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Data publishing provides an innovative model for data sharing. To gain better insights into data publishing, this paper uses a literature review and case studies to build up a citation metrics framework and conducts case studies on two Chinese data journals, Chinese Science Data and the Journal of Global Change Data and Discovery. This paper reveals the status of data publishing and reusability of the linked datasets in the two journals. And over thirty indicators have been analyzed, covering paper indicators (e.g. subjects, team size, and funding sources), data indicators (e.g. data sources, data scale, types, visits, and downloads), citation indicators (e.g. cited, citing, and time metrics), etc. The results unveil that, at the macro level, data publishing has become an important way for data sharing. It effectively promotes open cooperation, together with funding support, high data quality control, and facilitates the sharing of special collections of datasets across domains. Besides, national data centers play a vital role in supporting data publishing. At the micro level, datasets typically have strong associations with subjects but may still share commonalities across domains. Moreover, several indicators may contribute to decoding data reusability, such as combining data visits and downloads, and further links to citations. In addition, independent data publishing is on the rise in China, and we suggest nourishing data culture, diversifying business models, reinforcing data governance, developing robust platforms and technologies, and better incentives and metrics for data sharing in the long run.
Lili Zhang (Fri,) studied this question.