DOI: 10.5281/zenodo.7357747
title: [Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects

description: <strong>Explanation/Overview:</strong> Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in 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. The usernames have been anonymised. <strong>Purpose:</strong> The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R. <strong>Relatedness:</strong> 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'. <strong>Content:</strong> In this Zenodo entry, several files can be found. The structure is as follows (<code>files</code> and <strong>folders </strong>and<strong> </strong><em>descriptions</em>). <code>corresponding_calculations.html</code> <em>Quarto-notebook to view in browser</em> <code>corresponding_calculations.qmd</code> <em>Quarto-notebook to view in RStudio</em> <strong>assets</strong> <strong>data</strong> <strong>annotations</strong> <code>annotations.csv</code> <em>List of annotations made per day for each of the analysed projects</em> <strong>comments</strong> <code>comments.csv </code> <em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em> <strong>rolechanges</strong> <code>478_rolechanges.csv</code> <em>List of roles per user to determine number of role changes </em> <code>1104_rolechanges.csv</code> <em>...</em> <code>...</code> <strong>totalnetworkdata</strong> <strong>Edges</strong> <code>478_edges.csv</code> <em>Network data (edge set) for the given projects (without time slices)</em> <code>1104_edges.csv</code> <em>...</em> <code>...</code> <strong>Nodes</strong> <code>478_nodes.csv</code> <em>Network data (node set) for the given projects (without time slices)</em> <code>1104_nodes.csv</code> <em>...</em> <code>...</code> <strong>trajectories</strong> <em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em> <strong>478</strong> <strong>Edges</strong> <code>edges_4782016_q1.csv</code> <code>edges_4782016_q2.csv</code> <code>edges_4782016_q3.csv</code> <code>edges_4782016_q4.csv</code> <code>...</code> <strong>Nodes</strong> <code>nodes_4782016_q1.csv</code> <code>nodes_4782016_q4.csv</code> <code>nodes_4782016_q3.csv</code> <code>nodes_4782016_q2.csv</code> <code>...</code> <strong>1104</strong> <strong>Edges</strong> <code>...</code> <strong>Nodes</strong> <code>...</code> ... <strong>scripts</strong> <code>datavizfuncs.R</code> <em>script for the data visualisation functions, automatically executed from within </em><code>corresponding_calculations.qmd</code> <code>import.R</code> <em>script for the import of data, automatically executed from within </em><code>corresponding_calculations.qmd</code> <strong>corresponding_calculations_files</strong> f<em>iles for the html/qmd view in the browser/RStudio</em> <strong>Grouping:</strong> The data is grouped according to given criteria (e.g., <code>project_title </code>or <code>time</code>). Accordingly, the respective files can be found in the data structure