The Molecular Biophysics Database (MBDB) is a specialised repository for storing, describing, sharing and reusing raw experimental data from molecular biophysics. The repository works with both raw and derived data, together with rich metadata, including detailed sample descriptions and their chemical environment, instrument settings and experimental setup, data analysis methods, resulting biophysical parameters, authors and funding information. Its metadata model combines a general section shared across all experimental techniques with technique-specific sections, enabling users to search across different measurement methods. MBDB currently supports BLI, ITC, MST, SPR and mass photometry, with additional techniques planned. Records undergo curatorial checks for completeness and consistency, while structured metadata provide a basis for systematic comparison of measurement protocols, development of analytical tools and the application of machine learning methods.

BioSim CZ is a specialised research repository for storing, publishing, sharing and providing access to structural and simulation data from biomolecular systems. It focuses primarily on outputs from atomistic and coarse-grained molecular dynamics simulations, including simulation trajectories, related structures and metadata, as well as datasets containing predicted biomacromolecular structures.

Researchers can deposit their own data and obtain persistent identifiers that can be used in scholarly publications. The repository supports different access modes, ranging from open access and time-limited embargoes to controlled access upon request. The data deposition process includes automated metadata extraction and validation, consistency checks of simulation files, and conversion into standardised formats.

Each deposited simulation is accompanied by a standard set of automatically generated analytical summaries describing the quality, stability and dynamic properties of the simulated system. These analyses include, for example, structural changes over time (RMSD), flexibility of individual molecular regions (RMSF), radius of gyration and solvent-accessible surface area (SASA). Standardised analytical outputs support not only direct visualisation and interpretation of individual simulations, but also comparison between simulations, systematic discovery and aggregated analyses across independent research projects.

The repository supports data discovery and interlinking through standard bioinformatics identifiers such as PDB IDs and UniProt IDs. Metadata for published datasets are automatically transferred to the European catalogue MDDBR.eu, enabling their discovery across participating repositories. BioSim CZ follows the FAIR principles and supports research reproducibility, interoperability with existing biological databases and the reuse of simulation data, including for large-scale comparative studies and the development of machine learning methods.

The ClinData repository provides a secure and structured environment for storing, managing and processing clinical data, i.e. information on human subjects collected as part of both basic and applied clinical research. A distinctive feature of the repository is its ability to link clinical information with multi-omics and imaging data relating to the same individual through references to internal or external storage systems. This enables a comprehensive view of an individual’s health status across different types of examinations. The repository will also provide long-term archiving of completed studies and connections to international disciplinary catalogues and infrastructures, creating a unique platform for the secure preservation and integration of human data that can be used across medicine, biology and the social sciences.

CzBIRD is the Czech national repository for biological imaging data operated by the CzechBioImaging (CzBI) research infrastructure. It provides a comprehensive platform for the curated sharing, discovery and access to high-quality imaging datasets across a wide range of biological and related disciplines, including molecular biology, immunology, microbiology, virology, embryology, environmental sciences and palaeobiology. CzBIRD supports comprehensive work with FAIR data, including so-called “negative results”. It uses internationally recognised schemas, standards, ontologies and controlled vocabularies, such as REMBI, DCAT-AP, DataCite, PROV-O, OBO Foundry and EDAM BioImaging, as well as open data formats including OME-TIFF and OME-NGFF/ZARR. CzBIRD also plans to provide mapping to the BioImage Archive.

DataHub serves as the central platform of the Faculty of Science at Charles University for sharing, visualising and analysing geographical data, particularly spatial data from the fields of social and physical geography and demography. Its distinctive feature is the ability to work with data in geographic information system environments, enabling not only data storage but, above all, their presentation through interactive map applications, analytical dashboards and map viewers – capabilities that standard data repositories typically do not provide. As part of its further development, a separate repository will be established, the platform will be connected to the National Metadata Directory, and data upload processes will be automated. A key enhancement will also be the addition of a new database containing detailed demographic data for the Czech Republic, providing harmonised, high-quality information on fertility, mortality, migration and population structure at different geographical levels.

This repository is designed for the secure storage, management and sharing of multimodal imaging and physiological data acquired primarily through magnetic resonance imaging, electroencephalography, positron emission tomography and computed tomography, from both human-subject research studies and animal studies. Particular emphasis is placed on protecting sensitive data through dataset-level access control. Different access modes – ranging from open access and automated approval to manual review by an administrator – reflect the varying levels of sensitivity of individual records. The repository will also be connected to a trusted computing environment, enabling secure interactive and batch processing of stored data. Selected metadata records are also expected to be exported to a European research infrastructure for neuroscience.

OmiCZ (Czech Omics Node) is being developed as a secure national repository and platform for the federated analysis of sensitive omics data, particularly genomic, transcriptomic, proteomic and flow cytometry data. Data are transferred directly from instruments through a secure gateway into a Trusted Research Environment (TRE) within the e-INFRA CZ infrastructure, where they undergo quality control and standardised preprocessing. Once the data description has been approved, the dataset is encrypted using the Crypt4GH standard developed by GA4GH and stored together with its metadata in an Invenio-based repository. The repository therefore contains encrypted data only, while unencrypted data never leave the TRE. Beyond the basic metadata model, OmiCZ is also exploring a flexible, semantically oriented model linked to AI for detailed description of omics data and their provenance. Access to data is decided by a Data Access Committee, which always includes the data producer. As the data custodian, the producer retains full control over the data and can define highly granular conditions for their sharing and analysis. Permissions are granted for a specific dataset and a specific analytical workflow through a GA4GH Visa in LS AAI. Users therefore do not receive the raw data directly; instead, they run approved analyses within the TRE. The development of the repository also includes the necessary legal framework, including agreements between data producers, the repository operator, the IT infrastructure provider and users. Implementation of the GA4GH Beacon standard is planned to support data discovery. OmiCZ is also exploring options for connecting to the Czech node of the Federated European Genome-phenome Archive (FEGA), which is being developed independently.

The repository is intended to support omics data from a broad range of disciplines, from plant and environmental research to medicine. Its primary use case, however, is the storage of human genomic data for molecular medicine. In this context, OmiCZ is expected to serve as a national node within the emerging European network for sharing and analysing genomic data under the 1+Million Genomes (1+MG) initiative, including involvement in the Genomic Data Infrastructure (GDI) and federated analysis pilots within the Genome of Europe project.

The repository is designed for storing and managing data from the field of chemical biology, with a primary focus on mass spectra of small molecules. It is being developed with the diverse requirements of research groups in mind, particularly in terms of data formats and workflows, with the aim of creating a flexible and widely applicable infrastructure.

The repository is intended to serve not only as a data archive, but also as a data resource for subsequent research projects. Its design includes close integration with an environment for the development, training and validation of machine learning models focused on mass spectra analysis. These models can be used, for example, for efficient spectrum searches based on spectral similarity.

Strong emphasis is also placed on compliance with the FAIR principles – Findable, Accessible, Interoperable and Reusable. Metadata describing deposited data will therefore be made available through semantic web technologies, enabling machine processing, interlinking and reuse.