With the rapid expansion of digital twins both in research and industrial settings, a common practice is to use proprietary software solutions for generating and running digital twins. However, although these software products are appropriate in certain cases, the user cannot typically have access to the source code in order to change or fully understand on how data is accessed and processed. As data is the cornerstone to the digital twin concept, transparency is often required for the user to have high levels of trust in the results. Without a transparent platform, issues such as data corruption or misinformation could erode trust in the results. As a result, the ability to inspect, and then remove and/or query data from the dataset is desirable, but not commonly available through proprietary software solutions. This type of data management includes data both from the physical twin via experiments and sensors, and simulation results from the digital twin. To have transparency in the data collection, meta-data, and data usage, an open-source digital twin operational platform framework is used to demonstrate how a user accesses this data and how the digital twin uses this data in simulations.