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Astronomical researchers often think of analysis and visualization as separate tasks. In the case of high-dimensional data sets, though, interactive exploratory data visualization can give far more insight than an approach where data…
Mixed integer bilinear programs (MIBLPs) offer tools to resolve robotics motion planning problems with orthogonal rotation matrices or static moment balance, but require long solving times. Recent work utilizing data-driven methods has…
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…
Performing astronomical data analysis using only personal computers is becoming impractical for the very large data sets produced nowadays. As analysis is not a task that can be automatized to its full extent, the idea of moving processing…
With ever-increasing computational capabilities, robust and automated research workflows have become essential for orchestrating large numbers of interdependent simulations. However, significant technical expertise is still required to…
A recent study showed that more than 70% of researchers fail to reproduce their peers's experiments and more than half fail to reproduce their own experiments. Obviously, from a perspective of scientific quality this is a more than…
Computational notebooks have become popular for Exploratory Data Analysis (EDA), augmented by LLM-based code generation and result interpretation. Effective LLM assistance hinges on selecting informative context -- the minimal set of cells…
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.…
The end of Dennard scaling combined with stagnation in architectural and compiler optimizations makes it challenging to achieve significant performance deltas. Solutions based solely in hardware or software are no longer sufficient to…
There has been a large focus in recent years on making assets in scientific research findable, accessible, interoperable and reusable, collectively known as the FAIR principles. A particular area of focus lies in applying these principles…
We report a user-friendly software environment for battery data science. It is designed to streamline data management, data cleaning, and data analysis to help bridge the gap between the domain expertise of most battery scientists and the…
Interactive computational notebooks (e.g., Jupyter notebooks) are widely used in machine learning engineering (MLE) to program and share end-to-end pipelines, from data preparation to model training and evaluation. However, environment…
The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision…
Jupyter Notebooks have become a mainstream tool for interactive computing in every field of science. Jupyter Notebooks are suitable as companion applications for Science Gateways, providing more flexibility and post-processing capability to…
Multimodal representation alignment is pivotal for large language models and robotics. Traditional methods are often hindered by cross-modal information discrepancies and data scarcity, leading to suboptimal alignment spaces that overlook…
The database community has developed numerous tools and techniques for data curation and exploration, from declarative languages, to specialized techniques for data repair, and more. Yet, there is currently no consensus on how to best…
At present, code recommendation tools have gained greater importance to many software developers in various areas of expertise. Having code recommendation tools has enabled better productivity and performance in developing the code in…
Programming environments typically separate the world of static code from the dynamic execution of programs. Developers must switch between writing code and observing its execution, often with limited tools to understand the relationship…
Jupyter notebooks enable developers to interleave code snippets with rich-text and in-line visualizations. Data scientists use Jupyter notebook as the de-facto standard for creating and sharing machine-learning based solutions, primarily…
It is important for researchers to understand precisely how data scientists turn raw data into insights, including typical programming patterns, workflow, and methodology. This paper contributes a novel system, called DataInquirer, that…