Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data

The massive increase in data in scientific research requires the development and application of robust tools for data analysis and m achine l earning (ML) that are findable, accessible, interoperable, re usable (FAIR) and interpretable. In domains, such as b iomaterials s cience, e ngineering, c hemistry, h ealthcare and b io sciences, data - driven discovery typically requires interdisciplinary teams . These teams collaborate to implement unbiased data pre - processing strategies, select appropriate modelling techniques, and interpret model outputs to accelerate and inform research outcomes and support rational design and decision - making. This process is often iterative, with experts providing feedback over long periods of time to refine models and optimise the methodology adopted . In cases where initial analysis identifies issues with the data, such as outliers, unbalance d data classes, or experimental measurement uncertainty, another round of data collection and pre - processing might be necessary . That means that data for the same problem are likely to be analysed multiple times using different dataset versions and methodological pipelines. For interdisciplinary co - development of analytic s, there is also a need for tools that allow domain experts to focus on interpreting and using analysis results, rather than developing code . The widespread use of ML and the overwhelming availability of thousands of community - driven open - source packages in Python and R increases the barrier for interoperable and reusable data analysis methodologies . To facilitate accurate analy tics, transparency, and modelling results comparison, there is a strong need for easy - to - use tools that automatically track data, all methodological choices, performance metrics, and corresponding results.