Data stewardship focuses on tactical coordination and implementation responsible for establishing data-quality metrics and other requirements regarding good data management. The ultimate goal is to provide high-quality data that is easily accessible in a consistent manner.Learn more
Building a data management plan and providing high-quality research data requires many considerations that can be overwhelming and hard to start with. In DSW, you do not have to write a lot of text. Instead, you answer understandable questions in smart questionnaires, get links to external resources, FAIR metrics indications and more. These questions can have some follow-ups based on your answers or can be connected to external resources.Explore
Nowadays, working in a team is crucial. DSW provides many options on how to share your project with other people. You can work together online, comment questions, and immediately see the changes made by your coworkers. You can quickly see who answered which questions, and all the changes are saved in the version history. You can then label specific versions or come back to any point in history
DSW's integration within the PaNOSC project has streamlined Data Management Planning (DMP) across various Research Infrastructures (RIs), exemplified by ESFR's creation of over 1150 DMPs with 60% auto-population. As more RIs consider making DMPs mandatory, the future holds the promise of machine-readable DMPs for enhanced data exploitation and knowledge graph development.
Chalmers University of Technology is one of the leading science and technology universities in northern Europe, with over 2,000 active researchers, coordinating around 400 new, often highly data intensive research projects yearly.
The University of Clermont Auvergne (UCA) offers a Master's degree in Bioinformatics. One of the teaching units deals with how to handle and share public data considering best practices and FAIR principles for reproducible sciences. DSW has a friendly web interface with various DMP flavors, which makes it rapidly usable by anyone without specific background.
The Institut Français de Bioinformatique (IFB – ELIXIR-FR) installed a DSW instance in the first half of 2021. We are in the process of creating a DMP template targeted to the French bioimagery community and we needed a user-friendly tool to help us build the structure of the DMP, experiment with it, and gather inputs and comments from the various actors.
BioData.pt is the Portuguese distributed e-infrastructure for biological data and the Portuguese ELIXIR node. Its mission is to support the national scientific system through best practices in data management and state of the art data analysis.
Norwegian funders require that research groups submit data management plans (DMPs) upon signing the contract for their research projects. Generating a DMP has been regarded by many researchers as a mere administrative burden, rather than a tool to revise their habits and support their projects.
Our story started in 2018 with the development of a data management plan (DMP) template targeted at projects generating NGS data at SciLifeLab. We wanted to provide this template to a wider audience and hence needed an online resource. Through the ELIXIR network we knew about the Data Stewardship Wizard, and decided to try it out.
Do you use DSW in your research or is it somehow included in your workflow? We would be happy to hear and publish your success story to inspire others!
If you write a paper where you would like to refer to the Data Stewardship Wizard, we would really appreciate if you cited our DSW paper. Here we provide more details about how to cite us.
You can easily contribute just by providing your language skills and help us translate DSW to various languages as well as maintain such translations.
We highly appreciate any form of feedback and support from the community. You or your organisation can support DSW development easily using GitHub Sponsors.
The operation and development of Data Stewardship Wizard is supported by ELIXIR CZ research infrastructure (MŠMT Grant No.: LM2018131, LM2023055).
The development of Data Stewardship Wizard is supported by Codevence Solutions.
Releases 3.5.0, 3.6.0, and 3.7.0 were supported by the ENVRI-FAIR to develop the FIP wizard to generate FAIR Implementation Profiles.
Releases 2.10.0, 2.11.0 and 2.12.0 were supported by the Centre for Digital Life Norway with funding from the Research Council of Norway under grant agreement 248810.