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Citizen science

Amateur scientific research

  • 69 min read
  • 12 chapters

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Through Research, Science and Social science

The applications and functions of citizen science in research projects are multifaceted. Citizen science can be used as a methodology in which public volunteers help in data collection and classification, thereby improving the scientific community's capacity. Citizen science observations can be used to uncover general trends in native habitats across broad spatial and temporal scales, extending data collection beyond the capacity of individual researchers. These observations can then be integrated with findings from designed experiments to provide a more complete understanding of natural variation and ecological processes. Citizen science can also involve more direct involvement from the public, with communities initiating projects researching environment and health hazards within their own communities. Participation in citizen science projects also educates the public about the scientific process and increases awareness about different topics. Some schools incorporate citizen science projects as part of their teaching curricula for this very purpose. More recently, libraries have also emerged as community hubs for citizen science.

Background

The first use of the term "citizen science" appeared in a January 1989 issue of the MIT Technology Review, which featured three community-based labs studying environmental issues. In the 21st century, the number of citizen science projects, publications, and funding opportunities has increased. Citizen science has been used more over time, a trend helped by technological advancements. Digital citizen science platforms, such as Zooniverse and iNaturalist, store large amounts of data for many projects and are a place where volunteers can learn how to contribute to projects. For some projects, participants are instructed to collect and enter data, such as the species they observed, into large digital global databases. For other projects, participants help classify data on digital platforms. Citizen science data is also being used to develop machine learning algorithms. An example is using volunteer-classified images to train machine learning algorithms to identify species. While global participation and global databases are found on online platforms, the uniformity of data from contributors across different locations is not guaranteed. Concerns over potential data quality issues in citizen science projects, including measurement errors and biases, are recognized in the scientific community. However, statistical solutions and best practices are available to assist in addressing these concerns.

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