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收集我们所做的一切数据的世界里。如果我们希望改善我们的健康，这些信息可能非常有用。然而，安全地做到这一点并不容易。有许多数据被滥用或对证据作出错误解释的例子。这一点在今年越来越明显，科学家和政府都在努力沟通他们对当前流行病的理解中的不确定性。由于现有证据有限，科学家们强调，需要更多的数据来比较地区、人群亚群，更重要的是，研究疫情随时间的演变。只有有了更多的数据，我们才能了解人口的变化，解释健康不平等和时间趋势。例如，为了评估地方或国家封锁是否有效，我们需要计算在特定时间内不同社区出现了多少病例，其中有多少人住院，有多少人死亡。理想情况下，我们应该跟踪每个人从诊断到住院，到康复或死亡，然后比较这些事件的发生率，例如，按地区，性别，职业和种族。为了实现这一点，我们需要考虑这些事件中的每一个发生的时间。这就需要将同一个人的信息与时间联系起来。 同样的原则也适用于其他疾病的研究。因此，获取相互关联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。然而，仅有数据是不够的。它们需要：（a）安全储存、清洁和准备分析;（B）适当分析;（c）与其他国家和其他已发表研究的证据一起解释。我们将这些步骤标记为：数据管理、分析和上下文。我们的建议旨在培训健康和社会数据科学家实现这些步骤所需的核心技能。我们将使用不同的格式，这些格式都将在线，以覆盖最广泛的数据科学家社区。我们将制作简短的介绍性视频（我们称之为“开胃菜”），然后在中级和更高级的水平上提供各种在线材料。其中一些将以录音讲座的形式，一些作为现场教程，其中讲座所涵盖的材料通过实际的计算机练习得到加强。我们还将举办关于专业主题的特定课程，包括与培训团队成员的现场（但在线）互动，以及参与者可以与我们进行一对一讨论的“数据诊所”。总之，我们将努力为参与管理，分析和解释复杂纵向生物社会数据的数据科学家开发和运行一个可访问的和包容性的培训计划。