PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
August 11, 2024International Journal of Data Science and Analytics3 citationsOpen Access

Sharing practices of software artefacts and source code for reproducible research

View Full Paper
CJClaire Jean-QuartierFJFleur JeanquartierSSSarah Stryeck

Key Points

Key points are not available for this paper at this time.

Abstract

Abstract While source code of software and algorithms depicts an essential component in all fields of modern research involving data analysis and processing steps, it is uncommonly shared upon publication of results throughout disciplines. Simple guidelines to generate reproducible source code have been published. Still, code optimization supporting its repurposing to different settings is often neglected and even less thought of to be registered in catalogues for a public reuse. Though all research output should be reasonably curated in terms of reproducibility, it has been shown that researchers are frequently non-compliant with availability statements in their publications. These do not even include the use of persistent unique identifiers that would allow referencing archives of code artefacts at certain versions and time for long-lasting links to research articles. In this work, we provide an analysis on current practices of authors in open scientific journals in regard to code availability indications, FAIR principles applied to code and algorithms. We present common repositories of choice among authors. Results further show disciplinary differences of code availability in scholarly publications over the past years. We advocate proper description, archiving and referencing of source code and methods as part of the scientific knowledge, also appealing to editorial boards and reviewers for supervision.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Jean-Quartier et al. (2024) studied this question.

synapsesocial.com/papers/68e5cb6fb6db6435875622cbhttps://doi.org/10.1007/s41060-024-00617-7
Ask AI
Helpful
Bookmark
Share
View Full Paper