Implementing and Learning from a Summer Research Data Management Training Program for Student Researchers

Objective – In consideration of emerging national Research Data Management (RDM) policy and infrastructure, this literature review seeks answers to the following questions: 1) What is the most effective way for a Canadian research university to build capacity among library and campus-wide research support staff, with a view towards providing coordinated RDM support services for our researcher community?2) What international training models and course offerings are available and appropriate for a local context?3) What national guidelines and best practices for pedagogical design and delivery can be adapted for a local context? Methods – This literature review synthesizes a total of 13 sources: 9 articles, 2 book chapters, and 2 whitepapers. The whitepapers were selected for a narrative literature review because of their focus on case studies detailing train-the-trainer models. Within the 13 sources we found 14 key case studies. This review serves as a supplement to the 2017 CARL Portage Training Expert Group white paper, “Research Data Management Training Landscape in Canada,” the focus of which was to identify RDM training gaps in order to recommend a coordinated approach to RDM training in a national environment. Results – The narrative review of case studies revealed three thematic areas. Firstly, pedagogical challenges were identified, including the need to target training to RDM support staff such as librarians and researchers, as they comprise distinct groups of trainees with divergent disciplinary vocabularies and incentives for training. Secondly, the case studies cover a broad range of pedagogical models including single or multiple sessions, self-directed or instructor-led, in-person or online instruction, and a hybrid of the two. Finally, RDM training also emerged as a key factor in community building within library staff units, among service units on campus, and with campus research communities. Conclusion – RDM training programs at local institutions should be guided by a set of principles aligned with the training methods, modes of assessment, and infrastructure development timeline outlined in a national training strategy. When adapting principles and training strategies to a local context, the following trends in the literature should be considered: librarians and researchers must have meaningful incentives to undertake training in RDM or to join a community of practice; disciplinary-specific instruction is preferable to general instruction; a librarian’s own training opportunities will influence their ability to provide discipline-specific RDM instruction to researchers; in-person training opportunities improve learning retention and produce beneficial secondary effects, whereas online instruction is most effective when paired with an in-person component; generalized third-party RDM training should be adapted to local context to be meaningful. Future directions for RDM training will integrate into open access and digital scholarship training, and into cross-disciplinary, open science communities of practice.

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.

BackgroundOpen Science Graphs (OSGs) are scientific knowledge graphs representing different entities of the research lifecycle (e.g. projects, people, research outcomes, institutions) and the relationships among them. They present a contextualized view of current research that supports discovery, re-use, reproducibility, monitoring, transparency and omni-comprehensive assessment. A Data Management Plan (DMP) contains information concerning both the research processes and the data collected, generated and/or re-used during a project’s lifetime. Automated solutions and workflows that connect DMPs with the actual data and other contextual information (e.g., publications, fundings) are missing from the landscape. DMPs being submitted as deliverables also limit their findability. In an open and FAIR-enabling research ecosystem information linking between research processes and research outputs is essential. ARGOS tool for FAIR data management contributes to the OpenAIRE Research Graph (RG) and utilises its underlying services and trusted sources to progressively automate validation and automations of Research Data Management (RDM) practices.ResultsA comparative analysis was conducted between the data models of ARGOS and OpenAIRE Research Graph against the DMP Common Standard. Following this, we extended ARGOS with export format converters and semantic tagging, and the OpenAIRE RG with a DMP entity and semantics between existing entities and relationships. This enabled the integration of ARGOS machine actionable DMPs (ma-DMPs) to the OpenAIRE OSG, enriching and exposing DMPs as FAIR outputs.ConclusionsThis paper, to our knowledge, is the first to introduce exposing ma-DMPs in OSGs and making the link between OSGs and DMPs, introducing the latter as entities in the research lifecycle. Further, it provides insight to ARGOS DMP service interoperability practices and integrations to populate the OpenAIRE Research Graph with DMP entities and relationships and strengthen both FAIRness of outputs as well as information exchange in a standard way.

Researchers of all disciplines produce, share, and reuse data as part of everyday research. Most funders require them to manage and document their data using data management plans (DMPs). DMPs are often static documents that researchers create by answering questions in predefined templates at the beginning of the research and, therefore, may become outdated and obsolete as the project progresses. It is essential to keep the DMP up to date at all stages of the research lifecycle, since numerous stakeholders and various services participate in data management that depend on information from them. In this article, we propose a conceptual service architecture that uses machine-actionable data management plans to automate the exchange and synchronization of information between different semi-automated research data management services acting on behalf of different stakeholders. To solve the stated problem, we analyze typical use cases in which the DMPs change and formulate requirements based on which we developed the conceptual architecture. We depict the designed architecture through a set of views, namely physical, development, logical, and process, using Unified Modeling Language and Business Process Models and Notation representation that describe the processes required to synchronize DMP information among multiple services. We instantiate it by implementing a service that connects a data repository and a DMP tool. Thus, we evaluate to what extent the defined processes help in keeping DMP contents up to date and which criteria must be fulfilled to keep them highly automated. The result of the article feeds into a larger discussion on streamlining interconnectivity and machine-actionability across planning, tracking, and assessing research phases. It also facilitates consensus building on enhancing the Research Data Alliance’s recommendation for machine-actionable DMPs.

Objectives/Goals: NIH requires researchers submit Data Management and Sharing (DMS) Plans with their grant applications. Librarians developed an assessment tool for the plans and completed a pilot assessment in order to leverage the plans and understand current institutional research data management and sharing. Methods/Study Population: The assessment tool includes questions related to evaluations of DMS Plans as well as questions related to the content of the plans. Evaluation questions were adapted from the Federation of American Societies for Experimental Biology evaluation rubric developed for the DataWorks! Data Management Plan (DMP) Challenge. Fields were added to collect information on the content of DMS Plans, including data type, institutional resources, data repositories, data standards, and data dissemination timelines. The assessment tool was tested in a pilot implementation. Seven library workers were trained and completed paired review samples of 27 DMS Plans (54 evaluations total) in order to test for tool reliability. Results/Anticipated Results: Results include findings on the reliability of the tool as well as preliminary results from an assessment of DMS Plans. Findings on the reliability of the tool include assessments of the paired reviewers for each question included in the tool. Paired reviewers generally agreed, but tended to differ on specific questions, including questions pertaining to the data types generated or used in a research project. Questions with high levels of agreement included subjects of study and code sharing practices. Results on the content of the DMS Plans include information such as data repositories used, data oversight responsibilities, and data and metadata standards employed. Discussion/Significance of Impact: DMS Plans present an opportunity to better understand data management and sharing practices, and good data management supports high-quality, reproducible research. Developing and testing assessment tools for these plans is a key step toward understanding and improving current research data management practices.

This paper presents the findings, lessons learned and next steps associated with the implementation of the immersiveInformatics pilot: a distinctive research data management (RDM) training programme designed in collaboration between UKOLN Informatics and the Library at the University of Melbourne, Australia. The pilot aimed to equip a broad range of academic and professional staff roles with RDM skills as a key element of capacity and capability building within a single institution.

The 2016 Annual Report for the University of Auckland's Centre for eResearch.Message from the Director, Professor Mark Gahegan:This has been a rather busy year. The Centre for eResearch has grown during 2016 to support four core services across the University, funded via the University Strategic Projects Office (USPO). These services are: 1. Research data management, including sharing ‘working’ data, publishing and discovering data, data management planning and training. 2. Virtual compute platform services, including research virtual machines, the Docker/Rancher and Jupyter Notebooks. 3. Research visualisation and analytics, offering a suite of visualisation platforms including a tiled display, large 3D display, Vive, HoloLens and zSpace virtual reality platforms. We now also offer a deep learning appliance with 2 NVIDIA 1080 GPUs. 4. The Research Hub, which is a capstone project that pools much of the knowledge about the many IT services that researchers use into an advisory: how to access them, how to get support, how they fit into the research lifecycle.We have also increased our special projects work, most specifically with the School of Population Health, with whom we have been building Mobile apps, creating mapping and analytics tools, running group knowledge elicitation workshops and delivering a web portal to support statistical analysis for the Growing Up in New Zealand project.During 2017, we plan to create another core service around digital research skills development to meet the increasing demand from our research community for more specialist training. We will also be offering the ResBaz (Research Bazaar) event again over the summer, and Winter Bootcamp for those who want to gain a number of proficiencies in one short burst.

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 ELIXIR research infrastructure bundles not only the databases and tools of bioinformatics, but it also brings together life science data expertise. The assembled expertise can form a fantastic resource for researchers making a data management plan (DMP). Currently, this expertise is hard to discover and access. Several of the ELIXIR nodes are looking for ways to offer DMP services to their communities. The technical coordinators in these nodes are planning to build these services together with training coordinators. Current plans call for a data management planning (data stewardship planning) portal and trainings on many different data management (planning) topics. Authors on this poster are representatives from six ELIXIR nodes that are initially participating in this work, other people from these ELIXIR nodes have contributed to the plans.

ABSTRACTDeveloping a data management plan is an important part of planning any research endeavor, and increasingly, grant institutions require these kinds of formal, written documents. Medical and academic libraries have begun reaching out and offering help to researchers developing plans for managing research data. Several tools are available to aid researchers and librarians alike in both building the skills needed to make well-informed choices when it comes to managing research data and to walk users step-by-step through the process of creating a formal data management plan. Data Research MANTRA is an online course for those who manage, or plan to manage, research data. DMPTool and DMPonline are step-by-step guides to creating data management plan documents that adhere to DMP best practices, as well as to the requirements of specific grantors and institutions. These tools can help librarians and the researchers with whom they work to both meet institutional requirements and to make informed decisions about handling research data that will help ensure its availability and usefulness in the years to come.

A FAIRification roadmap for ELIXIR Software Management Plans


 A three-phase Delphi study was used to investigate an emerging community for research data management in Norway and their understanding and application of data management plans (DMPs). The findings reveal visions of what the DMP should be as well as different practice approaches, yet the stakeholders present common goals. This paper discusses the different perspectives on the DMP by applying Star and Griesemer’s theory of boundary objects (Star & Griesemer, 1989). The debate on what the DMP is and the findings presented are relevant to all research communities currently implementing DMP procedures and requirements. The current discussions about DMPs tend to be distant from the active researchers and limited to the needs of funders and institutions rather than to the usefulness for researchers. By analysing the DMP as a boundary object, plastic and adaptable yet with a robust identity (Star & Griesemer, 1989), and by translating between worlds where collaboration on data sharing can take place we expand the perspectives and include all stakeholders. An understanding of the DMP as a boundary object can shift the focus from shaping a DMP which fulfils funders’ requirements to enabling collaboration on data management and sharing across domains using standardised forms.

ABSTRACTHow can the library contribute to developing research data services by focusing on training? This poster presents experiences and best practices with organizing research data management (RDM) training through the University of Oslo Library. The work is based on recommendations from the report “The Data Explosion –A Great Challenge and a Big Opportunity” that mapped existing data practices at the University of Oslo (UiO) (Working group storing and sharing of research data UiO, 2015). Based on these recommendations, a division of labor among the research support services at the university was decided upon and the university library (UL) was tasked with developing courses and training for better data practices. By establishing contact with existing initiatives, inviting in experts and holding meetings for library staff to create ownership and interest in the subject the library now has a portfolio of courses in data practices and data management that is constantly expanding.

The University of Cape Town's institutional Research Data Management Policy, passed in March 2018, mandates that postgraduate students and researchers must complete Data Management Plans (DMPs) in which they describe their plans for gathering, storing, archiving and where appropriate publishing their research data. This presentation between the Health Sciences Library and the Digital Library Services unit was presented at a workshop for postgraduate students in the Faculty of Health Sciences, outlining the various support mechanisms and platforms provided by UCT Libraries to assist in complying with the policy. The presentation focuses on the use of three specific processes and their associated tools, namely:1) Data Management Planning, through the use of UCT's DMPonline tool based on the Digital Curation Centre's software2) Collaborative research, using UCT's institutional instance of the Centre for Open Science's Open Science Framework tool3) Open data publishing using ZivaHub, UCT's open data repositoryDLS provides research data management support through presentations, online resources, workshops and small group consultation sessions. UCT students and researchers looking for RDM support can contact DLS at dls@uct.ac.za.

Recently, the topic of research data management (RDM) has emerged at the forefront of Open Science. Funders and publishers posit new expectations on data management planning and transparent reporting of research. At the same time, laboratories rely upon undocumented files to record data, process results and submit manuscripts which hinders repeatable and replicable management of experimental resources. In this study, we design a forensic process to reconstruct and evaluate data management practices in scientific laboratories. The process we design is named Laboratory Forensics (LF) as it combines digital forensic techniques and the systematic study of experimental data. We evaluate the effectiveness and usefulness of Laboratory Forensics with laboratory members and data managers. Our preliminary evaluation indicates that LF is a useful approach for assessing data management practices. However, LF needs further developments to be integrated into the information systems of scientific laboratories.