We describe the four publications we have tried to make reproducible and discuss how each paper has changed our workflows, practices, and collaboration policies. The fundamental insight is that paper artifacts must be made reproducible from the start of the project; artifacts are too difficult to make reproducible when the papers are (1) already published and (2) authored by researchers that are not thinking about reproducibility. In this paper, we present the best practices adopted by our research laboratory, which was sculpted by the pitfalls we have identified for the Popper convention. We conclude with a "call-to-arms" for the community focused on enhancing reproducibility initiatives for academic conferences, industry environments, and national laboratories. We hope that our experiences will shape a best practices guide for future reproducible papers.
A recent widespread realization that software experiments are not as easily replicated as once believed brought software execution preservation to the science spotlight. As a result, scientists, institutions, and funding agencies have recently been pushing for the development of methodologies and tools that preserve software artifacts. Despite current efforts, long term reproducibility still eludes us. In this paper, we present the requirements for software execution preservation and discuss how to improve long-term reproducibility in science. In particular, we discuss the reasons why preserving binaries and pre-built execution environments is not enough and why preserving the ability to replicate results is not the same as preserving software for reproducible science. Finally, we show how these requirements are supported by Occam, an open curation framework that fully preserves software and its dependencies from source to execution, promoting transparency, longevity, and re-use. Specifically, Occam provides the ability to automatically deploy workflows in a fully-functional environment that is able to not only run them, but make them easily replicable.
Psychology is currently experiencing a "renaissance" where the replication and reproducibility of published reports are at the forefront of conversations in the field. While researchers have worked to discuss possible problems and solutions, work has yet to uncover how this new culture may have altered reporting practices in the social sciences. As outliers can bias both descriptive and inferential statistics, the search for these data points is essential to any analysis using these parameters. We quantified the rates of reporting of outliers within psychology at two time points: 2012 when the replication crisis was born, and 2017, after the publication of reports concerning replication, questionable research practices, and transparency. A total of 2235 experiments were identified and analyzed, finding an increase in reporting of outliers from only 15.7% of experiments mentioning outliers in 2012 to 25.0% in 2017. We investigated differences across years given the psychological field or statistical analysis that experiment employed. Further, we inspected whether outliers mentioned are whole participant observations or data points, and what reasons authors gave for stating the observation was deviant. We conclude that while report rates are improving overall, there is still room for improvement in the reporting practices of psychological scientists which can only aid in strengthening our science.
Open data and open-source software may be part of the solution to sciences reproducibility crisis, but they are insufficient to guarantee reproducibility. Requiring minimal end-user expertise, encapsulator creates a "time capsule" with reproducible code (right now, only supporting R code) in a self-contained computational environment. encapsulator provides end-users with a fully-featured desktop environment for reproducible research.
In 1942, Robert Merton wrote that "Incipient and actual attacks upon the integrity of science" meant that science needed to "restate its objectives, seek out its rationale". Some 77 years later we are similarly in an environment where “the people of this country have had enough of experts". It is essential that science is able to withstand rigorous scrutiny to avoid being dismissed, pilloried or ignored. Transparency and reproducibility in the scientific process is a mechanism to meet this challenge and good research data management is a fundamental factor in this.
The "Crisis of Reproducibility" has received considerable attention both within the scientific community and without. While factors associated with scientific culture and practical practice are most often invoked, I propose that the Crisis of Reproducibility is ultimately a failure of generalization with a fundamental scientific basis in the methods used for biomedical research. The Denominator Problem describes how limitations intrinsic to the two primary approaches of biomedical research, clinical studies and pre-clinical experimental biology, lead to an inability to effectively characterize the full extent of biological heterogeneity, which compromises the task of generalizing acquired knowledge. Drawing on the example of the unifying role of theory in the physical sciences, I propose that multi-scale mathematical and dynamic computational models, when mapped to the modular structure of biological systems, can serve a unifying role as formal representations of what is conserved and similar from one biological context to another. This ability to explicitly describe the generation of heterogeneity from similarity addresses the Denominator Problem and provides a scientific response to the Crisis of Reproducibility.