NICHE: A curated dataset of engineered machine learning projects in Python

• Main Authors: WIDYASARI, Ratnadira, YANG, Zhou, THUNG, Ferdian, SIM, Sheng Qin, WEE, Fiona, LOK, Camellia, PHAN, Jack, QI, Haodi, TAN, Constance, LO, David, David LO
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Daily lives; Engineered software project; High quality; Labeled dataset; Learning projects; Machine-learning; Open source platforms; Open source projects; Software engineering practices; Software project; Computer and Systems Architecture; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8570; https://ink.library.smu.edu.sg/context/sis_research/article/9573/viewcontent/niche.pdf
• Institution: Singapore Management University

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This dataset can help researchers understand the practices that are adopted in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.