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@InProceedings{fangohr:icalepcs2019-tucpr02,
author = {H. Fangohr and M. Beg and M. Bergemann and V. Bondar and S. Brockhauser and A. Campbell and C. Carinan and R. Costa and F. Dall'Antonia and C. Danilevski and J.C. E and W. Ehsan and S.G. Esenov and R. Fabbri and S. Fangohr and E. Fernandez-del-Castillo and G. Flucke and C. Fortmann-Grote and D. Fulla Marsa and G. Giovanetti and D. Goeries and A. Götz and J. Hall and S. Hauf and D.G. Hickin and T. Holm Rod and T. Jarosiewicz and E. Kamil and M. Karnevskiy and J. Kieffer and Y. Kirienko and A. Klimovskaia and T.A. Kluyver and M. Kuster and L. Le Guyader and A. Madsen and L.G. Maia and D. Mamchyk and L. Mercadier and T. Michelat and I. Mohacsi and J. Möller and A. Parenti and E. Pellegrini and J.F. Perrin and M. Reiser and J. Reppin and R. Rosca and D.B. Rück and T. Rüter and H. Santos and R. Schaffer and A. Scherz and F. Schlünzen and M. Scholz and M. Schuh and J.R. Selknaes and A. Silenzi and G. Sipos and M. Spirzewski and J. Sztuk and J. Szuba and J.W. Taylor and S. Trojanowski and K. Wrona and A.A. Yaroslavtsev and J. Zhu},
% author = {H. Fangohr and M. Beg and M. Bergemann and V. Bondar and S. Brockhauser and A. Campbell and others},
% author = {H. Fangohr and others},
title = {{Data Exploration and Analysis with Jupyter Notebooks}},
booktitle = {Proc. ICALEPCS'19},
pages = {799--806},
paper = {TUCPR02},
language = {english},
keywords = {FEL, data-analysis, experiment, software, detector},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-TUCPR02},
url = {https://jacow.org/icalepcs2019/papers/tucpr02.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-TUCPR02},
abstract = {Jupyter notebooks are executable documents that are displayed in a web browser. The notebook elements consist of human-authored contextual elements and computer code, and computer-generated output from executing the computer code. Such outputs can include tables and plots. The notebook elements can be executed interactively, and the whole notebook can be saved, re-loaded and re-executed, or converted to read-only formats such as HTML, LaTeX and PDF. Exploiting these characteristics, Jupyter notebooks can be used to improve the effectiveness of computational and data exploration, documentation, communication, reproducibility and re-usability of scientific research results. They also serve as building blocks of remote data access and analysis as is required for facilities hosting large data sets and initiatives such as the European Open Science Cloud (EOSC). In this contribution we report from our experience of using Jupyter notebooks for data analysis at research facilities, and outline opportunities and future plans.},
}