Research Data Management Support Research Articles

Who Does What? – Research Data Management at ETH Zurich

We present the approach to Research Data Management (RDM) support for researchers taken at ETH Zurich. Overall requirements are governed by institutional guidelines for Research Integrity, funders’ regulations, and legal obligations. The ETH approach is based on the distinction of three phases along the research data life-cycle: 1. Data Management Planning; 2. Active RDM; 3. Data Publication and Preservation. Two ETH units, namely the Scientific IT Services and the ETH Library, provide support for different aspects of these phases, building on their respective competencies. They jointly offer trainings, consulting, information, and materials for the first phase. The second phase deals with data which is in current use in active research projects. Scientific IT Services provide their own platform, <em>openBIS</em>, for keeping track of raw, processed and analysed data, in addition to organising samples, materials, and scientific procedures. ETH Library operates solutions for the third phase within the infrastructure of ETH Zurich’s central IT Services. <em>The Research Collection</em> is the institutional repository for research output including Research Data, Open Access publications, and ETH Zurich’s bibliography.

Research data management literacy amongst lecturers at Strathmore University, Kenya

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.

Data librarianship in South African academic and research libraries: a survey

PurposeThe purpose of this study was to establish the current skills base of librarians working in research data management services in academic and research libraries in South Africa. The purpose was also to determine the relevance of courses and programmes that are currently being offered by library and information studies programmes in response to the needs of research data management services and make recommendations on curriculum improvement.Design/methodology/approachAbout 13 institutions which were considered early adopters of research data management services were identified as participants in an online survey. In addition, a review of Web pages of existing library and information studies schools was carried to establish courses that would support research data management services. Data collected through the two approaches were analysed and presented quantitatively and qualitatively.FindingsThe findings reveal an environment in a developmental stage, with limited skilled personnel to run research data management services. The findings also show an absence of specific data librarianship courses within existing library and information studies programmes and a very limited scope for the full range of data management courses within professional development programmes.Originality/valueThe paper provides information on approaches to further develop existing curriculum and contribute to the data management needs and support governments, funders and publishers' requirements for the discoverability and re-use of research data across research domains.

Case report of research data management support services in Australian university libraries

Case report of research data management support services in Australian university libraries

Development of a Domain-Specific Ontology to Support Research Data Management for the Tailored Forming Technology

The global trend towards the comprehensive digitisation of technologies in product manufacturing is leading to radical changes in engineering processes and requires a new extended understanding of data handling. The amounts of data to be considered are becoming larger and more complex. Data can originate from process simulations, machines used or subsequent analyses, which together with the resulting components serve as a complete and reproducible description of the process. Within the Collaborative Research Centre "Process Chain for Manufacturing of Hybrid High Performance Components by Tailored Forming", interdisciplinary work is being carried out on the development of process chains for the production of hybrid components. The management of the generated data and descriptive metadata, the support of the process steps and preliminary and subsequent data analysis are fundamental challenges. The objective is a continuous, standardised data management according to the FAIR Data Principles so that process-specific data and parameters can be transferred together with the components or samples to subsequent processes, individual process designs can take place and processes of machine learning can be accelerated. A central element is the collaborative development of a domain-specific ontology for a semantic description of data and processes of the entire process chain.

Supporting Research Data Management and Open Science in Academic Libraries: a Data Librarian’s View

The ‘data revolution’ has impacted researchers across the disciplines. As if the traditional work of teaching, competing for grants and promotion, doing research and publishing results was not challenging enough, researchers are required to make fundamental changes in the way they do all of these things. A similar shift can be seen for academic librarians. Librarians who were taught to meet the needs of their users based on information scarcity now need to retrain themselves to help users deal with information overload. Moreover, librarians increasingly find themselves ‘upstream’ in the research process, trying to assist their users in managing unwieldy amounts of data when their comfort zone is firmly ‘downstream’ in the post-publication stage. Unsettling as it may be, these are exciting developments for the library profession.

Research data management support

Good management and sharing of research data is a key principle for UiT The Arctic University of Norway, rooted in the value of increased transparency, reproducibility and reuse as well as increased quality of research. Meeting this aspiration requires operational support services, infrastructure, competence and a road map for different stakeholders. In line with these requirements, UiT has taken important steps to implement the ambition of FAIR research data management. These include the establishment of UiT Open Research Data archive in September 2016. Since then, more than 600 datasets with more than 5000 files have been uploaded, curated and made openly available. Moreover, UiT has been conducting a senior research data project that aims to preserve research data from senior researchers and make them available for future use. Additionally, UiT has adopted a policy for research data management that came into effect in September 2017. The poster outlines and reviews these and other efforts by UiT The Arctic University of Norway to provide support services for FAIR research data management.

Progress in Research Data Services

University libraries have played an important role in constructing an infrastructure of support for Research Data Management at an institutional level. This paper presents a comparative analysis of two international surveys of libraries about their involvement in Research Data Services conducted in 2014 and 2018. The aim was to explore how services had developed over this time period, and to explore the drivers and barriers to change. In particular, there was an interest in how far the FAIR data principles had been adopted.&#x0D; Services in nearly every area were more developed in 2018 than before, but technical services remained less developed than advisory. Progress on institutional policy was also evident. However, priorities did not seem to have shifted significantly. Open ended answers suggested that funder policy, rather than researcher demand, remained the main driver of service development and that resources and skills gaps remained issues. While widely understood as an important reference point and standard, because of their relatively recent publication date, FAIR principles had not been widely adopted explicitly in policy.

Research data management and the evolutions of scholarship

&#x0D; This case study critically examines ongoing developments in contemporary scholarship through the lens of research data management support at KU Leuven, and KU Leuven Libraries in particular. By means of case-based examples, current initiatives for fostering sound scientific work and scholarship are considered in three associated domains: support for policy-making, the development of research infrastructures, and digital literacy training for students, scientists and scholars. It is outlined how KU Leuven Libraries collaborates with partner services in order to contribute to KU Leuven’s research data management support network. Particular attention is devoted to the innovations that facilitate such collaborations. These accounts of initial experiences form the basis for a reflection on best practices and pitfalls, and foreground a number of pertinent challenges facing the domain of research data management, including matters of scalability, technology acceptance and adoption, and methods for effectively gauging and communicating the manifold transformations of science and scholarship.&#x0D;

Maturing research data services and the transformation of academic libraries

PurposeA major development in academic libraries in the last decade has been recognition of the need to support research data management (RDM). The purpose of this paper is to capture how library research data services (RDS) have developed and to assess the impact of this on the nature of academic libraries.Design/methodology/approachQuestionnaire responses from libraries in Australia, Canada, Germany, Ireland, the Netherlands, New Zealand, the UK and USA from 2018 are compared to a previous data set from 2014.FindingsThe evidence supports a picture of the spread of RDS, especially advisory ones. However, future ambitions do not seem to have seen much evolution. There is limited evidence of organisational change and skills shortages remain. Most service development can be explained as the extension of traditional library services to research data. Yet there remains the potential for transformational impacts, when combined with the demands implied by other new services such as around text and data mining, bibliometrics and artificial intelligence. A revised maturity model is presented that summarises typical stages of development of services, structures and skills.Research limitations/implicationsThe research models show how RDS are developing. It also reflects on the extent to which RDM represents a transformation of the role of academic libraries.Practical implicationsPractitioners working in the RDM arena can benchmark their current practices and future plans against wider patterns.Originality/valueThe study offers a clear picture of the evolution of research data services internationally and proposes a maturity model to capture typical stages of development. It contributes to the wider discussion of how the nature of academic libraries are changing.

The Impact of Targeted Data Management Training for Field Research Projects – A Case Study

We present a joint effort at Virginia Tech between a research group in the Department of Fish and Wildlife Conservation and Data Services in the University Libraries to improve data management for long-term ecological field research projects in the Florida Panhandle. Consultative research data management support from Data Services in the University Libraries played an integral role in the development of the training curriculum. Emphasizing the importance of data quality to the field workers at the beginning of this training curriculum was a vital part of its success. Also critical for success was the research group’s investment of time and effort to work with field workers and improve data management systems. We compare this case study to three others in the literature to compare and contrast data management processes and procedures. This case study serves as one example of how targeted training and efforts in data and project management for a research project can lead to substantial improvements in research data quality.

Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia.

Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research. This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia. A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle. Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams. Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group. This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.

Supporting the Interdisciplinary, Long-Term Research Project ‘Patterns in Soil-Vegetation-Atmosphere-Systems’ by Data Management Services

Science conducted in cross-institutional, interdisciplinary, long-term research projects requires active sharing of data, documents and further information. Thus, within the Collaborative Research Centre/Transregio 32 ‘Patterns in Soil-Vegetation-Atmosphere Systems’, funded by the German Research Foundation, research data management (RDM) services have been available since early 2007. These services were established to support all researchers during their entire individual research studies. They cover provision of general guidance, support and training for RDM. To fulfil the scientists’ needs and requests with regard to storage, backup, documentation, search and sharing of data with other project members, a project-specific RDM system was designed and implemented. This system was developed and continuously modified in collaboration with the scientists to facilitate their system acceptance. Besides the mentioned services, the system supports further common services such as controlled access to data, rights management, data publication with DOI and data statistics (on repository and single data level). All RDM services provided for the scientists are thus bundled and available to the users in one system: a ‘one-stop-shop’. After more than ten years of RDM service provision for the CRC/TR32, the repository statistics clearly visualize the use of the diverse RDM system services. Furthermore, it has been shown that an RDM adapted to the needs of interdisciplinary researchers can be fruitful and indispensable when scientists conduct their research study e.g. with a time lag. RDM services established at an early stage can contribute to a successful long-term research project.

Professional and institutional support for RDM: A case of the National Institutes of Health (NIH)

ABSTRACTThis study explored biomedical researchers' Research Data Management (RDM) practices and investigated how institute and professional services support their practices through the RDM processes with a case of NIH. It was found that institutional information technology (IT) and library services are focused on certain stages of RDM processes. Also, it was found that having a close collaboration among service units within an institute is important to maximize their resources for services, reduce duplicated services, and publicize their services to a wider range of users.

Preferred but not Required: Examining Research Data Management Roles in Health Science Librarian Positions

Introduction: Research data management (RDM) is being recognized as an increasingly important role for librarians. In this paper, the role of health science librarians in supporting research data management endeavors is examined.&#x0D; Methods: All job postings currently (as of April 5th, 2018) available on the University of Toronto’s Faculty of Information (iSchool) job site were analyzed to identify positions related to health science librarianship. The job responsibilities and descriptions were then examined to identify instances where research data management was mentioned.&#x0D; Results: Thirty-two postings from the search results were identified as meeting the inclusion criteria. Of these thirty-two health science librarian postings which were included in the analysis, eight included supporting research data management services, in some capacity, as part of the position description.&#x0D; Discussion/ Conclusion: Through the job posting analysis, a picture emerges where RDM is not consistently seen as a role for health science librarians. However, the literature indicates that in many instances, research data management is already being done by health science librarians, and is a trend which is likely to continue in the future. As such, it is important that research data management services start being acknowledged and reflected in education and job description opportunities.

Quadcopters or Linguistic Corpora

During an international library conference in 2017 the authors had many productive exchanges about similarities and differences in Swedish and German higher-education libraries. Since research data management (RDM) is an emerging topic on both sides of the Baltic Sea, we find it valuable to compare strategies, services, and workflows to learn from each other’s practices. Aim: In this paper, we aim to compare the practices and needs of small-scale data producers in engineering and the humanities. In particular, we try to answer the following research questions: • What kind of data do the small-scale data producers produce? • What do these producers need in terms of RDM support? • What then can we librarians help them with? Hypothesis: Our research hypothesis is that small-scale data producers have similar needs in engineering and the humanities. This hypothesis is based on the similarities in demands from funding agencies on (open) research data and on the assumption that research in different subjects often creates results which are different in content but similar in structure. Method: We study the current strategies, practices, and services of our respective universities (KTH Royal Institute of Technology Stockholm and Westfalische Wilhelms-Universitat Munster). We also study the work and initiatives done on a more advanced level by universities, libraries, and other organisations in Sweden and Germany. Results: The paper will give an overview of how we did the groundwork for the initial services provided by our libraries. We focus on what we are doing and why we are doing it. We find that we are following in the leading footsteps of other university libraries. The experiences shared by colleagues help us to adapt their best practices to our local demands, making them better practices for KTH and WWU researchers. Limitation: We restrict ourselves to studying only researchers who create data on a small scale, since the large-scale data producers handle the RDM on their own.

Supporting FAIR Data Principles with Fedora

Making data findable, accessible, interoperable, and re-usable is an important but challenging goal. From an infrastructure perspective, repository technologies play a key role in supporting FAIR data principles. Fedora is a flexible, extensible, open source repository platform for managing, preserving, and providing access to digital content. Fedora is used in a wide variety of institutions including libraries, museums, archives, and government organizations. Fedora provides native linked data capabilities and a modular architecture based on well-documented APIs and ease of integration with existing applications. As both a project and a community, Fedora has been increasingly focused on research data management, making it well-suited to supporting FAIR data principles as a repository platform. Fedora provides strong support for persistent identifiers, both by minting HTTP URIs for each resource and by allowing any number of additional identifiers to be associated with resources as RDF properties. Fedora also supports rich metadata in any schema that can be indexed and disseminated using a variety of protocols and services. As a linked data server, Fedora allows resources to be semantically linked both within the repository and on the broader web. Along with these and other features supporting research data management, the Fedora community has been actively participating in related initiatives, most notably the Research Data Alliance. Fedora representatives participate in a number of interest and working groups focused on requirements and interoperability for research data repository platforms. This participation allows the Fedora project to both influence and be influenced by an international group of Research Data Alliance stakeholders. This paper will describe how Fedora supports FAIR data principles, both in terms of relevant features and community participation in related initiatives.

Developing Research Data Management Services and Support for Researchers: A Mixed Methods Study

This mixed method study determined the essential tools and services required for research data management to aid academic researchers in fulfilling emerging funding agency and journal requirements. Focus groups were conducted and a rating exercise was designed to rank potential services. Faculty conducting research at the University of Toronto were recruited; 28 researchers participated in four focus groups from June– August 2016. Two investigators independently coded the transcripts from the focus groups and identified four themes: 1) seamless infrastructure, 2) data security, 3) developing skills and knowledge, and 4) anxiety about releasing data. Researchers require assistance with the secure storage of data and favour tools that are easy to use. Increasing knowledge of best practices in research data management is necessary and can be supported by the library using multiple strategies. These findings help our library identify and prioritize tools and services in order to allocate resources in support of research data management on campus.

Is Democracy the Right System? Collaborative Approaches to Building an Engaged RDM Community

When developing new products, tools or services, one always need to think about the end users to ensure a wide-spread adoption. While this applies equally to services developed at higher education institutions, sometimes these services are driven by policies and not by the needs of end users. This policy-driven approach can prove challenging for building effective community engagement. The initial development of Research Data Management support services at the University of Cambridge was policy-driven and subsequently failed in the first instance to engage the community of researchers for whom these services were created. In this practice paper, we describe the initial approach undertaken at Cambridge when developing RDM services, the results of this approach and lessons learnt. We then provide an overview of alternative, democratic strategies employed and their positive effects on community engagement. We summarise by performing a cost-benefit analysis of the two approaches. This paper might be a useful case study for any institutions aiming to develop central support services for researchers, with conclusions applicable to the wider sector, and extending beyond Research Data Management services.

Academic Dental Librarianship in Canada: Taking Stock, Planning the Future

The Oral Health Interest Group/Groupe d’intérêt en santé buccale of CHLA/ABSC, established in 2016, aims to act as a source of networking for dental librarians in Canada, conduct research, and advocate for the specialty. In the present article, the first produced by OHIG, the authors describe the current landscape of academic dental librarianship in Canada using data resulting from an informal consultation of all OHIG members. Examples of distinctive practice are highlighted through a series of vignettes, and the overview is set in context through a literature review of dental librarianship, focussing on Canadian contributions to the speciality. The article concludes with the authors’ reflections into possible directions the specialty may take over the next few years, noting the importance of increased embedded collaboration with faculty and the need to develop new skills, for example, to support research data management and new trends in scholarly communications.