Preliminary data and collaborations: Working towards submitting a grant

1. Preliminary data and collaborations: Working towards submitting a grant David Finn, PhD Lecturer in Pharmacology, SFI Principal Investigator Co-Director of the Centre for Pain Research David.Finn@Nuigalway.ie Tel: +353 (0)91 495280 http://www.nuigalway.ie/pharmacology/Dr_David_Finn.html

2. Talk overview • Grant applications: Ingredients for success? • Preliminary (pilot) data: • Why? • Where? • How much? • When? • How to maximise impact? • Collaborations: • Why? • Who? How to choose? • How to initiate contact? • Roles and expectations • Features of successful collaboration • Collaboration with Industry • Summary

3. Grant applications: Ingredients for success I • A great idea • Novel approach to a big/important question or problem • High probability of success • High risk vs high gain? • Excellence • Originality • Logical • Well-designed experiments • State-of-the-art approach employed? • Likely to yield strong, important conclusions? • Potential for high impact?

4. Grant applications: Ingredients for success II • Impact • Potential for publication in the best journals? • Make a positive difference to: • Health • Society • Economy • Human resource dimension – PhD/Postdoc training etc • Commercial impact • Collaboration with Industry, facilitate progression to market, IP, spin-out companies • Strong track record of applicant (or applicant team) • Publication record in the area (quantity and quality) • Citations • Invited conference presentations • Positions held and other measures of esteem • Supervisory record • Record in attracting grant funding • Institutional support and thematic alignment

5. Prelim data Collaborators Gain Risk Preliminary data and collaborators • Two ways in which you can mitigate risk and deal with potential shortcomings in your own expertise

6. Preliminary data – Why? • Demonstrate to reviewers that there is substance to your idea • May play a key role in shaping your hypothesis • Proof of principle • Data that suggest a high probability of success for the main project • Feasibility • Demonstrate your technical capability • Feasibility

7. Preliminary data – Where? • Usually in one or more of the following sections: • Background • Current state-of-the-art • Work leading up to the Project • Possibly also in the “Methodology/Approach” section if wish to demonstrate capability in a specific technique • But keep page counts in mind! • Appendices?

8. Preliminary data – How much? • Enough to convince reviewers that your project has substance and a high chance of success • May depend on the scheme you are applying for and the size, scale and ambition of the proposed project • Too much better than too little

9. Preliminary data – How to maximise impact? • Use strong data that support your hypothesis • Prepare very nice figures, with accurate and informative figure legends • Number the figures and refer to them in the text • If possible, position them close to the relevant text • Generally avoid using colour unless essential • A schematic that pulls the main themes, mechanisms, aims together

10. Preliminary data – When? • Allow sufficient time for generation • Identify the grant submission deadline and timetable generation of preliminary data from there • Often you might be drawing on data recently generated • Also possible to use published data to support the case for the project • Collaborators might be another source of data

11. The Grant Writing Process Preliminary data Preliminary data http://writingcenter.unc.edu/handouts/grant-proposals-or-give-me-the-money/

12. Collaboration

13. Collaboration – Why? • Cutting-edge 21st Century science is often a team effort • Can’t do everything yourself (and not expected to!) • Many papers published in the World’s best journals have multiple authors from multiple institutions and countries • Bring in essential expertise that you don’t have • Technical • Intellectual • Better solutions/experiments/ideas/papers usually emerge • Support and advice when things aren’t going according to plan • Co-supervision of students (and/or assist with training)

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15. Reviewers’ comments

16. Collaboration – Who? • Choose/invite the best person (or organisation) for the job • Proven track record in area • Currently active • People who you know you could work well with • Previously worked with? • Previously met? • Recommended by someone you trust? • Whose work you respect • People you can trust • To get the job done on time • To maintain confidentiality • To collaborate rather than compete • People who will bring fresh ideas and new ways of thinking

17. D Finn Collaborations • NUI, Galway • Dr Michelle Roche (Physiology) • Dr John Kelly (Pharmacology) • Dr Eilís Dowd (Pharmacology) • Prof Larry Egan (Pharmacology) • Prof Peter Dockery (Anatomy) • Prof Abhay Pandit (Biomed Eng) • Dr Brian McGuire (Psychology) • University of Nottingham • Prof Victoria Chapman • Dr Steve Alexander • Prof David Kendall • University of Nice • Prof Rene Garcia • Ms OphelieNachon • Univ College London • - Prof Stephen Hunt • University College Cork • Prof George Shorten (Anaesthesia) • University of Granada • Dr José Manuel Baeyens • Dublin City University • Dr Nick Gathergood • Prof Oliver Dolly • University of Indiana • Prof Ken Mackey • Trinity College Dublin • Prof Marina Lynch • University of Bristol • Dr David Jessop • University of Madrid • Dr Maria-Paz Viveros

18. Collaboration – How to initiate contact? • Look carefully at grant eligibility criteria • Collaborator vs Co-applicant/Co-PI • Best if you can meet at least once in person (especially if never met before) • Conferences, phone, Skype/video-con, email • Invite them to be a collaborator • Basic outline of idea and their role to begin with • Further detail later if they show interest • What are you asking of them and what can they expect in return? (see next slide) • Make contact as early as possible • If they sign up, keep them in the loop at all stages thereafter • Letter of support required? • Write it for them and send it as a draft, asking them to review and change as they see fit • Signatures in good time

19. Collaboration – Roles and Expectations • Be crystal clear on these • In writing in an e-mail • Probably also in the letter of support and proposal itself • What exactly is collaborator required to do and when? • Will they (can they?) receive funding? • Agree budget up front where possible • Contract research vs collaboration • Co-authorship on publications • Ownership of IP? • Student/researcher visits?

20. Collaborations with Industry • Can be very rewarding • Translation • Commercialisation • Relevance • Milestone driven, deliverables, deadlines • Often short duration projects • Company priorities and focus can change quickly • IP Agreements and Contracts essential

21. Features of Successful Collaboration • A positive and productive partnership • Joint publications • Joint funding • Shared supervision/training of students • Joint IP • Mutual trust and respect • Mutually beneficial • Regular contact, discussions, meetings • Each party delivers on their commitments in a timely manner • Opens up new ideas and projects

22. Summary • Preliminary data and collaborators are very important factors for most grant proposals • Preliminary data gives substance to your ideas and helps convince reviewers • Collaborators bring additional expertise • Collaboration can be a lot of fun and very rewarding!