Interoperability issues concerning observational data have gained attention in recent times. Automated data integration is important when it comes to the scientific analysis of observational data from different sources. However, it is hampered by various data interoperability issues. We focus exclusively on semantic interoperability issues for observational characteristics. We propose a use-case-driven approach to identify general classes of interoperability issues. In this paper, this is exemplarily done for the use-case of citizen science fireball observations. We derive key concepts for the identified interoperability issues that are generalizable to observational data in other fields of science. These key concepts contain several modeling challenges, and we broadly describe each modeling challenges associated with its interoperability issue. We believe, that addressing these challenges with a set of ontology design patterns will be an effective means for unified semantic modeling, paving the way for a unified approach for resolving interoperability issues in observational data. We demonstrate this with one design pattern, highlighting the importance and need for ontology design patterns for observational data, and leave the remaining patterns to future work. Our paper thus describes interoperability issues along with modeling challenges as a starting point for developing a set of extensible and reusable design patterns.

HL7 is holding its annual conference on genomics February 20-21 in Washington, D.C. The theme this year is focused on the intersection of clinical genomics and artificial intelligence. To preview the meeting, I recently interviewed Grant Wood, a member of the HL7 Clinical Genomics Work Group and a senior strategist at Intermountain Healthcare's Clinical Genetics Institute, who is chairing the meeting. HCI: Why did HL7 choose to focus on the confluence of clinical genomics and AI this year? Wood: AI and machine learning are getting a lot of hype now.