Posts about reproducible paper (old posts, page 9)

Analytic reproducibility in articles receiving open data badges at Psychological Science: An observational study

For any scientific report, repeating the original analyses upon the original data should yield the original outcomes. We evaluated analytic reproducibility in 25 Psychological Science articles awarded open data badges between 2014-2015. Initially, 16 (64%, 95% confidence interval [43,81]) articles contained at least one “major numerical discrepancy” (>10% difference) prompting us to request input from original authors. Ultimately, target values were reproducible without author involvement for 9 (36% [20,59]) articles; reproducible with author involvement for 6 (24% [8,47]) articles; not fully reproducible with no substantive author response for 3 (12% [0,35]) articles; and not fully reproducible despite author involvement for 7 (28% [12,51]) articles. Overall, 37 major numerical discrepancies remained out of 789 checked values (5% [3,6]), but original conclusions did not appear affected. Non-reproducibility was primarily caused by unclear reporting of analytic procedures. These results highlight that open data alone is not sufficient to ensure analytic reproducibility.

Neural Citation Recommendation: A Reproducibility Study

Context-aware citation recommendation is used to overcome the process of manually searching for relevant citations by automatically recommending suitable papers as citations for a specified input text. In this paper, we examine the reproducibility of a state-of-the-art approach to context-aware citation recommendation, namely the neural citationnetwork (NCN) by Ebesu and Fang. We re-implement the network and run evaluations on both RefSeer, the originally used data set, and arXiv CS, as an additional data set. We provide insights on how the different hyperparameters of the neural network affect the model performance of the NCN and thus can be used to improve the model’s performance. In this way, we contribute to making citation recommendation approaches and their evaluations more transparent and creating more effective neural network-based models in the future.

Reproducible Software Environment: a tool enabling computational reproducibility in geospace sciences and facilitating collaboration

The Reproducible Software Environment (Resen) is an open-source software tool enabling computationally reproducible scientific results in the geospace science community. Resen was developed as part of a larger project called the Integrated Geoscience Observatory (InGeO), which aims to help geospace researchers bring together diverse datasets from disparate instruments and data repositories, with software tools contributed by instrument providers and community members. The main goals of InGeO are to remove barriers in accessing, processing, and visualizing geospatially resolved data from multiple sources using methodologies and tools that are reproducible. The architecture of Resen combines two mainstream open source software tools, Docker and JupyterHub, to produce a software environment that not only facilitates computationally reproducible research results, but also facilitates effective collaboration among researchers. In this technical paper, we discuss some challenges for performing reproducible science and a potential solution via Resen, which is demonstrated using a case study of a geospace event. Finally we discuss how the usage of mainstream, open-source technologies seems to provide a sustainable path towards enabling reproducible science compared to proprietary and closed-source software.

Toward Enabling Reproducibility for Data-Intensive Research Using the Whole Tale Platform

Whole Tale is a web-based, open-source platform for reproducible research supporting the creation, sharing, execution, and verification of "Tales" for the scientific research community. Tales are executable research objects that capture the code, data, and environment along with narrative and workflow information needed to re-create computational results from scientific studies. Creating reproducible research objects that enable reproducibility, transparency, and re-execution for computational experiments requiring significant compute resources or utilizing massive data is an especially challenging open problem. We describe opportunities, challenges, and solutions to facilitating reproducibility for data-and compute-intensive research, that we call "Tales at Scale," using the Whole Tale computing platform.We highlight challenges and solutions in frontend responsiveness needs, gaps in current middleware design and implementation, network restrictions, containerization, and data access. Finally, we discuss challenges in packaging computational experiment implementations for portable data-intensive Tales and outline future work.

Assessing the impact of introductory programming workshops on the computational reproducibility of biomedical workflows

Introduction: As biomedical research becomes more data-intensive, computational reproducibility is a growing area of importance. Unfortunately, many biomedical researchers have not received formal computational training and often struggle to produce results that can be reproduced using the same data, code, and methods. Programming workshops can be a tool to teach new computational methods, but it is not always clear whether researchers are able to use their new skills to make their work more computationally reproducible. Methods: This mixed methods study consisted of in-depth interviews with 14 biomedical researchers before and after participation in an introductory programming workshop. During the interviews, participants described their research workflows and responded to a quantitative checklist measuring reproducible behaviors. The interview data was analyzed using a thematic analysis approach, and the pre and post workshop checklist scores were compared to assess the impact of the workshop on computational reproducibility of the researchers' workflows. Results: Pre and post scores on a checklist of reproducible behaviors did not increase in a statistically significant manner. The qualitative interviews revealed that several participants had made small changes to their workflows including switching to open source programming languages for their data cleaning, analysis, and visualization. Overall many of the participants indicated higher levels of programming literacy and an interest in further training. Factors that enabled change included supportive environments and an immediate research need, while barriers included collaborators that were resistant to new tools and a lack of time. Conclusion: While none of the participants completely changed their workflows, many of them did incorporate new practices, tools, or methods that helped make their work more reproducible and transparent to other researchers. This indicate that programming workshops now offered by libraries and other organizations contribute to computational reproducibility training for researchers

A Realistic Guide to Making Data Available Alongside Code to Improve Reproducibility

Data makes science possible. Sharing data improves visibility, and makes the research process transparent. This increases trust in the work, and allows for independent reproduction of results. However, a large proportion of data from published research is often only available to the original authors. Despite the obvious benefits of sharing data, and scientists' advocating for the importance of sharing data, most advice on sharing data discusses its broader benefits, rather than the practical considerations of sharing. This paper provides practical, actionable advice on how to actually share data alongside research. The key message is sharing data falls on a continuum, and entering it should come with minimal barriers.