Project Jupyter was born out of the IPython Project in 2014 as it evolved to support interactive data science and scientific computing across all programming languages.
rOpenSci is a software collective that provides R-based tools to enable access to scientific data repositories, full text of articles, and science metrics and also facilitate a culture shift in the scientific community toward reproducible research practices.
Software Carpentry is a volunteer organization whose goal is to make scientists more productive and their work more reliable by teaching them basic computing skills.
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
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.