This is the summary description of this new research infrastructure, submitted as part of the SERI Research Infrastructure Roadmap 2023.

Switzerland is at the forefront of the data revolution in the life sciences. To support this transformation, Swiss universities and research institutes have massively invested in local data-generating and data-processing platforms and embraced open research data principles. However, effective data sharing and reuse necessitate commonly adopted quality and operational standards and close collaboration between domain experts and data scientists.

SwissBioData ecosystem (SBDe) is a decentralised infrastructure that addresses these challenges and boosts Switzerland’s capacity to convert research data into knowledge and innovation, with the aims of (a) increasing the quality, standardisation, and efficiency across the data value chain - from data production to knowledge generation - through platform federation; (b) providing state-of-the-art support to the Swiss research community to make their data, methods, software tools, and workflow FAIR (findable, accessible, interoperable, reusable); and (c) establishing new resources that will reinforce Switzerland's international competitiveness and standing in data infrastructure for life sciences. Through better coordination across the national and international data science ecosystem, SBDe strives to avoid duplicating efforts and instead utilise existing infrastructure and initiatives.

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2022 01 11 SwissBioData map v2 web 

SBDe is structured in four main pillars:

  1. Production: SBDe will harmonise best practices for data acquisition, implementation of quality control strategies, analysis and constant updating of the data format and metadata landscape across 44 platforms, core facilities and research groups at 16 Swiss institutions.
  2. Analysis: SBDe supports FAIR data processing, analysis and predictive modelling by helping researchers to build, adapt and deploy software tools and workflows on distributed computing infrastructure, providing expert multi-level support to aid researchers in all steps of their data processing and analysis, promoting software components reuse through maintenance, documentation, and training, and supporting standardisation, deployment and sharing of trained machine learning models.
  3. Integration: SBDe experts help Swiss researchers to structure, describe, and share data in a way that maximises FAIR prospects, and to automate data query and integration procedures. The SwissBioData Knowledge Graph (SBD-KG) is a new resource to the Swiss and worldwide communities that connects related datasets currently scattered across different repositories and include both metadata and ontologies to establish semantic interoperability and allow the identification and combination of complex datasets.
  4. Cloud services: SBDe services and resources rely on an interoperable technology layer. Using virtualisation technologies, SBDe supports federated data processing, combining local infrastructure with computational resources available on external clouds. Every researcher has access to a FAIR space consisting of data, workflows, methods, software tools, and machine learning models.

Across pillars, SBDe resources and services are supported by a rich training programme aimed at core facilities and end-users, and by driver projects with open calls to strengthen the data acquisition, modelling, dissemination, and/or analysis aspects of existing projects - thus also reinforcing the link between SBDe and the Swiss research community.

Incentives for data sharing within the infrastructure, critical for the project’s success, will build on national and international experience on the measurement of the impact of databases and will include appropriate attribution mechanisms in case of reuse of datasets and the provision of usage statistics.

The SIB Swiss Institute of Bioinformatics will coordinate SBDe, building upon decades of expertise in developing widely used bioinformatics databases and resources, in particular state-of-the-art tools and workflows for biology, in decentralised data modelling and dissemination, and training. SIB’s contribution also builds upon the recent experience of developing the Swiss-wide BioMedIT/SPHN infrastructure for biomedical data, both on the technical and organisational levels.

By federating data production platforms, data reservoirs and computing resources, SBDe builds synergies that will propel Swiss life sciences to the next level, pulling Swiss bioindustries—including the pharmaceutical sector—along. Internationally, SBDe encourages collaborations and synergies and acts as a “magnifying lens” to highlight Swiss contributions, and foster new ones by raising the standing of its partners and users.