Musings from The Power List: Ansgar Korf

Could reusable mass spectrometry data transform analytical science?

Category	Detail
Condition	Not applicable; the source discusses analytical science and untargeted small-molecule mass spectrometry, not a medical condition.
Key Mechanisms	MZmine develops computational methods and open infrastructure to process untargeted small-molecule mass spectrometry data into structured, reusable data. The interviewee identifies matrix effects, ion suppression, batch drift, and storage-related degradation as systematic errors that models may learn as signal.
Target Population	Researchers working with untargeted small-molecule mass spectrometry data.
Care Setting	Analytical science research and laboratory data processing; no patient-care setting is discussed.

The interviewee says the MZmine team develops computational methods and open infrastructure to address data-processing bottlenecks in untargeted mass spectrometry.
FAIR-MS is described as applying MZmine processing capabilities to make mass spectrometry data findable and reusable at the scale needed for machine learning.
Untargeted datasets can be revisited years later to address questions that were not anticipated when data were acquired.
The interviewee says targeted methods remain essential wherever a validated answer is required.
The interviewee says study design, sampling, sample preparation, acquisition, and processing each constrain what the other workflow stages can achieve.

Not applicable; the article concerns analytical science and mass spectrometry research, not patient care.

No treatments or prescribing information are discussed.

Consider study design, sampling, sample preparation, acquisition, and processing as interdependent parts of the analytical workflow.
Address systematic sources of error across the workflow; the source cautions that scale alone does not remedy systematic error.
Make mass spectrometry data comparable and reusable to support large-scale machine-learning applications.