TY - JOUR
T1 - Data Management Documentation in Citizen Science Projects: Bringing Formalisation and Transparency Together
AU - Thuermer, Gefion
AU - González Guardia, Esteban
AU - Reeves, Neal
AU - Corcho, Oscar
AU - Simperl, Elena
N1 - Funding Information:
The overwhelming majority of projects that have a DMP are institutionally obliged to it. The EC Horizon framework and US National Science Foundation require project DMPs at different stages: within the first six months of funded projects, with regular updates thereafter (EC n.d.), and as part of applications (NSF n.d.), respectively. The EC requires details about the data, its adherence to FAIR principles, and related resources, security, and ethical aspects; the US requires details on how data will be shared and made available to other researchers. While institutional pressure may not be the only driver for DMPs in projects, it is an efficient one: All H2020 projects that responded to our query had one.
Funding Information:
DMPs define the relation between the different stakeholders involved in data management and their respective responsibilities. This applies especially where personal data are concerned, as they define measures to ensure legal compliance. They outline how the research project host, staff, and citizen scientists work with the data, and how external researchers, policy-makers, or companies can interact with it. They are mandatory for research projects funded by most public funding agencies (e.g., the European Commission in the Horizon framework (EC n.d.), the Agency for Healthcare Research and Quality, the Department of Energy, or NASA; Adler 2015). Even where they are not required, they are recommended by funders like the Wellcome Trust (EAGDA 2017). Moreover, many research institutions require them as part of their data management strategy and risk assessments.
Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement numbers 824603 and 101058677.
Publisher Copyright:
© 2023 The Author(s).
PY - 2023/6/5
Y1 - 2023/6/5
N2 - 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.
AB - 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.
KW - citizen science
KW - data management
KW - data management documentation
KW - data management plans
KW - data quality
UR - http://www.scopus.com/inward/record.url?scp=85165162081&partnerID=8YFLogxK
U2 - 10.5334/cstp.538
DO - 10.5334/cstp.538
M3 - Article
VL - 8
JO - Citizen Science: Theory & Practice
JF - Citizen Science: Theory & Practice
IS - 1
M1 - 25
ER -