The Knowledge Catalyst
The Knowledge Catalyst
Podcast Description
The Knowledge Catalyst is an open science podcast and an EMBL 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 openness and transparency.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.
Podcast Insights
Content Themes
The podcast focuses on critical themes such as open science practices, reproducibility, and the FAIR principles, with episodes like the inaugural release featuring Dr. Mahnoor Zulfiqar discussing her FAIR computational workflows in metabolomics, emphasizing the challenges and solutions within scientific transparency.
The Knowledge Catalyst is an open science podcast and an EMBL 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 openness and transparency.
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.
In the very first 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.
For the accompanying resources, please visit: https://www.embl.org/about/info/open-science/mahnoor-zulfiqar-tackling-the-bottleneck-in-metabolomics-with-a-fair-computational-workflow/
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