Physical Science

Project Jupyter

Project Jupyter is a community of open-source developers, scientists, educators, and data scientists. Its goal is to build open-source tools and create community that facilitates scientific research, reproducible and open workflows, education, computational narratives, and data analytics. Jupyter supports over 100 programming languages, and connects data analytics tools across a range of disciplines and communities.

There are several core projects of Jupyter that the Berkeley Institute for Data Science supports:

Cesium ML

Cesium is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions. Cesium has two main components—a Python library, and a web application platform that allows interactive exploration of machine learning pipelines. Take control over the workflow in a Python terminal or Jupyter notebook with the Cesium library, or upload your time-series files, select your machine learning model, and watch Cesium do feature extraction and evaluation right in your browser with the web application.

Opportunities for Biological Discovery From Quantitative Analysis of Vast Data Sets Acquired with Advanced Microscope Technologies

BIDS Machine Shop

Motivation

As more scientific fields move to intersect with computation, a need arises for software tools that can bridge the gap between the matter under investigation and computational principles/software engineering. Many scientists are specialists trained in their respective domains, so finding contributors with the necessary practical experience to implement computational tools—be it for statistical analysis, data wrangling, machine learning, visualization, or data management—can be difficult.

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