772 research outputs found
Why Change My Design: Explaining Poorly Constructed Visualization Designs with Explorable Explanations
Although visualization tools are widely available and accessible, not
everyone knows the best practices and guidelines for creating accurate and
honest visual representations of data. Numerous books and articles have been
written to expose the misleading potential of poorly constructed charts and
teach people how to avoid being deceived by them or making their own mistakes.
These readings use various rhetorical devices to explain the concepts to their
readers. In our analysis of a collection of books, online materials, and a
design workshop, we identified six common explanation methods. To assess the
effectiveness of these methods, we conducted two crowdsourced studies (each
with N = 125) to evaluate their ability to teach and persuade people to make
design changes. In addition to these existing methods, we brought in the idea
of Explorable Explanations, which allows readers to experiment with different
chart settings and observe how the changes are reflected in the visualization.
While we did not find significant differences across explanation methods, the
results of our experiments indicate that, following the exposure to the
explanations, the participants showed improved proficiency in identifying
deceptive charts and were more receptive to proposed alterations of the
visualization design. We discovered that participants were willing to accept
more than 60% of the proposed adjustments in the persuasiveness assessment.
Nevertheless, we found no significant differences among different explanation
methods in convincing participants to accept the modifications.Comment: To be presented at IEEE VIS 202
Bias-Aware Design for Informed Decisions: Raising Awareness of Self-Selection Bias in User Ratings and Reviews
People often take user ratings and reviews into consideration when shopping
for products or services online. However, such user-generated data contains
self-selection bias that could affect people decisions and it is hard to
resolve this issue completely by algorithms. In this work, we propose to raise
the awareness of the self-selection bias by making three types of information
concerning user ratings and reviews transparent. We distill these three pieces
of information (reviewers experience, the extremity of emotion, and reported
aspects) from the definition of self-selection bias and exploration of related
literature. We further conduct an online survey to assess the perceptions of
the usefulness of such information and identify the exact facets people care
about in their decision process. Then, we propose a visual design to make such
details behind user reviews transparent and integrate the design into an
experimental website for evaluation. The results of a between-subjects study
demonstrate that our bias-aware design significantly increases the awareness of
bias and their satisfaction with decision-making. We further offer a series of
design implications for improving information transparency and awareness of
bias in user-generated content
FoodWise: Food Waste Reduction and Behavior Change on Campus with Data Visualization and Gamification
Food waste presents a substantial challenge with significant environmental
and economic ramifications, and its severity on campus environments is of
particular concern. In response to this, we introduce FoodWise, a
dual-component system tailored to inspire and incentivize campus communities to
reduce food waste. The system consists of a data storytelling dashboard that
graphically displays food waste information from university canteens, coupled
with a mobile web application that encourages users to log their food waste
reduction actions and rewards active participants for their efforts.
Deployed during a two-week food-saving campaign at The Hong Kong University
of Science and Technology (HKUST) in March 2023, FoodWise engaged over 200
participants from the university community, resulting in the logging of over
800 daily food-saving actions. Feedback collected post-campaign underscores the
system's efficacy in elevating user consciousness about food waste and
prompting behavioral shifts towards a more sustainable campus. This paper also
provides insights for enhancing our system, contributing to a broader discourse
on sustainable campus initiatives
The state of the Martian climate
60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
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