Data is being collected and created at the fastest rate in human history; by the far the vast majority of this is in digital format. Allied with this, what was previously "offline" information can now be digitised quickly and cheaply e.g. old manuscripts, maps etc. This vast collection of existing and new information creates new opportunities and also difficulties. For a lot of this information to be useful it must be categorised and annotated in some way, so that sense can be made of the data and also so that the correct data can be accessed more easily. It is possible to complete this categorisation by hand with human annotators, but this effort can be expensive in terms of time, money and resources. This is especially true for large data sets or for data sets that require niche expertise to annotate. With this expense in mind, many have turned to machine learning to annotate data; however machine learning approaches still require human intervention to both create training sets for algorithms and judge the output of algorithms. Thus it is inevitable that human intervention is involved at some stage of the categorisation and annotation process. In this project we aim to gain a better understanding of this annotation process so that we can provide guidelines, approaches and processes for providing the most cost effective and accurate annotations for data sets. We propose to work with the three main types of unstructured data faced in big data: text, image, and video. The first challenge is to better understand the process assessors go through when annotating and judging different types of material. This will be carried out using a mixture of qualitative and quantitative techniques, using smaller scale lab-based studies. By better understanding the process by which individuals annotate and classify material, we hope to provide insights which can be used to make the annotation process more efficient, and identify a set of initial factors which affect annotation performance, such as degree of domain expertise and time. Based on this initial work, the aim is to then investigate which of these factors most affect assessment, using large scale crowdsourcing style methods. The final challenge is related to the classification task: how should annotation be approached, to give the best results when used in machine learning? Based on this, the project aims to create a set of guidelines for the creation of annotation and relevance sets.

Over the last decade, the creative industries have been revolutionised by the Internet and the digital economy. The UK, already punching above its weight in the global cultural market, stands at a pivotal moment where it is well placed to build a cultural, business and regulatory infrastructure in which first movers as significant as Google, Facebook, Amazon or iTunes may emerge and flourish, driving new jobs and industry. However, for some creators and rightsholders the transition from analogue to digital has been as problematic as it has been promising. Cultural heritage institutions are also struggling to capitalise upon new revenue streams that digitisation appears to offer, while maintaining their traditional roles. Policymakers are hampered by a lack of consensus across stakeholders and confused by partisan evidence lacking robust foundations. Research in conjunction with industry is needed to address these problems and provide support for legislators. CREATe will tackle this regulatory and business crisis, helping the UK creative industry and arts sectors survive, grow and become global innovation pioneers, with an ambitious programme of research delivered by an interdisciplinary team (law, business, economics, technology, psychology and cultural analysis) across 7 universities. CREATe aims to act as an honest broker, using open and transparent methods throughout to provide robust evidence for policymakers and legislators which can benefit all stakeholders. CREATe will do this by: - focussing on studying and collaborating with SMEs and individual creators as the incubators of innovation; - identifying "good, bad and emergent business models": which business models can survive the transition to the digital?, which cannot?, and which new models can succeed and scale to drive growth and jobs in the creative economy, as well as supporting the public sector in times of recession?; - examining empirically how far copyright in its current form really does incentivise or reward creative work, especially at the SME/micro level, as well as how far innovation may come from "open" business models and the "informal economy"; - monitoring copyright reform initiatives in Europe, at WIPO and other international fora to assess how they impact on the UK and on our work; - using technology as a solution not a problem: by creating pioneering platforms and tools to aid creators and users, using open standards and released under open licences; - examining how to increase and derive revenues from the user contribution to the creative economy in an era of social media, mash-up, data mining and "prosumers"; - assessing the role of online intermediaries such as ISPs, social networks and mobile operators to see if they encourage or discourage the production and distribution of cultural goods, and what role they should play in enforcing copyright. Given the important governing role of these bodies should they be subject to regulation like public bodies, and if so, how?; - consider throughout this work how the public interest and human rights, such as freedom of expression, privacy, and access to knowledge for the socially or physically excluded, may be affected either positively or negatively by new business models and new ways to enforce copyright. To investigate these issues our work will be arranged into seven themes: SMEs and good, bad and emergent business models; Open business models; Regulation and enforcement; Creators and creative practice; Online intermediaries and physical and virtual platforms; User creation, behaviour and norms; and, Human rights and the public interest. Our deliverables across these themes will be drawn together to inform a Research Blueprint for the UK Creative Economy to be launched in October 2016.