Lambda-Miner: Enhancing Reproducible Natural Organic Matter Data Processing with a Semi-Automatic Web Application

As the volume and complexity of data in environmental sciences continue to grow, the need for data management and reproducible processing methods becomes increasingly crucial. In the specific research domain of natural organic matter (NOM), there is currently no standardized tool for data processing, particularly for the management of data and its respective metadata. We developed and present the Lambda-Miner - a semi-automatic web application for data processing of ultrahigh-resolution mass spectrometry data of NOM. The platform provides an end-to-end data processing pipeline and supersedes manual steps via standardized data and metadata management. It empowers users to execute interactive workflows for mass spectra calibration, assignment of molecular formulas by specific rules to peak masses, and validation of these formulas according to specific sets of rules. Peak data as well as sample and measurement metadata are stored in a relational database management system (RDBMS). The Lambda-Miner thus facilitates reproducible, standardized data processing which builds a common repository for mass data, metadata (such as sample type and geolocation), intermediate, and final results in a format suitable for subsequent analyses. The combination of this information in one place enables meta-analyses such as long-term quality control studies and software optimization assays. The Lambda-Miner supports domain-specific requirements for research data management and contributes to achieving FAIR data principles in the domain of NOM analytics. The Lambda-Miner allows researchers to process their ultrahigh-resolution mass spectrometry data of NOM within minutes and linking it to features such as extraction efficiency, accumulation time, and relation of total assigned current to total ion current. Processed data can be downloaded in an interoperable format, facilitating individual data processing or visualization. The current implementation of the Lambda-Miner is designed for studying NOM with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) allowing formula assignments with widely used elemental compositions of NOM in the mass range from 0 to 1000 Da. But its modular structure makes it easy to adjust and extend the implementation for other kind of analyses or instrumentations. With its adaptability and focus on reproducibility, the Lambda-Miner introduces a valuable tool for advancing standardized data storage, processing, and analysis in the study of Natural Organic Matter.