@inproceedings {26121,
	title = {Binder 2.0 - Reproducible, interactive, sharable environments for science at scale},
	journal = {Python in Science Conference. Proceedings of the 17th Python in Science Conference},
	year = {2018},
	note = {​See the presentation at SciPy US 2018:\&nbsp;https://youtu.be/KcC0W5LP9GM},
	month = {07/2018},
	publisher = {SciPy},
	address = {Austin, Texas},
	abstract = {Binder is an open source web service that lets users create sharable, interactive, reproducible environments in the cloud. It is powered by other core projects in the open source ecosystem, including JupyterHub and Kubernetes for managing cloud resources. Binder works with pre-existing workflows in the analytics community, aiming to create interactive versions of repositories that exist on sites like GitHub with minimal extra effort needed. This paper details several of the design decisions and goals that went into the development of the current generation of Binder.},
	keywords = {binder, jupyter, open science, reproducibility},
	doi = {10.25080/Majora-4af1f417-011},
	url = {https://conference.scipy.org/proceedings/scipy2018/project_jupyter.html},
	author = {Bussonier, Matthias and Forde, Jessica and Freeman, Jeremy and Granger, Brian and Head, Tim and Holdgraf, Chris and Kelley, Kyle and Nalvarte, Gladys and Osheroff, Andrew and Pacer, M and Panda, Yuvi and Perez, Fernando and Ragan-Kelley, Benjamin and Willing, Carol}
}
@misc {25095,
	title = {Interactive CSE with IPython and Jupyter},
	howpublished = {SIAM CSE 2017, Atlanta, USA},
	year = {2017},
	publisher = {SIAM Conference on Computational Science and Engineering},
	abstract = {IPython and Jupyter provide tools for interactive computing that are widely used in science and education, and can benefit any CSE instructor, student, or researcher. We will show how to use IPython to improve your interactive explorations in Python, and how Jupyter notebooks enable interactive exploration, teaching, learning, and reproducibility, in Python as well as other languages such as MatLab, Julia, and R.The first part of the tutorial will focus on using IPython to get the most out of the tools provided by the Scientific Python community. The second part will cover Jupyter more broadly, including how working with the notebook document format can enable shareable, reproducible analyses.},
	url = {http://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=61492},
	author = {Ragan-Kelley, Benjamin and Jupyter, Project}
}
@misc {25093,
	title = {Jupyter and IPython facilitating open access and reproducible research},
	howpublished = {2nd Conference on Non-Textual Information, Hannover, Germany},
	year = {2017},
	abstract = {Jupyter notebooks provide a document-based interactive environment for performing and recording computation.\&nbsp;Notebook documents contain not only the code that is run, but prose and mathematics for describing the analysis, as well as recording the outputs of the computation, from plain text output to rich interactive media, such as HTML and javascript or images and video. Jupyter notebooks are being deployed widely as computational companions for publications, facilitation reproduction of results, and interactive exploration and modification of analyses, including by prominent scientific discoveries such as the LIGO experiment.\&nbsp;Being freely available, open source software, Jupyter and IPython aim to improve the accessibility of reproducible practices in computational science.},
	author = {Ragan-Kelley, Benjamin and Jupyter, Project}
}
@misc {25094,
	title = {Jupyter Notebooks Facilitating Productivity, Sustainability, and Accessibility of Data Science},
	year = {2017},
	type = {Poster},
	address = {SIAM Conference on Computational Science and Engineering, Atlanta, USA},
	abstract = {Jupyter notebooks provide a document-centric web-based environment for data science and education. The web-based platform allows Jupyter to be used as a local desktop application or portal to large scale computing resources. The document aspect facilitates recording, sharing, and reproducing analyses. The integration of notebooks into existing CSE research and education workflows can improve reproducible workflows and communication and collaboration. The interactive nature of the notebook environment enables productive exploration of libraries and analyses, and being based on web technologies allows the same productivity to extend even to large-scale computing resources, which often pose a challenge to productivity and accessibility. The document nature of Jupyter notebooks facilitates preserving and sharing computations and results. Being a free, public, open source project enables equal access to all students and researchers, and the web environment is more familiar and accessible to a much wider set of current and prospective researchers than traditional terminal environments. Due to Jupyter{\textquoteright}s language agnostic protocol, with support from dozens of programming languages, notebooks can be used in many scientific and mathematical domains, which can have diverse language preferences.},
	keywords = {data science, ipython, jupyter, sustainability},
	doi = {10.6084/m9.figshare.4696414.v1},
	url = {http://meetings.siam.org/sess/dsp_talk.cfm?p=82980},
	author = {Ragan-Kelley, Benjamin and Jupyter, Project}
}
@misc {25043,
	title = {Jupyter and its Horizons},
	howpublished = {EuroSciPy},
	year = {2016},
	abstract = {Project Jupyter provides building blocks for interactive and exploratory computing. These building blocks help make science reproducible across over 40 programming language (Python, Julia, R, etc.). Central to the project is the Jupyter Notebook, a web-based interactive computing platform that allows users to author data- and code-driven narratives - computational narratives - that combine live code, equations, narrative text, visualizations, interactive dashboards and other media.Jupyter is an active and growing collection of projects, split from IPython in 2015. Come learn about some of the exciting recent developments in the Jupyter project, as well as what what cool new things are coming soon. JupyterLab is the next generation of the Jupyter notebook interface. It is built on a modular javascript framework intended to allow building new applications using the Jupyter architecture, facilitating new uses of notebooks, as well as building other applications using the Jupyter architecture that are independent of notebooks. JupyterHub is a growing project for running a hosted notebook service, for classrooms or research groups, currently deployed at numerous schools and companies, big and small. Dashboards allow building interactive web applications from notebooks. ...And much more!},
	keywords = {jupyter, notebook},
	url = {https://www.euroscipy.org/2016/schedule/sessions/20/},
	author = {Ragan-Kelley, Benjamin and Jupyter, Project}
}
@inproceedings {25047,
	title = {Jupyter Notebooks: a publishing format for reproducible computational workflows},
	journal = {20th International Conference on Electronic Publishing},
	year = {2016},
	pages = {87-90},
	publisher = {IOS Press},
	abstract = {It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents.\&nbsp;},
	keywords = {notebook, reproducibility, research software},
	doi = {10.3233/978-1-61499-649-1-87},
	url = {http://ebooks.iospress.nl/publication/42900},
	author = {Kluyver, Thomas and Ragan-Kelley, Benjamin and Perez, Fernando and Granger, Brian and Bussonier, Matthias and Frederic, Jonathan and Kelley, Kyle and Hamrick, Jessica and Grout, Jason and Corlay, Sylvain and Ivanov, Paul and Avila, Dami{\'a}n and Abdallah, Safia and Willing, Carol and Jupyter, Project}
}
@misc {25039,
	title = {Jupyter: Notebooks in Multiple Languages for Data Science},
	howpublished = {PyData Amsterdam},
	year = {2016},
	publisher = {PyData},
	abstract = {Jupyter notebooks have become an invaluable tool for all kinds of data science. Originally developed as part of the IPython project, notebooks have evolved from a Python specific tool to support many programming languages; more than 50 different execution kernels have now been published. For all of these languages, notebooks are a way to record and describe a data science workflow, and then share it, publicly or privately, allowing the recipients to easily modify and execute the code.We\&rsquo;ll describe the architectural changes and decisions involved in the transition to supporting multiple languages, as well as our own experience in supporting data science languages ranging from C++ to R to Bash. You\&rsquo;ll also get a high-level understanding of how to create a new kernel, if a language you\&rsquo;re excited about is not yet supported.We\&rsquo;ll also highlight some of the current development work taking place in and around Jupyter, including redesigned UI, mechanisms for collaboration on notebooks, ways to share live, executable notebooks online, and projects that reuse the Jupyter machinery in different user interfaces.},
	keywords = {data science, jupyter, notebook, python},
	url = {http://pydata.org/amsterdam2016/schedule/presentation/19/index.html},
	author = {Ragan-Kelley, Benjamin and Kluyver, Thomas and Jupyter, Project}
}
@misc {25045,
	title = {JupyterHub: Deploying Jupyter Notebooks for students and researchers},
	howpublished = {JupyterDay Orsay},
	year = {2016},
	abstract = {Since the Jupyter notebook is a web-based environment, the notebook server can be run remotely, not just on your local machine. JupyterHub is a multi-user server, aimed at helping research groups and instructors host notebook servers for their users or students. By default, JupyterHub uses the local system users and PAM authentication, but it can be customized to use any authentication system, including GitHub, CILogon, Shibboleth, and more. The way single-user servers are spawned can also be customized to use services such as Docker, Kubernetes, or HPC cluster queuing systems. The tutorial will cover a basic deployment of JupyterHub on a single machine, then extending it to use docker and GitHub authentication, as well as general best practices for JupyterHub deployment.},
	keywords = {jupyter, jupyterhub, notebook},
	url = {http://reseau-loops.github.io/journee_2016_12_jupyter.html},
	author = {Ragan-Kelley, Benjamin and Jupyter, Project}
}
@misc {25040,
	title = {JupyterHub: Deploying Jupyter Notebooks for students and researchers},
	howpublished = {PyData London},
	year = {2016},
	type = {Tutorial},
	abstract = {The Jupyter Notebook is an interactive web-based tool for interactive programming and writing code-centric documents. Being a web-based environment, the notebook server can be run remotely, not just on your local machine. JupyterHub is a multi-user server, aimed at helping research groups and instructors host notebook servers for their users or students. By default, JupyterHub uses the local system users and PAM authentication, but it can be customized to use any authentication system, including GitHub, CILogon, Shibboleth, and more. The way single-user servers are spawned can also be customized to use services such as Docker, Kubernetes, or HPC cluster queuing systems. The tutorial will cover a basic deployment of JupyterHub on a single machine, then extending it to use docker and GitHub authentication, as well as general best practices for JupyterHub deployment.},
	keywords = {jupyter, jupyterhub, notebook},
	url = {http://pydata.org/london2016/schedule/presentation/59/},
	author = {Ragan-Kelley, Benjamin and Kelley, Kyle and Kluyver, Thomas}
}
@misc {25044,
	title = {Using IPython and Jupyter for reproducible research},
	howpublished = {Swedish Bioinformatics Workshop},
	year = {2016},
	abstract = {IPython and Jupyter are free, open source tools for interactive computing, reproducible research, and communication. Learn how the web-based Jupyter notebook can help you record, share, and publish your analysis, while IPython helps with the interactive exploration, enabling more productive and more reproducible workflows. Jupyter notebooks use an open protocol, allowing you to work in many languages, including Python, R, Julia, and more.},
	keywords = {jupyter, notebook, reproducibility},
	url = {http://sbw2016.se},
	author = {Ragan-Kelley, Benjamin and Jupyter, Project}
}
