Nigeria Machinery: A Low-Resource Industrial Dataset with a Domain-Grounded Reasoning Layer
Quick Answer
This paper shows that The Nigeria Machinery Usage and Failures Dataset provides 89 records across 28 indicators for Nigeria's industrial sectors, facilitating quantitative analysis and language model training.
Quick Take
The Nigeria Machinery Usage and Failures Dataset provides 89 records across 28 indicators for Nigeria's industrial sectors, facilitating quantitative analysis and language model training. It includes a reasoning layer that improves domain-grounded prompts from 1 out of 78 to 94 out of 94, ensuring accurate retrieval answers.
Key Points
- Dataset covers Nigeria's manufacturing and oil sectors from 2006 to 2025.
- Includes 89 machine-level records with 28 indicators.
- Chain-of-thought reasoning examples improve prompt accuracy.
- Data adaptation work conducted by Adaption Labs.
- Dataset released under CC-BY-4.0 with clear limitations.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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-trace rows. In every row, the prompt names the real indicator, subsector, year, and source of the record it comes from. The data adaptation work was carried out by Adaption Labs. Along the way we describe a problem that is common when language models are used to build datasets. The prompts can match the real numbers while saying nothing about the real domain. We show that fixing this raises the share of domain-grounded prompts from 1 out of 78 in an earlier release to 94 out of 94, and that every retrieval answer now matches its source value (84 out of 84). We release the data, the reasoning layer, and a per-row provenance file under CC-BY-4.0. We are clear about the limits. With 89 records and 17 indicators that have only one observation, this is a reference and seed dataset, not a large training set. Most reasoning rows are retrieval rather than multi-step computation.
| Comments: | 10pages, 2 tables |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.07883 [cs.AI] |
| (or arXiv:2607.07883v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.07883 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vincent Fakiyesi [view email]
[v1]
Wed, 8 Jul 2026 19:38:54 UTC (269 KB)
— Originally published at arxiv.org
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