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https://novonordiskfonden.dk/en/grants/data-science-collaborative-research-programme-2021/

Data Science Collaborative Research Programme 2021

Data Science Collaborative Research Programme 2021

Area
Biomedicine and health sciences
Biotechnology
And 2 more
Place of research
Denmark
Amount
Up to DKK 25 million per grant
Recurring call
December 2021
Call opens
December
22
2020
Deadline
March
16
2021, 2pm (CET)
How to apply Apply
Call opens
22 Dec 2020
Deadline
16 Mar 2021, 2pm (CET)
How to apply
Amount
Up to DKK 25 million per grant
Announcement of results:
End of December 2021
Apply

Purpose 

The Data Science Collaborative Research Programme aims to support synergistic research collaborations rooted in data science which:  

  • lead to new or improved core data science algorithms, methods and technologies.
    and/or
  • explore and expand data science applications to real-world scientific problems within the scope of the NNF Data Science Initiative (see Areas of Support)

The ideal projects will bring together data scientists and “domain-experts” in other scientific fields (like medicine, biology, plant science, biotechnology, physics, chemistry, etc.) or involve collaborations between different data science research groups in a synergistic effort to solve important scientific problems. 

Part of the NNF Data Science Initiative 

The Data Science Collaborative Research Programme is one of 4 pillars of the NNF Data Science Initiative, through which the Foundation aims to strengthen the Danish academic research environment within data science and artificial intelligence, as well as support the education and training of the next generation of data scientists.

Other calls in open competition include the Data Science Research Infrastructure Programme and the Data Science Investigator Programme. In addition, an academy for data science, with the purpose of establishing a network and distributing fellowships, is under design.

Areas of support

The collaborative research proposals must be linked to ongoing research and be within the scope of the NNF Data Science Initiative: 

  1. Development of new algorithms, methods and technologies within data science, artificial intelligence (incl. machine learning and deep learning), data engineering, data mining, statistics, applied math, computer science, big data analytics, etc. 
  2. Applications of data science (as defined above) within the Foundation’s core scientific areas: Biomedical and health science, life science and industrial applications promoting sustainability, as well as natural and technical sciences with potential application in biotechnology or biomedicine.

For projects mainly concerned with data science methods development, it is important that the applicants clearly show the relevance for potential future application and impact within life science, health science, or biotechnology. Vice versa, projects which have their primary focus on application of data science methods must describe and explain the novelty and impact of their data science approach, be it development of novel methods or novel applications of existing methods.

Eligibility 

The Data Science Collaborative Research Programme supports excellent scientists from different research departments – that of the main applicant and up to three others – that will collaborate in a dynamic structure, exploiting their mutual synergy to solve a specific, shared research question which is within the scope of the NNF Data Science Initiative. The number of co-applicants can be up to 5.

It is critical that all collaborators contribute significantly to advancing the science in the proposal. The collaborations can be across institutions and national borders but have to involve at least one Danish-based data science group. The main applicant need not be conducting data science research, but the project is expected to have a data science-driven focus. 

The state-of-the-art research programme must also lead to data science teaching programmes and/or training initiatives at Danish universities. Therefore, at least one of the collaborators (main applicant plus co-applicants) must have a current teaching portfolio at a Danish university that consists of undergraduate, graduate and/or training courses in data science-related topics 

Funding 

Each Data Science Collaborative Research grant can receive up to DKK 25 million over a minimum of 4 and up to 5 years, and the total allowed budget for the consortium is calculated based on the following:

  • Up to 4 different departments (including that of the main applicant) can receive funding.
  • Each department that participates with a single independent applicant increases the consortium total budget by up to DKK 1.0 million per project year.
  • Each department that participates with multiple independent applicants increases the consortium total budget by up to DKK 1.5 million per project year.
  • Up to 6 applicants (main applicant and 5 co-applicants) can be involved in the collaboration.

It is allowed to redistribute the funding of the total budget between participants and over the project period.

The total 2021 grant budget is up to DKK 60 million.

Application to other NNF programmes

It is possible for researchers to apply (as either a main or co-applicant) for each of the different data science calls under the NNF Data Science Initiative, but: 

  • The applicant must indicate which other submitted proposals includes her/him as a main or co-applicant  
  • The applications should not be contingent on each other 
  • The same research proposal cannot be submitted to different calls and any overlap in the project descriptions should be indicated clearly 

 In addition, dual submission for the NNF Interdisciplinary Synergy Programme is not allowed.  

Application process 

The application must be completed and submitted using the foundation’s application system NORMA that can be accessed via the link “Apply now”. 

Please read “Information and Guidelines for Applicants” carefully before initiating the application process. The Guidelines provide essential information on the call and the application content and process.