Pilots

TIER2's Decision Aid will provide clarity on the meaning, relevance, and feasibility of ‘reproducibility’ for researchers to aid them in identifying what type of reproducibility is relevant for their research and indicate what they must consider regarding how feasible such ‘reproducibility’ would be for them. The tool will be piloted with two researcher groups (qualitative and machine learning researchers).

Reproducibility requires more than data management: software, hardware, methods, workflows, and their connections and executors must be documented. The Reproducibility Management Plan (RMP) fills this gap by extending DMPs into comprehensive reproducibility planning tools. Co-created with 89 participants across 15 countries from 4 regions and deployed in the production-ready ARGOS platform, it address the full research lifecycle. CHIST-ERA pioneered adoption in the ICT projects, requiring unified Data & Software Management Plans as RMPs. The framework structures thinking around what will be produced, how it will be documented, where it will be deposited, who can access it, and how others can reuse it, thus creating an actionable blueprint that adapts to any research context. Built into ARGOS, it produces machine-actionable exports using the DMP Common Standard, integrates FAIRsharing for standards and policies, and creates qualified references that connect datasets to software, methods, and workflows. This approach helps researchers prevent downstream problems, enables funders to monitor reproducibility, and shifts reproducibility from reactive afterthought to proactive planning.

Computational experiments are essential for modern research, yet their complexity often hinders reproducibility. The TIER2 Pilot 3 publication “A Virtual Laboratory for Managing Computational Experiments” introduces SCHEMA lab, an open-source virtual environment that enables the design, execution, and tracking of containerised experiments with full provenance. By capturing configurations, datasets, software environments, and performance metrics, SCHEMA lab enhances reproducibility and transparency across disciplines. It supports individual researchers and research infrastructures in organising, comparing, and reusing computational workflows, fostering credible, reusable, and FAIR digital science practices.

Computational social scientists can enhance the transparency and reproducibility of their work using the simplified Reproducibility Checklist integrated into Methods Hub. The checklist provides essential documentation requirements—covering data, code, computational environment, and sharing—ensuring that methods are easier to understand, reuse, and evaluate. It is embedded in the Methods Hub submission workflow and publicly available in the method guidelines. Developed through literature review and informed by survey insights, the checklist has been refined for usability and tested through user studies and an experimental evaluation. Workshops, training modules, and a published paper support adoption and promote reproducible computational research.

The Reproducibility Promotion Plan for Funders (RPP) provides a comprehensive policy template with actionable recommendations to help funders foster reproducible research practices across three key areas of their work: evaluation and monitoring, policy and definitions, and incentives. The RPP serves as both guidance and inspiration for research funders to develop robust internal practices that strengthen their funding processes, while also managing external expectations by clearly communicating reproducibility requirements to researchers. Designed to be adaptable, the RPP offers best practice examples and practical tools that funders can tailor to their specific institutional contexts and levels of experience. Ultimately, the RPP aims to catalyse cultural and procedural change within funding organisations, empowering them to support researchers in conducting rigorous, transparent, and reproducible research.

The Reproducibility Monitoring Dashboard Pilot aims to develop tools that enable funding agencies to track and monitor the reusability of research artifacts across various projects, programs, topics, and disciplines. This auto-generated dashboard assesses the impacts of policies related to data and code sharing. Furthermore, we are establishing essential requirements to make the dashboard user-friendly for publishers.

The Editorial Workflows to Increase Data Sharing aim at helping authors make their research more transparent and reproducible. The workflow provides practical guidance on sharing data in trusted repositories, sent automatically when a revision to a manuscript submitted to a journal is requested. Acting at this key stage, it encourages immediate data sharing without affecting the editorial decision. Light-touch, scalable, and adaptable across disciplines, it supports journals and researchers in improving compliance with data sharing policies and fostering a culture of openness. 

The Editorial Reference Handbook informs and assists scholarly publishers in supporting the sharing of digital research objects and in operationalising findable, accessible, interoperable and reusable (FAIR) research practices by addressing gaps in editorial workflows, policy implementation and stakeholder alignment. The Handbook comprises three interrelated components—a checklist, detailed guidance, and a flowchart—intended primarily for in-house editorial staff while also providing value to reviewers, authors, and service providers.  

The Pilot included representatives of Cambridge University Press, Cell Press, EMBO Press, F1000 (Taylor & Francis), GigaScience Press, Lancet, Oxford University Press, PLOS, Springer Nature, Wiley.