For a positive culture change in life science research
The Knowledge Catalyst is a podcast and an Open Science Community project, where we hear from the voices of researchers about how they tackle some of the biggest challenges in their field with open science.
We get to know EMBL’s open science champions, in how and why they started their open science journey. Each episode is a starter pack with useful resources and links for applying open science and the FAIR principles in a specific use case.
Production: Victoria Yan, Anandhi Iyappan
Audio Technician and Editing: Felix Fischer
Original music: Sergio Alcaide, Felix Fischer
Graphics: Holly Joynes
Web Design: Victoria Yan, Szymon Kaspryzk
Please contact Victoria Yan: victoria.yan@embl.de
In this episode of the Knowledge Catalyst, Malvika Sharan, an EMBL alumnus, Senior Researcher at the Alan Turing Institute and co-founder of Open Life Science, discusses the value of research support roles, how to empower community leaders who help operationalize open science, and how inclusive approaches can shape the future of research.
In this episode of the Knowledge Catalyst, Jean-Karim Heriché (JK), the Computational Support at EMBL’s Cell Biology and Biophysics Unit, shares insights from his career on how to make your data FAIR and your research more reproducible. He emphasizes that open science begins with consistent, thorough documentation, essential for managing datasets of any size or complexity. JK also advocates for more dedicated resources and support for scientists, recognizing data stewardship as a core part of their work.
In this episode of The Knowledge Catalyst, Dr. Mahnoor Zulfiqar, postdoctoral fellow at EMBL, shares how she builds FAIR computational workflows to tackle challenges in metabolomics. From identifying unknown compounds using marine-derived metabolites to integrating tools across platforms with Docker and CWL, Mahnoor’s work enhances reproducibility and supports drug discovery. We explore how standardized, interoperable workflows can improve confidence in data analysis—and rebuild public trust in science.