Measuring Data Reusability in the Open Science Era

Open data, among the key pillars of Open Science, is one of the drivers for science and society. To promote open data, FAIR metrics should be ready to measure the performance of data-sharing works. However, considering the complex process of lifelong data management, how can we guarantee metrics of reusability for data that could objectively and truly reflect the reusability of data? The current data metrics may focus on different aspects of data, which include quality-based metrics, such as data accuracy, completeness, and usability measurement; impact-based metrics, including interest-based metrics, such as visits to databases and tweets in social media, and effort-sparing actions, such as downloads; knowledge-based metrics, such as citations in publications and patents; and value-based metrics, such as those highly praised by their functionality as social capital, or direct monetary return on capital assets driven by open data work. Considering the lifelong data management process, basic principles should be developed to balance different roles and maximize the total benefits. This way, inspired by Ranganathan's laws of library science, considering the intrinsic and extrinsic value of data and the current measuring practices adopted, the basic guiding principles will be developed and discussed. The core values of open science are also mapped to ensure the openness and inclusiveness of the principles and to guarantee a better flow of data across the science community and society.