The Partnership

The University of Reading and The Natural History Museum, London, home to a vast range of life and earth science specimens, have entered into an exciting long-term research collaboration.
Inspired by the planned move of 28 millions specimens from the Museum’s collections to an ambitious new science and digitisation centre on the Thames Valley Science Park (TVSP), the partnership provides a framework for joint initiatives which align with the specialisms and aims of both institutions.

This is a collaboration agreement at institutional level, which goes beyond the development at TVSP to form a strategic partnership and covers areas such as:

  • development of joint education and training provision, research proposals and postgraduate opportunities
  • mutual access to and potential development of research infrastructure
  • delivery of joint events to highlight research synergies
  • exploration of potential innovations to improve professional and public engagement with each institution’s Collections
  • collaboration to deliver benefit to the communities around TVSP and in the wider local area

Governance

Joint Governance Board membership

University of Reading:
Dominik Zaum, Pro-Vice Chancellor (Academic Planning and Resource)
Tom Oliver, Research Dean (Environment)
John Gibbs, Research Dean (Heritage and Creativity)
Charlotte Johnson, Senior Research Development Manager
Beth Steiner, Research Facilitation Manager

Natural History Museum:
Tim Littlewood, Director of Science
Ken Norris, Deputy Director of Science (Research)
Caroline Smith, Head of Collections
Kathryn Packer, Programme Director (NHM Unlocked)
Paola Ricciardi, Research Themes and Partnerships Manager

Priority Areas

The partnership aims to make progress aligned with the specialisms and goals of both organisations, against six priority areas:

Research expertise for global environmental challenges 

ECR and Postgrad development 

Local and regional engagement

Facilities to transform our science 

Creative approaches for broad audiences 

Data science innovation