Accelerating the adoption of research data management strategies

The need for good research data management (RDM) practices is becoming more recognized as a critical part of research. This may be attributed to the 5V challenge in big data: volume, variety, velocity, veracity, and value. The materials science community is no exception to these challenges as it heralds its new paradigm of data-driven science, which uses artificial intelligence to accelerate materials discovery but requires massive datasets to perform effectively. Hence, there are efforts to standardize, curate, preserve, and disseminate these data in a way that is findable, accessible, interoperable, and reusable (FAIR). To understand the current state of data-driven materials science and learn about the challenges faced with RDM, we gather user stories of researchers from small- and large-scale projects. This enables us to provide relevant recommendations within the data-driven research life cycle to develop and/or procure an effective RDM system following the FAIR guiding principles.