@misc {26190,
	title = {Binder 2.0: Next Gen of Reproducible Scientific Environments w/ repo2docker \& BinderHub},
	howpublished = {SciPy, USA},
	year = {2018},
	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.},
	url = {https://www.youtube.com/watch?v=KcC0W5LP9GM},
	author = {Holdgraf, Chris and Ragan-Kelley, Benjamin}
}
@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}
}
@inproceedings {26122,
	title = {Reproducible Research Environments with Repo2Docker},
	journal = {ICML 2018 Reproducible Machine Learning},
	year = {2018},
	month = {07/2018},
	publisher = {ICML},
	abstract = {Reproducibility challenges in machine learning often center on questions of software engineering practices. Researchers struggle to reproduce another scientist{\textquoteright}s work because they cannot translate a paper into code with similar results or run an author{\textquoteright}s code. repo2docker provides a simple tool for checking the minimum requirements to reproduce a paper by building a Docker image based on a repository path or URL.  Its goal is to minimize the effort needed to convert a static repository into a working software environment. By inspecting a repository for standard configuration files used in contemporary software engineering and leveraging containerization methods, repo2docker deterministically reproduces the environment of the author so the researcher can reproduce the author{\textquoteright}s experiments.},
	keywords = {docker, jupyter, python, reproducibility},
	url = {https://openreview.net/forum?id=B1lYOwuoxm},
	author = {Forde, Jessica and Head, Tim and Holdgraf, Chris and Panda, Yuvi and Nalvarte, Gladys and Pacer, M and Perez, Fernando and Ragan-Kelley, Benjamin and Sundell, Erik}
}
