Where Do We Go From Here: Choosing a Framework for Assessing Research Data Services and Training

Evaluation of Research Data Management (RDM) services in academic libraries of India: A triangulation approach

Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.

In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), “Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment”. However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-to-day research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices. This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft.

Big data-driven investigation into the maturity of library research data services (RDS)

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

As significant players in the production of scholarly work, academic researchers are increasingly gathering and producing data rapidly, exceeding the development of the knowledge and skills required for proper data storage and management. However, the focus has typically been on research publication outputs rather than the research data determining the academic's research output. This study conducted an audit survey on research data management in a University of Technology faculty context to gain insight into their research data management practices. The study was guided by the Data Audit Framework (DAF), the Community Capability Model Framework (CCMF), and the User-Centered Research Data Management Framework (UCRDMF). The study utilized an explanatory sequential mixed method research design incorporating quantitative and qualitative components. In the quantitative phase, an online survey was administered to postgraduate students pursuing Master's and Doctoral degrees between 2015 and 2020. On the other hand, the qualitative phase involved conducting a meta-analysis of research repositories across global Higher Education Institutions (HEIs) and conducting online interviews with postgraduate supervisors who play a crucial role in postgraduate study and research administration. The quantitative data were analyzed using the Statistical Package for the Social Sciences (SPSS), while the qualitative data were analyzed using NVivo software. The collected data were then analyzed respectively with theoretical frameworks and existing literature. The findings revealed that managing research data was primarily a personal matter. The main reason for the difficulties was that the faculty lacked a research data management investment. The use of emails, external hard drives, and personal laptops are additional examples that showcased that the faculty had not established centralized systems for managing research. However, participants did recognize the benefits of managing research data, such as scientific advances, enhanced data repurpose, and simplicity for data reusers. Findings also showed that the lack of platforms permitting data sharing and reuse was one of the main reasons that most researchers had not shared or reused other researchers' research data. Researchers, however, wanted to share their data without restrictions. The study's conclusions were also used to create and document a conceptual framework for the faculty research data repository platform. The proposed model aims to guide the conception of a repository that will secure the storage of faculty digital information that can be easily retrieved.

The complexity and privacy issues inherent in social science research data makes research data management (RDM) an essential skill for future researchers. Data management training has not fully addressed the needs of graduate students in the social sciences. To address this gap, this study used a mixed methods design to investigate the RDM awareness, preparation, confidence, and challenges of social science graduate students. A survey measuring RDM preparedness and training needs was completed by 98 graduate students in a school of education at a research university in the southern United States. Then, interviews exploring data awareness, knowledge of RDM, and challenges related to RDM were conducted with 10 randomly selected graduate students. All participants had low confidence in using RDM, but United States citizens had higher confidence than international graduate students. Most participants were not aware of on-campus RDM services, and were not familiar with data repositories or data sharing. Training needs identified for social science graduate students included support with data documentation and organization when collaborating, using naming procedures to track versions, data analysis using open access software, and data preservation and security. These findings are significant in highlighting the topics to cover in RDM training for social science graduate students. Additionally, RDM confidence and preparation differ between populations so being aware of the backgrounds of students taking the training will be essential for designing student-centered instruction.

PurposeThe purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level.Design/methodology/approachPurposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used.FindingsThe authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare.Research limitations/implicationsResearch could be expanded to a wider population, include other stakeholders and use qualitative methods.Practical implicationsResults are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group.Originality/valueThe findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia.

The emergence of data intensive science and the establishment of data management mandates have motivated academic libraries to develop research data services (RDS) for their faculty and students. Here the results of two studies are reported: librarians' RDS practices in U.S. and Canadian academic research libraries, and the RDS-related library policies in those or similar libraries. Results show that RDS are currently not frequently employed in libraries, but many services are in the planning stages. Technical RDS are less common than informational RDS, RDS are performed more often for faculty than for students, and more library directors believe they offer opportunities for staff to develop RDS-related skills than the percentage of librarians who perceive such opportunities to be available. Librarians need opportunities to learn more about these services either on campus or through attendance at workshops and professional conferences.

PurposeThe purpose of this article is to give an overview of the development of a Virtual Research Environment (VRE) conceptual model for the management of research data at a South African university.Design/methodology/approachThe research design of this article consists of empirical and non-empirical research. The non-empirical part consists of a critical literature review to synthesise the strengths, weaknesses (limitations) and omissions of identified VRE models as found in literature to develop a conceptual VRE model. As part of the critical literature review concepts were clarified and possible applications of VREs in research lifecycles and research data lifecycles were explored. The empirical part focused on the practical application of this model. This part of the article follows an interpretivist paradigm, and a qualitative research approach, using case studies as inquiry method. Case studies with a positivist perspective were selected through purposive sampling, and inferences were drawn from the sample to design and test a conceptual VRE model, and to investigate the management of research data through a VRE. Investigation was done through a process of participatory action research (PAR) and included semi-structured interviews and participant observation data collection techniques. Evaluation of findings was done through formative and summative evaluation.FindingsThe article presents a VRE conceptual model, with identified generic component layers and components that could potentially be applied and used in different research settings/disciplines. The article also reveals the role that VREs play in the successful management of research data throughout the research lifecycle. Guidelines for setting up a conceptual VRE model are offered.Practical implicationsThis article assisted in clarifying and validating the various components of a conceptual VRE model that could be used in different research settings and disciplines for research data management.Originality/valueThis article confirms/validates generic layers and components that would be needed in a VRE by synthesising these in a conceptual model in the context of a research lifecycle and presents guidelines for setting up a conceptual VRE model.

PurposeThe purpose of this study was to analyse the skills required by lecturers to be able to support research data management effectively; assess the research data management literacy levels amongst lecturers at Strathmore University; and suggest how research data management capacity can be strengthened to mitigate the knowledge gaps identified.Design/methodology/approachThis study was conducted as a mixed methods research. Explanatory sequential mixed methods approach was used to collect, analyse and interpret quantitative and qualitative data from lecturers at Strathmore University in Nairobi, Kenya. Quantitative data was collected using questionnaires while qualitative data was collected through focus group discussions. Quantitative data was analysed using SPSS while qualitative data was analysed thematically.FindingsThe findings of this study indicate varied levels of research data management literacy amongst lecturers at Strathmore University. Lecturers understand the need of having literacy skills in managing research data. They also participate in data creation, collection, processing, validation, dissemination, sharing and archiving. This is a clear indication of good research data management. However, the study also revealed gaps in research data management skills amongst the lecturers in areas such as sharing of research data on open access journals, data legislation and securing research data.Research limitations/implicationsThe study has been conducted in one university in Kenya. However, the findings have been contextualised in the global landscape through suitable references.Practical implicationsThe findings of this study may be used to attract the attention of lecturers and librarians to research data management. The findings may also be used to develop institutional policies on research data management at Strathmore University and beyond. The suggested ways of research data capacity strengthening can be adopted or adapted by other universities to enhance research data management.Originality/valueThis is an original study.

BackgroundPublic engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?ObjectiveThe aim of this article is to analyze research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.MethodsA scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers.ResultsHealth and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not prevalent in the review.ConclusionsThere is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle.

Management of research data is a service area of increasing interest to libraries. Librarians have begun to provide a range of services in this area and now teach data management to researchers, work with individual researchers to improve their data management practices, create data management subject guides, and assist in supporting funding agency and publisher data requirements. This paper is a primer on research data management for librarians who have little or no experience in this topic. It includes general background about research data, an overview of what is meant by data management, and suggestions for how to begin to move into this service area.

The study investigated veterinary medicine librarians' experience with and perceptions of research data services. Many academic libraries have begun to offer research data services in response to researchers' increased need for data management support. To date, such services have typically been generic, rather than discipline-specific, to appeal to a wide variety of researchers. An online survey was deployed to identify trends regarding research data services in veterinary medicine libraries. Participants were identified from a list of contacts from the MLA Veterinary Medical Libraries Section. Although many respondents indicated that they have a professional interest in research data services, the majority of veterinary medicine librarians only rarely or occasionally provide data management support as part of their regular job responsibilities. There was little consensus as to whether research data services should be core to a library's mission despite their perceived importance to the advancement of veterinary research. Furthermore, most respondents stated that research data services are just as or somewhat less important than the other services that they provide and feel only slightly or somewhat prepared to offer such services. Lacking a standard definition of "research data" and a common understanding of precisely what research data services encompass, it is difficult for veterinary medicine librarians and libraries to define and understand their roles in research data services. Nonetheless, they appear to have an interest in learning more about and providing research data services.

Data librarians are responsible for managing research data, who play an active role in improving the literacy and capacity of the research community and information professionals. Some Indonesian programmes have emerged, but their role and readiness have not been studied. The aspects of personnel readiness to support research data management (RDM) are further discussed in this research, as obligated on the National System for Science and Technology Act No. 11 of 2019. This study was conducted to investigate the readiness of librarians, academic communities, and information professionals in managing research data. A mixed method was used in this research. Firstly, a poll survey was digitally distributed in thematic seminar discussions on data management to librarians and scientists performing RDM tasks. Then, more detailed information about the role of librarians in managing research data was gathered from selected informants on FGD. The study found several points about the role of data librarians in improving data literacy and capacity through advocacy, workshops, seminars, training, and apprenticeship development. We then called for the involvement of all stakeholders, the scientific community, and information professionals to encourage them to continue working together in building a good data management ecosystem through innovative and collaborative programs. The results of this study could be a policy recommendation for the development of the status of library functions by the supervisory authority, the National Library of the Republic of Indonesia. The scientific community could use them to raise awareness and capacity for research data management from all institutions as data owners (scientific community) and data managers (data librarians).