The field of research is moving towards greater transparency and reproducibility, with a focus on developing frameworks and tools to facilitate the verification of scientific findings. One of the key areas of innovation is the development of open frameworks that incorporate containers, version control systems, and persistent archives to store and share research data, code, and figures. These frameworks aim to reduce the barriers to recreating figures and reproducing scientific findings, and to provide a reliable foundation for future research. Another area of advancement is the creation of curated datasets that provide a universal benchmark for evaluating and comparing the effectiveness of reproducibility tools. Additionally, there is a growing recognition of the importance of provenance information in ensuring the credibility and reproducibility of research findings, and efforts to develop comprehensive frameworks that combine workflow and data provenance. Noteworthy papers include:

• A paper introducing an open framework for archival, reproducible, and transparent science, which incorporates containers, version control systems, and persistent archives to improve the reproducibility and transparency of research.
• A paper presenting a curated dataset of computational experiments, which provides a standardized dataset for objectively evaluating and comparing the effectiveness of reproducibility tools.