Explanation/Overview: Corresponding graph files of the extracted Zooniverse networks described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: Collaboration Technologies and Social Computing and published as part of the Lecture Notes in Computer Science book series (LNCS,volume 13632) here. Usernames have been anonymised. The graph files are in .gexf (graph exchange XML format) and .gml (graph modeling language) formats which can be used by common graph/network-analysis and visualisation tools such as Gephi. Purpose: The purpose of this dataset is to provide the basis for possible further examinations of the network structure, involving additional (not yet analysed) features such as the content of the comments etc. Relatedness: The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'. Content: The dataset contains distinct graph files for each of the analysed projects. For each graph file, there are nodes and edges and their associated attributes (i.e., each edge can have an attribute). For the edges, apart from source and target, we have as attributes: weight project_title body (i.e., text) created_at userRoles discussion_title discussion_id user_id board_title relation target_role For the nodes, the attributes are: user_id userRoles degree_reply (i.e., degree for the reply relation) in_degree_reply out_degree_reply degree_comment in_degree_comment out_degree_comment degree_total in_degree_total out_degree_total target_role Grouping: Each graph file represents all the comments for the respective project across its lifespan irrespective of any time slices. Edges represent the comments and users represent the nodes. While the different boards are still contained within the data, all boards occur in the data.