The effective and ethical use of data to inform decision-making offers huge value to the public sector, especially when delivered by transparent, reproducible, and robust data processing workflows. One way that governments are unlocking this value is through making their data publicly available, allowing more people and organisations to derive insights. However, open data is not enough in many cases: publicly available datasets need to be accessible in an analysis-ready form from popular data science tools, such as R and Python, for them to realise their full potential. This paper explores ways to maximise the impact of open data with reference to a case study of packaging code to facilitate reproducible analysis. We present the jtstats project, which consists of R and Python packages for importing, processing, and visualising large and complex datasets representing journey times, for many modes and purposes at multiple geographic levels, released by the UK Department of Transport. jtstats shows how domain specific packages can enable reproducible research within the public sector and beyond, saving duplicated effort and reducing the risks of errors from repeated analyses. We hope that the jtstats project inspires others, particularly those in the public sector, to add value to their data sets by making them more accessible.

翻译：在公共部门中，以透明、可重复且稳健的数据处理流程为依托，以有效且合乎伦理的方式利用数据进行决策，能够创造巨大价值。政府解锁这一价值的途径之一，是公开其数据，以便更多个人和组织从中获取洞见。然而在许多情况下，仅开放数据并不足够：公开可用的数据集需以分析就绪的形式，通过常见的数据科学工具（如R和Python）访问，方能发挥其全部潜力。本文参考一个旨在促进可重复分析的代码封装案例，探讨如何最大化开放数据的影响。我们介绍了jtstats项目，该项目包含用于导入、处理和可视化英国交通部发布的多模式、多目的及多地理层级的大型复杂行程时间数据集的R和Python软件包。jtstats展示了领域专用软件包如何助力公共部门及更广泛领域内的可重复研究，从而节省重复劳动并降低反复分析带来的错误风险。我们希望jtstats项目能激励他人，尤其是公共部门人员，通过提高数据可获取性来为其数据集增值。