Open data, open methods, and reproducible and transparent research are increasingly endorsed by funders, publishers, institutions and learned societies. But what does making your research open, transparent and reproducible mean in practice? What are the advantages and the pitfalls?

During this Data Conversations meeting, we will dive into the topic of innovation in research contexts, and look at our general understanding of reproducibility and data sharing in open science, lessons learned about reproducibility in medical research, and how these lessons inform our general understanding of reproducibility.

During Data Conversations, researchers from different Faculties, Institutes, and disciplines have a chance to hear and learn from each other about their experiences with practical aspects of research, such as data management.

This Data Conversations meeting will be integrated in The Turing Way Bookdash. While attending the Data Conversations, you will have an opportunity to learn more about this handbook to reproducible, ethical and collaborative data science.

Speakers

Leighann Kimble (VU Amsterdam)

Maxine Mackintosh (Genomics England)

What to expect?

This edition of Data Conversations will feature two short presentations. Following the presentation part, we will have a moderated discussion. If you have questions, you are welcome to share them in the chat or to ask them directly. 

Who should attend?

Data Conversations brings together researchers, research support staff, and data management, and data science experts from all subject areas. Students, early career researchers, as well as experienced academics, are welcome to attend.

About Data Conversations

Data Conversations started at the University of Lancaster in the UK. The Data Conversations at the VU Amsterdam are intended to provide a forum for researchers from different subjects and disciplines to exchange practices and ideas around open data, FAIR data, research data management and related open science topics.

About The Turing Way

The Turing Way is an open source community-driven book on reproducible, ethical and collaborative data science. We involve and support a diverse community in participating, skill-building, exchanging ideas, developing resources and making data science inclusive as they collaborate with each other.