Analyzing the reproducibility of research-related Jupyter notebooks at scale
| dc.contributor.author | Mietchen, Daniel | |
| dc.contributor.author | Samuel, Sheeba | |
| dc.contributor.editor | Koziolek, Anne | |
| dc.contributor.editor | Lamprecht, Anna-Lena | |
| dc.contributor.editor | Thüm, Thomas | |
| dc.contributor.editor | Burger, Erik | |
| dc.date.accessioned | 2025-02-14T09:36:31Z | |
| dc.date.available | 2025-02-14T09:36:31Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | We address computational reproducibility of publication-associated Jupyter notebooks at 3 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article’s full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion. (iii) We imported the corpus into a knowledge graph with a public SPARQL endpoint that allows for fine-grained exploration of notebooks individually or in aggregation (e.g. by topic, by journal or by error type). In this talk, we zoom in on common problems and practices, highlight trends, and discuss potential improvements to Jupyter-related workflows associated with biomedical publications. | en |
| dc.identifier.doi | 10.18420/se2025-07 | |
| dc.identifier.eissn | 2944-7682 | |
| dc.identifier.issn | 2944-7682 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/45813 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | Software Engineering 2025 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-360 | |
| dc.subject | Knowledge Graph | |
| dc.subject | Computational reproducibility | |
| dc.subject | Jupyter notebooks | |
| dc.subject | FAIR data | |
| dc.subject | PubMed Central | |
| dc.subject | GitHub | |
| dc.subject | Python | |
| dc.subject | SPARQL | |
| dc.title | Analyzing the reproducibility of research-related Jupyter notebooks at scale | en |
| mci.conference.date | 22.-28. Februar 2025 | |
| mci.conference.location | Karlsruhe | |
| mci.conference.sessiontitle | Scientific Programme | |
| mci.reference.pages | 39-40 |
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