Marek SuchánekBoth funding agencies and research institutions commonly provide their own templates for composing data management plans (DMPs), encapsulating local requirements and researcher considerations. However, researchers often fill these templates to meet funder requirements without addressing domain-specific data management essentials. This blog post describes the challenges posed by diverse DMP templates and highlights how FAIR Wizard mitigates related issues for comprehensive and effective data management planning.
The first challenge researchers face when striving to create a solid data management plan is picking the right template. If we view planning not just as paperwork but as something vital for ensuring fair and proper data management, the question arises: which template should we go for? In Europe alone, there are over 25 different DMP templates used by international and national funders. Some templates focus on specific research fields, while others prioritize making data FAIR or are committed to Open Science. Here are a few examples:
So, which one should we pick? Should we choose based on our field, national rules, or the requirements of a specific European project? There's no clear answer when these templates are just seen as choices. This can make it feel like a chore for researchers, filling out the template for funding and then trying to manage data FAIRly.
Once a template has been selected, the process of filling it in begins, revealing the second challenge. It essentially becomes an exercise in reading instructions and writing essays, tasks that few enjoy. We're tasked with deciphering the guidance text, which dictates what each section of the plan should contain. For instance, in the data summary section, we're instructed to list all datasets along with their purpose, data type, and expected volume. However, these instructions often suffer from ambiguity, incompleteness, and even inconsistency, leaving us to interpret and fulfill the expressed requirements as best we can.
The final challenge arises when we commit to filling a particular DMP template, akin to experiencing a "DMP template lock-in" reminiscent of vendor lock-in scenarios. We invest time and effort into completing the chosen template, only to discover later that we require a different one to adhere to national regulations, institutional demands, or for submission to a different entity. While there's an inherent level of duplication between templates, nuances in what reviewing organizations require to evaluate a DMP inevitably exist. Thus, we often find ourselves facing the prospect of manually rewriting the DMP according to a different template.
Is there truly no way forward? Despite efforts to standardize DMP templates, particularly on a European scale under the Science Europe umbrella, as evidenced by the Practical Guide to the International Alignment of Research Data Management - Extended Edition released in 2021, the landscape remains fragmented. While some institutions have embraced this guide, numerous DMP templates persist, adding to the complexity. Moreover, institutions have adopted various templates, such as the Horizon Europe DMP template, often customizing or translating them to suit their specific requirements, further contributing to the confusion.
Another notable initiative is the RDA DMP Common Standard for machine-actionable DMPs, which provides a profile for storing essential DMP-related information in an interoperable manner, typically in JSON format. This includes critical details such as contributors, project information, datasets, distribution metadata, and costs, among others. While this standard has been embraced by many DMP platforms, it has yet to be widely adopted by funders and other organizations.
FAIR Wizard offers a unique approach compared to other DMP platforms, placing a strong emphasis on well-guided planning. By simply creating a project and inputting relevant details and data treatment plans, we kickstart the process. Institutions provide structured guidance within the questionnaire, aiding in informed decision-making and ensuring proper data management. Additionally, collaborative features enable seamless cooperation with colleagues, ensuring alignment and buy-in with the plan.
Obtaining a DMP document in FAIR Wizard is seamlessly integrated into the planning process itself. We can choose from various document templates tailored to meet funder requirements, national regulations, or institutional needs, including machine-actionable DMPs. These templates generate documents based on our questionnaire responses, providing a snapshot of our evolving data management plan. With FAIR Wizard, the concept of a "living document" truly comes to life through its smart and collaborative questionnaire format.
With FAIR Wizard, navigating the intricacies of crafting a comprehensive living data management plan becomes a breeze. Gone are the days of grappling with different DMP templates – this platform simplifies the entire process. It provides a user-friendly questionnaire coupled with clear guidance from within organizations using it, ensuring that you can confidently address all aspects of data management. Whether it's generating a detailed data management plan or any other essential document for your project, FAIR Wizard offers seamless customization to meet your specific needs. By streamlining the document generation process, we save you valuable time and effort, allowing you to focus on the core aspects of your research or project.
Furthermore, FAIR Wizard ensures that your data management planning is not only efficient but also well-guided. Every step of the way, you'll have access to step-by-step instructions and best practices, ensuring that your data is organized and managed effectively. With that, you can achieve peace of mind knowing that your data management endeavors are in good hands. Say goodbye to the frustration of essay-writing exercise and say hello to a streamlined, guided approach to data management planning.
