Abstract
Study information Design ideation study (N = 24) using eye tracking technology. Participants solved a total of twelve design problems while receiving inspirational stimuli on a monitor. Their task was to generate as many solutions to each problem and explain their solution briefly by thinking aloud. The study allows for getting further insight into how inspirational stimuli improve idea fluency during design ideation. This dataset features processed data from the experiment. Eye tracking data includes gaze data, fixation data, blink data, and pupillometry data for all participants. The study is based on the following research paper and follows the same experimental setup: Goucher-Lambert, K., Moss, J., & Cagan, J. (2019). A neuroimaging investigation of design ideation with and without inspirational stimuli—understanding the meaning of near and far stimuli. Design Studies, 60, 1-38. DOI Dataset Most files in the dataset are saved as CSV files or other human readable file formats. Large files are saved in Hierarchical Data Format (HDF5/H5) to allow for smaller file sizes and higher compression. All data is described thoroughly in 00_ReadMe.txt. The following processed data is included in the dataset: Concatenated annotations file of experimental flow for all participants (CSV). All eye tracking raw data in concatenated files. Annotated with only participant ID. (CSV/HDF5) Annotated eye tracking data for ideation routines only. A subset of the files above. (CSV/HDF5) Audio transcriptions from Google Cloud Speech-to-Text API of each recording with annotations. (CSV) Raw API response for each transcription. These files include time offset for each word in a recording. (JSON) Data for questionnaire feedback and ideas generated during the experiment. (CSV) Data for the post-experiment survey, including demographic information (TSV). Python code used for the open-source experimental setup and dataset construction is hosted at GitHub. Repository also includes code of how the dataset has been further processed.
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