Data Standards provide a way of expressing data in a uniform manner. In addition to standardizing a format for encoding data, data standards allow for data to be exchanged easily and meaningfully. Standards, commonly, enable applications to easily communicate and pass data to one another; however, this seamless communication between applications is impossible if applications rely on different data standards that encode data differently. This thesis proposes a workflow methodology for best-effort automatic conversion or translation of meta-data from one data standard to another while minimizing the loss of data. The objective of the methodology is to validate the conversion and determine the compatibility between two tools and their underlying data standards. The standard-enabled workflow and methodology created should analyze a given workflow of tools to see if data is lost within the workflow and ensure that the data is still compliant with a standard as the data flows through various tools. To determine how well the methodology works, Synthetic Biology tools are evaluated to see valid connections can be made with other tools while maintaining compliance within the data standard supported by the tool.