Abstract Data‐driven storytelling has gained prominence in journalism and other data reporting fields. However, the process of creating these stories remains challenging, often requiring the integration of effective visualizations with compelling narratives to form a cohesive, interactive presentation. To help streamline this process, we present an integrated authoring framework and system, D ata W eaver , that supports both visualization‐to‐text and text‐to‐visualization composition. D ata W eaver enables users to create data narratives anchored to data facts derived from “call‐out” interactions, i.e., user‐initiated highlights of visualization elements that prompt relevant narrative content. In addition to this “vis‐to‐text” composition, D ata W eaver also supports a “text‐initiated” approach, generating relevant interactive visualizations from existing narratives. Key findings from an evaluation with 13 participants highlighted the utility and usability of D ata W eaver and the effectiveness of its integrated authoring framework. The evaluation also revealed opportunities to enhance the framework by refining filtering mechanisms and visualization recommendations and better support authoring creativity by introducing advanced customization options.

Natural language and search interfaces intuitively facilitate data exploration and provide visualization responses to diverse analytical queries based on the underlying datasets. However, these interfaces often fail to interpret more complex analytical intents, such as discerning subtleties and quantifiable differences between terms like “bump’’ and “spike’’ in the context of COVID cases, for example. We address this gap by extending the capabilities of a data exploration search interface for interpreting semantic concepts in time series trends. We first create a comprehensive dataset of semantic concepts by mapping quantifiable univariate data trends such as slope and angle to crowdsourced, semantically meaningful trend labels. The dataset contains quantifiable properties that capture the slope-scalar effect of semantic modifiers like “sharply” and “gradually,” as well as multi-line trends (e.g., “peak,” “valley”). We demonstrate the utility of this dataset in SlopeSeeker, a tool that supports natural language querying of quantifiable trends, such as “show me stocks that tanked in 2010.” The tool incorporates novel scoring and ranking techniques based on semantic relevance and visual prominence to present relevant trend chart responses containing these semantic trend concepts. In addition, SlopeSeeker provides a faceted search interface for users to navigate a semantic hierarchy of concepts from general trends (e.g., “increase’’) to more specific ones (e.g., “sharp increase’’). A preliminary user evaluation of the tool demonstrates that the search interface supports greater expressivity of queries containing concepts that describe data trends. We identify potential future directions for leveraging our publicly available quantitative semantics dataset in other data domains and for novel visual analytics interfaces.

Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.’s semantic hierarchy model that catego-rizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model’s text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH’s text and chart integration capabilities when participants perform data exploration with the tool.

From dirty data to intentional deception, there are many threats to the validity of data-driven decisions. Making use of data, especially new or unfamiliar data, therefore requires a degree of trust or verification. How is this trust established? In this paper, we present the results of a series of interviews with both producers and consumers of data artifacts (outputs of data ecosystems like spreadsheets, charts, and dashboards) aimed at understanding strategies and obstacles to building trust in data. We find a recurring need, but lack of existing standards, for data validation and verification, especially among data consumers. We therefore propose a set of data guards: methods and tools for fostering trust in data artifacts.

Abstract Summary: Modern scientific investigation is generating increasingly larger datasets, yet analyzing these data with current tools is challenging. DIVE is a software framework intended to facilitate big data analysis and reduce the time to scientific insight. Here, we present features of the framework and demonstrate DIVE’s application to the Dynameomics project, looking specifically at two proteins. Availability and implementation: Binaries and documentation are available at http://www.dynameomics.org/DIVE/DIVESetup.exe. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

This paper describes exploration of uses of a computational storytelling environment on the Cardiology Unit of the Children's Hospital in Boston during the summer of 1997.Young cardiac patients ranging from age 7 to 16 used the SAGE environment to tell personal stories and create interactive characters, as a way of coping with cardiac ihness, hospitalizations, and invasive medical procedures.This pilot study is part of a larger collaborative effort between Children's Hospital and hZERL -A Mitsubishi Electric Research L&oratory to develop a web-based application, de Experience Journal, to assist patients and their families in dealing with serious medical illness.The focus of the paper is on young patients' uses of SAGE, on SAGE's aftordances in the context of the hospital, and on design recommendations for the development of future computational play kits.Preliminary analysis of children's stories indicates that children used different modes of interaction-direct, media&, and differ&-depending upon what personae the narrator chooses to take on.These modes seem to vary with the mindset and health condition of the Child.