BackgroundThe SCOPE trials (SCOPE 1, NeoSCOPE and SCOPE 2) have been the backbone of oesophageal RT trials in the UK. Many changes in oesophageal RT techniques have taken place in this time. The SCOPE trials have, in addition to adopting these new techniques, been influential in aiding centres with their implementation. We discuss the progress made through the SCOPE trials and include details of a questionnaire sent to participating centres. to establish the role that trial participation played in RT changes in their centre.MethodsQuestionnaires were sent to 47 centres, 27 were returned.Results100% of centres stated their departmental protocol for TVD was based on the relevant SCOPE trial protocol. 4DCT use has increased from 42 to 71%. Type B planning algorithms, mandated in the NeoSCOPE trial, were used in 79.9% pre NeoSCOPE and now in 83.3%.12.5% of centres were using a stomach filling protocol pre NeoSCOPE, now risen to 50%. CBCT was mandated for IGRT in the NeoSCOPE trial. 66.7% used this routinely pre NeoSCOPE/SCOPE 2 which has risen to 87.5% in the survey.ConclusionThe results of the questionnaires show how participation in national oesophageal RT trials has led to the adoption of newer RT techniques in UK centres, leading to better patient care.Electronic supplementary materialThe online version of this article (10.1186/s13014-019-1225-0) contains supplementary material, which is available to authorized users.

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This panel explores the many roles of data analytics in today's cross-domain collaborations. In some instances, cross-domain analytics are required to understand big data. In others, big data holds the key to understanding and evaluating how people collaborate across domains. Panelists will present their experiences with big data and collaboration, and discussion will be guided by the following questions: How does "understanding data through collaboration" relate to "understanding collaboration through data"? What modeling tools, visualizations, abstractions, and metrics can help facilitate collaborative discovery from big data? To what extent should these tools be tailored to specific domains or groups? How much do we need to know about another domain or group before we can intersect domain specific activities that constitute the context to various analytical processes, and cross-pollinate ideas for deeper analytics? What optimally would that other domain or group need to know about us? How much of this knowledge can we extract from data? PANELISTS SHORT BIOS & PHOTOS:Obinna Anya is a postdoctoral researcher in the Accelerated Discovery Lab at IBM Research -Almaden, San Jose, California, USA. His work lies in the areas of humancomputer interaction, collaborative workplaces, social informatics, and agent-based modeling. A computer scientist, his research blends methods of social science and computer science in the design of human-centred systems and environments for networkenabled organizations of the future. In particular, he examines interaction, emergence, and collaborative discovery in socio-computational systems. Obinna holds a PhD for his work on practice-centered approach to context-aware system design in e-health, an MSc in distributed systems -both from the University of Liverpool, UK -and a BSc in computer science from the University of Nigeria, Nsukka. He previously worked as a research scientist at Liverpool Hope University, UK, where he was the lead researcher on a British Council sponsored project on context-aware collaborative environments for e-health decision support. John V. Carlis is a professor of computer science and biomedical informatics and computational biology at the University of Minnesota. His research interests include developing better practices in data modeling and querying and developing extensions to database management systems in the context of complex evolving biomedical applications. Carlis received a PhD in business administration from the University of Minnesota. Contact him at carlis@umn.edu. Brent J. Hecht is an assistant professor of computer science and engineering at the University of Minnesota. With interests that lie at the intersection of human-computer interaction, geography, and big data, his research centers on the relationship between big data and human factors such as culture. A major focus of his work involves volunteered geographic information and its application in location-aware technologies. Dr. Hecht received a Ph.D. in computer science from Northwestern Un...