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Title	Data Exploration and Analysis with Jupyter Notebooks
Authors	H. Fangohr, M. Beg, M. Bergemann, V. Bondar, S. Brockhauser, C. Carinan, R. Costa, F. Dall’Antonia, C. Danilevski, J.C. E, W. Ehsan, S.G. Esenov, R. Fabbri, S. Fangohr, G. Flucke, C. Fortmann-Grote, D. Fulla Marsa, G. Giovanetti, D. Goeries, S. Hauf, D.G. Hickin, T. Jarosiewicz, E. Kamil, M. Karnevskiy, Y. Kirienko, A. Klimovskaia, T.A. Kluyver, M. Kuster, L. Le Guyader, A. Madsen, L.G. Maia, D. Mamchyk, L. Mercadier, T. Michelat, J. Möller, I. Mohacsi, A. Parenti, M. Reiser, R. Rosca, D.B. Rück, T. Rüter, H. Santos, R. Schaffer, A. Scherz, M. Scholz, A. Silenzi, M. Spirzewski, J. Sztuk, J. Szuba, S. Trojanowski, K. Wrona, A.A. Yaroslavtsev, J. Zhu EuXFEL, Schenefeld, Germany S. Brockhauser BRC, Szeged, Hungary A. Campbell, A. Götz, J. Kieffer ESRF, Grenoble, France H. Fangohr University of Southampton, Southampton, United Kingdom E. Fernandez-del-Castillo, G. Sipos The EGI Foundation, Amsterdam, The Netherlands J. Hall, E. Pellegrini, J.F. Perrin ILL, Grenoble, France T. Holm Rod, J.R. Selknaes, J.W. Taylor ESS, Copenhagen, Denmark J. Reppin, F. Schlünzen, M. Schuh DESY, Hamburg, Germany
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.
Funding	With support from EU’s H{2}020 grants 823852 (PaNOSC) and #676541 (OpenDreamKit), the Gordon and Betty Moore Foundation GBMF #4856, the EPSRC’s CDT (EP/L015382/1) and program grant (EP/N032128/1).
Paper	download TUCPR02.PDF [4.653 MB / 8 pages]
Slides	download TUCPR02_TALK.PDF [15.943 MB]
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Conference	ICALEPCS2019
Series	International Conference on Accelerator and Large Experimental Physics Control Systems (17th)
Location	New York, NY, USA
Date	05-11 October 2019
Publisher	JACoW Publishing, Geneva, Switzerland
Editorial Board	Karen S. White (ORNL, Oak Ridge, TN, USA); Kevin A. Brown (BNL, Upton, NY, USA); Philip S. Dyer (BNL, Upton, NY, USA); Volker RW Schaa (GSI, Darmstadt, Germany)
Online ISBN	978-3-95450-209-7
Online ISSN	2226-0358
Received	24 September 2019
Accepted	20 October 2019
Issue Date	30 August 2020
DOI	doi:10.18429/JACoW-ICALEPCS2019-TUCPR02
Pages	799-806
Copyright	Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.