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Population data science: advancing the safe use of population data for public benefit

机译:人口数据科学:促进安全使用人口数据以获取公共利益

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摘要

The value of using population data to answer important questions for individual and societal benefit has never been greater. Governments and research funders world-wide are recognizing this potential and making major investments in data-intensive initiatives. However, there are challenges to overcome so that safe, socially-acceptable data sharing can be achieved. This paper outlines the field of population data science, the International Population Data Linkage Network (IPDLN), and their roles in advancing data-intensive research. We provide an overview of core concepts and major challenges for data-intensive research, with a particular focus on ethical, legal, and societal implications (ELSI). Using international case studies, we show how challenges can be addressed and lessons learned in advancing the safe, socially-acceptable use of population data for public benefit. Based on the case studies, we discuss the common ELSI principles in operation, we illustrate examples of a data scrutiny panel and a consumer panel, and we propose a set of ELSI-based recommendations to inform new and developing data-intensive initiatives.We conclude that although there are many ELSI issues to be overcome, there has never been a better time or more potential to leverage the benefits of population data for public benefit. A variety of initiatives, with different operating models, have pioneered the way in addressing many challenges. However, the work is not static, as the ELSI environment is constantly evolving, thus requiring continual mutual learning and improvement via the IPDLN and beyond.
机译:使用人口数据回答重要问题以获取个人和社会利益的价值从未有过。全世界的政府和研究资助者都意识到了这一潜力,并在数据密集型计划中进行了重大投资。但是,仍有许多挑战需要克服,以便可以实现安全的,社会可接受的数据共享。本文概述了人口数据科学,国际人口数据链接网络(IPDLN)的领域及其在推进数据密集型研究中的作用。我们概述了数据密集型研究的核心概念和主要挑战,特别着重于道德,法律和社会影响(ELSI)。通过国际案例研究,我们展示了如何解决挑战,并汲取经验教训,以促进安全,社会可接受的人口数据用于公共利益的发展。在案例研究的基础上,我们讨论了操作中常见的ELSI原理,我们举例说明了数据审查小组和消费者小组的例子,并提出了一套基于ELSI的建议,以为新的和正在开发的数据密集型计划提供信息。尽管有许多ELSI问题需要克服,但从来没有比现在更好或更多的时间来利用人口数据的优势来实现公共利益。具有不同运营模式的各种举措开创了应对许多挑战的方式。但是,由于ELSI环境在不断发展,因此工作不是一成不变的,因此需要通过IPDLN进行持续的相互学习和改进。

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