Data Management Documentation in Citizen Science Projects: Bringing Formalisation and Transparency Together

Q1 Multidisciplinary
Gefion Thuermer, Esteban González Guardia, Neal Reeves, Óscar Corcho, E. Simperl
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引用次数: 1

Abstract

Citizen science (CS) is a way to open up the scientific process, to make it more accessible and inclusive, and to bring professional scientists and the public together in shared endeavours to advance knowledge. Many initiatives engage citizens in the collection or curation of data, but do not state what happens with such data. Making data open is increasingly common and compulsory in professional science. To conduct transparent, open science with citizens, citizens need to be able to understand what happens with the data they contribute. Data management documentation (DMD) can increase understanding of and trust in citizen science data, improve data quality and accessibility, and increase the reproducibility of experiments. However, such documentation is often designed for specialists rather than amateurs. This paper analyses the use of DMD in CS projects. We present analysis of a qualitative survey and assessment of projects’ DMD, and four vignettes of data management practices. Since most projects in our sample did not have DMD, we further analyse their reasons for not doing so. We discuss the benefits and challenges of different forms of DMD, and barriers to having it, which include a lack of resources, a lack of awareness of tools to support DMD development, and the inaccessibility of existing tools to citizen scientists without formal scientific education. We conclude that, to maximise the inclusivity of citizen science, tools and templates need to be made more accessible for non-experts in data management.
公民科学项目中的数据管理文档:将形式化和透明度结合在一起
公民科学(CS)是开放科学过程的一种方式,使其更容易获得和更具包容性,并将专业科学家和公众聚集在一起共同努力推进知识。许多倡议让公民参与数据的收集或管理,但没有说明这些数据会发生什么。在专业科学领域,开放数据越来越普遍和必要。为了与公民进行透明、开放的科学研究,公民需要能够理解他们贡献的数据会发生什么。数据管理文档(DMD)可以增加对公民科学数据的理解和信任,提高数据质量和可访问性,并增加实验的可重复性。然而,这样的文档通常是为专家而不是业余爱好者设计的。本文分析了DMD在CS项目中的应用。我们对项目DMD的定性调查和评估进行了分析,并介绍了数据管理实践的四个要点。由于我们样本中的大多数项目没有DMD,我们进一步分析了他们没有这样做的原因。我们讨论了不同形式的DMD的好处和挑战,以及拥有它的障碍,包括缺乏资源,缺乏对支持DMD发展的工具的认识,以及没有正规科学教育的公民科学家无法获得现有工具。我们的结论是,为了最大限度地提高公民科学的包容性,需要让非数据管理专家更容易获得工具和模板。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Citizen Science Theory and Practice
Citizen Science Theory and Practice Multidisciplinary-Multidisciplinary
CiteScore
4.70
自引率
0.00%
发文量
25
审稿时长
45 weeks
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