalize_embeddings=True)
scores = embs @ q.T
top = meta.with_columns(pl.Series("score", scores.flatten())).sort("score", descending=True).head(10)
```

## Licence

Released under [**CC-BY-4.0**](https://creativecommons.org/licenses/by/4.0/). You may share and adapt the data for any purpose, including commercial use, provided you give appropriate credit.

The underlying source data is published by the World Bank and UNESCO Institute for Statistics under their respective open-data terms — please consult the original providers for indicator-level licensing.

## Citation

```
@misc{lima_osaa_metrics_2026,
  author       = {Mirian Lima},
  title        = {OSAA Metrics — Economic \& Trade Indicators},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/spencerlima/osaa-metrics},
  note         = {Companion code: \url{https://github.com/mirianlima/osaa-metrics}}
}
```

## Links

- **Source code:** [github.com/mirianlima/osaa-metrics](https://github.com/mirianlima/osaa-metrics)
- **Upstream data:** [World Bank Open Data](https://data.worldbank.org/), [UNESCO UIS](https://uis.unesco.org/)
- **Embedding model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)