DOI: 10.5061/dryad.dfn2z34z3
title: SciStarter: exploring project connections across the citizen science landscape: a social network analysis of shared volunteers

description: Research on citizen science volunteers has historically focused on single
 projects, but emerging research suggests many volunteers engage in
 multiple projects. Platforms that host thousands of projects, like
 SciStarter.org, enable the exploration of volunteer activity across
 multiple projects. To learn more about the phenomenon of multi-project
 engagement, we carried out a descriptive social network analysis using
 digital trace data depicting volunteer activity on SciStarter.org from
 2017 to 2018. During this time period, our sample included 624 citizen
 science projects and 3,650 unique volunteers who engaged in these
 projects. We used these data to visualize and analyze project connection
 networks formed when volunteers join multiple projects. Volunteers joined
 an average of 2.93 projects spanning many different scientific disciplines
 (e.g., topics such as Health & Medicine, Ecology &
 Environment) and modes of participation (e.g., online, offline); 73% of
 volunteers joined 2 or more projects. Volunteer engagement in citizen
 science produced a complex network of project connections with low network
 centrality, low levels of homophily and clustering, and ample evidence of
 boundary spanning (e.g. based on topic or mode). The projects most central
 in the network, which were also the most popular, were those featured as
 affiliates on the website or in promotional email campaigns. By using a
 network approach to analyze digital trace data, our research illustrates
 the extent of multi-project, multi-disciplinary engagement on a
 third-party platform, laying the groundwork for researchers and platform
 managers to explore and facilitate multi-project engagement and its
 implications for the larger field of citizen science.
description: All data in these files were provided as anonymized digital trace
 data from SciStarter.org. See the description of the project coding
 protocol in Futch et al. (in review) for more details about the project
 codes.
description: All data in these two files are cleaned and ready to use. See the
 CodeBook tab on the Project Attribute file for more details about
 variables on that spreadsheet.
description: # SciStarter: exploring project connections across the citizen science
 landscape: a social network analysis of shared volunteers
 [https://doi.org/10.5061/dryad.dfn2z34z3](https://doi.org/10.5061/dryad.dfn2z34z3) ## Description of the data and file structure This dataset consists of digital trace data extracted from SciStarter.org (which existed as SciStarter.com at the time data collection was initiated). On Dec 6th, 2018, our research team received anonymized digital trace data of SciStarter members’ activity on the website between Sept 19th, 2017, and Dec 3rd, 2018. Sept 19th, 2017 marked the launch of SciStarter 2.0, which introduced the added functionality of member accounts, dashboards, and profile pages. Use of these secondary data was approved by the NC State University Institutional Review Board (IRB Protocol # 20934) prior to analysis. At the time of data collection, volunteers could click buttons to either “join” or “bookmark” projects of interest on the SciStarter website. Additionally, volunteers could check a box to indicate that they had previously joined a project. Clicking “join” sent the volunteer to the project’s website (unless that project was exclusively hosted on SciStarter), and it automatically added the project to a list of joined projects on a volunteer’s profile. Thus, the join function in SciStater could be considered “conversion”, or an expression of interest in joining a project. Clicking “bookmark” added the project to a list of bookmarked projects on the volunteers’ profile. Volunteer activities like “joins” and “bookmarks” were recorded in the digital trace data, along with an anonymized participant ID number. Analysis of the data contributions showed that “joining” a project is a better predictor of subsequent contributions than “bookmarking” a project; we were able to confirm that at least 30% of volunteers who clicked “join” on an affiliate project later contributed to that project, while only 10% of volunteers who clicked “bookmark” ended up contributing. These estimates may be low, however, as volunteers would have to be logged in through SciStarter for their keystrokes to be accurately recorded. Thus, given the SciStarter metrics available through the API at the time of data collection, “join” was the best proxy for project participation. ### Files and variables #### File: Futch.2020\_NSNA-ProjectsAttributes.xlsx **Description:** Attributes associated with all citizen science projects in the dataset ##### Variables * Project Name = Common name of the project * Project ID = unique project ID number from SciStarter * R_online = online or offline project (1 = online, 0 = offline) * project_topic = topic of project (1 = Earth & Life sciences, 2 = Behavioral & Social Sciences, 3 = Engineering & Physical Sciences, 4 = Health & Medicine) * event = event or project (1 = event, 0 = actual project) * affiliate = SciStarter affiliate status (0 = not affiliate, 1 = part-time affiliate, 2 = affiliate) * campaign = whether or not project was featured in marketing campaign (0 = never featured, 1 = featured before data collection, 2 = featured during data collection, 3 = featured both during and before data collection) #### File: Futch.2020\_SNA-Edgelist.csv **Description:** Participants associated with each unique citizen science project ##### Variables * participant = participant ID number * project_name = Common name of project ## Code/software All data can be viewed in MS Excel ## Access information Data was derived from the following sources: * SciStarter.org