REVISITING SHAKESPEARE'S WORLD: OPTIMIZING DATA OUTCOMES AND INVESTIGATING CONTRIBUTOR DYNAMICS

Abstract

In this study, we present our work processing data output from Shakespeare's World (2015-2019), an early transcription project hosted on the Zooniverse online crowdsourcing platform. We refined the dataset to make it more amenable to low-code tools such as OpenRefine, enabling easier exploration and reuse. Utilizing the cleaned dataset, we also explored Shakespeare's World volunteers’ contribution patterns. By documenting our process of cleaning the outcome dataset, we provide steps and insights that may be useful for other transcription projects working with data derived from the Zooniverse platform. In addition to offering one plausible way to clean and analyze Zooniverse outcome data, our study also reveals the significant contributions from both anonymous and registered Shakespeare’s World volunteers; the challenges in maintaining participation over the project’s lifespan; and how the original aggregation protocol, which was designed specifically to combine multiple transcriptions by Shakespeare’s World volunteers, resulted in fewer successfully transcribed lines than expected. These findings have broader implications for project design, volunteer engagement, and data management practices in online crowdsourced transcription projects.