Synthetic biology is a movement to standardize genetic engineering and make it more reproducible and accessible by using functional descriptions of desired circuits. Such descriptions can then be converted to genetic designs via genetic design automation tools. Subsequently, the genetic designs can be used to generate models for in silico experimentation using automatic model generators. Both of these technologies rely on access to libraries of genetic part information encoded in standard, machine-readable ways. The Synthetic Biology Open Language (SBOL) can be used together with SynBioHub (a genetic part repository) to encode and store the information. However, the use of SynBioHub for the storage and reuse of parts is still very limited. This is due to insufficient metadata (making it difficult to find parts or judge their usefulness) and the effort required to submit parts to the repository. This dissertation aims to decrease the barriers to part reuse and thus enable a more automated synthetic biology workflow. Hence, an integrated curation workflow is proposed based on the contributions of the dissertation. The contributions are: a proposed SBOL Data Content Standard, tools for working with genetic parts in spreadsheets, a framework to modularly extend the SynBioHub part repository, and the lessons learned from the analysis and curation of data from existing genetic data repositories.