Many kinds of different scientific data are being produced every day by research institutes across the globe. Scientists are interested in using this data, but often have difficulties when trying to obtain access to data that has been created and is stored by external organizations, due to incompatible data management standards. The Findability, Accessibility, Interoperability, Re-usability (FAIR) principles are guiding principles for scientific data management and stewardship, which have been developed to facilitate knowledge discovery by introducing common standards for human and machine interaction with data, utilizing Persistent Identifiers (PIDs) and metadata. Several technologies and services have been introduced which leverage these principles. However, all aforementioned standards, technologies, and services are intended for static data and do not provide adequate support for dynamic and evolutionary data, e.g. software source code, which is often managed by Version Control Systems (VCSs) such as Git and Subversion. This research investigated the current approaches to managing persistently identified data through VCSs and found them to be lacking in diversity of supported VCSs and persistent publishing systems, and proposed a novel system which allows for direct publishing of repositories from multiple VCSs to multiple, external publishing systems through a web-accessed interface. This initial idea has also been published as a poster in the 2019 eScience Proceedings , which originated from an industry problem posed by Grasple . Additionally, at the end of the thesis, several assertions and conclusions about the state of the art of persistent publishing of evolutionary data, most notably software source code, are made which detail important problems that need additional solutions.