TOPIC : Data integration and data-driven in-silico models for enabling personalised medicine - a European standardization framework
|Publication date:||27 October 2017|
|Types of action:||CSA Coordination and support action|
|DeadlineModel: Opening date:||single-stage 07 November 2017||Deadline:||18 April 2018 17:00:00|
|Time Zone : (Brussels time)|
Topic DescriptionSpecific Challenge:
Big data relevant to personalised medicine encompasses many different, heterogeneous and complex data sets. The challenge is to harness and understand this abundance and diversity of data to produce medical benefits tailored to the individual or stratified patient groups.
To meet this challenge it is necessary that best practices are defined and widely adopted when using new technologies. For example, health research data production should be compliant with community-based quality standards, coupled with interoperable approaches for data integration and appropriate in-silico models to make sense of the data and produce results of medical relevance. Computational/in-silico models can be used to predict disease evolution, treatment response, and ultimately enable the personalisation of medical interventions
Standards, standard operating procedures or harmonisation strategies are part of the knowledge economy that facilitates innovation and the broader adoption of new technologies by European industry and by the regulatory authorities when approving new medicinal products and/or medical devices. Standards are key elements to facilitate competitiveness of European industry and the success of clinical research.Scope:
The proposal should establish a forum for in-silico methodologies applied in translational and clinical research, where different transnational initiatives should meet and debate on their standardisation strategies. The project should evaluate the data integration and data-driven in-silico models strategies and identify best practices for integrating and modelling heterogeneous human disease data transnationally. The project should focus on those heterogeneous types of human data which are best structured (addressing relevant ethical implications and sex and gender differences) and thus pose fewer technical challenges for transnational sharing of data. Such data could be in principle biological and clinical data and the models should comprise of several computational models e.g. systems biology, physiological modelling, network analysis etc.
The proposal should deliver recommendations for flexible/adaptable standardisation guidelines for European collaborative research for heterogeneous data integration and data-driven in-silico models with predictive capability to interpret the human disease data while respecting legal and ethical requirements for data protection. In addition to the research standards the project should also ensure that the standardisation guidelines delivered address the regulatory needs in terms of data-driven in-silico models. Such guidelines should be based on open access principles and on interoperable solutions to those standards existing in the industry and used by the regulatory authorities. Inclusion of regulatory authorities could lead to an increased impact of the research proposed, and this will be considered in the evaluation of the proposal.
The action should also aim to organise awareness workshops during which scientists and policy makers and regulatory authorities would debate on future developments of in-silico models in health research.
The proposal should adhere to the general concepts of the FAIR principles, establish links with relevant initiatives already supported by the European Commission and create a collaboration with the relevant ESFRI European infrastructures, IMI projects and the relevant standardisation initiatives e.g. European Metrology Programme for Innovation and Research.
For grants awarded under this topic for Coordination and Support Actions it is expected that results could contribute to European or international standards. Therefore, the respective option of Article 28.2 of the Model Grant Agreement will be applied.
The Commission considers that a proposal requesting a contribution from the EU of between EUR 1.5 and 2 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.Expected Impact:
- Harmonisation of health disease data integration and data-driven in-silico models in Europe.
- Accelerate the use of academic research data in clinical research and the broader adaptation by research, regulatory authorities and industry community.
- Contribution to the sustainability of health research by using the power of health data.
- Growth of the European data-driven economy.
Topic conditions and documents
1. Eligible countries: described in Annex A of the Work Programme.
A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon 2020 projects. See the information in the Online Manual.
Proposal page limits and layout: please refer to Part B of the proposal template in the submission system below.
- Evaluation criteria, scoring and thresholds are described in Annex H of the Work Programme.
- Submission and evaluation processes are described in the Online Manual.
The thresholds for each criterion in a single stage process will be 4 (Excellence), 4 (Impact) and 3 (Implementation). The cumulative threshold will be 12.
4. Indicative time for evaluation and grant agreements:
Information on the outcome of evaluation (single-stage call): maximum 5 months from the deadline for submission.
Signature of grant agreements: maximum 8 months from the deadline for submission.
5. Proposal templates, evaluation forms and model grant agreements (MGA):
Coordination and Support Action:
6. Additional provisions:
Members of consortium are required to conclude a consortium agreement, in principle prior to the signature of the grant agreement.
7. Open access must be granted to all scientific publications resulting from Horizon 2020 actions.
Where relevant, proposals should also provide information on how the participants will manage the research data generated and/or collected during the project, such as details on what types of data the project will generate, whether and how this data will be exploited or made accessible for verification and re-use, and how it will be curated and preserved.
Open access to research data
The Open Research Data Pilot has been extended to cover all Horizon 2020 topics for which the submission is opened on 26 July 2016 or later. Projects funded under this topic will therefore by default provide open access to the research data they generate, except if they decide to opt-out under the conditions described in Annex L of the Work Programme. Projects can opt-out at any stage, that is both before and after the grant signature.
Note that the evaluation phase proposals will not be evaluated more favourably because they plan to open or share their data, and will not be penalised for opting out.
Open research data sharing applies to the data needed to validate the results presented in scientific publications. Additionally, projects can choose to make other data available open access and need to describe their approach in a Data Management Plan.
Projects need to create a Data Management Plan (DMP), except if they opt-out of making their research data open access. A first version of the DMP must be provided as an early deliverable within six months of the project and should be updated during the project as appropriate. The Commission already provides guidance documents, including a template for DMPs. See the Online Manual.
Eligibility of costs: costs related to data management and data sharing are eligible for reimbursement during the project duration.
The legal requirements for projects participating in this pilot are in the article 29.3 of the Model Grant Agreement.
8. Additional documents:
No submission system is open for this topic.
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