In recent years, the reproducibility of scientific research has become increasingly important, both for external stakeholders and for the research communities themselves. They all demand that empirical data collected and used for scientific research is managed and preserved in a way that research results are reproducible. In order to account for this, the FAIR guiding principles for data stewardship have been established as a framework for good data management aiming at the findability, accessibility, interoperability, and reusability of research data. A special role is played by natural language processing and its methods, which are an integral part of many other disciplines working with language data: Language corpora are often living objects – they are constantly being improved and revised, and at the same time the processing tools are also regularly updated, which can lead to different results for the same processing steps. In this presentation I will first investigate CMC corpora, which resemble language learner corpora in some core aspects, with regard to their compliance with the FAIR principles and discuss to what extent the deposit of research data in repositories of data preservation initiatives such as CLARIN, Zenodo or META-SHARE can assist in the provision of FAIR corpora. Second, I will show some modern software technologies and how they make the process of software packaging, installation, and execution and, more importantly, the tracking of corpora throughout their life cycle reproducible. This in turn makes changes to raw data reproducible for many subsequent analyses.