Author: Holm Rod, T.
Paper Title Page
TUBPL02 Enabling Open Science for Photon and Neutron Sources 694
  • A. Götz, J. Bodera Sempere, A. Campbell, A. De Maria Antolinos, R.D. Dimper, J. Kieffer, V.A. Solé, T. Vincet
    ESRF, Grenoble, France
  • M. Bertelsen, T. Holm Rod, T.S. Richter, J.W. Taylor
    ESS, Copenhagen, Denmark
  • N. Carboni
    CERIC-ERIC, Trieste, Italy
  • S. Caunt, J. Hall, J.F. Perrin
    ILL, Grenoble, France
  • J.C. E, H. Fangohr, C. Fortmann-Grote, T.A. Kluyver, R. Rosca
    EuXFEL, Schenefeld, Germany
  • F.M. Gliksohn
    ELI-DC, Brussels, Belgium
  • R. Pugliese
    Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
  • L. Schrettner
    ELI-ALPS, Szeged, Hungary
  Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 823852
Photon and Neutron sources are producing more and more petabytes of scientific data each year. At the same time scientific publishing is evolving to make scientific data part of publications. The Photon and Neutron Open Science Cloud (PaNOSC*) project is a EU financed project to provide scientific data management for enabling Open Science. Data will be managed according to the FAIR principles. This means data will be curated and made available under an Open Data policy, findable, interoperable and reusable. This paper will describe how the European photon and neutron sources on the ESFRI** roadmap envision PaNOSC as part of the European Open Science Cloud***. The paper will address the issues of data policy, metadata, data curation, long term archiving and data sharing in the context of the latest developments in these areas.
slides icon Slides TUBPL02 [14.942 MB]  
DOI • reference for this paper ※  
About • paper received ※ 30 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
TUCPR02 Data Exploration and Analysis with Jupyter Notebooks 799
  • 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
  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).
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.
slides icon Slides TUCPR02 [15.943 MB]  
DOI • reference for this paper ※  
About • paper received ※ 24 September 2019       paper accepted ※ 20 October 2019       issue date ※ 30 August 2020  
Export • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)