update abstract

_publications/2024_loops.md

@@ -25,7 +25,7 @@ authors:
 - Alexander Lex
 - streit
 
-year: 2023
+year: 2024
 journal-short: OSF
 
 bibentry: article
@@ -62,9 +62,9 @@ pdf: 2024_loops.pdf
 code: https://github.com/jku-vds-lab/loops
 
 abstract: "
-Exploratory data science work is often described as an iterative process with cycles of obtaining, cleaning, profiling, analyzing, and interpreting data. These cycles create challenges within the linear structure of computational notebooks, leading to code quality, recall, and reproducibility issues.
-We present Loops, a set of visual support techniques for iterative and exploratory data analysis in computational notebooks. Loops leverages provenance information to provide direct feedback on the impact of changes made within the notebook. Through compact visual representations, we trace the evolution of the notebook over time, highlighting differences between versions. Detail views allow users to compare the cell content and output. Loops is compatible with various types of content present in notebooks, such as code, markdown, data, visualizations, or images.
-Loops not only improves the reproducibility of notebooks, but also supports analysts during their data science work by showing the effects resulting from changes and facilitating the comparison of multiple versions. We demonstrate our approach's utility and potential impact through two use cases and feedback from notebook users spanning various backgrounds.
+Exploratory data science is an iterative process of obtaining, cleaning, profiling, analyzing, and interpreting data. This cyclical way of working creates challenges within the linear structure of computational notebooks, leading to issues with code quality, recall, and reproducibility.
+To remedy this, we present Loops, a set of visual support techniques for iterative and exploratory data analysis in computational notebooks. Loops leverages provenance information to visualize the impact of changes made within a notebook. In visualizations of the notebook provenance, we trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and their respective differences. Analysts can explore these differences in detail in a separate view.
+Loops not only improves the reproducibility of notebooks but also supports analysts in their data science work by showing the effects of changes and facilitating comparison of multiple versions. We demonstrate our approach's utility and potential impact in two use cases and feedback from notebook users from various backgrounds.
 "
 
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