The article by Roger D. Peng discusses the importance of reproducibility in computational science, highlighting the challenges and potential solutions. Computational science has led to significant advancements, but the nature of these studies often makes full independent replication difficult or impossible. Reproducibility is proposed as a minimum standard to assess the validity of scientific claims, particularly when full replication is not feasible. This standard requires making data and analysis code available to others, which can help verify the quality of scientific findings. However, barriers to reproducibility include the lack of availability of computer code, the complexity of large datasets, and the need for substantial resources. The author suggests that journals can play a role in promoting reproducibility by implementing policies and encouraging authors to make their work reproducible. Additionally, the scientific community needs to develop a culture of reproducibility and create integrated infrastructure for sharing data and code. While significant changes may take time, incremental steps, such as publishing code and data, can significantly improve the reproducibility of computational research.The article by Roger D. Peng discusses the importance of reproducibility in computational science, highlighting the challenges and potential solutions. Computational science has led to significant advancements, but the nature of these studies often makes full independent replication difficult or impossible. Reproducibility is proposed as a minimum standard to assess the validity of scientific claims, particularly when full replication is not feasible. This standard requires making data and analysis code available to others, which can help verify the quality of scientific findings. However, barriers to reproducibility include the lack of availability of computer code, the complexity of large datasets, and the need for substantial resources. The author suggests that journals can play a role in promoting reproducibility by implementing policies and encouraging authors to make their work reproducible. Additionally, the scientific community needs to develop a culture of reproducibility and create integrated infrastructure for sharing data and code. While significant changes may take time, incremental steps, such as publishing code and data, can significantly improve the reproducibility of computational research.