Goals Rob Procter Dave Berry Anne Trefethen Paul Watson

Improve secondary use of data Preserve data Advertise data – metadata – open innovation models (data as IP) Barriers – Why should people share data? Reward & credit – Cleaning & adding Metadata (Discoverability) – Trust (see later goal) – Sustainability What to keep? Who pays (business model)? Who looks after it? Who curates it and the metadata (reward system)? Maintaining ways to access extract value from it ?

Access to new data collections Sharing as the Norm Transactional data – Purchasing Government data Clinical Data Real-Time data (sensors) Barriers – Socio-technical issues – Anonymity, security – Loss of business/researcher competitiveness? – Track provenance of data

Better Support for Collaborative problem solving Socio-technical issue Technological – Communications – Ease of use Configuring collaborations: networks, security, software.... Barriers: – Policy & organisation works against collaboration RAE/REF – Rewards for multi-disciplinary researchers – Security – policy, governance, trust

Reproducibility in Science Data Code Process People Barriers Long-term code & data preservation (business models) Manual processes Database updates Security & policy Tacit knowledge & Paper lab books

Trust & Understanding How do we know tools have been used properly? How much confidence should we have in the results? e.g. X-rays 3d-2d understanding (artefacts, properties of film) Errors? Ability to inspect process Capturing trust in data – Tacit knowledge Can people build up trust through collaborative tools – virtual encounters -> trust in collaborators

Position UK as competitively as possible in Global knowledge value chain UK-International value chain export knowledge & techniques source knowledge & techniques Virtualisation of knowledge work Barriers – Lack of international standardisation – Business models – how to value services we provide or buy (e.g. data)