Together we can maximize the value of translational research data.
The datasets that are generated today are complex, and involve many different types of data. The consequence is that the integration of multiple types of expertise is necessary.
Join us at the Bioinformatics and Translational Research and learn how to get to the analysis and interpretation of your data faster.Click here for details
See the event report here
The eTRIKS/ELIXIR-LU - IMI Data Catalogue
The eTRIKS/ELIXIR-LU—IMI Data Catalogue is unique in its kind because there is no such repository elsewhere that centralizes ongoing and past IMI project level metadata. It is part of the service that eTRIKS provides in its key knowledge management performance to bring power to the FAIR data concept starting with a focus on the findability of research study descriptions.Translational research scientists want to know what project study descriptions are available for disease areas and access the existing knowledge landscape. eTRIKS/ELIXIR-LU Data Catalogue is a metadata repository linking the massive data available in a global system that can be optimally leveraged to improve biomedical research. This will create value for public and private organisations/ translational researchers and drive research collaboration formation towards convergence and precision medicine. This is a collaborative project between eTRIKS and ELIXIR-Luxemburg Node.
Want to contribute to the discussion about the sharing and re-use of medical research data?
We are currently organizing discussion game sessions online and face to face.
Let us know you are interested
eTRIKS is supporting 27 projects
and we want to support 40 by the end of 2017
We have trained over 300 researchers
Find out more about training opportunities
We make data curation faster and data exploration easier
Help us create new tools in eTRIKS Labs
"eTRIKS provides the gel, without which we would be floating in excess pools of data."
Ian Adcock - U-BIOPRED Work Package 6 leader
eTRIKS Standards Starter Pack is in use broadly throughout the IMI
Get the eTRIKS starter pack here
New eTRIKS LAB established -
Create patient cohorts by setting constraints on components in high dimensional data sets.
Show your commitment to responsible management of medical research data
Portfolio of Resources
Unbiased comparison of data sets The Challenge Severe Asthma is often difficult to manage and many patients are unresponsive to treatment. Furthermore, it is thought that there are many different phenotypes of asthma that are not properly understood. U-BIOPRED aims to create ‘handprints’ that identify sub-phenotypes of asthma. The handprints can then be used to better understand the disease and lead to better targeted treatments for the individual. U-BIOPRED handprints include a wealth of diverse data on each patient and the researchers need to compare and combine them in an unbiased environment. This will permit identification of a wide range […]
- September 1, 2015
- View website
Analysis pipelines for very rich data sets The Challenge OncoTrack is looking at deep data sets for cancer patients to discover new markers for Colon Cancer. The goal of OncoTrack is to identify and characterize biomarkers that will help our understanding of the variable make-up of tumours and how this affects the way patients respond to treatment. This can be used to guide appropriate therapy choices for each individual patient. The data sets created are extremely rich containing clinical data, animal xenograft data and a wide range of genomic information including GWAS and NGS. Already hundreds of terabytes of data […]
- September 3, 2015
- View website
Comparing clinical data across multiple disease areas The Challenge Biopharmaceuticals provide a valuable new approach to disease management. But a significant limitation in their use is the development of anti-drug antibodies (ADA) in some patients. The development of ADA is seen to some extent across all diseases and with all treatments, but the individual data sets for any single disease and treatment have usually been small. ABIRISK seeks to bring these data sets together to understand the common underlying causes of ADA. To do this, disparate data sets from multiple disease areas and many different institutions need to be analysed […]
- September 4, 2015
- View website