Restart Data Workbench Motivation Survey

The Restart Data Workbench motivation study was conducted within the ongoing H2020 project named ACTION (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc. Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement. The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting soil pollution through waste reduction/management in the Restart Data Workbench pilot supported by the ACTION project. More info on the pilot available at https://actionproject.eu/citizen-science-pilots/restart-data-workbench/. The Restart Data Workbench motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (https://doi.org/10.5281/zenodo.5753092). The survey was designed and administered using the Coney toolkit. The research object adopts the RO-Crate specification. Files made available within the research object are: *-procedure.ttl contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the Survey Ontology *-results.ttl contains the RDF representation of the answers collected using the Survey Ontology *-survey.tll contains a comprehensive RDF representation of the survey data using the Survey Ontology *-results.csv contains the CSV of the collected answers *-script.R is the R script developed to analyse the collected answers *-mean-var-motivating-questions.csv contains the computed mean and average for each question considered (observable variables) *-mean-var-motivating-factor.csv contains the computed mean and average for each motivation factor considered (latent variables) *-correlation-factors-global-motivation.csv contains the correlation analysis between each motivation factor and the global motivatio