Collaborate, automate, and integrate data management plans in your group or organization to become FAIR. Advance with our enterprise-ready cloud platform and streamline your data management processes.
Start using the leading enterprise-ready solution for data management planning and
benefit from machine-actionable plan-driven data stewardship.
Data management plans (DMPs) are crucial for researchers and organizations as they ensure research integrity, improve data quality, and facilitate collaboration.
Enterprise DMPs deliver substantial benefits to all stakeholders through enabling automation and reducing the workload while applying best data stewardship practices.
Relinquish the burden of hosting on-premises and entrust your data management to our seasoned professionals, leveraging their expertise and scalable cloud infrastructure.
Uncover a range of additional features and explore the full capabilities of the system.
Data Stewardship Wizard (DSW) has become a leading tool for data management plans. FAIR Wizard uses its engine for data management planning, offers additional features, and provides infrastructure and support.
Learn more about the advantages of the FAIR Wizard compared to DSW.
See what our users say about FAIR Wizard.
FAIR Wizard allows us to streamline our scientific data management through planning integrated with our workflows.
FAIR Wizard has enabled us to provide effective support in one of the areas of Data Management - creating DMPs. We cover many different departments in Czech Academy of Sciences with this service, so we really appreciate the ability to group users. We also manage an institutional repository and the systematic management of machine-readable DMPs gives us an insight into the future planned use of our other service.
FAIR Wizard will help Stellenbosch University make data management plans the focal point for ensuring our institutional compliance with the POPIA Code of Conduct for Research. To date, the implementation process met our requirements for both stability of system performance and the functionality needed to adhere to the FAIR Data Principles.