2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.SE2026
ReproScore: Separating Readiness from Outcome in Research Software Reproducibility Assessment
Sheeba Samuel, Daniel Mietchen, Jungsan Kim +2
Digital libraries curate millions of research software artefacts yet lack scalable infrastructure for assessing whether those artefacts remain executable. Existing automated assess…
cs.SE2026
Containing the Reproducibility Gap: Automated Repository-Level Containerization for Scholarly Jupyter Notebooks
Sheeba Samuel, Daniel Mietchen, Hemanta Lo +1
Computational reproducibility is fundamental to trustworthy science, yet remains difficult to achieve in practice across various research workflows, including Jupyter notebooks pub…
cs.CE2024★ 2 cited
FAIR Jupyter: a knowledge graph approach to semantic sharing and granular exploration of a computational notebook reproducibility dataset
Sheeba Samuel, Daniel Mietchen
The way in which data are shared can affect their utility and reusability. Here, we demonstrate how data that we had previously shared in bulk can be mobilized further through a kn…