3d_jupyter.bib

@inproceedings{10.1145/3093338.3104159,
author = {Milligan, Michael},
title = {Interactive HPC Gateways with Jupyter and Jupyterhub},
year = {2017},
isbn = {9781450352727},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3093338.3104159},
doi = {10.1145/3093338.3104159},
abstract = {MSI at the University of Minnesota has adopted a goal of supporting Interactive HPC as a first class service. This paper describes the implementation of a gateway for user-friendly, reproducible computing in an HPC environment using the Jupyter notebook server and Jupyterhub. For this project, we developed novel components and techniques enabling interoperability of Jupyter with the distinct requirements of an institutional HPC center. These include modules providing integration with batch job scheduling, control of job profiles, and integration with a central authentication service. To achieve software sustainability, we designed these elements for generic applicability, and several have now been accepted as components of the Jupyterhub open source project.},
booktitle = {Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact},
articleno = {63},
numpages = {4},
keywords = {management, High performance computing, interfaces, Jupyter},
location = {New Orleans, LA, USA},
series = {PEARC17}
}

@article{10.1093/bioinformatics/btx789,
    author = {Nguyen, Hai and Case, David A and Rose, Alexander S},
    title = "{NGLview–interactive molecular graphics for Jupyter notebooks}",
    journal = {Bioinformatics},
    volume = {34},
    number = {7},
    pages = {1241-1242},
    year = {2017},
    month = {12},
    abstract = "{NGLview is a Jupyter/IPython widget to interactively view molecular structures as well as trajectories from molecular dynamics simulations. Fast and scalable molecular graphics are provided through the NGL Viewer. The widget supports showing data from the file-system, online data bases and from objects of many popular analysis libraries including mdanalysis, mdtraj, pytraj, rdkit and more.The source code is freely available under the MIT license at https://github.com/arose/nglview. Python packages are available from PyPI and bioconda. NGLview uses Python on the server-side and JavaScript on the client. The integration with Jupyter is done through the ipywidgets package. The NGL Viewer is embedded client-side to provide WebGL accelerated molecular graphics.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btx789},
    url = {https://doi.org/10.1093/bioinformatics/btx789},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/34/7/1241/25119547/btx789.pdf},
}

@article{perkel_why_2018,
	title = {Why {Jupyter} is data scientists’ computational notebook of choice},
	volume = {563},
	issn = {0028-0836, 1476-4687},
	url = {http://www.nature.com/articles/d41586-018-07196-1},
	doi = {10.1038/d41586-018-07196-1},
	language = {en},
	number = {7729},
	urldate = {2021-03-15},
	journal = {Nature},
	author = {Perkel, Jeffrey M.},
	month = nov,
	year = {2018},
	pages = {145--146},
}

@misc{ipyvolume,
    author    = "Maarten Breddels",
    title     = "Ipyvolume",
    url       = "https://github.com/maartenbreddels/ipyvolume"
}


@misc{pythreejs,
    title     = "pythreejs",
    url       = "https://github.com/jupyter-widgets/pythreejs"
}