Reusing Interactive Analysis Workflows

Gadhave, Kiran; Cutler, Zach; Lex, Alexander

doi:10.31219/osf.io/udqjr

Cited by 3 publications

(5 citation statements)

References 29 publications

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“…For example, if a dataset is refined over time, possibly because of discrepancies recorded as data hunches, a new version of a dataset could be overlaid with data hunches recorded for the old version of the dataset, to see whether the hunches expressed still apply. Overlaying data hunches could be combined with an explicit comparison of datasets [24]. However, such a workflow would incur additional visual complexity and hence would require dedicated methods to manage that complexity.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…By identifying data hunches as productive and insightful expert knowledge, we can re-interpret past work on collaborative visualization tools with this framing. For example, a visualization designer can incorporate commenting and discussion features to promote externalization of data hunches [30,47]; apply provenance tracking to record actions they took based on hunches to wrangle the data [15,19,24,25]; use visualization techniques like linked views and visualization states to show a collection of data hunches [35,46,67]. We see a wealth of opportunities for incorporating data hunches into old and new ways of visually analyzing data.…”

Section: Data Hunchesmentioning

confidence: 99%

See 1 more Smart Citation

Data Hunches: Incorporating Personal Knowledge into Visualizations

Lin¹,

Akbaba²,

Meyer³

et al. 2022

Preprint

Self Cite

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The trouble with data is that it frequently provides only an imperfect representation of a phenomenon of interest. Experts who are familiar with their datasets will often make implicit, mental corrections when analyzing a dataset, or will be cautious not to be overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as data hunches. We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Data Hunchesmentioning

confidence: 99%

Data Hunches: Incorporating Personal Knowledge into Visualizations

Lin¹,

Akbaba²,

Meyer³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Computational notebooks, such as Jupyter Notebooks [40] and R Markdown [41,42] are often discussed as a remedy for the issues we discuss here: they can be used to describe datasets and analysis steps, contain visualizations, and also contain executable code that (in theory [43]) ensures reproducibility of analysis. Due to these advantages, significant research has been devoted to understanding how analysts use computational notebooks [44,45,46] and to improve them [47,48,49,50].…”

Section: Methods Of Documenting Data and Analysismentioning

confidence: 99%

“…First, we need to make GUI-based visual analysis tools reproducible. The GUI-based tools our participants use do not support annotated histories or workflows, unlike various research prototypes [74,75,76,47]. For example, there is typically no way to comment on why some data was filtered out in the tools used by our participants.…”

Section: Design Opportunitiesmentioning

confidence: 99%

Here’s what you need to know about my data: Exploring Expert Knowledge’s Role in Data Analysis.

Lin¹,

Lisnic²,

Akbaba³

et al. 2023

Preprint

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Data driven decision making has become the gold standard in science, industry, and public policy. Yet data alone, as an imperfect and partial representation of reality, is often insufficient to make good analysis decisions. Knowledge about the context of a dataset, its strengths and weaknesses, and its applicability for certain tasks is essential. In this work, we present an interview study with analysts from a wide range of domains and with varied expertise and experience inquiring about the role of contextual knowledge. We provide insights into how data is insufficient in analysts workflows and how they incorporate other sources of knowledge into their analysis. We also suggest design opportunities to better and more robustly consider both, knowledge and data in analysis processes.

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“…Recent works have advocated for the development of interactive visualizations in such computational environments in order to support reproducibility, streamline analysis and increase adoption of visualization systems [43,65]. To support these goals, tools have been developed that help users embed interactive visualizations [65], create dashboards [64,67], and reuse workflows [14] in JupyterLab. Studies have also developed tools to condense notebooks for better collaboration [51] and communication through interactive data comics [37] and presentation slides [71].…”

Section: Visualizations In Computational Environmentsmentioning

confidence: 99%

Causalvis: Visualizations for Causal Inference

Guo

Karavani

Endert

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Causal inference is a statistical paradigm for quantifying causal effects using observational data. It is a complex process, requiring multiple steps, iterations, and collaborations with domain experts. Analysts often rely on visualizations to evaluate the accuracy of each step. However, existing visualization toolkits are not designed to support the entire causal inference process within computational environments familiar to analysts. In this paper, we address this gap with Causalvis, a Python visualization package for causal inference. Working closely with causal inference experts, we adopted an iterative design process to develop four interactive visualization modules to support causal inference analysis tasks. The modules are then presented back to the experts for feedback and evaluation. We found that Causalvis effectively supported the iterative causal inference process. We discuss the implications of our findings for designing visualizations for causal inference, particularly for tasks of communication and collaboration.

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Reusing Interactive Analysis Workflows

Cited by 3 publications

References 29 publications

Data Hunches: Incorporating Personal Knowledge into Visualizations

Data Hunches: Incorporating Personal Knowledge into Visualizations

Here’s what you need to know about my data: Exploring Expert Knowledge’s Role in Data Analysis.

Causalvis: Visualizations for Causal Inference

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