Computationally reproducing results from meta-analyses in ecology and evolutionary biology using shared code and data.

Many journals in ecology and evolutionary biology encourage or require authors to make their data and code available alongside articles. In this study we investigated how often this data and code could be used together, when both were available, to computationally reproduce results published in articles. We surveyed the data and code sharing practices of 177 meta-analyses published in ecology and evolutionary biology journals published between 2015-17: 60% of articles shared data only, 1% shared code only, and 15% shared both data and code. In each of the articles which had shared both (n = 26), we selected a target result and attempted to reproduce it. Using the shared data and code files, we successfully reproduced the targeted results in 27-73% of the 26 articles, depending on the stringency of the criteria applied for a successful reproduction. The results from this sample of meta-analyses in the 2015-17 literature can provide a benchmark for future meta-research studies gauging the computational reproducibility of published research in ecology and evolutionary biology.

Predicting and reasoning about replicability using structured groups.

This paper explores judgements about the replicability of social and behavioural sciences research and what drives those judgements. Using a mixed methods approach, it draws on qualitative and quantitative data elicited from groups using a structured approach called the IDEA protocol ('investigate', 'discuss', 'estimate' and 'aggregate'). Five groups of five people with relevant domain expertise evaluated 25 research claims that were subject to at least one replication study. Participants assessed the probability that each of the 25 research claims would replicate (i.e. that a replication study would find a statistically significant result in the same direction as the original study) and described the reasoning behind those judgements. We quantitatively analysed possible correlates of predictive accuracy, including self-rated expertise and updating of judgements after feedback and discussion. We qualitatively analysed the reasoning data to explore the cues, heuristics and patterns of reasoning used by participants. Participants achieved 84% classification accuracy in predicting replicability. Those who engaged in a greater breadth of reasoning provided more accurate replicability judgements. Some reasons were more commonly invoked by more accurate participants, such as 'effect size' and 'reputation' (e.g. of the field of research). There was also some evidence of a relationship between statistical literacy and accuracy.

Predicting reliability through structured expert elicitation with the repliCATS (Collaborative Assessments for Trustworthy Science) process.

As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.

Examining tensions in the past and present uses of concepts.

Examining tensions between the past and present uses of scientific concepts can help clarify their contributions as tools in experimental practices. This point can be illustrated by considering the concepts of mental imagery and hallucinations: despite debates over their respective referential reliabilities remaining unresolved within their interdependent histories, both are used as independently stable concepts in neuroimaging experiments. Building on an account of how these concepts function as tools structured for pursuit of diverging goals in experiments, this paper explores this tension by re-examining the continued reliance of each concept on inverse characterisations inherited from the nominally-discarded 'mediator-view' of sensory-like mental phenomena (SLMP). In doing so, I seek to demonstrate how examining unresolved tensions can help highlight that entrenched associations can remain both integral to, and obscured by, the uses of concepts as goal-directed tools within experimental practices.