BioMedIT hosts a full repository of documentation on the BioMedIT wiki space (confluence). Access is restricted to users.

BioMedIT nodes also provide standard data management solutions on-demand basis for the research projects based on OpenBis, LabKey, SLIMS etc. Please contact BioMedIT or your assigned BioMedIT node for more details. 

BioMedIT secure HPC service enables researchers to connect secure (HPC) clusters in the BioMedIT nodes directly from their home organizations to execute compute or memory intensive workflows leveraging the large CPU, memory and GPU resources of the nodes. BioMedIT secure HPC service is setup in an access-controlled, private environment offering network isolation, data isolation and computational resources isolation ensuring high standards of data security. Please E-mail BioMedIT or your assigned BioMedIT node for more details on this service. 

BioMedIT secure remote desktop service enables researchers to connect to a remote server in the BioMedIT nodes from their home organisation using a web browser. Please E-mail BioMedIT or your assigned BioMedIT node for more details.

The BioMedIT Code Repository Service (based on Git), is accessible to registered BioMedIT users to create, collaborate and share their application codes with other BioMedIT users.

Confluence is a collaboration space and repository for the working- and implementation groups. To use confluence, users need a SWITCH edu-ID or valid aii credentials from a Swiss university or hospital.

Provides a secure, easy-to-use registry for hosting, vulnerabilities scanning and sharing of OCI compatible container images. E-mail BioMedIT to request access.

The SPHN Data Coordination Centre has produced a set of templates for legal agreements for data transfers and cross institutional collaboration within the scope of BioMedIT.

The single point of entry into the BioMedIT environment allowing access to central services, node services, user management for collaborative projects and data transfer request management.

sett is BioMedIT's bespoke secure encyption and transfer tool. This tool allows users to package and move their data around the BioMedIT network in a safe and legal manner.

The SPHN Dataset contains the main concept definitions for clinical and genomic data used in SPHN.

The SPHN RDF Schema enables the semantic encoding of clinical routine data in a FAIR (findable, accessible, interoperable, and reusable) format, using other existing standard ontologies. 

SPHN provides a set of tools to enable schema visualization, quality control, automatic SPARQL query generation, etc. Setup and installation of these tools are a time consuming process, which is why we developed an easy-to-use web service enabling users to get all files from a single application. This web service includes the following tools: Dataset2RDF, SHACLer, SPHN Schema Visualization Tool, SPARQLer.

The Dataset2RDF Tool generates an SPHN compliant RDF Schema based on a Dataset input in Excel format.

SPHN provides two templates: the Dataset Template and the RDF Schema Template. The Dataset Template can be used to design your project specific concepts in Excel; this file can be used as input for the SPHN Schema Forge to get your Project RDF Schema. Alternatively, the RDF Schema Template helps projects to develop upon it their project-specific RDF schema in tools like Protégé. This template imports the SPHN RDF schema and other required RDF libraries as well as pre-filled basic metadata annotations.

The SPHN Schema Visualization Tool generates a human-readable HTML document describing the project's RDF schema directly from the schema. It is based on pyLODE and covers detailed information about the classes, object, datatype and annotation properties and the named individuals.

The SPHN data quality control files contain a set of SHACL rules and statistical SPARQL queries to validate the compliance of the RDF data produced.

The Java-based Quality Check tool facilitates the validation process of SPHN RDF data at the data provider level. Based on the SHACL file generated by the SHACLer and statistical queries in SPARQL, it generates a human-friendly report with information about data conformance to the schema and some basic statistics.

The SHACLer is a Python tool that extracts SHACL rules from an SPHN-compliant input schema for facilitating data validation. The SHACL rules are generated to check the following constraints: validity of classes and properties, cardinality constraints, restriction (sequence paths), literal type constraints and restricting on individuals/instances.

The DCC Terminology Service provides SPHN compatible, machine-readable versions of national (CHOP or ICD-10 GM) and international (SNOMED CT, LOINC, ATC, SO, GENO, HGNC, UCUM) terminologies and classifications in RDF format. They are available directly in the individual project spaces on the BioMedIT nodes or for download through the BioMedIT Portal (via SWITCH edu-ID). E-mail the DCC for more details.

Triple store and knowledge discovery tools enabling the visualization, exploration, knowledge interference and querying of specific data in RDF on a local instance. 

These tools are available on request in BioMedIT project spaces. E-mail the DCC for more details.

SPARQL is the semantic web standard query language used exploring the content of a data set. A set of ‘helper’ queries are generated in SPHN for better understand and deciphering the content of SPHN-related data sets.

The SPARQLer is a program that extracts SPARQL queries for each concept from an SPHN compliant input RDF Schema. Included are queries for flattening SPHN concepts and some basic statistical queries (e.g. counting instances per concept and predicates, minimum and maximum values/dates per predicate and a list and count of all used codes). The tool is integrated into the SPHN Schema Visualization Tool.

The SPHN Connector is a tool developed to facilitate the creation of valid SPHN-compliant graph data based on relational, semantically described data from data-providing institutions.The connector creates an input interface based on an RDF Schema conforming to the SPHN Framework. The ingested data is converted into RDF, validated to check its conformity with the schema, and de-identified. E-mail the DCC for more details.

Funding agencies commonly require researchers to submit a data management plan (DMP) when writing research proposals. The SPHN DMP guidelines offer general recommendations for the use of sensitive biomedical data to help researchers to plan the entire life cycle of their data, with particular focus on SPHN-funded projects using health-related data for research purposes, and data processing on the BioMedIT infrastructure.

The Swiss Cohort consortia is a network of Swiss Cohorts available on the internationally renowned Maelstrom metadata catalogue.

The SPHN Federated Query System allows researchers to assess, whether and where patients or patient data potentially suitable for a specific research question exists at Swiss University Hospitals.