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(100)

Knowledge sharing on deep learning in physics research using VISPA / Beer, Max (RWTH Aachen U.) ; Eich, Niclas (RWTH Aachen U.) ; Erdmann, Martin (RWTH Aachen U.) ; Fackeldey, Peter (RWTH Aachen U.) ; Fischer, Benjamin (RWTH Aachen U.) ; Hafner, Katharina (RWTH Aachen U.) ; Noll, Dennis Daniel Nick (RWTH Aachen U.) ; Rath, Yannik Alexander (RWTH Aachen U.) ; Rieger, Marcel (CERN ; RWTH Aachen U.) ; Temme, Alexander (RWTH Aachen U.) et al.
The VISPA (VISual Physics Analysis) project provides a streamlined work environment for physics analyses and hands-on teaching experiences with a focus on deep learning. VISPA has already been successfully used in HEP analyses and teaching and is now being further developed into an interactive deep learning platform. [...]
2020 - 5 p. - Published in : EPJ Web Conf. 245 (2020) 05040 Fulltext: PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.05040

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(91)

Facilitating collaborative analysis in SWAN / Tejedor, Enric (CERN) ; Bocchi, Enrico (CERN) ; Castro, Diogo (CERN) ; Gonzalez, Hugo (CERN) ; Lamanna, Massimo (CERN) ; Mato, Pere (CERN) ; Moscicki, Jakub (CERN) ; Piparo, Danilo (CERN)
SWAN (Service for Web-based ANalysis) is a CERN service that allows users to perform interactive data analysis in the cloud, in a “software as a service” model. It is built upon the widely-used Jupyter notebooks, allowing users to write - and run - their data analysis using only a web browser. [...]
2019 - 7 p. - Published in : EPJ Web Conf. 214 (2019) 07022 Fulltext from publisher: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.07022

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(91)

Data Science Environments: JupyterLab sharing and collaborative editing / Sieprawski, Marcin (speaker)
2023 - 651. HEP Computing; CS3 2023 - Cloud Storage Synchronization and Sharing External links: Talk details; Event details In : CS3 2023 - Cloud Storage Synchronization and Sharing

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(75)

JupyterLab+ScienceMesh: Collaborative Data Science in sync-and-share environment. / Sieprawski, Marcin (speaker) (Software Mind)
Collaborative Data Science becomes increasingly important, as organizations continue to become more data-driven, and Data Science projects/models become more complex. In the report **Critical Capabilities for Data Science and Machine Learning Platforms** (March 2021) Gartner predicts, that in near future collective intelligence in Data Science and cloud-based AI infrastructure will be among key factors for competitive advantage. This talk presents Distributed Data Science environments (part of [ScienceMesh](https://sciencemesh.io/)), which allow collaboration on [Jupyter Notebooks](https://jupyter.org/) in sync-and-share environment. Jupyter Notebook has become No1 platform used by data scientists to build interactive applications and to work with big data and AI. [...]
2022 - 1378. HEP Computing; CS3 2022 - Cloud Storage Synchronization and Sharing External links: Talk details; Event details In : CS3 2022 - Cloud Storage Synchronization and Sharing

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(70)

Integration of Jupyter Notebook Gallery with JupyterLab Interface of SWAN - Service for Web Based Analysis. / Nabil, Yasser
CERN SWAN, Service for Web based ANalysis, is a platform to perform interactive data analysis in the cloud. [...]
CERN-STUDENTS-Note-2022-127. - 2022
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(70)

Providing on-demand interactive notebook-based environments with transparent access to cloud storage and specialised hardware through the INFN Cloud Platform / Ciangottini, Diego (speaker) (INFN, Perugia (IT))
The Italian National Institute for Nuclear Physics (INFN) has a long history of designing and implementing large-scale computing infrastructures and applications. INFN has spent the past ten years heavily investing in developing solutions to enable, optimise and simplify transparent access to a multi-site federated Cloud infrastructure. [...]
2023 - 1354. HEP Computing; CS3 2023 - Cloud Storage Synchronization and Sharing External links: Talk details; Event details In : CS3 2023 - Cloud Storage Synchronization and Sharing

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(69)

A Web-Based Development Environment for Collaborative Data Analysis / Erdmann, M (Louvain U., CP3) ; Fischer, R (Louvain U., CP3) ; Glaser, C (Louvain U., CP3) ; Klingebiel, D (Louvain U., CP3) ; Komm, M (Louvain U., CP3) ; Müller, G ; Rieger, M ; Steggemann, J (CERN) ; Urban, M (Aachen, Tech. Hochsch.) ; Winchen, T (Aachen, Tech. Hochsch.)
Visual Physics Analysis (VISPA) is a web-based development environment addressing high energy and astroparticle physics. It covers the entire analysis spectrum from the design and validation phase to the execution of analyses and the visualization of results [...]
2014 - 5 p. - Published in : J. Phys.: Conf. Ser. 523 (2014) 012021
In : 15th International Workshop On Advanced Computing And Analysis Techniques In Physics Research, Beijing, China, 16 - 21 May 2013, pp.012021

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(62)

SWAN, Rucio, and Jupyter / Lassnig, Mario (speaker) (CERN)
The LHC experiments at CERN produce an enormous amount of scientific data. One of the main computing challenges is to make such data easily accessible by scientists and researchers. [...]
2021 - 978. HEP Computing; CS3 2021- Cloud Storage Synchronization and Sharing External links: Talk details; Event details In : CS3 2021- Cloud Storage Synchronization and Sharing

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(60)

Machine learning developments in ROOT / Bagoly, A (Eotvos U.) ; Bevan, A (Queen Mary, U. of London) ; Carnes, A (Florida U.) ; Gleyzer, S V (Florida U.) ; Moneta, L (CERN) ; Moudgil, A (Hyderabad, IIIT) ; Pfreundschuh, S (Chalmers U. Tech.) ; Stevenson, T (Queen Mary, U. of London) ; Wunsch, S (KIT, Karlsruhe) ; Zapata, O (Antioquia U.)
ROOT is a software framework for large-scale data analysis that provides basic and advanced statistical methods used by high-energy physics experiments. It includes machine learning tools from the ROOT-integrated Toolkit for Multivariate Analysis (TMVA). [...]
2017 - 8 p. - Published in : J. Phys.: Conf. Ser. 898 (2017) 072046 Fulltext: PDF;
In : 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, San Francisco, Usa, 10 - 14 Oct 2016, pp.072046

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(54)

A deep learning based search for a heavy CP-even Higgs boson in dileptonic H $\rightarrow$ WW decays with the CMS experiment / Fackeldey, Manfred Peter
CERN-THESIS-2018-249 -

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