Dataset for Design Ideation Study

DOI

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.

Pupil Capture, 3.3.0

Psychopy, 2021.1.4

Python, >3.6

Identifier
DOI https://doi.org/10.18710/PZQC4A
Metadata Access https://dataverse.no/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18710/PZQC4A
Provenance
Creator Abelson, Filip Gornitzka ORCID logo; Dybvik, Henrikke ORCID logo; Steinert, Martin ORCID logo
Publisher DataverseNO
Contributor Abelson, Filip Gornitzka; Dybvik, Henrikke; TrollLABS; Steinert, Martin; NTNU – Norwegian University of Science and Technology
Publication Year 2021
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact Abelson, Filip Gornitzka (TrollLABS, Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU)); Dybvik, Henrikke (TrollLABS, Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU))
Representation
Resource Type Eye tracking data; Dataset
Format text/plain; text/tab-separated-values; application/x-h5; application/zip; application/pdf
Size 13093; 767327; 1935109; 49209334; 540715825; 510702725; 1336354; 25860340; 286920385; 272694817; 581532; 33267; 7501; 2010; 295160
Version 1.1
Discipline Construction Engineering and Architecture; Design; Engineering; Engineering Sciences; Fine Arts, Music, Theatre and Media Studies; Humanities
Spatial Coverage Trondheim, Norway