Data Sharing in China: What do researchers think?

Late last year, we conducted further research with researchers in China to better understand the challenges and opportunities of data sharing. Last month, we published the results in both Chinese and English, now publicly available in the form of a white paper, Challenges and Opportunities for Data Sharing in China. We have also produced an infographic.
Data Sharing in China: What do researchers think?

You can access the white paper, infographic and survey dataset on figshare. The white paper attracted significant interest in China and was reported in more than 20 media outlets including Xinhua, China Daily, and Science and Technology Daily.

Writing a post for members of a community dedicated to research data, I’m preaching to the choir when I say that sharing data has many benefits. It helps to ensure transparency, openness and efficiency in the scientific process as well as having additional benefits to the economy, the society and the research community as a whole. It promotes partnership amongst researchers which is manifested by them sharing their research data with their fellow counterparts and as such creating a collective foundation upon which research can be built on.

2,000 researchers working in China took part in our survey and shared their experiences on data sharing. We would like to thank the National Science Library of the Chinese Academy of Science for being an integral part of this collaborative project.

So without any further ado, what did the research actually find?

The good news is that the majority of researchers surveyed (93%) in China are creating data management plans (DMPs), detailing how data will be collected, stored and shared. However the frequency of this planning varies widely, with only 58% doing this for half or more than half of their research. This compares to a global average of 70% who have created DMPs, according to the 2018 State of Open Data report from Digital Science and figshare.

For 36% of researchers working in China, DMPs are created only rarely. Among those researchers who have never created DMPs (7%), 50% say they have not heard of a DMP before, and 40% say they do not know how to create one.

However, 69% of researchers in China are “extremely likely” or “likely” to create a DMP in the next two years. This suggests that further education on how to create DMPs would have a positive impact by increasing the proportion of researchers who create these regularly as part of their research. There is an opportunity from within institutions, with 48% of researchers saying that research offices are best placed to provide them with guidelines and help with research data.

Data sharing is important to the majority of researchers in China, with 79% of respondents rating the discoverability of their data as being at least somewhat important to them, which is comparable to the global levels we found in our Practical Challenges report.

Private sharing of data with immediate colleagues and collaborators is more common than wider public sharing of data for researchers in China. Only 7% of researchers in China have never shared their data either privately or publicly.

The top two reasons why researchers were motivated to share their data were “to progress research in their field” (46%) and “increased visibility for their research” (44%). Lack of journal requirements was cited by 35% as the main reason why researchers had not shared their data, whilst “concerns about misuse of my data” (48%), and being “unsure about copyright and licensing” (32%) were the main concerns researchers faced with sharing data

The survey helps us to understand how Chinese researchers are sharing their data, and especially what they’re concerned about. We are committed to working with funding organizations and universities to develop collaborative solutions, for example on data management and education, to support China in its ambitious goals to ensure community best practices in the sharing and archiving of research data.

Working towards such a goal, Springer Nature has adopted four types of research data policy which are standardised across all of our journals, and made the research data policy texts available for reuse by the research data community under a Creative Commons attribution license. Springer Nature also offers Research Data Support and training options alongside its data publishing offerings in Scientific Data and BMC Research Notes.

If you would like to learn more about research data, our research data helpdesk provides free advice on policies of funders, institutions and journals and on finding research data repositories. Or visit our shiny new homepage on the Springer Nature website.

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