TITLE: NICHE: A Curated Dataset of Engineered Machine Learning Projects in Python 

ABSTRACT: Machine learning (ML) has gained much attention and 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 in understanding
ML projects. To help clear this obstacle, we present NICHE, a manually labelled
dataset consisting of 572 ML projects. Based on evidences 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 followed in high-quality ML projects. It can also be used as a benchmark
for classifiers designed to identify engineered ML projects.
Machine learning (ML) has gained much attention and 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 in understanding
ML projects. To help clear this obstacle, we present NICHE, a manually labelled
dataset consisting of 572 ML projects. Based on evidences 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 followed in high-quality ML projects. It can also be used as a benchmark
for classifiers designed to identify engineered ML projects.
NICHE: A Curated Dataset of Engineered Machine Learning Projects in Python
http://arxiv.org/abs/2303.06286v1