Our concept will be to strategically align data providers, users, tool developers and relevant infrastructure platforms at major German Research sites, which share our common research goals. Moreover, the network-focussed approach implies that all German Research Institutes in any region can be associated with our activities, which will serve to maximise the impact of DBD across the scientific community. Users will be able to address their scientific questions making use of a larger data repository than previously, and, within the network have access to a defined collection of machine-learning and artificial intelligence-based tools and workflows. This will allow users to generate, test and validate general prediction models and/or processes in their specific data domain. The higher aggregation levels achievable in this way will pave the way to more precise models and enhance our capabilities to probe and understand the fundamental determinants of cell and tissue function and the deviations associated with (pre-) disease states.
Together with identified interested German stakeholders and in cooperation with other NFDI projects, the consortium will identify the technologies which best can be adapted and adopted by single institutional users to allow single researchers to:
- be attracted to share (un)published data without losing their provenance and acknowledgement
- retain data on owner servers whilst data is also made accessible through maintained and sustainable web services or documented application programming interface (API) for data retrieval
- have access to common ontologies, standards and quality controls for their data (e.g. detailed metadata and resource description language (RDF)
- use a central access process to access enabling the user to search and interoperate/integrate their workflows to expanded datasets which can be subsequently aggregated with complementary resources
- have access to the full spectrum of qualified target biology and pre-clinical research data from target genetics, medicinal chemistry, antibody design, bioinformatics workflows, “omics” databases, cellular phenotype data resources, to in-vivo efficacy, toxicological and ADME studies
- stimulate drug discovery by improving knowledge about biological mechanisms which will allow for development of novel therapeutic options
- predict toxic effects due to interfering with biological system and/or with ecosystems and identify targets for therapy or avoidance of toxicities
- drive translation potential by means of future integration and alignment with data infrastructures dealing with clinical data, a feature which will become increasingly in focus as GRDP-compliant data are routinely deployed in the future
- share/annotate data to go beyond uploading and subsequently forgetting about data and eventually allow researchers to integrate their data/tools with any other data/tools in the resource.