The ability to cite software and give credit to its authors and contributors is increasingly important. While the number of online open-source software repositories has grown rapidly over the past few years, few are being properly cited when used due to the difficulty of creating appropriate citations and the lack of automated techniques. This paper presents GitCite, a model for software citation with version control which enables citations to be inferred for any project component based on a small number of explicit citations attached to subdirectories/files, and an implementation that integrates with Git and GitHub. The implementation includes a browser extension and a local executable tool, which enable citations to be added/modified/deleted to software project repositories and managed through functions such as fork/merge/copy.

Various domains such as business intelligence and journalism have made many achievements with help of data analytics based on machine learning (ML). While a lot of work has studied how to reduce the cost of training, storing, and deploying ML models, there is little work on eliminating the data collection and purchase cost. Existing data markets provide only simplistic mechanism allowing the sale of fixed datasets with fixed price, which potentially hurts not only ML model availability to buyers with limited budget, but market expansion and thus sellers' revenue as well. In this work, we demonstrate Nimbus, a data market framework for ML model exchange. Instead of pricing data, Nimbus prices ML models directly, which we call model-based pricing (MBP). Through interactive interfaces, the audience can play the role of sellers to vend their own ML models with different price requirements, as well as the role of buyers to purchase ML model instances with different accuracy/budget constraints. We will further demonstrate how much gain of sellers' revenue and buyers' affordability Nimbus can achieve with low runtime cost via both real time and offline results.