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Title Data Exploration and Analysis with Jupyter Notebooks
  • 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
Creative Commons CC logoPublished 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.