Rwanda's data,
unburied.

What is imibare?

Imibare is the Kinyarwanda word for "numbers" or "statistics." It is also an open data infrastructure project that extracts, cleans, and republishes Rwandan government statistical data in machine-readable form, so you do not have to spend a Tuesday afternoon copy-pasting numbers out of a PDF.

Why does it exist?

Rwanda's government institutions publish genuinely useful data. The problem is finding it, downloading it, and doing anything programmatic with it. Data is buried in scanned PDFs, inconsistent Excel files, and poorly maintained portals. imibare fixes the infrastructure. The data was always there; this just makes it accessible.

Who is behind this?

One person, for now. I publish without my real name because the work matters more than who is doing it. What is verifiable: the data, its sources, its methodology, and the date it was published. My name is not important; the data is.

Is imibare affiliated with the government?

No. This matters, so it is worth saying clearly. imibare is completely independent. It is not funded by, endorsed by, or affiliated with MINICT, RISA, NISR, or any other Rwandan government institution. The data on this site comes from those institutions (they remain the authoritative source), but the infrastructure, the decisions about what to publish and how, and the editorial independence are entirely mine.

For what it is worth, Rwanda's Ministry of ICT published a National Data Sharing Policy in May 2025 that explicitly defines public open data as a complement to their inter-agency infrastructure. imibare is exactly that complement. We are not competing; we are building different parts of the same future.

How is it funded?

Mostly stubbornness. The infrastructure cost is manageable while traffic stays modest. The work (writing the extraction pipelines, cleaning the data, maintaining the catalog) is done on evenings and weekends for now.

imibare has no advertisers, no government funders, and no sponsors with an interest in what gets published. The goal is to keep it that way.

Is the data accurate?

It comes from official government institutions (NISR, BNR, MINECOFIN, RSSB, RRA, RSE, etc.), who are the authoritative sources. imibare extracts, cleans, and standardises it, then publishes the result with full provenance: where it came from, when it was retrieved, what changed in cleaning.

That said: imibare is not infallible. Extraction pipelines can have bugs. Source data can have errors. If you are relying on imibare data for something consequential (published research, journalism, policy), please verify against the original source. And if you find an error, tell me.

Can I use the data?

Yes. imibare data is published under CC-BY 4.0. Use it, share it, build on it, publish it, for any purpose including commercial. The only requirement is attribution: credit the original institution as the data source, and imibare as the pipeline.

Example: "Source: NISR (statistics.gov.rw), processed by imibare (imibare.org)"

See the documentation for per-institution citation formats.

How do I access the data programmatically?

Three ways, depending on your workflow:

Python: pip install imibare, then imi.load("rw.nisr.cpi.monthly"). Returns a pandas or polars DataFrame.

Direct download: every dataset page has a download button. CSV, JSON, and Parquet available.

REST API: documented at /docs. Returns JSON metadata and presigned download URLs.

I found an error. What do I do?

Please email TK@imibare.org with the dataset ID, the value you believe is wrong, and what you think it should be. Include the original source if you have it. Every error report is taken seriously: the whole point of this project is that the data should be trustworthy.

Can I contribute a dataset?

Yes, contributions are welcome, especially from people with institutional knowledge of Rwandan data sources. You do not need to write code. If you know where a dataset lives, can download it, and can fill in a structured template, that is a meaningful contribution. Get in touch and we can figure out the best way to get it in.

What about other African countries?

Rwanda is instance one. The architecture is country-agnostic from the start: every dataset ID begins with a country code (rw.), and adding Kenya, Uganda, or Tanzania is a configuration change, not a rewrite. The expansion path is EAC first, then Sub-Saharan Africa. The criterion for adding a country: someone who knows the institutions and the data landscape well enough to own the pipeline for it. If that person is you, let's talk.

Are these questions actually asked frequently?

No. But they are the questions I would ask if I landed here for the first time, so here we are.