Learn to Discover (L2D): A Training Platform in Data Sciences and Machine Learning for Biomedicine and Health Researchers.

学习发现 (L2D)：面向生物医学和健康研究人员的数据科学和机器学习培训平台。

• 批准号: MR/V039229/1
• 负责人: Geraint Thomas
• 金额: $ 118.61万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV039229%2F1
• 关键词: Learn; Discover; L2D; Training; Platform

Digital skills need to improve to optimise competitive potential from accelerating applications of digital technology. Biological and medical science is becoming more automated to tackle bigger and more complex problems in discovery and applied health and medical sciences. The need for routine adoption of radically new ways of working and optimisation of their impact in this critical sector will only increase. We need action to re-skill the UK scientific workforce continuously since the deployment of digital skills offers very strong growth opportunities. The UK Government has committed to a technological future and to sectors that can deliver economic growth and has learnt lessons from elsewhere: 50% of all growth in the US economy over the last 50 years has come from the 5% of the workforce in STEM disciplines. Fusing the adoption of digital tech skills to the especially high-value STEM disciplines of discovery bioscience, biomedical and health sciences, where the UK already punches well above its weight, has tremendous potential for meaningful and measurable economic and social impact. However, tackling a digital skills crisis is not a trivial undertaking "Britain's chronic supply issues requires radical action. Working as a data expert requires knowing your maths, coding and computer science as well as problem solving, resilience and communication." (The Royal Society: - May 2019). Since 2011 SysMIC (http://sysmic.ac.uk) - funded initially by the BBSRC - has taken advantage of the digital technology, internet access, and distant communication infrastructure widely available for most professionals to address half of this skills problem amongst active bioscience and health researchers. We delivered high-quality, e-learning and training in mathematical, computational and statistical methods. The Learn 2 Discover (L2D) project will combine the expertise of leading health and bioscience-facing computational and data scientists with the remote learning acumen and resources of SysMIC to solve the remaining part of the challenge. L2D we will deliver data science, machine learning and AI training in a highly accessible, flexible, modular format, suitable for a very wide range of starting expertise - including beginners - and study regimes. Our modules will draw on established real-world examples yet deliver widely applicable skills and general computational self-confidence for effective application well beyond the course. Delivery through the web offers resilience to disruptions of HE systems and leverages remote work and study competence developed across the R&D workforce in 2020 Participation of UK bioindustry stakeholders in the design of the programme will promote movement and sharing of talent between academic and commercial sectors. Collaboration and alignment of our modules with the work of UK centres of research excellence, infrastructure and resource networks will promote visibility, confidence and demand. L2D will squarely address the digital productivity puzzle and promote knowledge exchange and its translation into impact in society and the economy and will offer opportunities for cross-sector spill over of benefits from training. Failure to respond effectively to the digital skills challenge is a major risk to business growth, innovation and broader societal development. A shortage in suitable digital skills persists in the UK labour market - research biomedical and health sciences is not an isolated case. Demand for workers with specialised data sciences and computational skills has been growing 6.5-fold faster than all other requirements. The best option is to nurture talent in the bio-, biomedical and health research sectors in situ with first class CPD of the sort proposed in L2D. A persistent digital skills wage differential makes impactful, broad digitals skills training attractive and very good value for money since over 75% of job openings at any skill level request digital skills.

数字技能需要提高，以通过加速数字技术的应用来优化竞争潜力。生物和医学科学正在变得更加自动化，以解决发现和应用健康和医学科学中更大和更复杂的问题。例行采用全新的工作方式并优化其在这一关键领域的影响的需求只会增加。我们需要采取行动，不断重新培养英国科学工作者的技能，因为数字技能的部署提供了非常强劲的增长机会。英国政府致力于技术未来和能够实现经济增长的行业，并从其他地方吸取了经验教训：过去50年美国经济增长的50%来自STEM学科的5%劳动力。将数字技术技能与发现生物科学，生物医学和健康科学等特别高价值的STEM学科融合在一起，英国已经远远超出了其重量，具有巨大的潜在意义和可衡量的经济和社会影响。然而，解决数字技能危机并不是一件小事，“英国长期的供应问题需要采取激进的行动。作为一名数据专家，你需要了解数学、编码和计算机科学，以及解决问题、适应力和沟通能力。（皇家学会：-2019年5月）。自2011年以来，SysMIC（http：sysmic.ac.uk）-最初由BBSRC资助-利用数字技术，互联网接入和远程通信基础设施广泛提供给大多数专业人员，以解决活跃的生物科学和健康研究人员中的一半技能问题。我们提供了高质量的在线学习和数学，计算和统计方法的培训。Learn 2 Discover（L2 D）项目将联合收割机结合领先的健康和生物科学计算和数据科学家的专业知识与SysMIC的远程学习智慧和资源，以解决挑战的剩余部分。L2 D将以高度可访问，灵活，模块化的格式提供数据科学，机器学习和人工智能培训，适用于非常广泛的入门专业知识-包括初学者-和学习制度。我们的模块将借鉴已建立的现实世界的例子，但提供广泛适用的技能和一般的计算自信心，有效的应用远远超出了课程。通过网络交付提供了对高等教育系统中断的弹性，并利用2020年在研发人员中开发的远程工作和学习能力英国生物产业利益相关者参与该计划的设计将促进学术和商业部门之间的人才流动和共享。我们的模块与英国卓越研究中心，基础设施和资源网络的合作和协调将促进知名度，信心和需求。L2 D将直接解决数字生产力难题，促进知识交流并将其转化为对社会和经济的影响，并将为培训带来的跨部门溢出效应提供机会。未能有效应对数字技能挑战是企业增长、创新和更广泛社会发展的主要风险。英国劳动力市场持续缺乏合适的数字技能-研究生物医学和健康科学并不是一个孤立的案例。对具有专业数据科学和计算技能的员工的需求增长速度是所有其他需求的6.5倍。最好的选择是培养生物，生物医学和健康研究领域的人才，并在L2 D中提出一流的持续专业发展。持续的数字技能工资差异使得有影响力的广泛数字技能培训具有吸引力，并且非常物有所值，因为任何技能水平的职位空缺中有超过75%需要数字技能。