Related papers: Facilitating Bioinformatics Reproducibility
Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big…
Background, enhancing interoperability of bioinformatics knowledge bases is a high priority requirement to maximize data reusability, and thus increase their utility such as the return on investment for biomedical research. A knowledge base…
Ensuring the reproducibility of scientific work is crucial as it allows the consistent verification of scientific claims and facilitates the advancement of knowledge by providing a reliable foundation for future research. However,…
Although a standard in natural science, reproducibility has been only episodically applied in experimental computer science. Scientific papers often present a large number of tables, plots and pictures that summarize the obtained results,…
Scientific research frequently involves the use of computational tools and methods. Providing thorough documentation, open-source code, and data -- the creation of reproducible computational research -- helps others understand a…
Bioinformatics is a new discipline that addresses the need to manage and interpret the data that in the past decade was massively generated by genomic research. This discipline represents the convergence of genomics, biotechnology and…
We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use…
The emergence of Cloud computing provides a new computing paradigm for scientific workflow execution. It provides dynamic, on-demand and scalable resources that enable the processing of complex workflow-based experiments. With the ever…
We describe a unique environment in which undergraduate students from various STEM and social science disciplines are trained in data provenance and reproducible methods, and then apply that knowledge to real, conditionally accepted…
Reproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge. It is widely considered that many fields of science are undergoing a reproducibility crisis. This has led to the…
How many times have you tried to re-implement a past CAV tool paper, and failed? Reliably reproducing published scientific discoveries has been acknowledged as a barrier to scientific progress for some time but there remains only a small…
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 exponential growth in computational power and accessibility has transformed the complexity and scale of bioinformatics research, necessitating standardized documentation for transparency, reproducibility, and regulatory compliance. The…
Various fields of science face a reproducibility crisis. For quantum software engineering as an emerging field, it is therefore imminent to focus on proper reproducibility engineering from the start. Yet the provision of reproduction…
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…
Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various…
To reproduce eScience, several challenges need to be solved: scientific workflows need to be automated; the involved software versions need to be provided in an unambiguous way; input data needs to be easily accessible; High-Performance…
Reproducibility and comparability of empirical results are at the core tenet of the scientific method in any scientific field. To ease reproducibility of empirical studies, several benchmarks in software engineering research, such as…
Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating…
Artificial Intelligence (AI) systems are increasingly dependent on complex, multi-layered software supply chains that introduce challenges for reproducibility, transparency, and security assurance. This study presents an Artificial…