Prioritising measures and interventions to strengthen research reproducibility: a Delphi consultation study

Abstract Background Research reproducibility – the ability of others to independently verify scientific findings by following the same methods and data – is a fundamental aspect of research integrity. When this fails, trust in evidence is undermined and research resources are wasted. Reproducibility measures describe the standards that define reproducible research, and reproducibility interventions are the actions used to improve those standards. There is little agreement on which practices and initiatives should be prioritised for implementation in practice. This study aimed to establish expert consensus on the key measures and interventions that should be prioritised to strengthen research reproducibility. Methods We conducted a Delphi consensus study as part of the EU Horizon Europe iRISE project. Experts from five stakeholder groups (researchers, editors, publishers, funders, and policymakers) evaluated reproducibility measures and interventions identified through prior literature mapping across two online survey rounds, followed by a final virtual consensus panel. Items were rated on a 10-point Likert scale, with consensus defined a priori as agreement by at least 70% of panellists assigning high-priority scores, corresponding to scores of eight to ten on the Likert scale. Results Seventy-three panellists from 34 countries participated in the first round, with high retention rates in the subsequent round. Consensus was achieved on eight reproducibility measures and six reproducibility interventions in the first round. Prioritised measures included methodological quality, reporting quality, code and data availability and reuse, computational reproducibility, transparency of research plan, reproducible workflow practices, trial registration, and materials availability and reuse. Prioritised interventions included data management training, data quality checks/feedback, statistical training, data sharing policy/guidelines, protocol/trial registration, and reproducible code/analysis training. Conclusions The prioritised measures and interventions provide a structured foundation for improving research reproducibility across disciplines. The findings can inform the development of institutional training curricula in data management and statistical methods, policies supporting data sharing and trial registration, and future empirical evaluation of these practices across research contexts.