Related papers: In Pursuit of Total Reproducibility
Software citation contributes to achieving software sustainability in two ways: It provides an impact metric to incentivize stakeholders to make software sustainable. It also provides references to software used in research, which can be…
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…
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…
Event-based datasets are crucial for cybersecurity analysis. A key use case is detecting event-based signatures, which represent attacks spanning multiple events and can only be understood once the relevant events are identified and linked.…
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…
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…
Crowdsourcing is a multidisciplinary research area including disciplines like artificial intelligence, human-computer interaction, database, and social science. To facilitate cooperation across disciplines, reproducibility is a crucial…
Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analytics) in the cloud.…
Reproducing computational research is often assumed to be as simple as rerunning the original code with provided data. In practice, missing packages, fragile file paths, version conflicts, or incomplete logic frequently cause analyses to…
We propose a dataset for event coreference resolution, which is based on random samples drawn from multiple sources, languages, and countries. Early scholarship on event information collection has not quantified the contribution of event…
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…
As reproducibility becomes a greater concern, conferences have largely converged to a strategy of asking reviewers to indicate whether code was attached to a submission. This is part of a larger trend of taking action based on assumed…
Recent developments in the commercial open source community have catalysed the use of Linux containers for scalable deployment of web-based applications to the cloud. Scientific software can be containerized with dependencies, configuration…
This document explores the advantages of integrating open source software and practices in managing a scientific lab, emphasizing reproducibility and the avoidance of pitfalls. It details practical applications from website management using…
A model is a simplified representation of portion of reality that hides a system s nonessential characteristics. It provides a means for reducing complexity as well as visualization and communication and a basis for building it. Most models…
Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data…
Software repositories is one of the sources of data in Empirical Software Engineering, primarily in the Mining Software Repositories field, aimed at extracting knowledge from the dynamics and practice of software projects. With the…
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior…
Computational experiments have become essential for scientific discovery, allowing researchers to test hypotheses, analyze complex datasets, and validate findings. However, as computational experiments grow in scale and complexity, ensuring…
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…