TRANSPATH - Data Management Plan - M6

A FAIRification roadmap for ELIXIR Software Management Plans

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create by answering questions in predefined templates at the beginning of the research and, therefore, may become outdated and obsolete as the project progresses.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.

Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent. Recent studies have highlighted the transformative potential of incorporating the FAIR (Findable, Accessible, Interoperable, and Reusable) principles into workflows, particularly in contexts like software and machine learning model development, to promote open science. This study aims to introduce a comprehensive framework, designed to calibrate existing reporting guidelines against the FAIR principles. The goal is to enhance reproducibility and promote open science by integrating these principles into the scientific reporting process. We employed the "Best fit" framework synthesis approach which involves systematically reviewing and synthesizing existing frameworks and guidelines to identify best practices and gaps. We then proposed a series of defined workflows to align reporting guidelines with FAIR principles. A use case was developed to demonstrate the practical application of the framework. The integration of FAIR principles with established reporting guidelines through the framework effectively bridges the gap between FAIR metrics and traditional reporting standards. The framework provides a structured approach to enhance the findability, accessibility, interoperability, and reusability of scientific data and outputs. The use case demonstrated the practical benefits of the framework, showing improved data management and reporting practices. The framework addresses critical challenges in scientific research, such as data leakage and reproducibility issues. By embedding FAIR principles into reporting guidelines, the framework ensures that scientific outputs are more transparent, reliable, and reusable. This integration not only benefits researchers by improving data management practices but also enhances the overall scientific process by promoting open science and collaboration. The proposed framework successfully combines FAIR principles with reporting guidelines, offering a robust solution to enhance reproducibility and open science. This framework can be applied across various contexts, including software and machine learning model development stages, to foster a more transparent and collaborative scientific environment.


 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.

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.

<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.

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.

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.

What is the relationship between Data Management Plans (DMPs), DMP guidance documents, and the reality of end-of-project data preservation and access? In this short paper we report on some preliminary findings of a 3-year investigation into the impact of DMPs on federally funded science in the United States. We investigated a small sample of publicly accessible DMPs (N=14) published using DMPTool. We found that while DMPs followed the National Science Foundation's guidelines, the pathways to the resulting research data are often obscure, vague, or not obvious. We define two “data pathways” as the search tactics and strategies deployed in order to find datasets.

As of October 2017 SNSF funding applications must include a Data Management Plan (DMP). The DMP holds information on what data will be collected or generated, how the data will be handled during the project, and how the data will be published and preserved. Additionally, the SNSF expects that research data will be published and made freely available whenever possible. Dr. Sarah Gerster and Dr. Martin von Arx will present the new SNSF guidelines on Open Research Data and answer your questions.

&amp;lt;p&amp;gt;Making research data FAIR (Findable, Accessible, Interoperable, and Reusable) is critical to maximizing its impact. However, since the FAIR principles are designed as guidelines and do not specify implementation rules, it is difficult to verify the practice of these principles. Therefore, metrics and associated tools need to be developed to enable the assessment of FAIR compliance of services and datasets. Such practical solutions are important for many stakeholders to assess the quality of data-related services. They are important for selecting such services, but can also be used to iteratively improve data offerings, e.g., as part of FAIR advisory processes. With the increasing number of published datasets and the need to test them repeatedly, there is a growing body of literature that recognizes this importance of automated FAIR assessment tools. Our goal is to contribute to this area of FAIR through the development of an open source tool called F-UJI.&amp;amp;#160; F-UJI supports programmatic FAIR assessment of research data based on a set of core metrics against which the implementation of FAIR principles can be assessed. This paper presents the development and application of F-UJI and the underlying metrics. For each of the metrics, we have designed and implemented practical tests based on existing standards and best practices for research data. The tests are important to our expanded understanding of how to test FAIR metrics in practice that have not been fully addressed in previous work on FAIR data assessment. We demonstrate the use of the tool by assessing several multidisciplinary datasets from selected trusted digital repositories, followed by recommendations for improving the FAIRness of these datasets. We summarize the experience and lessons learned from the development and testing.&amp;lt;/p&amp;gt;

Data generated by the rapidly evolving human biomonitoring (HBM) programmes are providing invaluable opportunities to support and advance regulatory risk assessment and management of chemicals in occupational and environmental health domains. However, heterogeneity across studies, in terms of design, terminology, biomarker nomenclature, and data formats, limits our capacity to compare and integrate data sets retrospectively (reuse). Registration of HBM studies is common for clinical trials; however, the study designs and resulting data collections cannot be traced easily. We argue that an HBM Global Registry Framework (HBM GRF) could be the solution to several of challenges hampering the (re)use of HBM (meta)data. The aim is to develop a global, host-independent HBM registry framework based on the use of harmonised open-access protocol templates from designing, undertaking of an HBM study to the use and possible reuse of the resulting HBM (meta)data. This framework should apply FAIR (Findable, Accessible, Interoperable and Reusable) principles as a core data management strategy to enable the (re)use of HBM (meta)data to its full potential through the data value chain. Moreover, we believe that implementation of FAIR principles is a fundamental enabler for digital transformation within environmental health. The HBM GRF would encompass internationally harmonised and agreed open access templates for HBM study protocols, structured web-based functionalities to deposit, find, and access harmonised protocols of HBM studies. Registration of HBM studies using the HBM GRF is anticipated to increase FAIRness of the resulting (meta)data. It is also considered that harmonisation of existing data sets could be performed retrospectively. As a consequence, data wrangling activities to make data ready for analysis will be minimised. In addition, this framework would enable the HBM (inter)national community to trace new HBM studies already in the planning phase and their results once finalised. The HBM GRF could also serve as a platform enhancing communication between scientists, risk assessors, and risk managers/policy makers. The planned European Partnership for the Assessment of Risk from Chemicals (PARC) work along these lines, based on the experience obtained in previous joint European initiatives. Therefore, PARC could very well bring a first demonstration of first essential functionalities within the development of the HBM GRF.

This work analyses the perception and practice of sharing, reusing, and facilitating access to research data in the field of food science and technology. The study involved the coordination of a focus group discussion and an online survey, to understand and evince the behaviour of researchers regarding data management in that field. Both the discussion group and the survey were performed with researchers from several institutes of the Spanish National Research Council. The lack of a data sharing culture, the fear of being scooped, and confusion between the concepts of the working plan and the data management plan were some of the issues that emerged in the focus group. Respondents' previous experience with sharing their research data has been mainly in the form of appendices to peer‐reviewed publications. From the survey (101 responses), the most important motivations for publishing research data were found to be facilitating the reproducibility of the research, increasing the likelihood of citations of the article, and compliance with funding body mandates. Legal constraints, intellectual property, data ownership, data rights, potential commercial exploitation, and misuse of data were the main barriers to publishing data as open data. Citation in publications, certification, compliance with standards, and the reputation of the data providers were the most relevant factors affecting the use of other researchers' data. Being recent or recently updated, well documented, with quality metadata and ease of access were the most valued attributes of open research data.

We describe a new vision for a Data Management Plan (DMP) that incorporates controlled vocabularies and semantic descriptions of the scholarly objects to be produced by the proposed project. We implement this vision in an open‐source web‐based DMP tool, called ezDMP, at ezdmp.org . The integrated use of structured information in ezDMP permits several important goals. First, with minimal additional effort, researchers can create DMPs with more complete information about the scholarly objects to be produced. Second, research funders can productively query this structured information to learn about repository use and other patterns of scholarly objects creation. Finally, ezDMP puts a structure in place that can support the integration of information about digital scholars objects, in an organized and systematic way, into research data management environments.