Experiences with a training DSW knowledge model for early-stage researchers
Marie-Dominique Devignes1, Malika Smaïl-Tabbone1, Hrishikesh Dhondge1
1Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-5400, France.
Open Research Europe
|August 30, 2023
Summary
Training PhD students in data management plans (DMPs) is crucial for open science. The Data Stewardship Wizard (DSW) framework offers a simplified approach for young scientists to understand and create DMPs, enhancing data sharing practices.
Area of Science:
- Life Sciences
- Scientific Data Management
Background:
- Open science and FAIR data principles necessitate robust data management plans (DMPs).
- PhD students often lack clarity on DMP creation and purpose, hindering compliance.
Purpose of the Study:
- To train early-stage researchers (ESRs) in formulating Data Management Plans (DMPs).
- To adapt the Data Stewardship Wizard (DSW) framework for training young scientists in data management principles.
Main Methods:
- Engaged 10 ESRs in a DMP formulation training project.
- Utilized a simplified Life Sciences Knowledge Model within the DSW framework.
- Collected feedback from ESRs throughout the training exercise.
Main Results:
- Introduced a new life-sciences training DMP template for young scientists.
- Reported on investigator and ESR experiences, highlighting common DMP development challenges.
- Demonstrated the DS-wizard's utility in conveying DMP concepts.
Conclusions:
- The DS-wizard is effective for DMP training, facilitating understanding of terminology and concepts.
- Comprehensive DMP training requires foundational concept introduction and practical dataset publication guidance.
- The DS-Wizard tool proved essential for the project's DMP training initiative.
Keywords:
Data Management PlanFAIR principlesmetadatamolecular biology.open sciencestructural bioinformaticsstudent trainingMore Related Videos
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