Fact sheets with vivid graphical design and intriguing statistical insights are prevalent for presenting raw data. They help audiences understand data-related facts effectively and make a deep impression. However, designing a fact sheet requires both data and design expertise and is a laborious and time-consuming process. One needs to not only understand the data in depth but also produce intricate graphical representations. To assist in the design process, we present DataShot which, to the best of our knowledge, is the first automated system that creates fact sheets automatically from tabular data. First, we conduct a qualitative analysis of 245 infographic examples to explore general infographic design space at both the sheet and element levels. We identify common infographic structures, sheet layouts, fact types, and visualization styles during the study. Based on these findings, we propose a fact sheet generation pipeline, consisting of fact extraction, fact composition, and presentation synthesis, for the auto-generation workflow. To validate our system, we present use cases with three real-world datasets. We conduct an in-lab user study to understand the usage of our system. Our evaluation results show that DataShot can efficiently generate satisfactory fact sheets to support further customization and data presentation.

Salt stress critically affects the physiological processes and morphological structure of plants, resulting in reduced plant growth. Salicylic acid (SA) is an important signal molecule that mitigates the adverse effects of salt stress on plants. Large pink Dianthus superbus L. (Caryophyllaceae) usually exhibit salt-tolerant traits under natural conditions. To further clarify the salt-tolerance level of D. superbus and the regulating mechanism of exogenous SA on the growth of D. superbus under different salt stresses, we conducted a pot experiment to examine the biomass, photosynthetic parameters, stomatal structure, chloroplast ultrastructure, reactive oxygen species (ROS) concentrations, and antioxidant activities of D. superbus young shoots under 0.3, 0.6, and 0.9% NaCl conditions, with and without 0.5 mM SA. D. superbus exhibited reduced growth rate, decreased net photosynthetic rate (Pn), increased relative electric conductivity (REC) and malondialdehyde (MDA) contents, and poorly developed stomata and chloroplasts under 0.6 and 0.9% salt stress. However, exogenously SA effectively improved the growth, photosynthesis, antioxidant enzyme activity, and stoma and chloroplast development of D. superbus. However, when the plants were grown under severe salt stress (0.9% NaCl condition), there was no significant difference in the plant growth and physiological responses between SA-treated and non-SA-treated plants. Therefore, our research suggests that exogenous SA can effectively counteract the adverse effect of moderate salt stress on D. superbus growth and development.

Direct conversion of cellulose to fine chemicals has rarely been achieved. We describe here an eco-benign route for directly converting various cellulose-based biomasses to glycolic acid in a water medium and oxygen atmosphere in which heteromolybdic acids act as multifunctional catalysts to catalyze the hydrolysis of cellulose, the fragmentation of monosaccharides, and the selective oxidation of fragmentation products. With commercial α-cellulose powder as the substrate, the yield of glycolic acid reaches 49.3%. This catalytic system is also effective with raw cellulosic biomass, such as bagasse or hay, as the starting materials, giving rise to remarkable glycolic acid yields of ∼30%. Our heteropoly acid-based catalyst can be recovered in solid form after reaction by distilling out the products and solvent for reuse, and it exhibits consistently high performance in multiple reaction runs.

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Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.

Throughout history, storytelling has been an effective way of conveying information and knowledge. In the field of visualization, storytelling is rapidly gaining momentum and evolving cutting-edge techniques that enhance understanding. Many communities have commented on the importance of storytelling in data visualization. Storytellers tend to be integrating complex visualizations into their narratives in growing numbers. In this paper, we present a survey of storytelling literature in visualization and present an overview of the common and important elements in storytelling visualization. We also describe the challenges in this field as well as a novel classification of the literature on storytelling in visualization. Our classification scheme highlights the open and unsolved problems in this field as well as the more mature storytelling sub-fields. The benefits offer a concise overview and a starting point into this rapidly evolving research trend and provide a deeper understanding of this topic.

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Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TRIPPLANNER, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TRIPPLANNER works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TRIPPLANNER applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.