Related papers: Kishu: Time-Traveling for Computational Notebooks
Computational notebooks are intended to prioritize the needs of scientists, but little is known about how scientists interact with notebooks, what requirements drive scientists' software development processes, or what tactics scientists use…
In many real-world settings, problem instances that need to be solved are quite similar, and knowledge from previous optimization runs can potentially be utilized. We explore this for the Traveling Salesperson problem with time windows…
Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an…
This paper presents an empirical study on the feasibility of using Checkpoint/Restore In Userspace (CRIU) for run-time application migration between hosts, with a particular focus on edge computing and cloud infrastructures. The paper…
Using computational notebooks (e.g., Jupyter Notebook), data scientists rationalize their exploratory data analysis (EDA) based on their prior experience and external knowledge such as online examples. For novices or data scientists who…
Realistic simulations in engineering or in the materials sciences can consume enormous computing resources and thus require the use of massively parallel supercomputers. The probability of a failure increases both with the runtime and with…
Interactive development environments like Jupyter Notebooks enable incremental coding through cells with immediate feedback, but their linear structure and global namespace limit scalability for large software projects. We present CodePod,…
Computational reproducibility refers to obtaining consistent results when rerunning an experiment. Jupyter Notebook, a web-based computational notebook application, facilitates running, publishing, and sharing computational experiments…
Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows. The reproducibility of…
Jupyter notebooks are widely used for machine learning (ML) prototyping. Yet, few debugging tools are designed for ML code in notebooks, partly, due to the lack of benchmarks. We introduce JunoBench, the first benchmark dataset of…
Open science initiatives seek to make research outputs more transparent, accessible, and reusable, but ensuring that published findings can be independently reproduced remains a persistent challenge. In this paper we describe an AI-driven…
AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component of contemporary coding contexts. Among these environments, computational notebooks, such as Jupyter, are of particular interest as they provide rich…
Data exploration is an important aspect of the workflow of mixed-methods researchers, who conduct both qualitative and quantitative analysis. However, there currently exists few tools that adequately support both types of analysis…
This paper presents Lishu, a deployable web artifact for searching, monitoring, and interpreting literature from elite business and management journals. The system integrates the UTD-24 and Financial Times 50 (FT50) journal pools and…
Computational reproducibility is fundamental to trustworthy science, yet remains difficult to achieve in practice across various research workflows, including Jupyter notebooks published alongside scholarly articles. Environment drift,…
Despite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data…
Real-time collaborative editing in computational notebooks can improve the efficiency of teamwork for data scientists. However, working together through synchronous editing of notebooks introduces new challenges. Data scientists may…
Large language models (LLMs) have shown great promise in automating data science workflows, but existing models still struggle with multi-step reasoning and tool use, which limits their effectiveness on complex data analysis tasks. To…
Exploratory Data Analysis (EDA) is a routine task for data analysts, often conducted using flexible computational notebooks. During EDA, data workers process, visualize, and interpret data tables, making decisions about subsequent analysis.…
Computational notebooks have become increasingly popular for exploratory data analysis due to their ability to support data exploration and explanation within a single document. Effective documentation for explaining chart findings during…