IPR POLICY AND SCIENTIFIC RESEARCH KNOWLEDGE EXCHANGE REPORT FOR SCIENTIFIC POLICY MAKERS

The report provides recommendations to policy makers in science and scholarly research regarding IPR policy to increase the impact of research and make the outcomes more available. The report argues that the impact of publicly-funded research outputs can be increased through a fairer balance between private and public interest in copyright legislation. This will allow for wider access to and easier re-use of published research reports. The common practice of authors being required to assign all rights to a publisher restricts the impact of research outputs and should be replaced by wider use of a non-exclusive licence. Full access and re-use rights to research data should be encouraged through use of a research-friendly licence.

Review of Open Access Policy Dependencies

Knowledge Exchange analysed the extent to which OA policies are dependent on a number of non-commercial, compliance-enabling services used by researchers and institutions. This work offers clear evidence to policy makers on the importance of a number of non-commercial services to the successful implementation of OA policies. It also shows that many of these services are at risk and warrant further support in financial and/or governance terms. The summary report (available here) includes an analysis of a wide range of OA services and policies currently in use and presents: • an analysis of the common elements found in the current OA policies adopted by research funders and institutions • a set of case studies that illustrate the direct or indirect dependency of OA policies on key services • the views of stakeholders on the key services that enable compliance with OA policies • use cases, presented in accessible formats and language for a non-technical audience • a set of priorities for action if OA policies are to be successfully implemented

MIRACLE Project Plan

Social scientists have used agent-based models (ABMs) to explore the interaction and feedbacks among social agents and their environments. The bottom-up structure of ABMs enables simulation and investigation of complex systems and their emergent behaviour with a high level of detail; however the stochastic nature and potential combinations of parameters of such models create large non-linear multidimensional “big data,” which are difficult to analyze using traditional statistical methods. Our proposed project seeks to address this challenge by developing algorithms and web-based analysis and visualization tools that provide automated means of discovering complex relationships among variables. The tools will enable modellers to easily manage, analyze, visualize, and compare their output data, and will provide stakeholders, policy makers and the general public with intuitive web interfaces to explore, interact with and provide feedback on otherwise difficult-to-understand models.