Description

Where scientists engage with citizen science, their main drive is usually to lay hands on the commonly voluminous data sources that citizen scientists are producing. That is when the fun starts.

Or not. It has been argued [Kosmala 2016] that data produced by citizen science and other data volunteering portals, while often very rich in content, large in volume, and up-to-date, can suffer from a lack of scientific experimental design. Since some citizen science projects do not adhere to robust data acquisition principles and standards, the data produced cannot be used for conducting “real” science.

Many different citizen science projects and corresponding portals exist, illustrating the large diversity of volunteers, all contributing to their best capacity, and each in her/his own way and strength.

Nice examples are OpenStreetMap (though not strictly citizen science, one of the largest volunteer data projects), ebird.org, and in the Netherlands also waarneming.nl and its nephew site observation.org, which caters for the international community.

The idea of this M.Sc. project proposal is to study the contributor bases in the understanding that they are not amorphous, but can be expected to display patterns in temporal behavior, spatial behavior, and inter-contributor processes. For instance, contributors may only accidentally contribute, or regularly, or excessively. Some contributors are or become experts, some become true specialists and even make it to validator ranks. One idea is to consider such different contributor roles as species, and study the contributor base with ecological metrics for species abundance [Grinberger 2019].

This study will look at patterns in the contributor base of waarneming.nl. We are particularly interested in three questions:

1. What is a useful definition of a contributor (sub)community and corresponding measurement metrics (e.g. size, geographic spread, age distribution, species richness indicators), if we want to be able to recognize distinct communities within the contributor base?

2. How can one computationally determine communities and community membership, and is there evidence of patterns or a community hierarchy within the overall contributor base?

3. What are the possible universal laws that we either see in the data, or that we expect to see? Do such laws also hold in other platforms, especially in OpenStreetMap?

Objectives and Methodology

Study patterns in space, time, behaviour of communities of contributors to citizen science. Specifically in context of a Dutch citizen science community.

Further reading

Kosmala, M., Wiggins, A., Swanson, A., Simmons, B., 2016. Assessing data quality in citizen science. Frontiers in Ecology and the Environment 14, 551–560. https://doi.org/10.1002/fee.1436
Grinberger A.Y., Minghini M., Mooney P., Juhász L. and Yeboah G. (2019) Bridging the Map? Exploring Interactions between the Academic and Mapping Communities in OpenStreetMap. Proceedings of the Academic Track at State of the Map 2019, Heidelberg (Germany), September 21-23, 2019, 1-2. https:/doi.org/10.5281/zenodo.3408639