CoCalc: Collaborative Calculation in the Cloud

Overview

logo CoCalc

Collaborative Calculation and Data Science

CoCalc is a virtual online workspace for calculations, research, collaboration and authoring documents. This includes working with the full scientific Python stack, SageMath, Julia, R Statistics, Octave, and many more. It also offers capabilities to author documents in LaTeX, R/knitr or Markdown, storing and organizing files, a web-based Linux Terminal, communication tools like a chatrooms, course management and more. It is the best choice for teaching remote scientific courses.

Website

Install CoCalc on your server or computer

You can easily use CoCalc on your own computer for free by running a Docker image.

History

CoCalc was formerly called SageMathCloud. It started to offer way more than just SageMath and hence outgrew itself. The name was coined in fall 2016 and changed around spring 2017.

Contributors

Current very active contributors

  • Harald Schilly
  • Hal Snyder
  • William Stein

Past contributors

  • Travis Scholl
  • John Jeng
  • Greg Bard
  • Rob Beezer
  • Keith Clawson
  • Tim Clemans
  • Andy Huchala
  • Jon Lee
  • Simon Luu
  • Nicholas Ruhland
  • Todd Zimmerman

... and many others: See https://github.com/sagemathinc/cocalc/graphs/contributors

Copyright/License

The copyright of CoCalc is owned by SageMath, Inc., and the source code here is released under the GNU Affero General Public License version 3+ subject to the "Commons Clause" License Condition v1.0.

See the included file LICENSE.md and Commons Clause.

None of the frontend or server dependencies of CoCalc are themselves GPL licensed; they all have non-viral liberal licenses. If want to host your own CoCalc at a company, and need a different AGPL-free license, please contact [email protected].

To clarify the above in relation to the "commons clause":

  • you can setup CoCalc at your own educational institution for teaching and research
  • any kind of work you do on CoCalc itself is not impacted
  • if you are unsure about whether your use of CoCalc is not allowed by "commons clause", do not hesitate to email us at [email protected].

Trademark

"CoCalc" is a registered trademark of SageMath, Inc.

Development

The scripts here might be helpful.  We do all of our development of CoCalc on https://cocalc.com itself.

Acknowledgements

Browserstack

We are grateful to BrowserStack for providing infrastructure to test CoCalc.

Google

We thank Google for donating over $150K in cloud credits since 2014 to support this project.

Owner
SageMath, Inc.
The company behind "CoCalc", a service to collaboratively use open source math software, Jupyter, LaTeX, and terminals in your browser
SageMath, Inc.
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8.1k Dec 30, 2022
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

Kenji Hiranabe 3.2k Jan 08, 2023
Read-only mirror of https://gitlab.gnome.org/GNOME/pybliographer

Pybliographer Pybliographer provides a framework for working with bibliographic databases. This software is licensed under the GPLv2. For more informa

GNOME Github Mirror 15 May 07, 2022
Doing bayesian data analysis - Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke

Doing_bayesian_data_analysis This repository contains the Python version of the R programs described in the great book Doing bayesian data analysis (f

Osvaldo Martin 851 Dec 27, 2022
Efficient Python Tricks and Tools for Data Scientists

Why efficient Python? Because using Python more efficiently will make your code more readable and run more efficiently.

Khuyen Tran 944 Dec 28, 2022
Python Data Science Handbook: full text in Jupyter Notebooks

Python Data Science Handbook This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. How to Use th

Jake Vanderplas 36.9k Dec 28, 2022
Discontinuous Galerkin finite element method (DGFEM) for Maxwell Equations

DGFEM Maxwell Equations Discontinuous Galerkin finite element method (DGFEM) for Maxwell Equations. Work in progress. Currently, the 1D Maxwell equati

Rafael de la Fuente 9 Aug 16, 2022
An interactive explorer for single-cell transcriptomics data

an interactive explorer for single-cell transcriptomics data cellxgene (pronounced "cell-by-gene") is an interactive data explorer for single-cell tra

Chan Zuckerberg Initiative 424 Dec 15, 2022
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Dec 31, 2022
Zipline, a Pythonic Algorithmic Trading Library

Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backte

Quantopian, Inc. 15.7k Jan 07, 2023
Data intensive science for everyone.

InVesalius InVesalius generates 3D medical imaging reconstructions based on a sequence of 2D DICOM files acquired with CT or MRI equipments. InVesaliu

Galaxy Project 1k Jan 08, 2023
A computer algebra system written in pure Python

SymPy See the AUTHORS file for the list of authors. And many more people helped on the SymPy mailing list, reported bugs, helped organize SymPy's part

SymPy 9.9k Jan 08, 2023
AnuGA for the simulation of the shallow water equation

ANUGA Contents ANUGA What is ANUGA? Installation Documentation and Help Mailing Lists Web sites Latest source code Bug reports Developer information L

Geoscience Australia 147 Dec 14, 2022
A framework for feature exploration in Data Science

Beehive A framework for feature exploration in Data Science Background What do we do when we finish one episode of feature exploration in a jupyter no

Steven IJ 1 Jan 03, 2022
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 915 Dec 29, 2022
A modular single-molecule analysis interface

MOSAIC: A modular single-molecule analysis interface MOSAIC is a single molecule analysis toolbox that automatically decodes multi-state nanopore data

National Institute of Standards and Technology 35 Dec 13, 2022
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

PyMC 7.2k Dec 30, 2022
collection of interesting Computer Science resources

collection of interesting Computer Science resources

Kirill Bobyrev 137 Dec 22, 2022
An open-source application for biological image analysis

CellProfiler is a free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure

CellProfiler 734 Jan 08, 2023
SeqLike - flexible biological sequence objects in Python

SeqLike - flexible biological sequence objects in Python Introduction A single object API that makes working with biological sequences in Python more

186 Dec 23, 2022