Reproducible Research in Computational Science

By Roger D. Peng. Science, 2011

1,427 citations recorded by the source corpus.

Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.

Subfields: Information Systems, Information Systems and Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e4625641d1072542315c14083befb43cd7096aba

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Citations in the corpus

SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research (2017, background): “An essential requirement for confirming any conclusions described in a scientific publication, including biomedical image analysis findings, is reproducibility [7, 21].”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, background): “The conjunction of the availability of both the data and the source code of the algorithm is what is called “Open Science” [110, 111, 112], and it is an increasingly popular approach to ensuring replicability in science, so that we can make better and better science.”
pyGIMLi: An open-source library for modelling and inversion in geophysics (2017, background): “Even technically versatile users commonly end up building individually tailored solutions by linking various existing (and potentially commercial) software packages through scripts, which hinders the reproducibility of scienti ﬁ c ﬁ ndings (Peng, 2011).”
rfishbase: exploring, manipulating and visualizing FishBase data from R. (2012, background): “…Journal of Fish Biology © 2012 The Fisheries Society of the British Isles, Journal of Fish Biology 2012, 81, 2030–2039 data coupled with scriptable analyses can help ensure that research is more easily reproduced and also facilitate extending the work in future studies (Merali, 2010; Peng, 2011).”
Reproducibility of computational workflows is automated using continuous analysis (2017, background): “It has been proposed that open science could aid reproducibility 3,11 .”
Facts Are More Important Than Novelty (2014, background): “A December 2011 special section in Science (Jasny, Chin, Chong, & Vignieri, 2011) discussed replication and its application in primate cognition (Tomasello & Call, 2011), field biology (Ryan, 2011), computer science (Peng, 2011), and genomics (Ioannidis & Khoury, 2011).”
Anserini (2018, background): “These two threads are related in that making source code available is touted across a broad range of scientific disciplines as one important step toward re-producibility [25, 44, 46].”