Structured elicitation protocols, such as the IDEA protocol, are used to elicit probabilistic judgements from multiple domain experts about uncertain events across fields including ecology, biosecurity risk assessment, and metascience. Individual expert judgements must subsequently be mathematically aggregated into a single group forecast. While the simplest case involves combining a set of point-estimates from multiple individuals, this process is further complicated when judgements include uncertainty bounds, or when elicitation is conducted across multiple rounds. This paper presents aggreCAT, an open-source R package that provides 29 aggregation methods for combining individual expert judgements into a single probabilistic estimate, accommodating designs ranging from single-round point estimates to multi-round three-point elicitation. The package follows tidy data principles, enabling straightforward integration with existing R workflows for application at scale. Methods range from unweighted arithmetic combinations to performance-weighted schemes and Bayesian models, with weights derived from uncertainty intervals, shifts in judgements between elicitation rounds, and breadth of expert reasoning. We provide worked examples illustrating the mechanics of representative aggregation methods, a general workflow for batch aggregation across multiple forecasts and methods, and built-in functions for evaluating and visualising forecast performance against known outcomes. aggreCAT fills a substantive gap in open software for mathematically aggregating expert judgement, and is intended to support researchers and decision analysts in rapidly and rigorously synthesising outputs from structured elicitation exercises.

This paper reports two approaches to forecasting replicability for a corpus of 3000 published social science papers, using large-scale human assessments. Replication Markets used incentivized surveys and prediction markets where participants traded assets linked to replication outcomes and market prices were interpreted as replication predictions. The repliCATS project used a structured group deliberation protocol, including interactive discussion, and mathematically aggregated forecasts to generate replication predictions. Accuracy for both approaches was validated against a subset of (n=37) independent, high-power replication studies. The predictive accuracy (median [range]) achieved for AUC by Replication Markets was 0.76 [0.72-0.82], and 0.76 [0.68-0.81] by repliCATS. Replication Markets achieved a classification accuracy of 73% [68-76%] and repliCATS achieved 68% [59-78%]. These results place the performance of both teams within the accuracy range achieved in prior replication forecasting studies. We conclude that informative forecasts can be elicited by both methods, but there are trade-offs between scale and accuracy.

Contributor Roles Taxonomy (CRediT) has recently changed how author contributions are acknowledged. To extend and complement CRediT, we propose MeRIT, a new way of writing the Methods section using the author’s initials to further clarify contributor roles for reproducibility and replicability. Lack of information on authors’ contribution to specific aspects of a study hampers reproducibility and replicability. Here, the authors propose a new, easily implemented reporting system to clarify contributor roles in the Methods section of an article.

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.

Structured protocols offer a transparent and systematic way to elicit and combine/aggregate, probabilistic predictions from multiple experts. These judgements can be aggregated behaviourally or mathematically to derive a final group prediction. Mathematical rules (e.g., weighted linear combinations of judgments) provide an objective approach to aggregation. The quality of this aggregation can be defined in terms of accuracy, calibration and informativeness. These measures can be used to compare different aggregation approaches and help decide on which aggregation produces the "best" final prediction. When experts' performance can be scored on similar questions ahead of time, these scores can be translated into performance-based weights, and a performance-based weighted aggregation can then be used. When this is not possible though, several other aggregation methods, informed by measurable proxies for good performance, can be formulated and compared. Here, we develop a suite of aggregation methods, informed by previous experience and the available literature. We differentially weight our experts' estimates by measures of reasoning, engagement, openness to changing their mind, informativeness, prior knowledge, and extremity, asymmetry or granularity of estimates. Next, we investigate the relative performance of these aggregation methods using three datasets. The main goal of this research is to explore how measures of knowledge and behaviour of individuals can be leveraged to produce a better performing combined group judgment. Although the accuracy, calibration, and informativeness of the majority of methods are very similar, a couple of the aggregation methods consistently distinguish themselves as among the best or worst. Moreover, the majority of methods outperform the usual benchmarks provided by the simple average or the median of estimates.

Structured protocols, such as the IDEA protocol, may be used to elicit expert judgments in the form of subjective probabilities from multiple experts. Judgments from individual experts about a particular phenomena must therefore be mathematically aggregated into a single prediction. The process of aggregation may be complicated when uncertainty bounds are elicited with a judgment, and also when there are several rounds of elicitation. This paper presents the new R package \pkg{aggreCAT}, which provides 22 unique aggregation methods for combining individual judgments into a single, probabilistic measure. The aggregation methods were developed as a part of the Defense Advanced Research Projects Agency (DARPA) ‘Systematizing Confidence in Open Research and Evidence’ (SCORE) programme, which aims to generate confidence scores or estimates of ‘claim credibility’ for 3000 research claims from the social and behavioural sciences. We provide several worked examples illustrating the underlying mechanics of the aggregation methods. We also describe a general workflow for using the software in practice to facilitate uptake of this software for appropriate use-cases.

Crowd-sourced human judgments about the trustworthiness of research claims may be used to generate forecasts of the probability of those claims being successfully replicated . Predictive models are less time and resource-intensive methods of generating forecasts about the likely replicability of research claims , however, are likely to be less accurate than human judgments. In this paper we propose and demonstrate a method that combines both crowd-sourced human judgments and model-based predictions of the likely replicability of research claims. We employ a Bayesian model that takes the model-based predictions as the prior data, and updates these values using crowd-sourced human judgments to generate predictions of replication outcomes using an annotated database of eight large-scale replication studies.