Synaesthetic Engagement of Artificial Intelligence with Digital Arts and its Audience (AI TRACE): Questionnaire data and timing-and-tracking data gathered from digital arts exhibition visitors

The data presented in this data project were collected in the context of the research project “AI TRACE - Synaesthetic Engagement of Artificial Intelligence with Digital Arts and its Audience”. The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.)under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 782). AI TRACE aimed at developing an ethically compliant behavioural analysis and visualization tool in the form of a metalanguage that can be used in the museum sector to track, analyse and present data collected from exhibition visitors in the form of a personalized 3D digital object. AI TRACE showcases Artificial Intelligence subsystems. The data presented in this data project were collected during the Preparatory Activity event that took place in October 2021 during the 17th edition of the Athens Digital Arts Festival (ADAF). The research activity was hosted at the new premises of the Museum of Modern Greek Culture, at a specially designed exhibition space. The purpose of this activity was to collect data for methodological testing and for feeding the AI subsystem. The data files derived from the research activities and provided here are: Data that was gathered from a pre-visit questionnaire which was completed by 231 survey participants. The questionnaire explores visitors' motivations for visiting and preferences of a digital arts exhibition, their relationship with digital arts, their mood and attitude before the visit, and certain demographic characteristics. For the purpose of the research, visitors of the Athens Digital Arts Festival who voluntarily participated in the survey were approached. The dataset can be used to analyse various characteristics of museum visitors, such as their motivation to visit, their interests and their relationship with art, in relation to their personal characteristics (gender, age, status). Timing-and-tracking (T) data -more specifically time spent in each exhibit and trajectory- which were extracted from video recordings in the exhibition space. Regular festival visitors were recruited at the entrance of the exhibition area by the research assistants. Participants (N=273) were asked to sign an informed consent form before filling the online anonymous questionnaire and entering the exhibition space. A custom-made software was used to blur their faces during video recording for data anonymization purposes. The tracking data were extracted via BORIS (Behavioral Observation Research Interactive Software), an open-source event logging software for video/audio coding and live observations that is developed by the University of Torino. The datasets can be used to analyse visitors’ behavior in relation to demographic characteristics, their motivations, interests and relation to arts, as well as their pre-visit mood and attitudes. The data file containing both questionnaire data and timing-and-tracking (T) data for a subset of the sample (N=223). This data can be used in combination to analyse the behaviour of museum visitors in relation to their demographic characteristics, motivations for visiting, their preferences and their relationship with digital arts, as well as in relation to their mood and attitude before the visit.

本数据集项目所呈现的数据，源自研究项目“AI TRACE——人工智能与数字艺术及其受众的联觉互动（AI TRACE - Synaesthetic Engagement of Artificial Intelligence with Digital Arts and its Audience）”。该研究项目由希腊研究与创新基金会（Hellenic Foundation for Research and Innovation, H.F.R.I.）资助，属于其“支持博士后研究者的研究项目第2轮征集”范畴，项目编号为782。AI TRACE旨在开发一款符合伦理规范的行为分析与可视化工具，以元语言（metalanguage）的形式呈现，可应用于博物馆领域，用于追踪、分析并展示从展览观众处收集的、可转化为个性化3D数字对象的数据，同时该项目展示了人工智能子系统。本数据集项目所呈现的数据，采集自2021年10月举办的第17届雅典数字艺术节（Athens Digital Arts Festival, ADAF）筹备活动，本次研究活动在新落成的现代希腊文化博物馆的专属展览空间内开展，活动核心目的是为方法学测试及人工智能子系统的训练采集数据。本研究活动产生并在此提供的数据文件包括两类：其一为预访问问卷数据，共计231名受访者完成该问卷，问卷内容涵盖观众参观数字艺术展的动机与偏好、其与数字艺术的关联、参观前的情绪与态度，以及部分人口统计学特征，本次调研邀请雅典数字艺术节的观众自愿参与，参与者均为主动报名者；其二为时序追踪（Timing-and-tracking, T）数据，具体为从展览空间的录像中提取的各展区停留时长与游览轨迹数据。研究助理在展区入口招募了常规艺术节观众，共计273名参与者，所有参与者在填写线上匿名问卷并进入展区前，均签署了知情同意书。为保障数据匿名性，录制视频时使用定制开发的软件对参与者面部进行了模糊处理。上述追踪数据通过BORIS（行为观察研究交互软件，Behavioral Observation Research Interactive Software）提取，该软件为都灵大学开发的开源视频/音频编码与现场观察事件记录软件。本数据集可用于分析博物馆观众的多维度特征，包括其参观动机、兴趣爱好、与艺术的关联，结合其人口统计学特征（性别、年龄、身份）展开研究；亦可结合观众参观前的情绪与态度开展相关行为分析。此外，本数据集包含部分样本（N=223）的问卷数据及时序追踪（T）数据整合文件，可联合用于分析博物馆观众的行为特征，结合其人口统计学特征、参观动机、偏好、与数字艺术的关联，以及参观前的情绪与态度展开深入研究。