This article provides recommendations for writing empirical journal articles that enable transparency, reproducibility, clarity, and memorability. Recommendations for transparency include preregistering methods, hypotheses, and analyses; submitting registered reports; distinguishing confirmation from exploration; and showing your warts. Recommendations for reproducibility include documenting methods and results fully and cohesively, by taking advantage of open-science tools, and citing sources responsibly. Recommendations for clarity include writing short paragraphs, composed of short sentences; writing comprehensive abstracts; and seeking feedback from a naive audience. Recommendations for memorability include writing narratively; embracing the hourglass shape of empirical articles; beginning articles with a hook; and synthesizing, rather than Mad Libbing, previous literature.
This article uses the framework of Ioannidis (2005) to organise a discussion of issues related to the ‘reproducibility crisis’. It then goes on to use that framework to evaluate various proposals to fix the problem. Of particular interest is the ‘post‐study probability’, the probability that a reported research finding represents a true relationship. This probability is inherently unknowable. However, a number of insightful results emerge if we are willing to make some conjectures about reasonable parameter values. Among other things, this analysis demonstrates the important role that replication can play in improving the signal value of empirical research.
The reproducibility crisis, that is, the fact that many scientific results are difficult to replicate, pointing to their unreliability or falsehood, is a hot topic in the recent scientific literature, and statistical methodologies, testing procedures and p‐values, in particular, are at the centre of the debate. Assessment of the extent of the problem–the reproducibility rate or the false discovery rate–and the role of contributing factors are still an open problem. Replication experiments, that is, systematic replications of existing results, may offer relevant information on these issues. We propose a statistical model to deal with such information, in particular to estimate the reproducibility rate and the effect of some study characteristics on its reliability. We analyse data from a recent replication experiment in psychology finding a reproducibility rate broadly coherent with other assessments from the same experiment. Our results also confirm the expected role of some contributing factor (unexpectedness of the result and room for bias) while they suggest that the similarity between original study and the replica is not so relevant, thus mitigating some criticism directed to replication experiments.
This presentation to LERU workshop: Nurturing a Culture of Responsible Research in the Era of Open Science considered the issue of the credibility of science being in question in a 'post-truth' world and how reproducibility is adding to the problem. Open Science offers a solution, but it is not easy to implement, particularly by research institutions. The main issues relate to language used in the open space, that solutions look different to different disciplines, that researchers are often feeling "under siege" and that we need to reward good open practice.
Most scientific papers are not reproducible: it is really hard, if not impossible, to understand how results are derived from data, and being able to regenerate them in the future (even by the same researchers). However, traceability and reproducibility of results are indispensable elements of highquality science, and an increasing requirement of many journals and funding sources. Reproducible studies include code able to regenerate results from the original data. This practice not only provides a perfect record of the whole analysis but also reduces the probability of errors and facilitates code reuse, thus accelerating scientific progress. But doing reproducible science also brings many benefits to the individual researcher, including saving time and effort, improved collaborations, and higher quality and impact of final publications. In this article we introduce reproducible science, why it is important, and how we can improve the reproducibility of our work. We introduce principles and tools for data management, analysis, version control, and software management that help us achieve reproducible workflows in the context of ecology.
When computational experiments include only datasets, they could be shared through the Uniform Resource Identifiers (URIs) or Digital Object Identifiers (DOIs) which point to these resources. However, experiments seldom include only datasets, but most often also include software, execution results, provenance, and other associated documentation. The Research Object has recently emerged as a comprehensive and systematic method for aggregation and identification of diverse elements of computational experiments. While an entire Research Object may be citable using a URI or a DOI, it is often desirable to cite specific sub-components of a research object to help identify, authorize, date, and retrieve the published sub-components of these objects. In this paper, we present an approach to automatically generate citations for sub-components of research objects by using the object’s recorded provenance traces. The generated citations can be used "as is" or taken as suggestions that can be grouped and combined to produce higher level citations.