Accelerating the Adoption of Research Data Management Strategies in Materials Science: Best Practices and Collaboration Hub (c)

Abstract

The need for good research data management (RDM) practices is becoming more recognized as a critical part of research attributed to the exponential rise in Big Data. The materials science community is no exception to this trend, as it embarks on a new paradigm of data-driven science, leveraging artificial intelligence to expedite materials discovery but necessitating large-scale datasets to perform effectively. Hence, there is a concerted effort to standardize, curate, preserve, and disseminate these materials data in a manner that adheres to the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. This thesis work offers two core contributions in this regard, including the recommendation of best practices within the data-driven materials research life cycle to develop and/or procure an effective and FAIR RDM system. These best practices are then applied to develop a data sharing platform dubbed Collaboration Hub © for catalysis surface reactions which are computationally expensive and valuable. This platform consists of a MongoDB database, Python FastAPI, and a React JS website, all bundled up within Docker containers and deployed on secure servers with the purpose to facilitate the long-term use and preservation of FAIR and sustainable data.

Date of Award	2023
Original language	American English
Awarding Institution	HBKU College of Science and Engineering