Purpose

Citizen Science – public participation in scientific projects – is becoming a global practice engaging volunteer participants, often non-scientists, with scientific research. Citizen Science is facing major challenges, such as quality and consistency, to reap open the full potential of its outputs and outcomes, including data, software and results. In this context, the principles put forth by Data Science and Open Science domains are essential for alleviating these challenges, which have been addressed at length in these domains. The purpose of this study is to explore the extent to which Citizen Science initiatives capitalise on Data Science and Open Science principles.

Design/methodology/approach

The authors analysed 48 Citizen Science projects related to pollution and its effects. They compared each project against a set of Data Science and Open Science indicators, exploring how each project defines, collects, analyses and exploits data to present results and contribute to knowledge.

Findings

The results indicate several shortcomings with respect to commonly accepted Data Science principles, including lack of a clear definition of research problems and limited description of data management and analysis processes, and Open Science principles, including lack of the necessary contextual information for reusing project outcomes.

Originality/value

In the light of this analysis, the authors provide a set of guidelines and recommendations for better adoption of Data Science and Open Science principles in Citizen Science projects, and introduce a software tool to support this adoption, with a focus on preparation of data management plans in Citizen Science projects.

中文翻译：

---

数据科学与开放科学视角下的污染公民科学项目分析

目的

公民科学——公众参与科学项目——正在成为一种全球实践，让志愿者参与者（通常是非科学家）参与科学研究。Citizen Science 正面临着质量和一致性等重大挑战，以充分发挥其产出和成果的潜力，包括数据、软件和结果。在这种情况下，数据科学和开放科学领域提出的原则对于缓解这些挑战至关重要，这些挑战已经在这些领域得到了详细的解决。本研究的目的是探索公民科学倡议在多大程度上利用数据科学和开放科学原则。

设计/方法/方法

作者分析了 48 个与污染及其影响相关的公民科学项目。他们将每个项目与一组数据科学和开放科学指标进行比较，探索每个项目如何定义、收集、分析和利用数据来呈现结果并为知识做出贡献。

发现

结果表明，与普遍接受的数据科学原则有关的几个缺点，包括缺乏对研究问题的明确定义和对数据管理和分析过程的有限描述，以及开放科学原则，包括缺乏必要的上下文信息来重用项目成果。

原创性/价值

根据这一分析，作者提供了一套指导方针和建议，以在公民科学项目中更好地采用数据科学和开放科学原则，并介绍了一种软件工具来支持这种采用，重点是准备数据管理计划在公民科学项目中。