In 2025, the bacterial diversity database BacDive is the leading database for strain-level bacterial and archaeal information. It has been selected as an ELIXIR Core Data Resource as well as a Global Core Biodata Resource. Since its initial release more than ten years ago, BacDive (https://bacdive.dsmz.de) has grown tremendously in content and functionalities, and is a comprehensive resource covering the phenotypic diversity of prokaryotes with data on taxonomy, morphology, physiology, cultivation, and more. The current release (2023.2) contains 2.6 million data points on 97 334 strains, reflecting an increase by 52% since the previous publication in 2021. This remarkable growth can largely be attributed to the integration of the world-wide largest collection of Analytical Profile Index (API) test results, which are now fully integrated into the database and searchable. A novel BacDive knowledge graph provides powerful search options through a SPARQL endpoint, including the possibility for federated searches across multiple data sources. The high-quality data provided by BacDive is increasingly being used for the training of artificial intelligence models and resulting genome-based predictions with high confidence are now used to fill content gaps in the database.

We present MediaDive (https://mediadive.dsmz.de), a comprehensive and expert-curated cultivation media database, which comprises recipes, instructions and molecular compositions of >3200 standardized cultivation media for >40 000 microbial strains from all domains of life. MediaDive is designed to enable broad range applications from every-day-use in research and diagnostic laboratories to knowledge-driven support of new media design and artificial intelligence-driven data mining. It offers a number of intuitive search functions and comparison tools, for example to identify media for related taxonomic groups and to integrate strain-specific modifications. Besides classical PDF archiving and printing, the state-of-the-art website allows paperless use of media recipes on mobile devices for convenient wet-lab use. In addition, data can be retrieved using a RESTful web service for large-scale data analyses. An internal editor interface ensures continuous extension and curation of media by cultivation experts from the Leibniz Institute DSMZ, which is interlinked with the growing microbial collections at DSMZ. External user engagement is covered by a dedicated media builder tool. The standardized and programmatically accessible data will foster new approaches for the design of cultivation media to target the vast majority of uncultured microorganisms.

BRENDA (https://www.brenda-enzymes.org/), the most comprehensive enzyme and ligand database for over nearly four decades, has seen major developments since 2021, further solidifying its role as an ELIXIR Core Data Resource and Global Core Biodata Resource in the life sciences. As part of the DSMZ Digital Diversity consortium (https://hub.dsmz.de) since 2023, BRENDA has introduced a prototype knowledge graph accessible via a public SPARQL endpoint, enabling semantic search, data integration, and improved reusability in line with FAIR principles. The database now hosts ∼5.8 million data points on 112 288 enzymes from 15 335 organisms and 173 164 references, alongside 278 840 ligands. Recent updates include the manual annotation of ∼16 000 references, the addition of 615 EC numbers—480 with newly curated data—and revisions to 4422 EC classes. The curated pathway collection has expanded to 195 metabolic maps, now supported by interactive pathway summary pages that organize key information into structured, user-friendly views. BRENDA now also incorporates DSMZCellDive data, visually linking enzyme classes to high-expressing human cell lines. A new gene-centric search interface allows streamlined access to enzyme-specific data using gene symbols or identifiers. These enhancements advance BRENDA’s interoperability and analytical capabilities, reinforcing its integration with major bioinformatics resources such as SILVA, BacDive, and LPSN.

Predicting prokaryotic phenotypes—observable traits that govern functionality, adaptability, and interactions—holds significant potential for fields such as biotechnology, environmental sciences, and evolutionary biology. In this study, we leverage machine learning to explore the relationship between prokaryotic genotypes and phenotypes. Utilizing the highly standardized datasets in the BacDive database, we model eight physiological properties based on protein family inventories, evaluate model performance using multiple metrics, and examine the biological implications of our predictions. The high confidence values achieved underscore the importance of data quality and quantity for reliably inferring bacterial phenotypes. Our approach generates 50,396 completely new datapoints for 15,938 strains, now openly available in the BacDive database, thereby enriching existing phenotypic resources and enabling further research. The open-source software we provide can be readily applied to other datasets, such as those from metagenomic studies, and to various applications, including assessing the potential of soil bacteria for bioremediation.

Human and animal cell lines serve as model systems in a wide range of life sciences such as cancer and infection research or drug screening. Reproducible data are highly dependent on authenticated, contaminant-free cell lines, no better delivered than by the official and certified biorepositories. Offering a web portal to high-throughput information on these model systems will facilitate working with and comparing to these references by data otherwise dispersed at different sources. We here provide DSMZCellDive to access a comprehensive data source on human and animal cell lines, freely available at celldive.dsmz.de. A wide variety of data sources are generated such as RNA-seq transcriptome data and STR (short tandem repeats) profiles. Several starting points ease entering the database via browsing, searching or visualising. This web tool is designed for further expansion on meta and high-throughput data to be generated in future. Explicated examples for the power of this novel tool include analysis of B-cell differentiation markers, homeo-oncogene expression, and measurement of genomic loss of heterozygosities by an enlarged STR panel of 17 loci. Sharing the data on cell lines by the biorepository itself will be of benefit to the scientific community since it (1) supports the selection of appropriate model cell lines, (2) ensures reliability, (3) avoids misleading data, (4) saves on additional experimentals, and (5) serves as reference for genomic and gene expression data.

Prokaryotic viruses represent the most diverse and abundant biological entities on Earth. So far, data on bacteriophages are not standardized, not readily available for comparative analyses and cannot be linked to the rapidly growing (meta)genomic data. We developed PhageDive (https://phagedive.dsmz.de), a comprehensive database for prokaryotic viruses gathering all existing data dispersed across multiple sources, like scientific publications, specialized databases or internal files of culture collections. PhageDive allows to link own research data to the existing information through an easy and central access, providing fields for various experimental data (host range, genomic data, etc.) and available metadata (e.g. geographical origin, isolation source). An important feature is the link between experimental data, the culture collection number and the repository of the corresponding physical bioresource. To date, PhageDive covers 1167 phages from three different world-renowned public collections (DSMZ, Félix d’Hérelle Reference Center for Bacterial Viruses and NCTC) and features an advanced search function using all data fields from the sections like taxonomy or morphology by controlled vocabulary and ontologies. PhageDive is fully interoperable with other resources including NCBI, the Viral Host Range database (VHRdb) of Institute Pasteur or the BacDive and MediaDive databases of DSMZ.