Nigeria Machinery: A Low-Resource Industrial Dataset with a Domain-Grounded Reasoning Layer
There is relatively little, public, and model-ready data on industrial machinery for African economies. This makes it hard to do quantitative analysis or to train language models on numeric tasks grounded in that setting. We release two things to help with part of this problem. The first is the Nigeria Machinery Usage and Failures Dataset: 89 machine-level records across 28 indicators, covering Nigeria's manufacturing and oil and gas sectors from 2006 to 2025. Every record names a public source and is decoded by a codebook. The second is a method for building chain-of-thought (CoT) reasoning examples from these sparse numeric values. The result is 94 prompt, completion, and reasoning-trac...
| Authors | Gospel Bassey, Vincent Fakiyesi |
|---|---|
| Primary category | cs.AI |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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