Computational notebooks combine code, visualizations, and text in a single document.
Researchers, data analysts, and even journalists are rapidly adopting this new medium. We …

Deep neural networks ('deep learning') have emerged as a technology of choice to tackle
problems in speech recognition, computer vision, finance, etc. However, adoption of deep …

Computational notebooks-such as Azure, Databricks, and Jupyter-are a popular, interactive
paradigm for data scientists to author code, analyze data, and interleave visualizations, all …

Literate programming tools are used by millions of programmers today, and are intended to
facilitate presenting data analyses in the form of a narrative. We interviewed 21 data …

PyTerrier is a Python-based retrieval framework for expressing simple and complex
information retrieval (IR) pipelines in a declarative manner. While making use of the long …

Many modern data science applications build on data lakes, schema-agnostic repositories
of data files and data products that offer limited organization and management capabilities …

While gene expression data at the mRNA level can be globally and accurately measured,
profiling the activity of cell signaling pathways is currently much more difficult …

Robotics is powered by software. Software tools control the rate of innovation in robotics
research, drive the growth of the robotics industry, and power the education of future …

The increasing availability and decreasing cost of high-throughput sequencing has
transformed academic medical microbiology, delivering an explosion in available genomes …

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the
remarkable success of dataframe libraries in Rand Python, dataframes face performance …