Data management plans as linked open data: exploiting ARGOS FAIR and machine actionable outputs in the OpenAIRE research graph

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create by answering questions in predefined templates at the beginning of the research and, therefore, may become outdated and obsolete as the project progresses.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 is an annotated data management plan (DMP) template for Science and Technology Facilities Council (STFC) grant application data management plans.This document (available in .pdf and .docx formats) was created using the DMPonline tool, which provides templates for structuring major research funders' DMPs. The document includes the guidance text provided in the tool, produced by the Digital Curation Centre (DCC), the STFC and the University of Sheffield Library.STFC expects that data resulting from the research it funds should be made openly available after any proprietary period. Proposals for projects that would result in the production or collection of scientific data should include a data management plan as an attachment to the JeS proforma. The data management plan attachment is mandatory against most STFC schemes (not Public Engagement) and should be no longer than two pages of A4. STFC does not stipulate a specific format for data management plans and applicants may structure them in the way most appropriate to the project. If it is felt that a DMP is not relevant to a proposal then an attachment explaining this should be uploaded to pass validation. This, together with any costs associated with it, will be considered and assessed by the normal peer review process. The data management plan should explain how the data will be managed over the lifetime of the project and, where appropriate, preserved and shared for future re-use. For more information, see the STFC scientific data policy, the STFC guidance on writing a data management plan and guidance on the data management review.The DCC also provides guidance on Data management Plans and How to Develop a Data Management and Sharing Plan.

Background This study explores a library-led research data management (RDM) training program at a Canadian post-secondary institution that targeted students participating in summer research assistantships as well as their faculty supervisors. This paper describes the program in detail and shares findings from a student reflection assignment about practicing RDM for the first time. Methods The RDM training program included four requirements: attending an introductory RDM session; attending a data management plan (DMP) workshop; submitting a DMP for feedback; and completing a reflection assignment. Where consent was obtained (n=19), reflection assignments were analyzed using a qualitative content analysis approach. Results 35 faculty supervisors registered 53 students to participate. 62.2% (n=33) of students completed all components of the program. Perceived benefits of completing a DMP included improved project planning, supporting best practices, potential for data reuse, and team communication. Perceived challenges included the inflexibility of DMPs, difficulty populating DMPs, demands on researchers’ time, and lack of long-term utility. 73.6% of students (n=14/19) reported that building a DMP helped them with their summer projects. Conclusion Through instruction, practical engagement, and reflection within the context of real-world research, the program supported participants in learning about and practicing RDM, and provided insights for academic librarians who wish to refine or develop training in their local contexts as they continue to navigate emerging expectations from funders and publishers.


 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.

A FAIRification roadmap for ELIXIR Software Management Plans

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.

Discussion on developing a data management plan and its key factors in clinical study based on electronic data capture system

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.

<strong>Objective:</strong> To examine the effects of research data services (RDS) on the quality of data management plans (DMPs) required for a campus-level faculty grant competition, as well as to explore opportunities that the local DMP requirement presented for RDS outreach. <strong>Methods:</strong> Nine reviewers each scored a randomly assigned portion of DMPs from 82 competition proposals. Each DMP was scored by three reviewers, and the three scores were averaged together to obtain the final score. Interrater reliability was measured using intraclass correlation. Unpaired t-tests were used to compare mean DMP scores for faculty who utilized RDS services with those who did not. Unpaired t-tests were also used to compare mean DMP scores for proposals that were funded with proposals that were not funded. One-way ANOVA was used to compare mean DMP scores among proposals from six broad disciplinary categories. <strong>Results: </strong>Analyses showed that RDS consultations had a statistically significant effect on DMP scores. Differences between DMP scores for funded versus unfunded proposals and among disciplinary categories were not significant. The DMP requirement also provided a number of both expected and unexpected outreach opportunities for RDS services. <strong>Conclusions: </strong>Requiring DMPs for campus grant competitions can provide important assessment and outreach opportunities for research data services. While these results might not be generalizable to DMP review processes at federal funding agencies, they do suggest the importance, at any level, of developing a shared understanding of what constitutes a high quality DMP among grant applicants, grant reviewers, and RDS providers.

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.

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.

Plano de Gerenciamento de Dados é o documento formal que descreve os processos de gestão dos dados integrando seu ciclo de vida. A elaboração do Plano de Gerenciamento de Dados auxilia os pesquisadores e os profissionais atuantes nos repositórios. O objetivo deste estudo foi investigar quantos e quais repositórios de dados das 100 melhores universidades do mundo disponibilizam Planos de Gerenciamento de Dados e identificar aspectos relacionados a possíveis benefícios gerados pelo seu uso. A metodologia teve como base a pesquisa bibliográfica concomitante à metodologia quantitativa e qualitativa. Foi utilizado o método exploratório para fazer o levantamento das universidades através do sítio webometrics e dos respectivos repositórios. Os resultados demostram que apenas 36 repositórios disponibilizam Planos de Gerenciamento de dados e que suas instruções variam dependendo das características dos repositórios e dos conjuntos de dados neles depositados. Conclui-se que seu uso proporciona diversos benefícios a todos os atores envolvidos na gestão de dados.