Data Management Sharing Plan: Fostering Effective Trans-Disciplinary Communication in Collaborative Research

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.

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.

Data Management and Sharing Plans (DMSPs) are typically viewed only as a requirement researchers must meet as part of grant proposals to funding agencies; machine actionable DMSPs (maDMSPs) offer the potential to enhance the value of research data produced at institutions by making it more discoverable and connected to other parts of the research ecosystem. In recent years, maDMSPs have emerged as key mechanisms for the United States federal funding agencies’ policies for public access to research data. This paper provides overviews and insights from four institutions that were part of the Machine Actionable Plans (MAP) Pilot Project with the goal to develop projects related to maDMSPs. The case studies cover using generative AI for DMSP feedback, organizing a cross-campus workshop on maDMSPs, attempting to track and connect campus systems related to grant-related research, and creating a proof-of-concept for networked RDM and DMSP workflows. Finally, this paper concludes by acknowledging the rapid changes happening in federal funding agencies in the United States, highlighting potential failure points, and emphasizing the importance of staying up to date with changes to US federal policies.

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.


 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.

In modern societies and their governance, many societal issues have become complex and require a multidisciplinary approach to law-making, as many legal matters require knowledge and application of many other fields of study. This article focuses on the critical importance of transdisciplinary communication in shaping robust and responsive legislative frameworks. Based on a review of relevant literature and empirical research, the article demonstrates how collaboration across diverse fields of expertise enhances the quality and efficacy of legislative processes. Transdisciplinary communication fosters dialogue among politicians, researchers, practitioners, and the public. This dialogue allows for a comprehensive understanding of societal challenges and the integration of research-based evidence, stakeholder insights, and ethical considerations into the legislative process, thereby promoting transparency, legitimacy, and accountability. Through case studies and theoretical analyses, the article illustrates how transdisciplinary communication can help avoid unintended consequences, address inequalities in the system, and advance the realization of the public interest. Furthermore, the article highlights the potential of this approach in navigating the complexities of emerging issues such as climate change, technological innovation, and social justice. The article critically examines the approval of Albania's Sex Offender Registry Law 62/2023, using it as a case study to show the disconnection between scientific research and the formulation of public policy. By tracing the historical origins of sex offender registries and comparing various international models, the study highlights the lack of coherence between the approved law and established research findings. The analysis reveals the overestimation and misrepresentation of statistical data, shortcomings in the legislative process, and the absence of a transparent, evidence-based approach. The failure to implement the law and establish the registry within the provided timeframe and the inherent limitations of the approved model further emphasize the challenges in creating effective legislation. The findings not only critique the flaws in Law 62/2023 but also reflect. broader concerns about the utilization of scientific research in shaping legal frameworks, underscoring the need for a more informed and evidence-driven approach to policymaking. In conclusion, the article advocates for a shift towards embracing transdisciplinary communication as a fundamental pillar of contemporary law-making, which would help foster more resilient, equitable, and adaptive governance systems.

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.

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

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.

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.

A FAIRification roadmap for ELIXIR Software Management Plans

By taking into account the requirement of smart education platform, it gives the implementation of data management with component-based software development (CBSD). First, the concept components were extracted as reuse goal, and then a facet-based model was established to describe components: later on components were developed in Model View Controller (MVC) pattern which makes components have relatively independent interfaces to support software reuse. And an example was used to show the development process, which successfully realized the effective data management. The result shows that this method used in smart education has achieved good performance in practice, and components are reused in systems successfully, in this way, it improves the development efficiency which contributes to data management.

BackgroundResearchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.MethodsWe conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations.ResultsOur findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types—ranging from sequencing and clinical data to proteomic and imaging data—highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards.ConclusionsThese findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

Purpose – The purpose of this paper is to present a case study of work performed at the London School of Hygiene and Tropical Medicine to set-up a Research Data Management Service and tailor it to the needs of health researchers. Design/methodology/approach – The paper describes the motivations for establishing the RDM Service and outlines the three objectives that were set to improve data management practice within the institution. Each of the objectives are explored in turn, stating how they were addressed. Findings – A university with limited resources can operate a RDM Service that pro-actively supports researchers wishing to manage research data by monitoring evolving support needs, identifying common trends and developing resources that will reduce the time investment needed. The institution-wide survey identified a need for guidance on developing data documentation and archiving research data following project completion. Analysis of ongoing support requests identifies a need for guidance on data management plans and complying with journal sharing requirements. Research limitations/implications – The paper provides a case study of a single institution. The results may not be generally applicable to universities that support other disciplines. Practical implications – The case study may be helpful in helping other universities to establish an RDM Service using limited resources. Originality/value – The paper outlines how the evolving data management needs of public health researchers can be identified and a strategy that can be adopted by an RDM Service to efficiently address these requirements.

<p>Integration of data management systems is a persistent problem in European projects that span multiple agencies. Months, if not years of projects are often expended on the integration of disparate database structures, data types, methodologies and outputs. Moreover, this work is usually confined to a single effort, meaning it is needlessly repeated on subsequent projects. The legacy effect of removing these barriers could therefore yield monetary and time savings for all involved, far beyond a single cross-jurisdictional project. </p><p>The European Union’s INTERREG VA Programme has funded the COMPASS project to better manage marine protected areas (MPA) in peripheral areas. Involving five organisations, spread across two nations, the project has developed a cross-border network for marine monitoring. Three of those organisations are UK-based and bound for Brexit (the Agri-Food and Biosciences Institute, Marine Scotland Science and the Scottish Association of Marine Science). With that network under construction, significant efforts have been placed on harmonizing data management processes and procedures between the partners. </p><p>A data management quality management framework (DM-QMF) was introduced to guide this harmonization and ensure adequate quality controls would be enforced. As lead partner on data management, the Irish Marine Institute (MI) initially shared guidelines for infrastructure, architecture and metadata. The implementation of those requirements were then left to the other four partners, with the MI acting as facilitator. This led to the following being generated for each process in the project:</p><p>Data management plan: Information on how and what data were to be generated as well as where it would be stored. </p><p>Flow diagrams: Diagrammatic overview of the flow of data through the project. </p><p>Standard Operating Procedures: Detailed explanatory documents on the precise workings of a process.</p><p>Data management processes were allowed to evolve naturally out of a need to adhere to this set standard. Organisations were able to work within their operational limitations, without being required to alter their existing procedures, but encouraged to learn from each other. Very quickly it was found that there were similarities in processes, where previously it was thought there were significant differences. This process of sharing data management information has created mutually benefiting synergies and enabled the convergence of procedures within the separate organisations. </p><p>The downstream data management synergies that COMPASS has produced have already taken effect. Sister INTERREG VA projects, SeaMonitor and MarPAMM, have felt the benefits. The same data management systems cultivated as part of the COMPASS project are being reused, while the groundwork in creating strong cross boundary channels of communication and cooperation are saving significant amounts of time in project coordination.</p><p>Through data management, personal and institutional relationships have been strengthened, both of which should persist beyond the project terminus in 2021, well into a post-Brexit Europe. The COMPASS project has been an exemplar of how close collaboration can persist and thrive in a changing political environment, in spite of the ongoing uncertainty surrounding Brexit.</p>