Research data management in universities of central China

Abstract

Purpose Revealing research data’s production and use, the status of research data management (RDM) and researchers’ service requirements in universities of Central China; this study aims to investigate the feasibility of university libraries in providing RDM services without any supporting policies from governments or funding agencies. Design/methodology/approach Using a stratified sampling method, faculties and graduate students from 11 universities were investigated. Four pilot subjects at Wuhan University (WHU) were chosen for whom a pilot RDM platform was to be constructed. Findings Research data at Chinese universities are small, sporadic and discontinuous. Such data are intensively or dispersedly under researcher’s management, with some unresolved problems regarding data security, data sharing and utilisation efficiency. Researchers’ needs for data services are strong. University libraries in China can develop RDM systems and provide related services. To realise this, more work should be done on service mechanism, service promotion, software development and staff training. Research limitations/implications The user survey covered 11 universities in central China, which may not reveal the real RDM status of researcher in different areas of China. Practical implications The practice at WHU could provide reference to other university libraries in China or other developing countries. Social implications The practice at WHU could provide reference to other university libraries in China or other developing countries. Originality/value The user survey is designed to be as comprehensive as possible and cover 902 researchers from 11 different types of Chinese universities. The practice at WHU is one of the first RDM initiatives led by university library in China.

Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania

“…Data management planning constitutes one of the important research funder requirements when applying for a research grant (Jones 2011, Jones et al 2013Fary & Owen 2013;Kelly & Hodgett 2015;Davidson 2016). Some research universities and organisations consider data management planning as an integral part of data management practices even without any supporting policies from government or a research funding agency (Liu & Ding 2016). Despite variations in funders' specific requirements, many require data management plans (DMPs) when a research proposal is submitted.…”

The Role of Academic Libraries in Scholarly Communication. A Meta-Analysis of Research

“…The main conclusion that can be drawn from this research is the lack of awareness among scholars but also among university administrators about the benefi ts of this type of activity, an issue which can become a substantial challenge but also an area within which librarians can act. Within this publication (Liu & Ding, 2016) as well as in the two which were previously mentioned (McLure et al, 2014;Weller & Monroe-Gulick, 2014) the need for special education of librarians, experts in the area of data management, has been highlighted. Similar conclusions were reached by Diekema, Wesolek, and Walters (2014) who also drew attention to the fact that neither the offi cial granting agency requirements nor the general conviction of academics about the benefi ts of sharing data from research impact their actual activities in this realm.…”

“…Importantly, scientists participating in research were not aware which activities within this realm were currently realized by the library. In turn, studies carried out at 11 Chinese universities (Liu & Ding, 2016) demonstrated that the majority of scholars do not posses appropriate skills to properly manage data and institutions in which they work do not offer them support in this area. A pilot project dealing with opening a research data management platform and offering an array of services connected to it at the Wuhan University Library was met with academics' considerable interest.…”

Data sharing for global, unspecified reuse

“…When it comes to assessing these claims, the sampling strategies employed by previous studies are instructive, as they offer a glimpse on how they have interpreted data (sharing) practices relative to modes of scientific organisation. These strategies range from (single) case studies relying heavily on ethnographic methods (interviews, participant observation) (Hsu et al 2015;Leonelli 2016;Myneni et al 2016;Wallis et al 2013); qualitative small-N analyses (Kurata et al 2017); quantitative studies of disciplinary data practices (Anderson et al 2007;Borghi and Van Gulick 2018;Chen and Wu 2017); comparative studies within institutions (Akers and Doty 2013;Cox and Williamson 2015;Mancilla et al 2019;Reichmann et al 2021;Schöpfel et al 2018); to large-scale national or international surveys (Aydinoglu et al 2017;Chigwada, Chiparausha, and Kasiroori 2017;Elsayed and Saleh 2018;Koopman and Jager 2016;Liu and Ding 2016;Tenopir et al 2011). The sampling strategies associated with these approaches correspond to (broadly) administrative versus (broadly) epistemic concerns, depending on the theoretical framework (research areas, specialties, disciplines, universities and departments) used.…”