A quality and popularity based ranking method for research datasets

Research outputs are the final products in the scientific research process and their quality is progressively being evaluated by various methods such as altmetrics, bibliometrics, impact factors and citation count etc. However, a significant component of scientific research involves creating/collecting/curating research datasets and globally, funding agencies and governments are mandating an open access policy on research datasets. Though repositories exist to store the datasets, there is no metricised guidance, indicating the quality of datasets for researchers wishing to reuse. We propose a novel method for ranking and visualising research datasets based on their quality and popularity, constructed through a normalised citation count since the year of origin, total cites and the impact factor of the journals which publish the articles citing the dataset. Additionally, we present the process flow for a proposed digital information system for the access of datasets according to their discipline and rank based on the variables. The proposed method is expected to assist researchers, globally, to choose the right datasets for their research, encourage researchers to share their datasets and promote interdisciplinary research.