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) 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)

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

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

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:

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. 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}}
}

OSAA Metrics

master.parquet — long-format indicator values

meta.parquet — per-indicator metadata + embeddings