Participation of Higher Education Institutions (HEIs) in Research Data Management: A Study

Executive summary: Ensuring the effective management of research data, where it encompasses "any information that has been collected, observed, generated, or created to validate original research findings" (University of Leeds, n.d.), has evolved into an essential demand within research institutions, notably in higher education institutions (HEIs), commonly known as universities. In this context, diligent care and meticulous management of research data are imperative to uphold the integrity of research and development activities in academia. Research data management (RDM) refers to the systematic approach of handling research data throughout its lifecycle, encompassing its plan, collection, documentation and organization, curation, storage, preservation, and sharing. It aims to ensure the integrity, accessibility, and usability of research data. Considering the importance of research data and the increasing focus on RDM in HEIs, we developed a strong interest in comprehending how extensively HEIs worldwide are adopting effective RDM practices. To explore this, we first conducted a thorough literature review. It has been observed that most of the state-of-the-literature concentrated on the practices of research data service and the role of libraries in academic institutes (Chiware et al., 2016; Cox et al., 2017; Flores et al., 2015; Pinfield et al., 2014; Si et al., 2015; Tenopir et al., 2014; Tripathi et al., 2017). Higman and Pinfield (2015) have investigated the relationship between RDM policy and services, and openness practices in HEIs. The examination of relevant literature has highlighted a noticeable absence of studies that concentrate on a comprehensive assessment and investigation of the current state of RDM in HEIs. This research aims to provide a detailed report on the current state of RDM in HEIs worldwide. This investigation involved analyzing the websites of top-50 universities as per the QS world university rankings-2018. Furthermore, a set of criteria was developed to evaluate and explore the RDM systems of the selected institutes. The study highlights various aspects of RDM such as policy and guidelines, data management planning, software used for developing the data repository or third-party repository service providers, responsible stakeholders in data management, metadata, licensing, identifiers used, and more. Key Findings and Discussion: The analysis of the results provides a comprehensive overview of RDM practices in the selected HEIs. The following key findings shed light on the current state of RDM adoption in HEIs worldwide: RDM Policy and Guidelines: Among the HEIs, 27 institutions (71.05%) have established RDM policies, demonstrating a significant commitment to organized data management practices. However, it is noteworthy that 11 institutions (28.95%) are yet to implement policy. This indicates that while a substantial portion of HEIs recognize the importance of RDM policy, there is still room for improvement in establishing policy and guidelines across the HEIs. DMP Tool Adoption: A notable 34 institutions (89.47%) have incorporated the use of data management planning (DMP) tools, showcasing a proactive approach towards managing their RDM requirements. Conversely, 4 institutions (10.53%) do not utilize DMP tools. This indicates a strong trend towards employing dedicated tools for systematic data management plan, which can enhance the overall quality and accessibility of research data. Software Landscape: The data management software landscape reveals Dspace as the dominant choice, with 7 institutions (18.42%) employing it. Dataverse follows closely with 5 institutions (13.16%), and Fedora with 3 institutions (7.89%). This diverse distribution suggests that institutions are making software choices based on their specific needs. However, the absence of software information for 12 repositories highlights the importance of transparently documenting software used for data management. Metadata Standards: Dublin Core stands out as the most prevalent metadata standard, adopted by 13 institutions. This likely stems from its versatility and simplicity, catering to various data types. DDI, included by 5 institutions, highlights a focus on comprehensive metadata for empirical research. While DataCite, MODS, RIOXX, SDMX, and ISA standards demonstrate diversity. Notably, for the majority of repositories (18 institutions), metadata remains unknown, indicating a potential area for improvement in documenting and providing metadata details. HEIs should prioritize consistent metadata documentation to optimize data usability and accessibility. Stakeholders in RDM: The results indicate that libraries play a pivotal role as major stakeholders in RDM across HEIs. Additionally, other stakeholders such as the Office of Research, IT Service, and Research Operation Office are also involved, emphasizing the interdisciplinary nature of research data management. This multi-stakeholder involvement emphasizes the collaborative effort required for effective RDM implementation in HEIs. Identifiers for Data: The most common identifier for research data is "DOI," used by 27 institutions. "Handle" is the second most popular, employed by 8 institutions. Some institutions use alternatives like "ark," "URI," and "Purl." Importantly, 4 repositories lack any specific identifier. This absence could hinder data discoverability and tracking. "DOI" emerges as the preferred and widely recognized choice, underlining its importance in enhancing data accessibility and scholarly communication within HEIs. Institutions without an identifier may benefit from adopting a persistent identification system to ensure data clarity and accessibility. Citation Download/Export: Only 12 repositories (31.58%) offer citation download/export facilities, while 26 repositories (68.42%) lack this feature. By adding citation download/export capabilities, HEIs can contribute to wider research visibility and impact. Access Policies: An impressive 34 repositories (89.47%) support open access, indicating a strong commitment to sharing research data openly. This aligns with the global trend towards open science and data democracy facilitating collaboration and knowledge dissemination. The prevalence of open access policies signifies HEIs' recognition of the importance of transparent and accessible research data. The findings of the study show the areas where RDM practices in HEIs require further improvement as well as the progress that has been accomplished. While a substantial number of HEIs have implemented RDM policies, adopted DMP tools, and advocated for open access, challenges remain in areas such as metadata documentation, citation export capabilities, and consistent software documentation. The coordination and participation of stakeholders from different units, including the library, office of research, IT service, research operation office, and other unit or department, emphasizes the teamwork needed for successful RDM implementation in HEIs. Finally, it is crucial to keep RDM policy up to date as technologies and time changes in order to promote a community-wide academic culture of efficient data management. References: Chiware, E. R. T., Mathe, Z., & Mathe, Z. (2016). Academic libraries’ role in Research Data Management Services: a South African perspective. South African Journal of Libraries and Information Science, 81(2). https://doi.org/10.7553/81-2-1563 Cox, A. M., Kennan, M. A., Lyon, L., & Pinfield, S. (2017). Developments in research data management in academic libraries: Towards an understanding of research data service maturity. Journal of the Association for Information Science and Technology, 68(9), 2182–2200. https://doi.org/10.1002/ASI.23781 Flores, J. R., Brodeur, J. J., Daniels, M. G., Nicholls, N., & Turnator, E. (2015). Libraries and the Research Data Management Landscape. In The Process of Discovery: The CLIR Postdoctoral Fellowship Program and the Future of the Academy 84 (pp. 82–102). https://www.clir.org/wp-content/uploads/sites/6/RDM.pdf Higman, R., & Pinfield, S. (2015). Research data management and openness: The role of data sharing in developing institutional policies and practices. Program, 49(4), 364–381. https://doi.org/10.1108/PROG-01-2015-0005 Pinfield, S., Cox, A. M., & Smith, J. (2014). Research data management and libraries: Relationships, activities, drivers and influences. PLoS ONE, 9(12). https://doi.org/10.1371/JOURNAL.PONE.0114734 Si, L., Xing, W., Zhuang, X., Hua, X., & Zhou, L. (2015). Investigation and analysis of research data services in university libraries. Electronic Library, 33(3), 417–449. https://doi.org/10.1108/EL-07-2013-0130 Tenopir, C., Sandusky, R. J., Allard, S., & Birch, B. (2014). Research data management services in academic research libraries and perceptions of librarians. Library and Information Science Research, 36(2), 84–90. https://doi.org/10.1016/J.LISR.2013.11.003 Tripathi, M., Shukla, A., & Sonker, S. K. (2017). Research data management practices in university libraries: A study. DESIDOC Journal of Library and Information Technology, 37(6), 417–424. https://doi.org/10.14429/djlit.37.6.11336 University of Leeds. (n.d.). What is research data? Retrieved December 28, 2018, from https://library.leeds.ac.uk/info/14062/research_data_management/61/research_data_management_explainedThis presentation was delivered at the IIT, Gandhinagar, during the International Conference on Changing Landscape of Science & Technology Libraries (CLSTL), which took place from February 28 to March 02, 2019