2016 saw a tremendous amount of discussion and development on the subject of scientific reproducibility. Were you able to keep up? If not, check out this list of 16 sources from 2016 to get you up to date for the new year! The reproducibility crisis in science refers to the difficulty scientists have faced in reproducing or replicating results from previously published scientific experiments. Although this crisis has existed in the scientific community for a very long time, it gained much more visibility in in the past few years. The terms “reproducibility crisis” and “replicability crisis” were coined in the early 2010s due to the growing awareness of the problem.
In recent years, the psychological and behavioral sciences have increased efforts to strengthen methodological practices and publication standards, with the ultimate goal of enhancing the value and reproducibility of published reports. These issues are especially important in the multidisciplinary field of psychophysiology, which yields rich and complex data sets with a large number of observations. In addition, the technological tools and analysis methods available in the field of psychophysiology are continually evolving, widening the array of techniques and approaches available to researchers. This special issue presents articles detailing rigorous and systematic evaluations of tasks, measures, materials, analysis approaches, and statistical practices in a variety of subdisciplines of psychophysiology. These articles highlight challenges in conducting and interpreting psychophysiological research and provide data-driven, evidence-based recommendations for overcoming those challenges to produce robust, reproducible results in the field of psychophysiology.
A team of Web and Internet Science (WAIS) researchers, from Electronics and Computer Science at Southampton, has been working with statistical colleagues at the Centre for Multilevel Modelling, University of Bristol, to develop new software technology that allows UK students and young researchers to access reproducible statistical 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.