Video, Slides, Webinar
A Gentle Introduction to DSW for Convergers
Data Stewardship Wizard brings together data stewards and researchers to efficiently compose data management plans (DMPs) for their research projects. Data stewards capture their knowledge and expertise in so-called knowledge models that are turned into smart questionnaires filled by researchers. They can then export their answers to human-readable documents following the well-known templates such as Science Europe or Horizon 2020; or machine-actionable files for further processing.
The ELIXIR CONVERGE project is funded by the European Commission and involves experts from all 23 ELIXIR Nodes in 22 European countries. Its mission is to help standardize life science data management across Europe. To achieve this, one of the project's main goals is to develop a data management toolkit for life scientists. This toolkit will help bring more research data in the public domain and give scientists access to more data, allow them to see some of the nowadays challenges from different perspectives, and therefore will help stimulate innovation in various life science fields. The technical solution for this bridge between scientists and the science data management standardization will be the DSW, which will provide the platform for the toolkit.
This introduction first covers an overview of researchers' and data stewards' essential workflows in DSW. Then, we have a live demonstration of specific features:
- How to set up an integration question, a question that can gather answers from external repositories or databases;
- Details about projects, online collaboration, and using different document templates;
- How to configure a submission service to submit exported documents from DSW to external services easily.
Video available here.
Licence: Creative Commons Attribution 4.0 International
Keywords: Data managment plan, data stewardship, data management, software tools
Target audience: data managers, Researchers, data stewards
Resource type: Video, Slides, Webinar
Scientific topics: Data management, Data governance