I built an open API for Nigeria's 752 universities, polytechnics, and colleges of education
If you've ever tried to find a clean, structured, up-to-date list of Nigerian tertiary institutions, you've probably run into the same wall I did: every result is a static blog post or a PDF from a few years ago, half of them contradict each other, and none of them are usable as data — just prose to be re-typed by hand. There wasn't an open API for this. So I built one. What it is…
Finding a reliable, up-to-date list of Nigerian tertiary institutions can be challenging. Previous sources were either outdated, contradictory, or not easily usable. In response, I created an open-source REST API and dataset called nigeria-tertiary-institutions-api, which includes every Nigerian university, polytechnic, and college of education, totaling 752 institutions.
The API provides information about each institution, such as category, state, year established, and department structure. It is self-hosted, allowing users to clone the repository, run one command, and have their own instance running locally or deployed wherever desired. The dataset is stored in three CSV files, and a build step compiles them into a local SQLite file at startup.
The API can be accessed via GET requests to retrieve institution statistics, search for institutions based on type and state, and search for departments across all institutions. Departments can also be queried for course-of-study data. The data model is organized in four layers, ensuring a clear structure. While the institution, school, and department data is verified through live research, course-of-study data is generated using a naming convention.
Departments with missing or unclear information are flagged as "inferred." A bug in the course-of-study generator was discovered during a sanity check, and a filter was added to prevent the generation of fake department rows. The API is open by default, but can be secured by setting an API_KEY environment variable.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.