Musings from The Power List: Ansgar Korf

Could reusable mass spectrometry data transform analytical science?

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Ansgar Korf’s team develops computational methods and open infrastructure through MZmine to reduce software bottlenecks in untargeted mass spectrometry.
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Korf is developing FAIR-MS to make mass spectrometry data findable and reusable for machine learning across laboratories and instruments.
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Korf expects untargeted measurement to become the default, allowing datasets to be revisited to answer questions not considered when they were acquired.
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Korf says analytical science is undervalued as a scientific contribution, limiting recognition of method development, expertise, and software infrastructure.
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Korf warns that AI cannot compensate for systematic errors in analytical workflows, including matrix effects, ion suppression, batch drift, and sample degradation.

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