DataHub Team at ELIXIR Belgium datahub@elixir-belgium.org Efficient metadata collection is vital for the discoverability and reusability of experimental data. Metadata collected following standardized, structured models, and machine-readable formats enhances (meta)data interoperability and prepare the data for seamless integration with ML/AI applications. However, existing open-source tools catering to this process are limited, lacking user-friendliness, and often too specific to certain use cases. With the development of the DataHub platform, our aim is to empower researchers and institutes by offering a streamlined, intuitive, and FAIR-by-design approach to metadata management. Flora D’Anna1, Kevin De Pelseneer1 , Rafael Andrade Buono1, SciLifeLab team2, FAIRDOM community3, Frederik Coppens1. 1 VIB Data Core, VIB Technologies, Technologiepark 75, 9052 Ghent, Belgium; 2 SciLifeLab-Data Centre, SciLifeLab, Tomtebodavägen 23, 171 65 Solna, Sweden; 3 https://fair-dom.org/people. How to structure experimental metadata in DataHub Investigation: overall goals of related studies Stream of Assays: test producing measurements via 1 technology type Study: information about - the subjects or observation units under study (sources) - the samples collection protocol - the resulting samples Sample: input material of assay Protocol 1: extraction Extract: output material from protocol 1 Protocol 2: library construction Library: output material from protocol 2 Data file: outputs from protocol 3 Protocol 3: sequencing Assay: one experimental step Assay 1 Assay 2 Assay 3 Sample collection (Protocol) Samples (Material) Sources (Material) Mass Spectrometry Sequencing DataHub platform Rationale of the approach: FAIR experimental metadata by-design by starting with the end in mind DataHub aims to integrate metadata required by public or local data repositories and archives (1). This will allow researchers to collect experimental metadata according to standards from the start of their experiments, one step at the time, while referencing data files stored in appropriate data storage systems (2). Structured and standardized metadata could then be easily deposited in data repositories by researchers (3). End-repositories ●End-repositories’ checklists, ontologies, controlled vocabularies ●Sample-level metadata ●Template customisation ●Describe 1 experimental step at the time ●Link to external data storage Local archive Streamlined metadata and data submission to end-repositories End-repositories’ metadata requirements and standards Collection of experimental metadata 1 2 3 https://datahub.elixir-belgium.org ELNs Object storage Future developments https://datahub-test.elixir-belgium.org • For active (meta)data collection of experimental conditions and samples by researchers • Samples lineage • Compliant with repositories’ checklists and customisable • Controlled vocabularies • Ontologies from Ontology Lookup Service (OLS) • Samples metadata in tabular format • Import and export • Creation and editing in batch via Excel spreadsheet Dynamic table Future developments Samples query "List all the sequencing files originating from an Illumina MiSeq instrument and derived from the material named 'Yeast culture 1'." DataHub user interface and features • Research laboratories or units: enhance data management by integrating DataHub with ELNs and other existing systems. • Technical core facilities: utilize and share metadata standards with users. • Research institutes and consortia: • keep track of and discover datasets generated by various laboratories • support the implementation of FAIR and Open Science principles. Who can benefit from using DataHub Method: FAIRDOM-SEEK and ISA-JSON We develop features in FAIRDOM-SEEK1 that support structuring of the experimental metadata according to the established ISA metadata framework, in JSON format2. 1 DOI 10.5281/zenodo.5653415 2 https://doi.org/10.1093/gigascience/giab060 FAIR metadata by-design as a service for life s