Eye-tracking data of the 2017 Aesthetic value project (NESP 3.2.3, Griffith Institute for Tourism Research)

This dataset consists of three data folders for the eye-tracking experiment conducted within the NESP 3.2.3 project (Tropical Water Quality Hub): Folder (1) The folder of Eye-tracking videos contains 66 Tobii recordings of participants’ eye movements on screen, Folder (2) The Heatmaps folder includes 21 heatmaps created by Tobii eye-tracking software on the basis of 66 participants’ data and Folder (3) The input folder has 21 original pictures used in eye-tracking experiment. Moreover, The dataset also includes 1 excel file representing eye-tracking data extracted from Tobii software and participant interview results, 1 SPV. file as the input of SPSS data analysis process and 1 SPV. file as the output of data analysis process. Methods: This dataset resulted from both input and output data of eye-tracking experiments. The input includes 21 underwater pictures of the Great Barrier Reef, selected from online searching with the keyword “Great Barrier Reef”. These pictures are imported to Tobii eye-tracking software to design the eye-tracking experiments. 66 participants were recruited using convenience sampling in this study. They were asked to sit in front of a screen-based eye-tracking equipment (i.e. Tobii T60 eye-tracker) after providing informed consent. Participants were free to look at each picture on screen as long as they wanted during which their eye movements were recorded. They also rated each picture on a 10-point beauty scale (1-Not beautiful at all, 10-Very beautiful) and a 10-point expectation scale (1-Not at all, 10-Very much). After the experiment, 40 subjects were also interviewed to identify the areas of interest (AOI) in each picture and to rate the beauty of these AOIs. Eye-tracking data was then extracted from Tobii eye-tracking device including participants’ eye-tracking recordings, heatmaps (i.e. images showing viewers’ attention focus) and raw eye-tracking measures (i.e. picture beauty, time to first fixation, fixation count, fixation duration and total visit time) using XLSX. download format. Raw eye-tracking data was then imported to IBM SPSS using SAV. format for data analysis which results in a SPV. output file. Further information can be found in the following publication: Scott, N., Le, D, Becken, S., and Connelly, R. (2018 Submitted) Measuring perceived beauty of the Great Barrier Reef using eye tracking. Journal of Sustainable Tourism. Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15. Format: The project dataset includes 132 eye-tracking videos of AVI. format, 21 heatmaps of PNG. format, 21 pictures of JPEG. format, 1 XLSX. format document representing raw eye-tracking measures and interview data, 1 SAV. format document as the input of data analysis and 1 SPV. format file showing data analysis results. Data Dictionary: Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10: Names of pictures used in the eye-tracking experiment 2. 3Q1, 3Q2, 3Q3, 3Q4, 3Q5, 3Q6, 3Q7, 3Q8, 3Q9, 3Q10, 3Q11: Names of pictures used in the eye-tracking experiment 3. Raw Eye tracking Measurements excel spreadsheets: Tab - Picture: INDEX: the 10-point scale showed to participants VALUE: meaning of the 10-point scale Q1.1: Beauty score Q1.2: Expectation score Tab - Area of Interest (AOI)" TIME TO FIRST FIXATION_Q1: Time to first fixation in the picture Q1 (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) TOTAL FIXATION DURATION_Q1: Fixation duration in the picture Q1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. FIXATION COUNT_Q1: Fixation count in the picture Q1 (i.e. the average number of fixations in the picture). TOTAL VISIT DURATION_Q1: Total time visit for the picture Q1 (i.e. the average time participants spent looking at a picture). TIME TO FIRST FIXATION_AOI1: Time to first fixation in the AOI identified in the picture Q1 (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) TOTAL FIXATION DURATION_AOI1: Fixation duration in the AOI identified in the picture Q1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. FIXATION COUNT_AOI1: Fixation count in the AOI identified in the picture Q1 (i.e. the average number of fixations in the picture). TOTAL VISIT DURATION_AOI1: Total time visit for the AOI identified in the picture Q1 (i.e. the average time participants spent looking at a picture). Tab - AOI interview: AOI IDENTIFIED: The AOI that is the most mentioned by participants NUMBER OF PARTICIPANTS: the number of participants who mentioned the AOI in the previous column. BEAUTY MEAN: The average beauty score of the correspondent AOI rated by 40 participants. AOI-1: The AOI identified by the correspondent participant. RATING: the beauty score associated to the AOI identified by the correspondent participant. Tab - Analysis: REC: Recording PICTURE: Picture number BEAUTY: The average beauty score of the correspondent picture by 66 participants EXPECTATION: The average expectation score of the correspondent picture by 66 participants AOI BEAUTY: The average beauty score of the AOI identified in the correspondent picture by interviewed participants. PICTURE 1st TIME: The average time to first fixation in the correspondent picture (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) by 66 participants PFDURATION: The average fixation duration in the correspondent picture (i.e. the average length of all fixations during all recordings in the whole picture) by 66 participants PFCOUNT: The average fixation count in the correspondent picture (i.e. the average number of fixations in the picture) by 66 participants PTING VISIT: The average of total time visit for the correspondent picture (i.e. the average time participants spent looking at a picture) by 66 participants AOI 1stTIME: The average time to first fixation in the AOI identified in the correspondent picture (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) by 66 participants AOIFDURATION: The average fixation duration in the AOI identified in the correspondent picture (i.e. the average length of all fixations during all recordings in the whole picture) by 66 participants AOIFCOUNT: The average fixation count in the the AOI identified in correspondent picture (i.e. the average number of fixations in the picture) by 66 participants AOITIMEVISIT: The average of total time visit for the AOI identified in the correspondent picture (i.e. the average time participants spent looking at a picture) by 66 participants References: Scott, N., Le, D, Becken, S., and Connelly, R. (2018 Submitted) Measuring perceived beauty of the Great Barrier Reef using eye tracking. Journal of Sustainable Tourism. Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15. Data Location: This dataset is filed in the eAtlas enduring data repository at: data\nesp3\3.2.3_Aesthetic-value-GBR

Principal Investigator
Becken, Susanne, Professor Griffith Institute for Tourism Griffith University
Co Investigator
Connolly, Rod, Professor School of Environment & Australian Rivers Institute - Coast & Estuaries Griffith University
Co Investigator
Stantic, Bela, Professor School of Information and Communication Technology, Griffith Sciences, Griffith University
Co Investigator
Scott, Noel, Professor Griffith Institute for Tourism Research Griffith University
Co Investigator
Mandal, Ranju, Dr School of Information and Communication Technology Griffith University
Co Investigator
Le, Dung Griffith Institute for Tourism Research Griffith University
Point Of Contact
Becken, Susanne, Professor Griffith Institute for Tourism Griffith University s.becken@griffith.edu.au

Data collected from 28 Jan 2017 until 28 Jan 2018


Data Usage Constraints
  • Attribution 3.0 Australia