The value of good data visualization has already been shown in several scenarios. Still, it is not always easy to obtain it, as it depends on factors such as the dataset, the amount of data, task types, the user profile, the type of interaction, etc. To mitigate the challenges addressed, automated or semi-automated systems have been proposed, emphasizing rule-based/heuristic approaches and machine-learning models. However, many of these applications require specialized knowledge and present results (data visualizations) that are not flexible for customization. Papers have highlighted the ease of tools like ChatGPT in creating various tasks, including creating data charts. This facility, in addition to the intelligent computational model involved, is also due to the expressiveness used in the requests to execute the tasks by the users since these tools use Natural Language Interfaces. Despite adopting these tools overgrowing in different scenarios of society, studies on the best way to use them, integrate them into existing processes, or evaluative studies on their effectiveness or efficiency are still incipient. Thus, this paper will evaluate the workload for creating data visualization using ChatGPT 3.5. For assessment, the NASA Task Load Index (Nasa TLX) methodology was applied, and users with experience creating data visualization created two proposed scenarios. The preliminary results showed high temporal and mental demand, mainly due to the vocabulary used and the completeness of the user instructions. The average time to create and perform InfoVis tasks in two proposed evaluation scenarios was 33 and 44 minutes, and 14 queries were applied on average for both scenarios. The direct consequence was that the users have redone the requests and improved the instructions at each new iteration, and all users completed the proposed tasks.

In recent years, the physicalization area has expanded rapidly and has been utilized in numerous contexts, mostly in big open public areas or data-related places. Most data physicalizations have visual restrictions and no interaction. Hence, multiple technologies, including augmented reality (AR), have been applied to physical visualizations to address the above-mentioned issues. This research proposes dynamic data physicalization using AR virtual content (visual data items). The dynamic bar chart allows the configuration of numerical data to height, categorical data to color, and categorical data to the x-axis. The mobile augmented reality (MAR) application performs some Infovis tasks, such as settings, filters, details on demand, etc. A cloud server selects data, calculates visual elements or additional visualizations, calculates scales for physicalizing data, and enables the communication between the MAR application and dynamic physicalization. Lastly, dynamic and augmented data physicalization characteristics and scenarios are shown.

Data Physicalization is the area that investigates how physical objects can encode data through their physical (geometry and materials) and visual features. In the information visualization field, color plays a fundamental role in communicating and encoding data, and it is no different for data physicalization. However, in the real world, color perception can be influenced by the material that composes the data physicalization, the environment’s brightness, and the characteristics of the light-emitting device. Thus, this article presents a process to evaluate and select colors to compose color palettes (categorical, sequential, and divergent) for data physicalizations, considering the perceptual distance between the chosen colors and the brightness of the light-emitting device for evaluating the influence of neighboring colors. Additionally, users perform Information Visualization tasks (identification, comparison, maximums, minimums, etc.) with different color palettes of the industry and literature in a physical 3D bar chart composed of LED strips. The initial results showed better performance by participants using the proposed color scales in Information Visualization tasks than those using traditional digital color scales. Finally, the steps carried out culminated in the proposition of a pipeline for evaluating and creating color scales for data physicalization, considering the features of the light-emitting device and the material used in the data physicalization.

This study presents a perceptually grounded method for designing vibrotactile scales. These scales consist of structured sets of calibrated vibrotactile patterns to facilitate reliable differentiation between the values and help users perceive and interpret variations in stimulus through touch. Systematic methods for designing vibrotactile patterns and scales that are perceptually valid and effective for data encoding are lacking. To address this, we investigated vibrotactile properties, frequency, duration, and rhythm to design perceptually distinct scales that support user identification and ordering tasks. We conducted an iterative experimental process involving the estimation of perceptual limits for frequency and duration, the construction of perceptible scales, and user evaluation of the proposed scales. We created a custom vibrotactile device with multiple actuators for experimental support, and a set of three scales using parameters: frequency (four, five, and six values) and duration/rhythm (four, six, and eight values). Participants identified all vibrotactile patterns with 100% accuracy. Ordering task accuracy reached 100% for four value scales, 91.6% for six value scales, and 88.1% for eight value scales for some properties. The findings indicate that lower-granularity scales yielded smaller perceptual errors and greater consistency, whereas higher-granularity scales increased perceptual load and error dispersion. These results validate the use of perceptual limits for designing vibrotactile scales and support their integration into vibrotactile interfaces for encoding data.

Recent literature reviews point out that active data physicalizations still lack evaluation scenarios. These evaluations mainly focus on the perception of visual and physical characteristics of physicalizations and users' performance of analysis tasks. This paper addresses a proposed accuracy assessment of data physicalization for data representation. The data physicalization used is a physical and dynamic bar chart using 3D printed models (3D bars) and LED strips inside the bars, both controlled by microcontrollers (Arduino), being possible to represent different datasets. The accuracy assessment of this physicalization is based on the height and color of the 3D bars. For this, distance sensors measure the actual height values of the bars, and a computer vision camera/algorithm collects and estimates the RGB value of the bars. From original and measured values datasets, bar charts images are built to perform a similarity comparison between the images. The similarity metric is used as an accuracy metric of the bar heights to represent data and obtained as initial results value higher than 96%. Furthermore, an evaluation scenario of the distance between the reference and measured colors 2000) classifies most of them as having proper perception. Additionally, the accuracy loss was evaluated on data physicalization in a scenario of continuous use (ten configurations without any calibration), and the variation of errors was around 2%.