streamFind
Flexible Data Analytics and Workflow Designer for Identifying Chemicals in the Water Cycle
Start: 09/2022
End: 08/2025
The streamFind project develops an open, flexible, and extensible software platform for the data-driven identification of chemicals in the water cycle. The focus is on non-target screening using high-resolution mass spectrometry, which enables the detection and assessment of unknown or previously unregulated substances, transformation products, and chemicals of emerging concern in water samples. To date, analysing this type of data has been complex, often dependent on proprietary software solutions and only feasible with advanced data science expertise. streamFind addresses this challenge by enabling domain experts from water analytics, environmental engineering and research to graphically compose modular workflows, process mass spectrometry data across different manufacturers, integrate metadata and document results in a comparable way. In doing so, the project strengthens the data skills of early-career researchers and creates a foundation for investigating pollutant inputs into wastewater, surface water, groundwater and drinking water more quickly, transparently and reproducibly in the future.
In the project, IUTA and the FZI Research Center for Information Technology combine their complementary expertise in environmental analytics, mass spectrometry, data processing, software architecture and artificial intelligence. The project develops a modular open-source platform that enables NTS workflows for various water-cycle-related research questions to be created, adapted, executed and shared. This includes basic modules for data import, peak selection, peak alignment, grouping, quality assessment and data quality control. In addition, advanced modules are developed, for example for data filtering, matrix correction, cross-project data matching, PCA-DA, prioritisation of relevant peaks and identification of prioritised signals. A central element is the integration of metadata such as sampling location, sampling time, environmental parameters or operating data from treatment processes. This allows NTS results to be interpreted more effectively and linked to temporal, spatial or process-related information. The platform is accompanied by documentation, tutorials, use cases and workshops so that it can continue to be used and extended by the scientific community beyond the end of the project.
In the streamFind project, FZI contributes its expertise in data science and software engineering, and is primarily responsible for the conception and implementation of the digital platform. The aim is to make the complex analysis of non-target screening data from water and environmental analytics more accessible, reproducible and extensible for domain experts. To this end, FZI develops the technical architecture, the infrastructure for processing and integration of mass spectrometry data and metadata, as well as a graphical frontend that allows modular analysis workflows to be created without in-depth programming knowledge.
Another key focus of FZI is the definition of robust interfaces, data formats, and execution mechanisms so that different analysis components, programming languages and data sources can be brought together in a common platform. In addition, FZI supports the integration of data science methods, for example for statistical analyses, prioritisation, visualisation and reporting. In this way, FZI creates the informatics foundation that enables new modules to be added and further developed by the scientific community even after the end of the project.
Together with IUTA, FZI supports the validation, documentation, and dissemination of the platform. In doing so, FZI helps to make the project results available as an open, transparent, and sustainably usable software solution and strengthens knowledge transfer between computer science, environmental analytics, and the water sector.
