Clinical trials are research studies involving patients or healthy volunteers which aim to test whether a new treatment is safe and 'better' than current treatment. Clinical trials are grouped into stages (or phases) and researchers aim to answer different questions at each of the phases. The earliest of these, Phase I, aims to test whether a treatment is safe, to investigate side effects and to and to recommend a dose of the treatment for further testing. Phase I trials involve small numbers of participants who may be healthy volunteers or patients. The first group of participants in a phase I trial are given a low dose of treatment and if shown to be safe and without many side effects, the next group of participants are given a higher dose. Further groups of participants are enrolled; with the dose, being raised for each successive group until a dose is reached that has too many negative side effects. The decision whether to give patients the same, a higher, or lower dose of treatment is carefully made before the treatment is given. These trials are called "dose-escalation" or "dose-finding" trials. Once a clinical trial is complete, the results are reported to the clinical research community and to the public. To make sure that these reports are reliable and helpful for further research, a set of guidelines of the important items to be reported, Consolidated Standards of Reporting Trials (or CONSORT for short) have been published. It sets out a standard way for authors to report how the trial is designed, analysed and interpreted and has been instrumental in promoting transparent reporting. The original CONSORT guidelines were developed for specific types of trials and their design features often differ from dose-finding trials. This is important as currently trial reports of Phase I trials often miss out important information about how they were designed and conducted, which can make it difficult for the reader to interpret and gauge the validity of the trials. This wastes time and resources, but more importantly, may unethically expose participants to ineffective or even harmful interventions. Making the best decisions in a Phase I trial, on whether to give a patient the same dose, or a higher or lower dose, is key to the success of the trial. The statistical methods used to guide these decisions have advanced significantly over the past 25 years, so that safer trials with more reliable results can be conducted. But these methods are often complex, and have additional transparency and reporting demands. To address these problems, we will develop an 'extension' to the CONSORT guidelines, which is relevant to all early phase dose-finding designs in different diseases. The main CONSORT Statement has now been extended in 7 other design areas, primarily in later phase trials (www.consort-statement.org/extensions). A CONSORT extension for dose-finding trials is long overdue. To develop an internationally agreed way to report such trials, we have brought together a multi-disciplinary, international team of experts in the design, running and reporting of early phase trials who work in academic institutions or pharmaceutical industry, and those with expertise in developing reporting guidelines of trials. Finally, to ensure that the developed CONSORT extension will be widely adopted, we will involve key groups throughout this research: the trial community, journal editors (who publish articles on dose-finding trials), peer reviewers (who assess research papers), regulators, patients and the public. We will publicise our outputs at relevant meetings and to local/national Patient and Pubic Involvement (PPI) groups, and will use social media to raise awareness. We will conduct practical workshops, highlighting common reporting flaws and how to use the new guideline. We will co-produce two lay papers with our PPI representative to effectively involve, engage and inform patients and the public about the importance of this work.

We live in the "Era of Mathematics" (UKRI, 2018), in which mathematics research has deep and widespread impact. Medical imaging is enhanced using the theory of inverse problems. Predicting sewage contamination in waterways after storms requires solving complicated systems of hydrodynamic equations. Machine learning tools are revolutionising data-intensive computing and, handled with proper mathematical care, have vast potential benefits for science and society. These are examples of the ongoing explosion in mathematical innovation driving, and being driven by, the analysis and modelling of data running through every aspect of life. Cutting-edge research now sits at the interface of data science and mathematical modelling. Methods and fields such as compressed sensing, stochastic optimisation, neural networks, Bayesian hierarchical models - to name but a few - have become interwoven and contributed to the delivery of a new domain of research. We refer to this research interface as "statistical applied mathematics". Established in 2014, the Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa, samba.ac.uk) delivers leading research and training in this space. In the development of this bid, we have consulted widely with academic, industrial, and governmental partners, who consistently report a large and widening gap between demand and supply for highly skilled graduates. Our vision is to create a new generation of statistical applied mathematicians ready to lead high-impact, data-driven, mathematically-robust research in academia and industry. We will nurture a vibrant culture of cohort learning, enabling internationally-leading training in modern mathematical data science. A particularly important research focus will be the synthesis of data-driven methods with robust mathematical modelling frameworks. Tomorrow's industrial mathematicians and statisticians must understand when machine learning tools are (and are not) appropriate to use and be able to conduct the underpinning research to improve these tools by integrating scientific domain knowledge. This research challenge is informed by deep partnerships with a range of industry and government bodies. Our long-term partners such as BT, Syngenta, Novartis, the NHS, and the Environment Agency co-create our vision and our training. They are emphatic that we must address the urgent need for mathematical data science talent in this key strategic area for the UK economy. Many of our students will work directly on industry challenges during their PhD either in their core research or with internships. Our unique Integrative Think Tanks are the key mechanism for exploring new research ideas with industry. These are week-long events where SAMBa students, leading academics, and partners work together on industrial and societal problems. SAMBa graduates will be able to develop and apply new ideas and methods to harness the power of data to tackle challenges affecting society, the economy, and the environment. Our students will move into academia, providing sustainability to the UK's capacity in this field, as well as industry and government, providing impact through societal benefits and driving economic growth. Many alumni now hold permanent positions at leading UK universities and senior positions in a range of businesses. The CDT will be embedded within the University of Bath's Department of Mathematical Sciences, where 98% of the research is world leading or internationally excellent (REF2021). The CDT is supported by 58 academics in maths, with similar numbers of co-supervisors from industry and other departments. The centre will be co-delivered with 22 industry and government partners. A vital international perspective is provided by a worldwide network of 11 academic institutions sharing our scientific vision.

Data and data science are transforming the world and data science expertise is in extremely high demand. Particularly within biomedical research, there is an urgent need for a shared framework of data functions, to enable skills mobility and recognition across different contexts (MRC strategic review 2022). This project will enable organisations to incorporate data science skills into their teams and work culture by establishing a greater understanding of the common language needed to describe skills and careers in biomedical data science. We will enable cross-domain working so that collaborative team science approaches lead the future of biomedical research. Our proposal to advance biomedical data science careers will focus on three key objectives: To evaluate skills gaps and identify priority areas for developing knowledge, skills and behaviours across the biomedical data science ecosystem. 2. To better understand roles, career pathways and team science approaches within the biomedical data science community and how these can improve access, resourcing and career offers. 3. To evaluate and recommend innovative approaches and ways of working that will drive forward capacity building and improve quality and standards in biomedical data science. We will conduct an extensive landscape mapping exercise to evaluate the biomedical data science ecosystem in terms of competencies, skills, career pathways and team science approaches. This will constitute a comprehensive basis to improve the development of biomedical data science skills and career offers and, ultimately, support innovation to improve capacity, quality and standards in biomedical data science. The Alan Turing Institute and EMBL's European Bioinformatics Institute (EMBL-EBI) are world leaders in biomedical data science with proven track records of innovative and impactful collaborative team science, and we will leverage existing cross-sector networks and interest groups to provide a wide range of partners to inform this work. The outcomes of this work have the potential to directly affect how biomedical data science is conducted as well as improve the research culture and career opportunities for biomedical data science across the UK. This improvement will positively impact sector porosity, supporting greater mobility between organisations in different sectors, increasing the overall workforce, and leading to greater efficiency in research. In addition, our approach will embed and champion equity, diversity and inclusion (EDI) by ensuring we conduct the project in a manner that enables diverse and inclusive input from the biomedical data science community and beyond. This will lead to more impactful outputs that will provide transparency of roles, career paths and ways of working, which will allow for democratisation of knowledge in terms of careers in biomedical data science and will create opportunities for a greater variety of people, skills and roles in teams leading to truly diverse teams.

SAMBa aims to create a generation of interdisciplinary mathematicians at the interface of stochastics, numerical analysis, applied mathematics, data science and statistics, preparing them to work as research leaders in academia and in industry in the expanding world of big models and big data. This research spectrum includes rapidly developing areas of mathematical sciences such as machine learning, uncertainty quantification, compressed sensing, Bayesian networks and stochastic modelling. The research and training engagement also encompasses modern industrially facing mathematics, with a key strength of our CDT being meaningful and long term relationships with industrial, government and other non-academic partners. A substantial proportion of our doctoral research will continue to be developed collaboratively through these partnerships. The urgency and awareness of the UK's need for deep quantitative analytical talent with expert modelling skills has intensified since SAMBa's inception in 2014. Industry, government bodies and non-academic organisations at the forefront of technological innovation all want to achieve competitive advantage through the analysis of data of all levels of complexity. This need is as much of an issue outside of academia as it is for research and training capacity within academia and is reflected in our doctoral training approach. The sense of urgency is evidenced in recent government policy (cf. Government Office for Science report "Computational Modelling, Technological Futures, 2018"), through the EPSRC CDT priority areas of Mathematical and Computational Modelling and Statistics for the 21st century as well as through our own experience of growing SAMBa since 2014. We have had extensive collaboration with partners from a wide range of UK industrial sectors (e.g. agri-science, healthcare, advanced materials) and government bodies (e.g. NHS, National Physical Laboratory, Environment Agency and Office for National Statistics) and our portfolio is set to expand. SAMBa's approach to doctoral training, developed in conjunction with our industrial partners, will create future leaders both in academia and industry and consists of: - A broad-based first year developing mathematical expertise across the full range of Statistical Applied Mathematics, tailored to each incoming student. - Deep experience in academic-industrial collaboration through Integrative Think Tanks: bespoke problem-formulation workshops developed by SAMBa. - Research training in a department which produces world-leading research in Statistical Applied Mathematics. - Multiple cohort-enhanced training activities that maximise each student's talents and includes mentoring through cross-cohort integration. - Substantial international opportunities such as academic placements, overseas workshops and participation in jointly delivered ITTs. - The opportunity for co-supervision of research from industrial and non-maths academic supervisors, including student placements in industry. This proposal will initially fund over 60 scholarships, with the aim to further increase this number through additional funding from industrial and international partners. Based on the CDT's current track record from its inception in 2014 (creating 25 scholarships to add to an initial investment of 50), our target is to deliver 90 PhD students over the next five years. With 12 new staff positions committed to SAMBa-core areas since 2015, students in the CDT cohort will benefit from almost 60 Bath Mathematical Sciences academics available for lead supervisory roles, as well as over 50 relevant co-supervisors in other departments.

Clinical pharmacologists are physicians and scientists whose focus is developing and understanding existing and new drug therapies; they work in a variety of settings in academia, the NHS, industry and government. In the clinical setting, they work directly with patients, participate in trials, and investigate how patients respond to drugs, including why certain patients develop side effects to drugs. The total number of academic clinical pharmacologists trained in the UK is small, and there is an imperative to continue to train more clinical pharmacologists and other specialists with expertise in clinical pharmacology who can work between academia, healthcare and industry. In 2010, the Government recognised that the provision of high quality-care and better interaction with Industry requires clinicians to be familiar with the relevant practices in clinical pharmacology. The universities of Liverpool and Manchester, in collaboration with industry partners, were awarded funding from the MRC to address this unmet need in clinical pharmacology: The North West England MRC Clinical Research Training Fellowship Programme in Clinical Pharmacology and Therapeutics. This programme has allowed 13 clinical fellows (high flying trainee doctors), rigorously selected from across all medical specialties e.g. dermatology, rheumatology, paediatrics etc., to study for PhDs on a variety of clinical pharmacology related research topics such as drug safety and stratified medicine - matching the right drug to the right patient. In addition to their research work the fellows received without walls training with industry and modular training in key aspects of clinical pharmacology. The programme has been a tremendous success with 56 scientific journal publications, 21 conference presentations, 13 prizes and interactions with 61 NHS Trusts. All fellows will also get a PhD. We now wish to renew the scheme with the MRC and with 4 industry partners - Lilly, Novartis, Roche and UCB Pharma, to appoint 13 more fellows. The previous successful format and structure will be retained as will joint leadership from the two universities. Refinements to the programme include: increasing the number of industry partners thereby allowing us to cover more therapeutic areas including cancer; lengthening of the recruitment process to give potential fellows, their supervisors and industry representatives more time to develop research projects with strong alignment; identification of a lead industry partner for each fellow from the beginning of the programme to develop a partnership from the outset; ensuring, where possible, that fellows can spend up to one year with the industry partner at their site(s) performing different aspects of their project. These changes will enhance fellows' training with industry, and also increase the input provided by industry in individual projects. There will be very strong patient and public engagement (as in the current scheme) with involvement of patients in the planning of research proposals, a number of public lectures and involvement of fellows at events such as the Manchester Science Spectacular. The Fellowship Programme will go some way to producing academics with expertise in clinical pharmacology and helping to optimise the safe, targeted prescribing of existing drugs and the development of new therapies for human disease.