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Researchers in Cambridge have developed a data standard for storing and exchanging data between different programs in the field of macromolecular NMR spectroscopy. The standard has been used as the foundation for the development of an open source software suite for NMR data analysis, leading to improved research tools which have been widely adopted by both industrial and academic research groups, who benefit from faster drug development times and lower development costs. The CCPN data standard is an integral part of major European collaborative efforts for NMR software integration, and is being used by the major public databases for protein structures and NMR data, namely Protein Data Bank in Europe (PDBe) and BioMagResBank.
The security of data in printing and network environments is an area of increasing concern to individuals, businesses, government organisations and security agencies throughout the world. Mathematical algorithms developed at the School of Mathematics at Cardiff University represent a significant step-change in existing data security techniques. The algorithms enable greater security in automatic document classification and summarisation, information retrieval and image understanding. Hewlett-Packard (HP), the world's leading PC vendor, funded the research underpinning this development and patented the resulting software, with the aim of strengthening its position as the market leader in this sector of the global information technology industry. Hewlett Packard has incorporated the algorithms in a schedule of upgrades to improve the key security features in over ten million of their electronic devices. Accordingly, the impact claimed is mitigating data security risks for HP users and clients and substantial economic gain for the company.
Advanced technologies for data visualisation and data mining, developed in the Unit in collaboration with national and international teams, are widely applied for development of medical services. In particular, a system for canine lymphoma diagnosis and monitoring developed with [text removed for publication] has now been successfully tested using clinical data from several veterinary clinics. The risk maps produced by our technology provide early diagnosis of lymphoma several weeks before the clinical symptoms develop. [text removed for publication] has estimated the treatment test, named [text removed for publication], developed with the Unit to add [text removed for publication] to the value of their business. Institute Curie (Paris), applies this data mapping technique and the software that has been developed jointly with Leicester in clinical projects.
Targeted Projection Pursuit (TPP) — developed at Northumbria University — is a novel method for interactive exploration of high-dimension data sets without loss of information. The TPP method performs better than current dimension-reduction methods since it finds projections that best approximate a target view enhanced by certain prior knowledge about the data. "Valley Care" provides a Telecare service to over 5,000 customers as part of Northumbria Healthcare NHS Foundation Trust, and delivers a core service for vulnerable and elderly people (receiving an estimated 129,000 calls per annum) that allows them to live independently and remain in their homes longer. The service informs a wider UK ageing community as part of the NHS Foundation Trust.
Applying our research enabled the managers of Valley Care to establish the volume, type and frequency of calls, identify users at high risk, and to inform the manufacturers of the equipment how to update the database software. This enabled Valley Care managers and staff to analyse the information quickly in order to plan efficiently the work of call operators and social care workers. Our study also provided knowledge about usage patterns of the technology and valuably identified clients at high risk of falls. This is the first time that mathematical and statistical analysis of data sets of this type has been done in the UK and Europe.
As a result of applying the TPP method to its Call Centre multivariate data, Valley Care has been able to transform the quality and efficiency of its service, while operating within the same budget.
This case study reports our work on the development, application and dissemination of innovative cloud-based technologies to industrial problem domains. First, decentralised scheduling is implemented within federated Clouds, to facilitate the new drug discovery process for a global pharmaceutical company. Second, multi-objective approaches to the management and optimisation of video processing and analysis workflows in distributed environments is described in the context of an SME organisation that is developing new products, services and markets. Both of these examples have attracted, and continue to attract, commercial funding, and demonstrate the efficacy of knowledge transfer into industry from University of Derby (UoD) research.
The research in this case study has pioneered knowledge management technology. It has had major impact on drug discovery and translational medicine and is widely adopted in the pharmaceutical and healthcare industries. The impacts are:
[text removed for publication], a developer of high-precision medical devices, have produced a new data annotation tool ([text removed for publication]) based on research in CSRI on data storage formats and activity recognition for applications within smart home environments. Within [text removed for publication] stereo-based cameras record activities in a specified environment (e.g. kitchen) which are then annotated using user-based pre-configured activity labels (e.g. prepare meal, wash dishes). [text removed for publication] is currently used by [text removed for publication] users and has yielded additional sales worth [text removed for publication]. [text removed for publication] have employed [text removed for publication] additional technical development staff to extend [text removed for publication] functionality, and through an MoU [text removed for publication] now supports automated annotation based on CSRI's research on activity recognition.
KCL research played an essential role in the development of data provenance standards published by the World Wide Web Consortium (W3C) standards body for web technologies, which is responsible for HTTP, HTML, etc. The provenance of data concerns records of the processes by which data was produced, by whom, from what other data, and similar metadata. The standards directly impact on practitioners and professional services through adoption by commercial, governmental and other bodies, such as Oracle, IBM, and Nasa, in handling computational records of the provenance of data.
Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.
The mapping and monitoring of land cover, habitats and forest structure through satellite-based observation by government and commercial organisations around the world has been enhanced by data analysis techniques and tools developed by the Earth Observation and Ecosystem Dynamics (EOED) Laboratory at Aberystwyth University (AU). This has allowed new commercial services to be provided and has change professional working practices. The key impacts include (i) improved knowledge and information about land cover and environmental change in forest and brigalow ecosystems in Australia, supporting effective management strategies; (ii) the completion of a comprehensive digital map of habitats in Wales to inform policy-making; and (iii) the increased capacity of the global remote sensing community in forest characterisation using open source software developed by AU.