NIST LabCAS: Data Driven Science Architecture and Resource

Biological data lie at the heart of innovation and artificial intelligence (AI) advances in the emerging biotechnology sector and serve as key strategic resources for achieving major breakthroughs in biomanufacturing, as summarized by the National Security Commission on Emerging Biotechnology's (NSCEB's) 2025 report.  The NIST Bioeconomy Lexicon defines biological data as "the information, including associated descriptors, derived from structure, function or process of (a) biological system(s) that is either measured, collected, or aggregated for analysis."  Laboratory data management, process annotation, and data structure are fundamental, enabling concepts for using biological data in AI systems and promoting data suitability for machine learning/automated learning (ML/AL) pipelines.

NIST LabCAS is a data science system/platform developed to integrate structured data and robust metadata from analytical methods and tools for repeatable data processing pipelines, data visualization, and data delivery.  NIST LabCAS adheres to FAIR data principles and data architecture standards by design and is modeled after the National Cancer Institute (NCI) Early Detection Research Network (EDRN) Cancer Biomarker Data Commons.

NIST LabCAS organizes raw and processed data from multiple NIST-led Consortia and reliably allows consortia members to archive, share, and analyze experimental data from large-scale consortium interlaboratory studies.  NIST LabCAS also as a platform to test data structures and annotation strategies to best implement database interoperability.

NIST LabCAS is part of broader efforts to advance measurement data organization strategies for the bioeconomy.  These structured and high-quality datasets (i.e., AI-ready data) are intended to serve as benchmarks to support standards development and AI model training.  NIST LabCAS supports extensive data features, such as technical replicates, donor replicates, instrument settings and calibration parameters, that promote the quality of interlaboratory study datasets and support robustness testing of both analytical and ML/AI methods.  The flexible LabCAS approach provides an integrated, searchable data processing, management, and analytics ecosystem for these complex scientific data and metadata.

NIST LabCAS is being developed for several primary use cases each with identifying data sets, described below, while maintaining congruent and disciplined metadata, common analytical tools and workflows, and unified front-end interface configurations.

NIST Flow Cytometry Standards Consortium (FCSC)

• FCSC Working Group 1 - Equivalent Reference Fluorophores (ERF) assignment of unknowns for flow cytometry instrument standardization across over 25 laboratories
• FCSC Working Group 2 - T-/B-/monocyte/NK-cell (TMBNK) flow cytometric assay for cell count and cell health

NIST Genome Editing Consortium (GEC)

• GEC Working Group 1 Specificity Measurements - Genome in a Bottle mixture interlaboratory study of assay accuracy for variant size and frequency
• GEC Working Group 2 Data & Metadata - formal metadata schema for genome editing experiments

NIST Microbial Metrology

• Portal for labeled datasets and structured experimental protocols, from integrated electronic notebooks, with data analysis pipelines and visualizations
• Provenance and reference data for microbial strains that researchers can obtain from the NIST Microbial Strain Collection

NIST Reference Material 8048 Human Fecal Material

• Metagenomics, metabolomics, and flow cytometry datasets and analyses supporting homogeneity and stability assessments of NIST RM 8048 

Cell Expansion Provenance

• Organized LIMS data collected during cell expansion of over 20 prototype cell-based reference materials.
• Sharable data models for capturing cell expansion bioprocess measurement data
• Provenance visualization tools