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Abstract In this paper, we explore the crucial role and challenges of computational reproducibility in geosciences, drawing insights from the Climate Informatics Reproducibility Challenge (CICR) in 2023. The competition aimed at (1) identifying common hurdles to reproduce computational climate science; and (2) creating interactive reproducible publications for selected papers of the Environmental Data Science journal. Based on lessons learned from the challenge, we emphasize the significance of open research practices, mentorship, transparency guidelines, as well as the use of technologies such as executable research objects for the reproduction of geoscientific published research. We propose a supportive framework of tools and infrastructure for evaluating reproducibility in geoscientific publications, with a case study for the climate informatics community. While the recommendations focus on future CIRCs, we expect they would be beneficial for wider umbrella of reproducibility initiatives in geosciences.
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Reproducibility Signals in Science: A preliminary analysis
Reproducibility is an important feature of science; experiments are retested, and analyses are repeated. Trust in the findings increases when consistent results are achieved. Despite the importance of reproducibility, significant work is often involved in these efforts, and some published findings may not be reproducible due to oversights or errors. In this paper, we examine a myriad of features in scholarly articles published in computer science conferences and journals and test how they correlate with reproducibility. We collected data from three different sources that labeled publications as either reproducible or irreproducible and employed statistical significance tests to identify features of those publications that hold clues about reproducibility. We found the readability of the scholarly article and accessibility of the software artifacts through hyperlinks to be strong signals noticeable amongst reproducible scholarly articles.
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- Award ID(s):
- 2022443
- PAR ID:
- 10483254
- Editor(s):
- Tirthankar Ghosal, Sergi Blanco-Cuaresma
- Publisher / Repository:
- Association for Computational Linguistics
- Date Published:
- Journal Name:
- The first Workshop on Information Extraction from Scientific Publications
- Subject(s) / Keyword(s):
- Reproducibility
- Format(s):
- Medium: X
- Location:
- https://aclanthology.org/2022.wiesp-1.16/
- Sponsoring Org:
- National Science Foundation
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