Citizen Science Month: Are you concerned about the data quality in citizen science?
The reliability of research is often based on high-quality research data. The quality of data and its challenges are regularly discussed when talking about citizen science research data. In addition to researchers and the academic community, funders and participating citizens set expectations for the quality of data. High-quality, reusable data is a wish and benefit for everyone, including citizens. Research data must be trustworthy, since data collected or produced by citizen science, co-research, mass observation, etc. - whatever term is used - are also creating new innovations and a basis for decision-making.
Data requirements
The same requirements apply to citizen science data as to other research data. Data quality is defined by its validity, reliability, accuracy and correctness. What is specific to citizen science is that the data are collected and processed by citizens who are not trained as researchers. However, this does not automatically make the data of lower quality, but it does impose some specific requirements on the research process and professional researchers. These include, for example, training citizen scientists to follow and otherwise handle the data protocol, communicating about the data on a project-by-project basis and agreeing on the rights associated with the data collected. For example, the data may include photographs taken by citizens, for which copyright must be agreed. In addition, the challenge of data in citizen science can be the handling of personal data of citizens participating in research. Sometimes it also involves the sensitivity of location data.
Citizens and researchers may also have different expectations about data sharing. While academic researchers would prefer to wait until scientific publication, citizens may want data to be shared quickly. Data sharing should therefore be agreed at an early stage. There are also demands from research funders that research data should be "as open as possible and as closed as necessary".
In addition to data quality, the accuracy and precision of the data depends on the contextualisation of the data (telling how and under what circumstances the data was generated) and enabling data reuse and interoperability. Data contextualisation also includes metadata and agreeing on data ownership.
Data Management Plan and FAIR principles
Good scientific practice includes the development of a data management plan (DMP) and the managing of research data. A well-designed data management plan helps to manage research data throughout its life cycle. DMP can also improve the quality and reusability of data collected in citizen science. Responsible data management also takes into account research ethical considerations, which can easily arise in citizen science for the reasons mentioned above.
Handling research data according to the FAIR (Findable, Accessible, Interoperable, Re-usable) principles is part of responsible data management. Understanding and following FAIR principles will increase the reliability of the data and the research. They emphasize the potential for re-use of data, but also the importance of understanding data protection and access restrictions where appropriate. Compliance with the FAIR principles therefore also helps the further use of data collected in citizen science.
Support from the library: Learn more, join trainings or ask!
The library offers support for data management. We offer a comprehensive Data management guide as part of the Researcher's guide to responsible and open science. The Research data services, coordinated by the Library, provides comments on data management plans, trainings and guidance on data management issues.
For help and questions, please contact researchdata [at] tuni.fi (researchdata[at]tuni[dot]fi)
More information on Citizen Science Month
References
Balázs, B. et al. (2021). Data Quality in Citizen Science. In: Vohland, K., et al. The Science of Citizen Science. Springer, Cham. https://doi.org/10.1007/978-3-030-58278-4_8
FAIR principles. Fairdata.fi. https://www.fairdata.fi/en/about-fairdata/fair-principles/
Hansen, J. S. et al. (2021) Research Data Management Challenges in Citizen Science Projects and Recommendations for Library Support Services. A Scoping Review and Case Study. Data science journal. 20 (1), 1-29. https://doi.org/10.5334/dsj-2021-025
Tampere University Library. Researcher's guide to responsible and open science. Data management. https://libguides.tuni.fi/researchers-guide/data-management
Authors: Paula Nissilä and Taina Peltonen, Tampere University Library
