BACKGROUND
In recent years, the scientific community has been shaken by a “replication crisis”, where many landmark studies failed to reproduce. With reproducibility becoming a standard expectation from scientific journals and funding agencies, it’s an essential skill for researchers in all disciplines and at every career stage. Reproducibility not only ensures the credibility of your work and elevate the visibility and impact of your research within the scientific community, but also enhances your efficiency as a researcher.
WORKSHOP GOAL
Are you facing the challenge of making your research reproducible due to requirements from journals or funding agencies and don’t know where to start? Do you need help to efficiently structure your work, safeguard against data and information loss, and ensure you can reproduce your research results at any time in the future?
This course is designed for you. It offers a comprehensive introduction to reproducibility, providing you with the essential tools and strategies to effectively navigate the reproducibility landscape. It offers guidance through all stages of your research process – before, during, and after data analysis and gives opportunities to reflect on and discuss the unique aspects and requirements of your own research.
By the end of the course, you will be able to select and apply the tools and best practices that are most relevant and practical for your research needs.
WORKSHOP CONTENT
- Reproducible, replicable, robust and generable research: Key distinctions and their implications for your work
- Current scientific discourse on reproducibility and open science: Insight into the latest discussions in the field
- Pre-registration and Registered Reports: Learn where, how and why to document and publish your research plans
- Storage and Organization: Best practices for organizing and storing your files and data efficiently
- Documentation of codes, data, and research environments: Discover effective methods for documenting every aspect of your research process
- Version control (e.g. Git): Gain insights into using version control tools to manage changes in your research files
- Software containers, Jupyter notebooks & co.: Learn about advanced tools to ensure reproducibility across different computing environments
- Transparent reporting: Principles for reporting your research transparently
- Preprint Servers: Understand the role of preprint servers in disseminating your work
- Data sharing and (data) repositories: Learn how and where to share your data responsibly and effectively
Please note that this workshop can only provide a brief overview of Git and Jupyter. For more in-depth learning, we offer specialized training on this topic.
TARGET AUDIENCE
Researchers of all disciplines and career stages – early career researchers (especially PhD students) or advanced researchers – with little to no prior knowledge about reproducibility.
ABOUT THE TRAINER
Dr. Susanne de Vogel is a data scientist for training and consulting at the DSC. She holds a diploma in Social Sciences from the University of Cologne (2013) and a PhD in Sociology from the Martin Luther University of Halle-Wittenberg (2019). Susanne has worked for over 10 years on the development and implementation of various panel studies at the German Center for Higher Education Research and Science Studies (DZHW) in Hanover. Her competencies lie in the collection, preparation, analysis, and management of (survey) data. As a higher education and science researcher, she is expert in research practices and conditions in academia.