“Data structures, algorithms, statistics, and scientific interpretation are all part of the same measurement chain.”
What does your research focus on, and what problem are you trying to solve?
My research focuses on making analytical data trustworthy from the moment a signal is recorded to the point where scientific conclusions are drawn. We develop data-processing and chemometric approaches that retain uncertainty, quality, and provenance, rather than reducing complex measurements to numbers whose origins are difficult to trace.
What aspect of your current research excites you most – and why is this an important moment for it?
What excites me most is treating data processing as an integral part of the analytical measurement rather than as something that happens afterward. Modern instruments generate extraordinary amounts of high-quality data, but every subsequent processing step – such as centroiding, feature detection, integration, and modeling – adds assumptions and uncertainty.
We are working on approaches that make those processing steps transparent and quantitative, including uncertainty estimates and quality measures that can be propagated through an analytical workflow. This is particularly important now because analytical science is moving rapidly toward automated workflows and AI-assisted interpretation. If we want machines to make meaningful use of analytical data, they need more than numbers: they need information about where those numbers came from and how trustworthy they are.
Looking five to 10 years ahead, what emerging trend do you think will have the greatest impact on analytical science?
AI will certainly have a major impact, but the more fundamental transformation will be the emergence of machine-actionable analytical data. Today, we often focus on making instruments more powerful and algorithms more sophisticated. At the same time, much of the context needed to interpret the resulting data remains hidden in proprietary formats, software settings, or scientists’ notebooks.
In the future, analytical results will increasingly have to carry their metadata, provenance, uncertainty, and semantic meaning with them. That will allow instruments, algorithms, and AI systems to interact much more autonomously. In my view, trustworthy AI in analytical science will ultimately depend less on ever-larger models and more on giving those models better-structured, traceable, and scientifically meaningful data.
What is the biggest challenge facing analytical science today?
We have become extremely good at producing data, but we are still not equally good at preserving their meaning. A modern analytical workflow can contain dozens of transformations between the original detector signal and the final result. Decisions made during preprocessing, peak detection, integration, or data filtering can substantially affect the outcome, yet these decisions and their uncertainties are often difficult to reconstruct afterward.
This becomes especially problematic as datasets grow and analysis becomes more automated. Analytical science therefore needs to treat data processing, metadata, provenance, and uncertainty with the same rigor we traditionally apply to calibration, validation, and instrumental performance.
Do you have any strong opinions with which the rest of the field tends to disagree?
FAIR data should not be the final step of a research project, but a property of the entire analytical workflow. Too often, we generate and process data first and only later try to make the results findable, accessible, interoperable, and reusable. By then, important context, provenance, and processing information may already have been lost.
Analytical workflows should be designed so that data remain FAIR and machine-actionable from acquisition onward. That will be essential not only for reproducibility, but also if we expect AI systems to work reliably with scientific data.
What decision, opportunity, or unexpected event has had the greatest influence on your career so far?
One of the most influential decisions was not to choose between analytical chemistry and data science. During my work with increasingly complex mass-spectrometric data, I realized that many of the questions I found most interesting existed at that interface. They could not be solved by treating software merely as a tool used after the chemical measurement.
That led me toward analytical data science and eventually to establishing my own research direction in this area. It fundamentally changed how I think about analytical chemistry: the measurement does not end at the detector. Data structures, algorithms, statistics, and scientific interpretation are all part of the same measurement chain.
What interest or skill outside science has made you a better researcher?
Knitting, perhaps surprisingly. Complex knitting patterns are built from a limited number of simple operations, but small mistakes can propagate and become surprisingly difficult to correct later. It teaches patience, pattern recognition, and the importance of understanding structure rather than simply following instructions.
I’ve also learned a research lesson: sometimes the most efficient way forward is to go back, find where something went wrong, and reconstruct it properly rather than patching the final result.
Gerrit Renner is Research Group Leader, Analytical Data Science, in Instrumental Analytical Chemistry at the Faculty of Chemistry, University of Duisburg-Essen, Germany.
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