Marek SuchánekData management planning often starts as a requirement imposed by funding agencies like the European Commission, NIH, ERC, or ZonMw. While fulfilling this mandate by having diversity of DMP templates at hand is essential, it can feel like a chore with little immediate payoff for researchers. FAIR Wizard transforms this perception by turning data management planning into an opportunity for broader benefits. Instead of merely producing a data management plan (DMP) to satisfy funders, FAIR Wizard empowers users to create outputs that serve multiple stakeholders — including researchers, data stewards, project managers, and institutional leaders.
Traditional data management planning has often focused on producing a single, static document. However, FAIR Wizard encourages researchers and data stewards to view it as a dynamic and ongoing process. We already uncovered how DMPs should be turned into truly living documents. By guiding users through data stewardship topics, FAIR Wizard ensures that the planning effort generates meaningful outputs beyond the DMP. The tool integrates seamlessly with existing systems, allowing researchers to collect and validate information at the right time and from the best sources.
The structured approach of FAIR Wizard empowers researchers to align their data management efforts with broader project objectives. It supports plan-driven methodologies that extend beyond traditional data management, encompassing areas such as project management and specialized topics like data security. By delivering actionable insights and streamlining workflows, FAIR Wizard reimagines the role of data management plans (DMPs). Instead of being confined to static documents, it encourages us to think beyond the box — collecting information dynamically (not just manually) and leveraging it to generate a range of valuable outputs tailored to diverse stakeholders and use cases, both during the project and beyond.
FAIR Wizard excels at simplifying complex domains by breaking them into manageable parts. It leverages knowledge models to structure questionnaires that efficiently guide users — researchers, project managers, support staff, and other contributors — through the information collection process. Features like nested questions, phase indicators, and metrics ensure that users can provide input in an informed and streamlined way. Its flexibility allows for the design of custom knowledge models capable of collecting any information necessary for generating tailored outputs.
Another major strength of FAIR Wizard is its ability to integrate with external systems. By connecting to institutional repositories, project management tools, metadata systems, and other resources, it can automatically retrieve relevant information. This reduces redundancy, enhances data accuracy, and significantly streamlines workflows. Users particularly value the time saved when information is auto-filled, eliminating the need for manual input.
To design an effective information collection process, several key considerations come into play:
With these principles in mind, FAIR Wizard transforms the data collection process into a purposeful, user-friendly, and highly efficient experience.
The collected information isn’t just stored — it can be transformed into outputs tailored to the needs of different stakeholders. In FAIR Wizard, it is possible to develop document templates for a variety of purposes, such as funder-specific DMPs, data-sharing plans, data protection impact assessments (DPIA), data management checklists, or institutional reports. Basically any textual document can go out of a project in FAIR Wizard, there is no limit to its contents.
The flexibility of FAIR Wizard extends to the wide array of formats it supports, making it adaptable to diverse needs and contexts. Outputs can be generated as polished PDFs for formal submissions, editable DOCX files for collaborative efforts, or machine-readable formats like JSON and RDF for seamless system integration. The possibilities for output formats are virtually limitless, allowing projects to deliver exactly what is needed for various stakeholders and purposes. Each document template can also support multiple formats, ensuring the generated outputs are both accessible and actionable in different scenarios.
When designing a document template, there are several key considerations to ensure it meets its intended purpose effectively:
Data management (or even broader) planning doesn't need to be perfect from the outset. FAIR Wizard embraces an iterative approach, enabling plans to start small and straightforward and then grow more sophisticated over time. For instance, a knowledge model can initially focus on a single document template, addressing only the most immediate needs. As institutional requirements and priorities evolve, additional questions and outputs can be incorporated, expanding the model to cover more comprehensive use cases.
This flexibility allows researchers to update and refine their plans as their projects progress, integrating new insights and adapting to shifting circumstances. With FAIR Wizard, these incremental improvements are straightforward and seamless, transforming data management into an ongoing, dynamic process. Updates and enhancements are naturally integrated into existing workflows, enabling users to build on their previous efforts without becoming overwhelmed. This evolutionary approach ensures that planning remains relevant, adaptable, and continually aligned with both project goals and institutional objectives.
Efficiency lies at the heart of FAIR Wizard’s philosophy. Acting as a single source of truth, it ensures consistency across all outputs and systems while reducing the need for repetitive manual tasks through automation. For instance, FAIR Wizard can push updates to institutional repositories, populate metadata schemas, or seamlessly integrate with other tools to streamline workflows.
This automation not only saves significant time but also minimizes errors, ensuring that data management practices align with institutional policies and funder requirements. Researchers and data stewards are freed to focus on high-value activities, confident that routine processes are handled reliably and efficiently.
FAIR Wizard supports a variety of integration methods to enhance efficiency of day-to-day workflows. For example, API integrations enable advanced connectivity with external systems, allowing data to flow seamlessly between FAIR Wizard and institutional platforms via API integration. Another approach is using integration questions, which can be configured to dynamically pull specific data directly into the system without manual effort. Additionally, value integrations make it easy to connect to commonly used resources and bring in essential data automatically. These are just a few examples of the flexible integration options FAIR Wizard provides, ensuring it adapts to the unique requirements of each institution or project.
By offering diverse integration methods, FAIR Wizard empowers users to automate their workflows effectively, turning data management planning into a seamless and highly efficient process.
FAIR Wizard elevates data management planning from a compliance-driven task to a valuable, multi-stakeholder process. By guiding researchers through structured topics, collecting information seamlessly, and producing tailored outputs, it ensures that the effort invested yields meaningful results. With its iterative and automated approach, FAIR Wizard not only meets funder requirements but also enhances the entire data stewardship process, driving better outcomes for research projects and institutions alike.
