A strong movement towards openness has seized science. Open data and methods, open source software, Open Access, open reviews, and open research platforms provide the legal and technical solutions to new forms of research and publishing. However, publishing reproducible research is still not common practice. Reasons include a lack of incentives and a missing standardized infrastructure for providing research material such as data sets and source code together with a scientific paper. Therefore we first study fundamentals and existing approaches. On that basis, our key contributions are the identification of core requirements of authors, readers, publishers, curators, as well as preservationists and the subsequent description of an executable research compendium (ERC). It is the main component of a publication process providing a new way to publish and access computational research. ERCs provide a new standardisable packaging mechanism which combines data, software, text, and a user interface description. We discuss the potential of ERCs and their challenges in the context of user requirements and the established publication processes. We conclude that ERCs provide a novel potential to find, explore, reuse, and archive computer-based research.

Scientific research is published in journals so that the research community is able to share knowledge and results, verify hypotheses, contribute evidence-based opinions and promote discussion. However, it is hard to fully understand, let alone reproduce, the results if the complex data manipulation that was undertaken to obtain the results are not clearly explained and/or the final data used is not available. Furthermore, the scale of research data assets has now exponentially increased to the point that even when available, it can be difficult to store and use these data assets. In this paper, we describe the solution we have implemented at the National Computational Infrastructure (NCI) whereby researchers can capture workflows, using a standards-based provenance representation. This provenance information, combined with access to the original dataset and other related information systems, allow datasets to be regenerated as needed which simultaneously addresses both result reproducibility and storage issues.

Improving the reliability and efficiency of scientific research will increase the credibility of the published scientific literature and accelerate discovery. Here we argue for the adoption of measures to optimize key elements of the scientific process: methods, reporting and dissemination, reproducibility, evaluation and incentives. There is some evidence from both simulations and empirical studies supporting the likely effectiveness of these measures, but their broad adoption by researchers, institutions, funders and journals will require iterative evaluation and improvement. We discuss the goals of these measures, and how they can be implemented, in the hope that this will facilitate action toward improving the transparency, reproducibility and efficiency of scientific research.

In this talk I will review a few examples of reproducibility challenges in computational environments and discuss their potential effects. Based on discussions in a recent Dagstuhl seminar we will identify different types of reproducibility. Here, we will focus specifically on what we gain from them, rather than seeing them merely as means to an end. We subsequently will address two core challenges impacting reproducibility, namely (1) understanding and automatically capturing process context and provenance information, and (2) approaches allowing us to deal with dynamically evolving data sets relying on recommendation of the Research Data Alliance (RDA). The goal is to raise awareness of reproducibility challenges and show ways how these can be addressed with minimal impact on the researchers via research infrastructures offering according services.

A team of scientists including Stanford’s John Ioannidis, MD, DSc, has proposed a set of principles to improve the transparency and reproducibility of computational methods used in all areas of research. The group’s summary of those principles, known as the Reproducibility Enhancement Principles, was published recently in a paper in Science.

Over the past two decades, computational methods have radically changed the ability of researchers from all areas of scholarship to process and analyze data and to simulate complex systems. But with these advances come challenges that are contributing to broader concerns over irreproducibility in the scholarly literature, among them the lack of transparency in disclosure of computational methods. Current reporting methods are often uneven, incomplete, and still evolving. We present a novel set of Reproducibility Enhancement Principles (REP) targeting disclosure challenges involving computation. These recommendations, which build upon more general proposals from the Transparency and Openness Promotion (TOP) guidelines (1) and recommendations for field data (2), emerged from workshop discussions among funding agencies, publishers and journal editors, industry participants, and researchers representing a broad range of domains. Although some of these actions may be aspirational, we believe it is important to recognize and move toward ameliorating irreproducibility in computational research.