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

Signe Gadegaard,Søren Møller,Gertrud Stougård Thomsen,Katrine Flindt Holmstrand,Asger Væring Larsen,Karsten Kryger Hansen,Jitka Stilund Hansen

doi:10.5334/dsj-2021-025

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.

Highlights

The citizen science (CS) method has broad perspectives in using citizen-driven data collection to answer research questions and address societal challenges in all fields of science
Two questions formed the base of a systematic literature search: 1) What challenges are CS projects facing in terms of research data management (RDM)? 2) Are the FAIR principles applied for data in CS projects?
RDM CHALLENGES IDENTIFIED FROM LITERATURE SEARCH Knowledge of and adherence to the FAIR principles

Summary

Introduction

The citizen science (CS) method has broad perspectives in using citizen-driven data collection to answer research questions and address societal challenges in all fields of science. From a scientific perspective, involving interested members of the public in the generation of large, spatially and temporally highly complex data sets is one of the greatest benefits of CS. CS projects are often initiated as a collaboration between scientists and lay people, but initiatives driven by non-academic individuals, communities or private organisations are widespread globally. With the availability of new easy-to-use technologies, data collection by the volunteers increases in volume and sophistication. CS projects are part of a new era of data aggregation and harmonisation that facilitates interconnections between different datasets. CS data have the potential to form the foundation of innovations, new discoveries and policymaking

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