Research Data Management Challenges in Citizen Science Projects and Recommendations for Library Support Services. A Scoping Review and Case Study

Q2 Computer Science

Data Science Journal Pub Date : 2021-08-18 DOI:10.5334/dsj-2021-025

J. S. Hansen, Signe Gadegaard, Karsten Kryger Hansen, Asger Væring Larsen, S. Møller, Gertrud Stougård Thomsen, Katrine Flindt Holmstrand

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引用次数: 5

Abstract

Citizen science (CS) projects are part of a new era of data aggregation and harmonisation that facilitates interconnections between different datasets. Increasing the value and reuse of CS data has received growing attention with the appearance of the FAIR principles and systematic research data management (RDM) practises, which are often promoted by university libraries. However, RDM initiatives in CS appear diversified and if CS have special needs in terms of RDM is unclear. Therefore, the aim of this article is firstly to identify RDM challenges for CS projects and secondly, to discuss how university libraries may support any such challenges. A scoping review and a case study of Danish CS projects were performed to identify RDM challenges. 48 articles were selected for data extraction. Four academic project leaders were interviewed about RDM practices in their CS projects. Challenges and recommendations identified in the review and case study are often not specific for CS. However, finding CS data, engaging specific populations, attributing volunteers and handling sensitive data including health data are some of the challenges requiring special attention by CS project managers. Scientific requirements or national practices do not always encompass the nature of CS projects. Based on the identified challenges, it is recommended that university libraries focus their services on 1) identifying legal and ethical issues that the project managers should be aware of in their projects, 2) elaborating these issues in a Terms of Participation that also specifies data handling and sharing to the citizen scientist, and 3) motivating the project manager to good data handling practises. Adhering to the FAIR principles and good RDM practices in CS projects will continuously secure contextualisation and data quality. High data quality increases the value and reuse of the data and, therefore, the empowerment of the citizen scientists.

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公民科学项目中的研究数据管理挑战和图书馆支持服务建议。范围界定综述和案例研究

公民科学（CS）项目是数据聚合和协调新时代的一部分，有助于不同数据集之间的互联。随着FAIR原则和系统研究数据管理（RDM）实践的出现，增加CS数据的价值和重用受到了越来越多的关注，这些原则和实践通常由大学图书馆推广。然而，CS的RDM举措似乎是多样化的，CS在RDM方面是否有特殊需求尚不清楚。因此，本文的目的首先是确定CS项目的RDM挑战，其次是讨论大学图书馆如何支持任何此类挑战。对丹麦CS项目进行了范围界定审查和案例研究，以确定RDM的挑战。选择48篇文章进行数据提取。四位学术项目负责人就其CS项目中的RDM实践接受了采访。审查和案例研究中发现的挑战和建议往往不是针对CS的。然而，寻找CS数据、吸引特定人群、确定志愿者的归属以及处理包括健康数据在内的敏感数据是CS项目经理需要特别关注的一些挑战。科学要求或国家实践并不总是包含CS项目的性质。基于已确定的挑战，建议大学图书馆将其服务重点放在1）确定项目经理在其项目中应该意识到的法律和道德问题，2）在参与条款中详细说明这些问题，该条款还规定了公民科学家的数据处理和共享，以及3）激励项目经理进行良好的数据处理实践。在CS项目中坚持FAIR原则和良好的RDM实践将持续确保情境化和数据质量。高数据质量增加了数据的价值和重复使用，从而增强了公民科学家的能力。

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

Data Science Journal Computer Science-Computer Science (miscellaneous)

CiteScore

5.40

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.