Related papers: Excel Spreadsheet Analyzer
The increasing availability of high-quality optical and near-infrared spectroscopic data, as well as advances in modelling techniques, have greatly expanded the scientific potential of spectroscopic studies. However, the software tools…
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python…
Most of the solar physicists use very expensive software for data reduction and visualization. We present hereafter a reliable freeware solution based on the Python language. This is made possible by the association of the latter with a…
A spreadsheet usually starts as a simple and single-user software artifact, but, as frequent as in other software systems, quickly evolves into a complex system developed by many actors. Often, different users work on different aspects of…
Spreadsheets are the most popular end-user programming software, where formulae act like programs and also have smells. One well recognized common smell of spreadsheet formulae is nest-IF expressions, which have low readability and high…
Computer simulation has become one of the most important tools in scientific research in many disciplines. Benefiting from the dynamical trajectories regulated by versatile interatomic interactions, various material properties can be…
All users of spreadsheets struggle with the problem of errors. Errors are thought to be prevalent in spreadsheets, and in some instances they have cost organizations millions of dollars. In a previous study of 50 operational spreadsheets we…
Exploratory visual data analysis tools empower data analysts to efficiently and intuitively explore data insights throughout the entire analysis cycle. However, the gap between common programmatic analysis (e.g., within computational…
Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists' burden on data preparation and model selection, few have targeted the…
AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining various established techniques, creating a unified,…
BEANS software is a web based, easy to install and maintain, new tool to store and analyse data in a distributed way for a massive amount of data. It provides a clear interface for querying, filtering, aggregating, and plotting data from an…
Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language models (LLMs). In response, we introduce SpreadsheetLLM,…
In the ever-evolving landscape of scientific computing, properly supporting the modularity and complexity of modern scientific applications requires new approaches to workflow execution, like seamless interoperability between different…
Machine learning applications, especially in the fields of me\-di\-cine and social sciences, are slowly being subjected to increasing scrutiny. Similarly to sample size planning performed in clinical and social studies, lawmakers and…
As the volume of publicly available data continues to grow, researchers face the challenge of limited diversity in benchmarking machine learning tasks. Although thousands of datasets are available in public repositories, the sheer abundance…
The AI revolution is data driven. AI "data wrangling" is the process by which unusable data is transformed to support AI algorithm development (training) and deployment (inference). Significant time is devoted to translating diverse data…
Spreadsheet engineering methodologies are diverse and sometimes contradictory. It is difficult for spreadsheet developers to identify a spreadsheet engineering methodology that is appropriate for their class of spreadsheet, with its unique…
Our term "structure discovery" denotes the recovery of structure, such as the grouping of cells, that was intended by a spreadsheet's author but is not explicit in the spreadsheet. We are implementing structure discovery tools in the…
We describe how Python can be leveraged to streamline the curation, modelling and dissemination of drug discovery data as well as the development of innovative, freely available tools for the related scientific community. We look at various…
We present a spreadsheet implementation, extended with sheet-defined functions, that allows users to define functions using only standard spreadsheet concepts such as cells, formulas and references, requiring no new syntax. This implements…