Datamations: Animated Explanations of Data Analysis Pipelines

Pu, Xiaoying; Kross, Sean; Hofman, Jake M.; Goldstein, Daniel G.

doi:10.1145/3411764.3445063

Cited by 20 publications

(18 citation statements)

References 42 publications

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“…Khan et al [29] develop "data tweening, " which involves animating the transformations occurring between two database queries. A "datamation" [41] animates plotted data points to showcase restructuring tasks, while Yang et al [54] propose fair-DAGs for identifying bias in preprocessing pipelines. These tools convey provenance information using sketches, interactivity, animation, and directed acyclic graphs (DAGs).…”

Section: Related Workmentioning

confidence: 99%

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Smallset Timelines: A Visual Representation of Data Preprocessing Decisions

Lucchesi

Kuhnert

Davis

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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Data preprocessing is a crucial stage in the data analysis pipeline, with both technical and social aspects to consider. Yet, the attention it receives is often lacking in research practice and dissemination. We present the Smallset Timeline, a visualisation to help reflect on and communicate data preprocessing decisions. A "Smallset" is a small selection of rows from the original dataset containing instances of dataset alterations. The Timeline is comprised of Smallset snapshots representing different points in the preprocessing stage and captions to describe the alterations visualised at each point. Edits, additions, and deletions to the dataset are highlighted with colour. We develop the R software package, smallsets, that can create Smallset Timelines from R and Python data preprocessing scripts. Constructing the figure asks practitioners to reflect on and revise decisions as necessary, while sharing it aims to make the process accessible to a diverse range of audiences. We present two case studies to illustrate use of the Smallset Timeline for visualising preprocessing decisions. Case studies include software defect data and income survey benchmark data, in which preprocessing affects levels of data loss and group fairness in prediction tasks, respectively. We envision Smallset Timelines as a go-to data provenance tool, enabling better documentation and communication of preprocessing tasks at large. CCS CONCEPTS• Human-centered computing → Visualization toolkits.

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Section: Related Workmentioning

confidence: 99%

“…Some support visualisation of the entire data pipeline [51], data lineage [8], or data flow [54]. Others are interactive [4,5,36] or animated [29,41]. To the best of our knowledge, none of the existing tools focus on visualising the decisions made during preprocessing in a way that is static and compact.…”

Section: Introductionmentioning

confidence: 99%

Smallset Timelines: A Visual Representation of Data Preprocessing Decisions

Lucchesi

Kuhnert

Davis

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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“…Xiaoying Pu is a Ph.D. candidate at the University of Michigan. In her research, she takes a human-centered approach to communicating uncertainty and statistics with visualizations [41,43]. She has organized a CHI 2021 SIG on visualization grammars [42] and contributed to a research transparency tutorial at IEEE VIS 2020.…”

Section: Instructorsmentioning

confidence: 99%

Transparent Practices for Quantitative Empirical Research

Wacharamanotham

Yang

et al. 2022

CHI Conference on Human Factors in Computing Systems Extended Abstracts

Self Cite

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Transparent research practices analytic methods, and data to be thoroughly evaluated and potentially reproduced. The HCI community has recognized research transparency as one quality aspect of paper submission and review since CHI 2021. This course addresses HCI researchers and students who are already knowledgeable about experiment research design and statistical analysis. Building upon this knowledge, we will present current best practices and tools for increasing research transparency. We will cover relevant concepts and skills in Open Science, frequentist statistics, and Bayesian statistics, and uncertainty visualization. In addition to lectures, there will be hands-on exercises: The course participants will assess transparency practices in excerpts of quantitative reports, interactively explore implications of analytical choices using RStudio Cloud, and discuss their fndings in small groups. In the fnal session, each participant will choose a case study based on their interest and assess its research transparency together with their classmates and instructors.enable the research design, materials, CCS CONCEPTS• Human-centered computing → HCI design and evaluation methods; Visualization techniques; Visualization design and evaluation methods.

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“…Secondly, some tools are proposed to visualize the semantics of data wrangling. For instance, Datamations [50] creates animated transitions for data transformations to explain the entire data analysis pipeline. SOMNUS [69] supports transformation inference based on function information and generates a glyph-based provenance graph to visualize the evolution of tables.…”

Section: Program Understanding In Data Wranglingmentioning

confidence: 99%

“…However, they cannot directly inform data workers of the type of transformation. Others, such as SOMNUS [69], Datamations [50], and Data Tweening [31], illustrate the semantics of data transformations using well-designed visualization. However, they rely on hand-crafted rules to parse scripts, resulting in poor generalizability and scalability.…”

Section: Introductionmentioning

confidence: 99%