Understand sensitive data and the challenges of using it in biomedical research
Services to support research using sensitive health related data
A secure IT environment for the responsible handling of health data as a service
Start here to find out all you need to know before setting up your BioMedIT user account
Find out about the training available to help you use BioMedIT
Meet the people behind the BioMedIT Network, and find out what we're trying to achieve
BioMedIT and the SPHN Data Coordination Center (DCC) offer a broad portfolio of training covering the use of BioMedIT tools and services, the responsible use of health-related data for research, and FAIR data
What does BioMedIT offer and how can it support your research endeavor?
Within the Swiss Personalized Health Network (SPHN) and related national initiatives researchers use patient data (i.e., confidential human data) in their research projects. Dealing with confidential human data requires awareness of data privacy, respective laws and information security. These courses explain the legal and regulatory context or personalised health research and what should be done in practice to protect the patients’ privacy when performing biomedical research on human data.
Completing the courses is mandatory for users of BioMedIT, and taking this course is highly recommended for all users of SPHN infrastructures.
Covering sett, images, large amounts of data, key management.
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Developed by the Health Ethics and Policy Lab at the ETH Zurich and based on the SPHN Ethical Framework for Responsible Data Processing in Personalized Health Research", this course will allow you to familiarize yourself with the ethical framework along with its key points about responsible usage of health data for research purposes.
Please note a SWITCH edu-ID is required to access this course.
Detailed advice how to set up legal agreements when using health-related data in multi-center projects in Switzerland.
This training covers the different types of agreements available, and what other legal aspects you need to consider when preparing your multi-center research project.
For support, contact the SPHN Data Coordination Center.
The SPHN Data Coordination Center (DCC) operated by the Personalized Health Informatics Group is responsible for the coordination and for the technical implementation of key milestones of the SPHN initiative, as well as for promoting harmonization efforts and process innovation throughout the Swiss health-data ecosystem.
The FAIR principles have been developed to enable a better data management and stewardship in research by Wilkinson et. al. in 2016. They consist of a list of necessary criteria for making data Findable, Accessible, Interoperable and Reusable. However, the understanding of these principles and their concrete implementation can sometimes be abstract and difficult.
In this training we offer a detailed example of the implementation of FAIR principles, demonstrating how the different principles and criteria have been applied to the various components of the SPHN framework.
Semantic standards gain importance when it comes to FAIR data sharing for research in Personalized Medicine and beyond. The training offers an insight into the most important clinical semantic standards that are relevant in Switzerland or internationally. Advantages, disadvantages and differences between the standards are highlighted and illustrated with practical examples.
This training will allow you to distinguish between classifications and ontologies and know the basics of CHOP, ATC, ICD-10, SNOMED CT and LOINC, and their area of application.
The SPHN Dataset contains all SPHN concepts, their general and contextualized descriptions, value sets and information about the connected semantic standards. The training explains the principles applied in the development of the concepts and illustrates the individual steps with a practical example.
This training will enable users to develop new concepts for the SPHN Dataset or for their project Dataset, following the guiding principles of concept design.
The SPHN RDF Schema, or ontology, enables the semantic encoding of clinical routine data in a FAIR (findable, accessible, interoperable, and reusable) format, using other existing standard ontologies.
This training provides an introduction to RDF in the context of the Swiss Personalized Health Network.
SPHN projects can extend the SPHN Dataset and its related RDF schema to fit the needs of the project.
This training highlights the different steps to undertake for extending the SPHN RDF schema with an example concept (Fluid Balance), using Protégé, a desktop-tool for editing ontologies.
Data exploration and visualization is often the first step of data analysis.
This training will enable researchers to learn how to explore and visualize their RDF graph data with GraphDB.
SPARQL is the official W3C recommended RDF query language for RDF data.
This training will enable researchers to learn how to query RDF data with GraphDB. It will highlight how the inference capabilities provided in RDF can be used with the knowledge contained in external terminologies like SNOMED CT and LOINC.
The programming languages R and Python can be used to analyze RDF graph data.
This training provides the basic tasks for consuming RDF data with R and Python: e.g. connecting to a SPARQL endpoint and run SPARQL queries against this endpoint.
SHACL is a W3C standard to validate RDF graphs against a set of conditions.
This training will enable researchers to learn how to validate an RDF data graph against a set of constraints expressed in SHACL in GraphDB, interpret a SHACL validation report, understand the possibilities and limitations of validation with SHACL and, use and understand the SHACLer tool.
The SPHN Federated Query System (FQS) allows queries on anonymized and nationally harmonized data coded in national or international terminologies. Researchers can assess whether and where patients or patient data potentially suitable for a specific research question exist at one or several of the Swiss University Hospitals.
This training will enable users to learn how to use the FQS, create queries and interpret the results.
The Maelstrom catalogue relies upon a powerful cataloguing toolkit to improve the findability and usability of cohort data. It is already serving the metadata dissemination needs of cohorts/studies from across the world, grouped into several international networks, and provides a user-friendly, web-based solution for data discovery.
This video is a demonstration of how to use the Maelstrom Metadata Catalogue.
