We introduce the Korea Institute of Science and Technology-Novel Materials Discovery (KISTNOMAD) platform, a materials data repository. We describe its functionality and novel features from an academic viewpoint. It is a data repository designed for computational material science, especially focusing on managing and sharing the results of molecular dynamics simulation results as well as quantum mechanical computations. It consists of three main components: a database, file storage, and

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... front end. The database hosts material properties, which are extracted from the computational results. The front end has a graphical user interface and an open application programming interface, which allow researchers to interact with the system more easily. KIST-NOMAD's panel displays the searched results on a well-organized and research-oriented web page. All the open access data and files are available for downloading in comma-separated value format as well as zipped archives. This automated extraction function was developed by utilizing database parsers and JSON scripts. KISTNOMAD also has an efficient option to download simulation and computation results on a large-scale. All of the above functions are designed to satisfy academic and research demands, and make highthroughput screening available, while incorporating machine learning for computational material engineering. We finally stress that the repository platform is user-driven and user-friendly. It is clearly designed to follow the modern big-data architecture and re-use principles for scientific data, such as being findable, accessible, and interoperable.