INCF is involved in a number of collaborative projects together with organizations such as the CENTER-TBI consortium, the Human Brain Project, the Kavli Foundation, General Electric, Janelia Farm, and Allen Institute for Brain Science.
Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) aims to better characterize TBI and identify the most effective clinical interventions, link patient profiles with effectiveness of treatments (toward “precision medicine”), and improve outcomes via comparative-effectiveness studies. The project has collected an unprecedented scale of data from 65 participating centers across 20 European countries, with data about 5400 patients being collected in the core study and 22760 in the Registry. INCF's role in the project has been to develop a data/informatics platform, internationalization of the Common Data Elements, implement appropriate standards developed by the INCF network, and facilitation of novel analytical methods.
HBP Medical Informatics Platform
In order to maximise European investments in both CENTER-TBI and Human Brain Project (HBP), and provide an enriched research environment for both communities of system users, INCF aim to align the CENTER-TBI data platform with the HBP Medical Informatics Platform (MIP). Researchers would use the analysis platform provided by MIP in conjunction with the data access platform to analyse subsets of data and even share analysis methods within the platform. In the process, INCF will help in standardising the Common Data Elements and other standards using the CENTER-TBI - MIP integration as a use case. Another European TBI study, Collaborative REsearch on ACute Traumatic brain Injury in intensiVe care medicine in Europe (CREACTIVE), is currently sharing data in the MIP. As part of this work, federated analysis of multiple studies (i.e. combining data from CENTER-TBI and CREATIVE to answer a research question) will be technically explored.
A joint development between HBP, INCF, and NIF. KnowledgeSpace aims to be a globally-used, community-based, data-driven encyclopedia for neuroscience that links brain research concepts to data, models, and the literature that support them. KnowledgeSpace is a framework that links HBP data and data from some of the world’s leading neuroscience repositories to general descriptions of neuroscience concepts found in wikipedia with more detailed content from NeuroLex. It then integrates the content from those two sources with the latest neuroscience citations found in PubMed ().
International Brain Initiative
The International Brain Initiative is a consortium aiming to coordinate between the large international brain initiatives with the purpose of maximizing reproducibility and minimizing duplication of effort. The current members of the consortium include the U.S. BRAIN Initiative, the E.U. Human Brain Project, the Korea Brain Project, the Japan Brain/MINDS Project, Israel Brain Technologies, and the Australian Brain Alliance. The Consortium is coordinated by the Kavli Foundation, assisted by INCF, IBRO, and the Australian Brain Alliance.
TrainingSpace (TS) is an online hub that aims to make neuroscience educational materials more accessible to the global neuroscience community developed in collaboration with INCF, HBP, SfN, FENS, IBRO, IEEE Brain, BD2K, OHBM, CONP, and iNeuro Initiative. As a hub, TS provides users with access to:
- Multimedia educational content from courses, conference lectures, and laboratory exercises from some of the world’s leading neuroscience institutes and societies
- Study tracks to facilitate self-guided study
- Tutorials on tools and open science resources for neuroscience research
- A Q&A forum, NeuroStars
- A neuroscience encyclopedia that provides users with access to over 1.000.000 publicly available datasets as well as links to literature references and scientific abstracts, KnowledgeSpace
Topics currently included in TS include: general neuroscience, clinical neuroscience, computational neuroscience, neuroinformatics, computer science, and data science.