Rigorous Training in Longitudinal Data Science (RADIANCE)

纵向数据科学的严格培训 (RADIANCE)

• 批准号: MR/V038885/1
• 负责人: Bianca De Stavola
• 金额: $ 111.22万
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV038885%2F1
• 关键词: Rigorous; Training; Longitudinal; Data; Science

We live in a world where data are collected on nearly everything we do. Such information has the potential to be extremely useful if we wish to improve our health. However, doing this safely is not easy. There are many examples where data have been misused or erroneous interpretation of the evidence has been drawn. This is increasingly apparent this year, where scientists and governments are struggling to communicate the uncertainties in their understanding of the current pandemic. Because the available evidence is limited, scientists stress that more data are needed to compare regions, subgroups of people and, crucially, study the evolution of the epidemic over time. Only with more data we will be able to understand variations in the population and explain health inequalities as well as time trends. For example, to assess if a local or national lock-down is working, we need to count how many cases arise in different communities over certain periods of time, how many of them are hospitalised, and how many die. Ideally, we should follow each individual from diagnosis, to hospitalization, to recovery or death, and then compare the incidence of each of these events, by region, sex, occupation, and ethnicity, for example. To achieve this, we need to account for when each of these events occurs. This requires linking information on the same individual over time. The same principle applies to the study of other diseases. For this reason, access to linked individual medical and administrative records is crucial for biomedical and public health research. Having the data is not sufficient, however. They need to be: (a) safely stored, cleaned, and prepared for analysis; (b) properly analysed; and (c) interpreted together with evidence from other countries and other published research. We label these steps: data stewardship, analysis, and context. Our proposal aims to train health and social data scientists in the core skills needed to achieve these steps. We will use different formats which will all be on-line to reach the broadest community of data scientists. We will produce short introductory videos (which we call "Appetisers"), and then various on-line material delivered at an intermediate and more advanced levels. Some of this will be in the form of recorded lectures, some as live tutorials where the material covered by the lectures is reinforced with practical computer-based exercises. We will also run specific courses on specialised topics which will include live (but on-line) interactions with members of the training team, and "data clinics" where participants can have one-to-one discussions with us.In summary, we will endeavour to develop and run an accessible and inclusive training programme for data scientists involved in the management, analysis and interpretation of complex longitudinal biosocial data.

我们生活在一个几乎所有所做的事情上收集数据的世界。如果我们希望改善自己的健康，此类信息有可能非常有用。但是，安全这样做并不容易。在许多示例中，已经提出了数据被滥用或错误解释的示例。今年，这是越来越明显的，科学家和政府正在努力在理解当前的大流行时努力传达不确定性。由于现有证据是有限的，因此科学家强调，需要更多数据来比较人的亚组，并至关重要的是研究流行病的演变。只有有更多数据，我们才能理解人群的差异，并解释健康不平等以及时间趋势。例如，要评估当地或国家锁定是否在起作用，我们需要计算在某些时期内不同社区中出现多少病例，其中有多少个住院以及有多少人死亡。理想情况下，我们应该跟随每个人从诊断到住院，恢复或死亡，然后按地区，性别，职业和种族进行比较。为了实现这一目标，我们需要考虑这些事件中的每一个。这需要随着时间的推移链接有关同一个人的信息。 相同的原则适用于其他疾病的研究。因此，访问链接的个人医疗和行政记录对于生物医学和公共卫生研究至关重要。但是，拥有数据还不够。他们需要：（a）安全存储，清洁和准备进行分析； （b）正确分析； （c）与其他国家和其他已发表研究的证据一起解释。我们标记这些步骤：数据管理，分析和上下文。我们的建议旨在以实现这些步骤的核心技能来培训健康和社会数据科学家。我们将使用不同的格式，这些格式都将在线到达最广泛的数据科学家社区。我们将制作简短的介绍性视频（我们称为“开胃菜”），然后以中间和更高级的水平交付的各种在线材料。其中一些将以记录的讲座的形式，有些是现场教程，其中讲座所涵盖的材料通过实用的基于计算机的练习加强。我们还将开设有关专业主题的特定课程，其中包括现场（但在线）与培训团队成员的互动，以及“数据诊所”，参与者可以与我们进行一对一的讨论。总而言之，我们将努力开发和开展一项可访问且包容性的培训计划，以涉及参与管理，分析和解释复杂纵向生长数据的数据科学家。