Related papers: A Guide to Computational Reproducibility in Signal…
With the increasing amount of data and use of computation in science, software has become an important component in many different domains. Computing is now being used more often and in more aspects of scientific work including data…
The reproducibility of software environments is a critical concern in modern software engineering, with ramifications ranging from the effectiveness of collaboration workflows to software supply chain security and scientific…
Study reproducibility is essential to corroborate, build on, and learn from the results of scientific research but is notoriously challenging in bioinformatics, which often involves large data sets and complex analytic workflows involving…
Reproducible builds are a set of software development practices that establish an independently verifiable path from source code to binary artifacts, helping to detect and mitigate certain classes of supply chain attacks. Although quantum…
Reproducibility and realistic datasets are crucial for advancing research. Unfortunately, they are often neglected as valid scientific contributions in many young disciplines, with computer science being no exception. In this article, we…
While clinical trials are the state-of-the-art methods to assess the effect of new medication in a comparative manner, benchmarking in the field of medical image analysis is performed by so-called challenges. Recently, comprehensive…
As NLP research attracts public attention and excitement, it becomes increasingly important for it to be accessible to a broad audience. As the research community works to democratize NLP, it remains unclear whether beginners to the field…
Achieving complete reproducibility in science, particularly in research fields such as biodiversity, is challenging due to analytical choices, bias and interpretation. Here, we examine examples of reproducibility in biological systematics,…
Signals analysis for cytometry remains a challenging task that has a significant impact on uncertainty. Conventional cytometers assume that individual measurements are well characterized by simple properties such as the signal area, width,…
Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a…
Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the…
The vast majority of scientific contributions in the field of computational systems biology are based on mathematical models. These models can be broadly classified as either dynamic (kinetic) models or steady-state (constraint-based)…
University research groups in Computational Science and Engineering (CSE) generally lack dedicated funding and personnel for Research Software Engineering (RSE), which, combined with the pressure to maximize the number of scientific…
Reproducibility is a cornerstone of science. FAIR (findable, accessible, interoperable, and reusable) data is often a vital step towards testing the reproducibility of results. The implementation of FAIR principles in the astrophysical…
Reproducibility is an ideal that no researcher would dispute "in the abstract", but when aspirations meet the cold hard reality of the academic grind, reproducibility often "loses out". In this essay, I share some personal experiences…
In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is…
How should software engineering be adapted for Computational Science (CS)? If we understood that, then we could better support software sustainability, verifiability, reproducibility, comprehension, and usability for CS community. For…
Machine learning (ML) reproducibility is often framed as a problem of incomplete artifact recording. This framing leads to systems that prioritize capturing datasets, code, configurations, and execution environments. However, in…
Open data and open-source software may be part of the solution to science's "reproducibility crisis", but they are insufficient to guarantee reproducibility. Requiring minimal end-user expertise, encapsulator creates a "time capsule" with…
Powered by advanced information technology, more and more complex systems are exhibiting characteristics of the Cyber-Physical-Social Systems (CPSS). Understanding the mechanism of CPSS is essential to our ability to control their actions,…