Replication Data for: Citizen scientists: unveiling motivations and characteristics influencing initial and sustained participation in an agronomic project

The dataset was gathered in the context of the “Soja in 1000 Tuinen” (Soy in 1000 Gardens) project which ran between May and October 2021.
For this, 1200 citizens were selected out of a pool of applicants to plant soybean in their gardens. These citizens had two main tasks.
Firstly, they reported on soybean growth and yield of ten soybean plants during five consecutive project steps. Secondly, halfway through the summer,
participants returned five out of ten plants back to the lab, where professional scientists checked them for root nodules. The project led to a big phenotypical dataset for each garden that could directly be related to the occurrence of nodules, as well as a collection of bacteria isolated from these nodules. Additionally, soil collected from each garden was analysed to determine its nutrient, chemical and microbial composition.
Combined with the phenotypical data, this provides a better fundamental insight into the environment needed for optimal soybean growth and nodulation.

With regards to societal impact, the project had two main goals:
(1) Increasing public awareness about the benefits of legumes and microbes for sustainable garden management and agriculture, and
(2) Characterising citizen scientists and the drivers behind their initial and sustained engagement, such that results from this study could inform recruitment strategies,
enhance project design, and foster long-term engagement in future citizen science initiatives.
For the latter, participants filled in a large survey at the beginning of the project.
In this survey, several constructs regarding motivation and other personal characteristics that could influence participation in the case study were questioned.
Further, the survey was also filled in by a panel of non-participants in the project, to be able to compare whether differences between citizen scientists and ‘the general public’ were present. Finally, the data set also contains measures of participation level for each participant, which were recorded during the project.

This dataset is the product of the survey and participation results.