UNLOCK’s large-scale open infrastructure for microbial research provides access to unique facilities and state-of-the-art equipment, enabling you to generate innovative, high-quality, and FAIR data. The FAIR data platform makes sure that your operational (meta) data (e.g., your bioreactor and instrument configurations, your run parameters, calibration records, your processing workflows) and your sample-specific (meta) data are structured using controlled vocabularies and standardized schemas. These records are persistently identified, versioned, and stored in a local FAIR-aligned repository to enable automated integration, traceability, and reproducible downstream analyses.

Access to your research resources is controlled via SURF Research Access Management (SRAM). UNLOCK users can log in using their existing institutional credentials, enabling secure collaboration with partners from other institutions or industry. Industry partners need to create an eduID for identity verification.

Within UNLOCK, community-approved standards are used to record sample and sequence metadata, ensuring your raw and processed data are consistent, interoperable, reproducible, and reusable over the long term. The FAIR Data Station is there to support and guide users throughout this process (Figure 1).

Figure 1: The FAIR Data Station helps you to organize your project metadata into three levels: Investigation, Study, and Assay. The Investigation level contains general project and administrative information and is intended for people to read and understand. The Study and Assay levels record details about your experimental setup, such as the study design (e.g. it is a time-series experiment), the type of analysis that will be performed (e.g. metagenomics), the library strategy (e.g. Whole Genome Sequencing), and the technology platform used (e.g. Oxford Nanopore). Because this information affects how the raw data will be processed, the FAIR Data Station helps you to record Study and Assay metadata in a machine-readable format, enabling automated downstream data processing and analysis while improving efficiency and reducing errors.

Omics data: Clear, step-by-step workflows are needed to turn raw omics data (like DNA sequences) into useful results (like gene function). At your request, the FAIR Data Platform can process your raw omics data using FAIR-compliant computational workflows. For this we have created standard FAIR workflows that not only produce these results but also include important data processing metadata, such as details about the tools and settings used and data product quality checks (e.g. MAG assembly-statistics, single-copy marker gene analysis).

Operational time-series data are continuous measurements, such as pH, temperature, and oxygen levels, collected during your experiments. These measurements provide the environmental context needed to interpret biological results. Within UNLOCK, operational data is automatically collected through the Lightweight Equipment Adapter Framework, visualized remotely in real time through Grafana dashboards, and stored in a FAIR-compliant format alongside the associated experimental metadata.

Operational data dashboard: Real-time graph of incoming operational data within the UNLOCK infrastructure.

Constraint-based genome scale metabolic models (GEMs) of microorganisms, enable researchers to predict metabolic phenotypes, flux distributions, and adaptive responses to environmental changes. These models identify essential genes and metabolic bottlenecks and can also shed light on metabolic interactions in microbial communities.

Several automated tools exist that can generate draft genome-scale models. However their quality is typically low because they rely heavily on genome annotations and generic database mappings which may propagate annotation errors, missing functions, and overgeneralized enzyme assignments directly into the model. Automated gap-filling may introduce reactions that are mathematically valid but biologically implausible, sometimes creating thermodynamically infeasible cycles while other critical components such as transport reactions are frequently misannotated or absent, preventing “growth” and/or unrealistic nutrient uptake and secretion.

Biomass equations simulate growth by accounting for cell dry weight composition, including amino acids, nucleotides, fatty acids, and sugars as well as energy requirements. However, these equations are typically borrowed from model organisms and may not reflect species- or condition-specific composition, leading to distorted flux predictions.

In summary, developing functional, predictive models demands significant human curation, effectively turning the process into a specialized field of study.