Understanding the Data Management Plan as a Boundary Object through a Multi-stakeholder perspective

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.

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.

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.

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.

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

<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.

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.

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.

The Horizon Europe Model Grant Agreement requires that a data management plan (DMP) is established and regularly updated. This DMP describes how research data are managed both throughout the lifecycle of the project and after the end of the project. It identifies procedures and minimum requirements to collect, store, analyse, and publish data in a consistent way according to the FAIR (Findable, Accessible, Interoperable and Reusable) principles. This document is a living document and will be regularly updated when necessary. Milestones are scheduled for M24 and a final report on M48. All project partners will be informed of the changes made to this document via the project Steering Group meetings and made available via the internal information processes and during the meeting of the next General Assembly. The basis for this document is the Horizon template for a Data Management Plan.

The DIA Clinical Data Management Community created a committee to develop a model standard operating procedure (SOP) for writing a data management plan. The goal of the committee was to develop a plan that could be used by industry and academic institutions. The model was based on contributed examples from committee members and their experiences with current practices and technologies. It is understood that as new clinical trial technology is implemented, the SOP will require modification. The model SOP and associated templates are presented as a starting point, and each company or institution will need to modify them to meet its individual needs. The model DMP SOP produced addresses most data management issues that are present in any phase clinical trial while providing a flexible framework.

Data management plans as expectations of the grant proposal process are still fairly novel, and the expected format and content of these plans is still evolving. The objective of this research is to gain a greater understanding of the expected content for data management plans submitted as part of grant proposals to federal funding agencies. This paper examines federal funding agencies’ data management plan guidelines in relation to the broad elements of data management identified by the Interagency Working Group on Digital Data: Description, Impact, Content & Format, Protection, Preservation, Access, and Transfer of Responsibility. Specifically, statements in agencies’ guidelines were categorized into the most applicable category (or categories). The representation of each category within each agency’s guidelines was addressed, and the statements falling in each category were analyzed. Some categories, including Access and Preservation, were represented in all or nearly all of the guidelines examined. Other categories — Impact and Transfer of Responsibility — were rarely addressed. The expectations for data management plans are evolving and will likely continue to evolve as more agencies require them.

Data management increasingly demands transparency with respect to data processing. Various stakeholders need information tailored to their needs, e.g. data management plans (DMP) for funding agencies or privacy policies for the public. DMPs and privacy policies are just two examples of documents describing aspects of data processing. Dedicated tools to create both already exist. However, creating each of them manually or semi-automatically remains a repetitive and cognitively challenging task. We propose a data-driven approach that semantically represents the data processing itself as workflows and serves as a base for different kinds of result-sets, generated with SPARQL, i.e. DMPs. Our approach is threefold: (i) users with domain knowledge semantically represent workflow components; (ii) other users can reuse these components to describe their data processing via semantically enhanced workflows; and, based on the semantic workflows, (iii) result-sets are automatically generated on-demand with SPARQL queries. This paper demonstrates our tool that implements the proposed approach, based on a use-case of a researcher who needs to provide a DMP to a funding agency to approve a proposed research project.