This chapter examines the implications of the Replication Crisis for corpus linguistics and humanities researchers working with language data. It highlights common issues and workflows in corpus linguistics that hinder reproducibility and offers guidance on improving data management, sharing practices and transparency of analyses. The discussion addresses several key issues prevalent in the field, including a lack of training on enhancing reproducibility, disorganized data management, analysis procedures that impede replication, and convoluted workflows compounded by limited resources. These challenges are particularly pronounced in qualitative and interpretive work, where specific attention is required, but also concern any type of data-intensive analysis. To address the issues, the chapter provides pointers to resources, practices, and tools that enhance data management and transparency of analyses in corpus linguistics, and it shows how these resources can be put into practice using the illustrative example of a study on adjective amplification in selected varieties of Asian Englishes. By adopting improved methods, researchers adhere to best practices and thereby enhance reproducibility, transparency, and the overall research quality in corpus linguistics.