Research data management in university libraries: The need for data literacy and technological revamp

Introduction

The advancement in technologies and the resultant effect of broadening the horizon of scholarly communication have driven universities to be more ‘research centric’, in line with their objective of advancing the frontiers of knowledge. As such, the production of research in universities has experienced a drastic increase in recent times. The International Association of Scientific, Technical and Medical Publishers (STM) in their 2021 global brief showed that the publication of research articles experienced a growth rate of between 5 and 6.5% (STM, 2021). This was corroborated by Elsevier (cited in Science Business, 2023) when they noted an important growth in scholarly production especially in the Global South. The growth in research articles could prompt a corresponding increase in research data. Taylor (2023) revealed a rapid growth of these data from 2010 to 2023 and projected a 19.2% compound annual growth by 2025. This growth, if not well handled, could result in ‘research data overload’, where researchers could be overwhelmed by the avalanche of research data in circulation, beyond their ability to effectively and efficiently utilize them. This reinforces the need for research data management (RDM), which has emerged as an issue of concern to researchers and academics in universities around the world.

The emergence and development of copious amounts of data and the consequent need for appropriate management on the part of stakeholders have spurred the ideation, recognition, adoption and application of RDM (Howie and Kara, 2020). One of the popular definitions of RDM was put forward by De Montfort University Library (2023) as the storage, access and preservation of data that emanated from scholarly investigations or research works. They further asserted that RDM services (RDMS) encompass the data life cycle of planning, digital curation, metadata creation and conversion. Therefore, RDM involves the deliberate management of activities within the research data life cycle, which Ashiq et al. (2021) described as covering the activities of planning, organizing, creating, storing, security, retrieval, sharing and reusing of data. Some of these activities fit into the conventional practices and function of university libraries, which made it easier for them to accommodate RDMS into their service structure.

A prominent research work that affirmed the relationship between RDM and university libraries was that of Andrikopoulou et al. (2022). An analysis of the study revealed that while libraries are well positioned to deliver RDMS and have great potential to do this, certain issues (drivers and factors) need to be addressed by further quantitative and qualitative work in order to strengthen the ongoing development of RDM in university libraries. Andrikopoulou et al. (2022) suggested that skill gaps and inadequate technological infrastructure can constrain the development of RDM programmes in university libraries. The basic element of any RDM project is data, and as such, competence in handling data is required for the successful execution of RDM projects. However, data literacy is not a common literacy-type, especially among university library personnel in the Global South (Global Voice Group, 2020; Moyo and Bangani, 2023), when compared to those of the Global North (Burress et al., 2020). However, a study carried out in Bulgaria, which is considered to be a Global North country by World Population Review (https://worldpopulationreview.com/country-rankings/global-north-countries), found that RDM awareness and skills are still far from satisfactory, implying a general need to address RDM concerns.

Inadequacy of technological infrastructure has continued to be an issue, particularly among universities in the Global South, which could hinder the development of RDM or the delivery of RDMS in these university libraries. Although some university libraries may already have technology processes that facilitate RDM, it is imperative to provide programmes that educate or train library and information science (LIS) professionals on the fundamentals of research data literacy (Nwabugwu and Godwin, 2020). This suggests that technological infrastructure and data literacy are essentials of RDM in university libraries. Consequently, this paper examines RDM in university libraries, and how data literacy and technological revamp are critical factors needed for the successful implementation of RDM in university libraries.

The paper begins by providing a synopsis or overview of RDM in university libraries, examining general issues around RDM and its deployment in university libraries. It then examines data literacy as a requirement for the effective implementation of RDM and for the successful delivery of RDMS in university libraries. Also, the paper examines the role of technological infrastructure in achieving RDM in university libraries.

Research objectives

The study aimed to theoretically explore data literacy and technological infrastructure as prerequisites for the successful implementation of RDMS in university libraries. Specifically, the study examined literature on the following:

Data management in university libraries.

Methodology

The study employed a qualitative research approach to synthesize and analyse existing literature. It examined data literacy and technological infrastructure as prerequisites for the successful implementation of RDMS in university libraries. The adoption of a qualitative method of literature review was informed by the need to understand existing knowledge and findings around the focus theme of this study, thereby aligning with Echedom and Okuonghae (2021). To ensure the study was carried out in a clear and well-defined manner, existing literature around the theme of the study was systematically selected using the following predefined keywords/search terms: ‘data literacy and research data management’, ‘technological infrastructure and research data management’ and ‘research data management services’. The selection of the literature was conducted from three academic databases, namely Emerald, ProQuest and Google Scholar. Emerald was selected because it contains a large amount of literature in the field of LIS and sits well with the constructs of the study. ProQuest and Google Scholar were selected because they contain a large amount of multi-disciplinary full text articles published by different academic publishers around the world. This aligns well with the global scope of this study. A primary inclusion criterion for literature used in this study was that the studies were published within the last five years (2019 to 2023). Studies published before 2019 were excluded from the study. The researchers reviewed and selected the relevant literature within a two-week period, and completed the entire study within a three-month period while adhering to the highest ethical standards.

Synopsis of research data management in university libraries

In this digital era, research data are viewed as a product and not just a means to an end. The usefulness of research data is no longer terminated at the point of data analysis and publication of research findings. Research data have become more useful by the practice of data sharing and data re-use in the academic community. The life cycle of research data is no longer linear but now cyclic, where research data are being used by other scholars even after a research project has been completed. Hence, the need for RDM cannot be overemphasized. RDM is explained as a research component specifically dealing with proper organization and preservation of research data for the purpose of current and future access and use (Chawinga and Zinn, 2020).

Research data management is simply explained as the intentional or deliberate processes involved in gathering, storing, protecting and disseminating data created or curated during the period of research (Nwabugwu and Godwin, 2020). The overall aim of this unique activity is ensuring there is no loss of valuable and viable research data, and also to promote data sharing and re-use. Appropriate RDM in university libraries today encourages the practice of reproducibility and re-use of data by scholars and researchers. RDM encompasses diverse techniques and approaches that are tailored towards preservation of the visibility of research data, easing access to these data even after the research has been completed. Proper RDM ensures that the data be not just retrievable, comprehensible and understandable during an ongoing research project but also remain that way and useful for future investigations and research studies.

Essentially, RDM creates a framework that extends beyond simple storage; it includes the proper organization, preservation and exchange of data in a way that permits their continued utilization and increases the likelihood of novel findings and scientific breakthroughs (Kuchma, 2021; Orr, 2023). The activities carried out during the processes of RDM serve as a springboard for collaborations, data reproducibility and the evolution of the research and scholarship landscape. This reiterates the benefits of proper management of research data in the form of RDM.

University libraries today are institutions trusted with the responsibilities of acquiring, organizing, storing, preserving and disseminating data and information, and these include research data. Payal and Manorama (2019) noted that libraries serve as prime stewards for these responsibilities given the expertise of their professionals in organizing and safeguarding information. Their core mission revolves around the preservation of knowledge, making them well equipped to handle data archiving and curation tasks effectively. Leveraging their extensive experience, librarians have adeptly established institutional repositories, showcasing their proficiency in managing vast amounts of data/information. Engaging in data archiving and curation seamlessly aligns with their established responsibilities, reflecting a natural extension of their long-standing commitment in these domains. Although Payal and Manorama's (2019) study explains the imperatives of RDM practices in academic libraries, the study offers limited practical guidance on how libraries can effectively implement these services into their operations. Case studies, examples of successful RDM initiatives and best practices would guide libraries from different regions in the implementation of RDMS. Payal and Manorama's (2019) study is limited to academic libraries in India, thus, the findings lack power of generalization to the global context, and as such, may not represent the global landscape of RDM practice.

The expertise of librarians in managing information aligns with their core mission of preserving knowledge for posterity. Library professionals, through their specialized training, possess the nuanced skills required for meticulous data organization and preservation. Hombali (2022) underscored this claim noting that librarians are trained to support scholars and researchers in the process of managing their research data effectively, further ensuring that these data are properly preserved, thereby making them easily available for re-use and sharing. Their experience in setting up and managing institutional repositories attest to their proficiency in handling large volumes of information. Thus, undertaking the tasks of data archiving and curation seamlessly aligns with their established expertise (Baily, 2023; Tammaro et al., 2019), marking a continuation of their efforts in maintaining and preserving valuable knowledge resources. However, Hombali’s (2022) assertion lacked empirical support as it relied solely on secondary sources. The lack of empirical evidence in the form of surveys or case studies limits the ability to reach an objective and strong conclusion about the effectiveness of librarians in the delivery of RDMS.

The unique and important function of RDM carried out by university libraries today centres around certain components such as the needed infrastructure and resources, policy formulations and framework trainings, data preservation and access, collaborations, etc. University libraries are expected to be custodians of research data and are responsible for their effective management. Various studies have been conducted on the practice of RDM in libraries today. One such study was carried out by Masinde et al. (2021) on the experiences of research librarians with data management activities in the University of Nairobi’s library. The study revealed the existence of policies that guided research data activities such as data capture, appraisal, description, preservation, access, re-use and sharing in the university library. The authors affirmed the need to frequently revisit the institution’s research data policy, to align it with the research needs of the scholars. Despite existing policies covering quality assurance, research data and intellectual property, the authors highlighted a significant gap: there were no explicit guidelines for managing each stage of data curation and capabilities (Masinde et al., 2021). However, a notable limitation of their study is that it relied on the interviews of only five participants, which is considered a very small sample size. Hence, the findings may not represent the true picture of RDM practices in Kenya. Moreso, the findings from the study are highly susceptible to social desirability bias given the manner in which the data were collected.

In order for university libraries to effectively and efficiently carry out the service of RDM, it is pertinent that requisite training of personnel be put in place to ensure its success. It is no news that LIS professionals have imbibed the needed skills to carry out certain basic functions centring around satisfying the diverse information needs of their clientele (Carvalho e Rodrigues and Mandrekar, 2021; Taufiq et al., 2020). However, there is still the need for the re-skilling and up-skilling of these professionals in university libraries. Machimbidza et al. (2022) unequivocally noted the need for in depth training in the area of data curation and management services. The authors noted that librarians need training and retraining in the area of data set inclusion in university library repositories. It was observed that some basic skills needed were evidently missing, which would be acquired through training of the professionals in university libraries. Such trainings should be targeted at the acquisition of specific skills like basic computer skills, metadata, digital preservation, curation, copyright and the publication process, as well as the skills for handling experimental data, computer modelled data, simulated.

The level of preparedness of university libraries may still be at an early stage since they still have a lot of work to do as it pertains to the provision of RDMS. Tang and Hu (2019) in their study on provision of RDMS in academic libraries underscored this claim. However, the study revealed that there are variations in the RDMS offered in institutions in the United States of America and in non-USA countries. Significantly more institutions in the USA (66.0%) provided data organization/curation services as compared to non-USA (35.7%) locations. Significantly more institutions in the USA (72.3%) provided metadata services compared to non-USA countries (42.9%). Finally, a significantly higher number of institutions in the USA (70.2%) provided data visualization services compared to institutions in non-USA countries (21.4%). By implication, libraries in highly developed countries like the USA could be more prepared to deliver RDMS than institutions in other countries, particularly in the Global South. Thus, there is a critical need for targeted and accessible professional development initiatives tailored to empower librarians with the competencies required for successful RDM service provision (Mavodza, 2022). However, it is worthy to note that Tang and Hu (2019) hinged their findings on self-reported data among librarians with varied levels of awareness of RDMS. This increases the likelihood of inaccurate reporting of data. Also, this method of data collection is considered a major limitation, as responses are likely to be subjective. A possible solution to this would be the triangulation of data using a combination of semi-structured interviews and surveys.

The development and delivery of RDMS in university libraries have not been without challenges. Tang and Hu (2019) noted that some challenges encountered in the provision of RDMS include paucity of staff (51.9%), poor marketing and outreach of RDM services (29.6%) and poor collaborative understanding among departments (29.6%). Chawinga and Zinn (2020) also opined that lack of skills among personnel is one of the major challenges clogging the advancement of RDM today. This deficiency was attributed to a lack of sufficient professional development opportunities tailored to equip librarians with the necessary competencies to excel in RDM-related roles. The absence of targeted training programmes and educational initiatives has hindered these professionals from acquiring the specialized skills required to perform optimally within the domain of RDMS. This implies that the possession of relevant skills and competences is germane to the development of RDMS in university libraries. Furthermore, while RDMS is applicable to different kinds of libraries, the constraints of RDM practices may vary significantly from one academic domain to the other. This therefore reinforces the need to examine and understand the challenges facing RDMS practices from a more general perspective, as the findings of Chawinga and Zinn (2020) were limited to health-related programmes and not all fields of human knowledge. Additionally, there is the concern of possible response bias, as the data were self-reported by the respondents.

Despite the need for librarians to continuously develop pertinent skills required for the effective delivery of RDM, some studies have shown the possession of relevant competencies by librarians. Chawinga and Zinn (2020), in their study on librarians’ RDM competencies, noted that they displayed moderate proficiency in various RDM tasks, particularly in tasks such as transitioning data to updated file formats (56.2%), crafting preservation metadata (56.2%), moving digital content to repositories (62.5%) and gathering data directly from creators (75%). These findings indicate that librarians and the university library as an institution hold significant potential to influence the RDM landscape within universities, by leveraging their existing expertise. They have the opportunity to demonstrate to researchers, through their activities, that they are ideally positioned within the university community to carry out RDM activities effectively.

Data literacy as a requirement for RDM implementation in university libraries

Data from research activities are the core element of every RDM practice. It is therefore critical that the deployment of RDM in university libraries require that the personnel who will be involved in RDMS have gained mastery in the handling of research data. This mastery, which connotes data literacy, encompasses ‘the ability to identify, collect, evaluate, analyze, interpret, critique, present, and protect data’ (Central Michigan University (CMU) Libraries, 2023). According to Koltay (2015), data literacy is the ability to access, comprehend, analyse, manage, critically evaluate and use research data in an ethical manner. Statistics Canada (cited in Mcelhone et al., 2022) noted that data literacy is a 21st century competency which connotes the capacity, abilities or skills required to deal with data, such as the capacity to read, analyse, interpret and visualize data as well as to motivate sound decision-making. Mcelhone et al. (2022) revealed that the components of data literacy are tripartite in nature, comprising communicating with data, working with data and reading data. The UN's Data Revolution website [https://www.undatarevolution.org/] describes it as the intersection or common joining point between information literacy, statistical literacy and technical literacy. This competency has become vital for potential data management professionals (Koltay (2017), including LIS professionals who are currently taking up RDM-related roles, particularly in university libraries.

Empowering librarians with data literacy competence would provide a solution to the existing gap in RDM understanding. Carmi et al. (2020), in their study, found a gap in the understanding (an understanding gap) of data management best practices and asserted that data literacy training would be a way of bridging this gap. Without a robust knowledge of the data, and by extension RDM, RDM implementation in university libraries will be an effort in futility. This aligns with the assertion of Koltay (2019) that without data literacy education, RDM is inconceivable since those who will utilize the data must be taught how to comprehend, analyse and apply their findings. By implication, LIS professionals need to be trained to become data literate as a prerequisite for the successful implementation of RDM programmes and delivery of RDMS in university libraries.

Since not all libraries can afford to engage specialized workers in the area of RDM, retraining library staff has become crucial to them in achieving data literacy (Anene and Ebifagha, 2021). The authors underscored the need for library professionals to become data literate in a bid to get involved in the range of data-related activities. Data literacy competence can be developed through professional development opportunities that enhance LIS professionals’ expertise, helping them to adapt to changing trends and to develop the necessary skills to address evolving challenges in handling research data. This ongoing training will ensure that data librarians remain well-equipped to effectively support researchers, manage data repositories, implement best practices in data curation and contribute meaningfully to the evolving landscape of information management. Nwabugwu and Godwin (2020) unequivocally noted from the findings of their study on RDM and information security that a number of the university libraries studied encouraged such training programmes as a stride towards the implementation of RDMS.

Subaveerapandiyan (2023) listed some of the data literacy skills that should be developed towards RDM practices in institutions, including metadata management, ontology, collaboration skills, data mining, re-use, visualization, retention and storage skills, and data management plan. Adika and Kwanya (2020) measured RDM literacy levels assessing planning, finding, organizing, storing, security and sharing research data. These are areas need to be covered in data literacy programmes. RDM instruction programmes aimed at training academic library professionals from the study of Xu (2022: 16) focussed on ‘data sharing, RDM overview, data storage, DMP, data documentation, RDM ethics, data visualization & analysis, data security and RDM tools’. The author affirmed that the demand for RDM training has increased since 2011, pointing out that training on the listed areas is expected to provide professionals with the competences needed to become data literate for the successful implementation of RDM programmes. Taş (2023), in a study on digital literacy education, revealed that respondents needed to be educated in areas such as ‘data evaluation/analysis, identifying the relevance of data and data protection in a sensitive manner (p.389)’. Additionally, the participants of the study stressed the significance of understanding interrelationships among data, modifying data for use in various contexts and combining diverse data in a strategic way, as areas of consideration in the development of data literacy.

As university libraries teach information literacy skills as part of their training programmes, they must begin to teach data literacy in the same manner, given that the trend in knowledge currently favors issues around research data. This is why Nwagwu (2024) advocates the teaching of data literacy in academic libraries, which would add to their research support portfolio. By organizing data literacy programmes with the aim of heightening awareness on data, increasing knowledge about data, enhancing proper handling and management of data, strengthening the effective use of data and their preservation, university libraries would certainly be engaging in RDMS. This aligns with the quantitative study of Palsdottir (2021), where findings revealed that researchers’ understanding of data management needed to be urgently increased (due to their poor level of knowledge) and that there was a need to provide them with training for the effective application of data management methods. When researchers within the university community be so trained, it will be easier for them to comply with policies and standards required by university libraries to successfully implement RDM. Therefore, for the overall success of RDM in university libraries, their personnel should be equipped with the requisite trainings to effectively handle data; and when they become data literate, they would be expected to train academics within the university community. However, Palsdottir’s (2021) study hinged on a limited scope of variables as it focussed only on aspects of data management such as metadata, data management training methods and data management plans. This limited scope provides a narrow view and understanding of RDM practices. It is the position of the present study that future research should expand on this and address other variables/factors influencing RDM practices in libraries.

Achieving RDM through technological revamp in university libraries

Research data management carries great value in the academic environment, especially while conducting academic research. Specifically, RDM is instrumental for the preservation of research output, reproducibility of research, enhancing collaborations and ensuring transparency in research methodologies. These functions can be financially easily attained with the acquisition and deployment of proper technological infrastructures. In other words, inappropriate, inadequate and obsolete technology will make RDM seem like a Herculean task and slow down its processes (Nwabugwu and Godwin, 2020). Without this, library personnel would spend more time troubleshooting, dealing with errors or manually compensating for technological shortcomings rather than focussing on the actual management of research data.

As university libraries advance technologically, these technologies transform the way information-related services are delivered. These technologies positively influence how libraries collect, store, process and disseminate data (Eiriemiokhale and Amzat, 2022). There is, however, much room for improvements and upgrade in the technologies available in university libraries for carrying out diverse information services, including RDM. Technological revamp refers to strategic efforts aimed at significantly upgrading or modernizing existing technological infrastructures, systems and tools within an organization or specific domain, in this context, the university library. In the context of RDM in university libraries, technological revamp involves deliberate actions to enhance the technological capabilities and resources available for managing research data effectively. An upgrade or improvement of the available technologies will definitely advance the process of carrying out RDM in university libraries.

A number of university libraries are still at developing stages in terms of available technological infrastructure put in place to enhance the provision of RDM. In order to effectively manage research data in university libraries, there is need for the availability of necessary technologies and their regular upgrade (Nwabugwu and Godwin, 2020). Thus, university libraries are expected to map out the required technologies pertinent to RDM and make them available in an adequate manner in order to successfully implement RDMS. Also, as with every other technology, university libraries will be expected to keep them upgraded in order to accommodate technical scalability. One of the technological infrastructures that enhance the delivery of RDMS in university libraries is cloud storage infrastructure, or facilities used to store and back up research data, which is well noted in the study of Avuglah and Underwood (2019). Therefore, the practice of cloud computing integration into RDM practices should be implemented in university libraries. This will allow for better and flexible data storage options, with scholars having access to data stored in the cloud from multiple locations, further enhancing the processes of collaborations and ultimately the promotion of RDMS delivery in university libraries.

Research data management can be financially easily achieved when there is an upgrade of the hardware components that help in this process. Some of these components include servers, storage systems, networking infrastructures, etc. It is pertinent that university library management invest in high performance computing facilities and resources capable of accommodating the large datasets that may be generated by researchers in the university environment. Masinde et al. (2021) noted that there is the need for robust technological infrastructure that effectively support the data curation, storage, preservation, access, re-use and sharing of research data. The authors specifically reported equipment with low storage capacities and deficiencies in preservation, backups and access, coupled with a considerable user population, as critical challenges to RDM implementation. Hence, the need for university libraries to revamp their technological infrastructure in line with RDM requirements cannot be overemphasized in the process of managing research data and deploying RDMS.

The deployment and revamp of technologies in university libraries towards the delivery of RDMS come with the challenge of data security. It is therefore vital that university libraries boost their data security measures. This will enhance data integrity (Ntja, 2022), encouraging researchers to trust university libraries to properly handle their research data across the RDM life cycle. The process of strengthening available security protocols, especially those related to RDM, is highly necessary. This will help to secure all the research data available in the university, particularly those of sensitive nature. In reinforcing data security on available technologies in university libraries, there is the need for data security policies, regular security software upgrade, installation of software and applications against hackers, setting up of access control, installation of firewall protection as well as to have a cyber-security unit or team for quick response to threats (Igbinovia and Ishola, 2023). Thus, infrastructures for data security are an important component of the technological infrastructure needed towards the successful implementation of RDM in university libraries.

An improvement in the metadata management system and network optimization infrastructure in university libraries is also necessary in the process of effectively offering RDM. The curation of research data or metadata curation practices are crucial to every RDM endeavor. As such, data curation infrastructures become germane to the successful management of research data (Ntja, 2022). Whereas infrastructures for network optimization is a collection of methods and instruments for enhancing the dependability and performance of networks (Petryschuk, 2023), the essence of optimizations is to meet performance requirements. It is necessary to ensure smooth data transfer and effective communication between systems and the institutional repository through an optimized network infrastructure. University libraries need to enhance these technologies as they help to achieve integrity, accessibility and longevity of valuable research data, fostering an environment conducive to robust academic inquiries and innovations.

It is pertinent to improve systems and interfaces in order to ensure a seamless access to research data without detriment to user convenience and satisfaction. The design of any system should give special attention to its usability, which has a significant relation to user satisfaction (Dianat et al., 2019). The technologies available for RDM should have an appealing user interface without jeopardizing the satisfaction of users. That is, RDM systems or infrastructures should have user-centric designs while allowing for the performance of complex searches. The interfaces should be highly adaptive and responsive irrespective of the devices being used. This will ensure that users can access research data repositories seamlessly from desktops, laptops, tablets and mobile devices, importantly enhancing accessibility.

Conclusion

The current data driven economy has amplified the relevance of research data and the subsequent need for proper management for scholarly and economic value. University libraries around the world have begun to set up systems and structures for the effective implementation and delivery of RDMS. The level of effectiveness of university libraries with respect to the delivery of RDMS varies from one country to another. However, for successful implementation of RDM, deliberate effort needs to be made by university libraries to build teams of data literate professionals possessing a wide range of data handling skills and competences. Besides institutional training programmes, professionals’ personal development is germane to the realization of RDM objectives in libraries. These training programmes and personal development are relevant for them and for all academics in university communities, as RDM should be handled as a university-based project.

Data literate librarians need adequate RDM technologies for optimal RDMS delivery. As such, university libraries cannot realize their full potential, even with data literate professionals, without addressing issues related to technological infrastructure. Technologies are required across all stages of the data management cycle and, as such, the technological capacity of university libraries will importantly influence the delivery of RDMS. This paper, therefore, supports the assertion that data literacy and technological infrastructures are required for the successful delivery of RDMS in university libraries.