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LPA Online Workshop on Control Systems and Machine Learning

24.–27. Jan. 2022
Europe/Berlin Zeitzone
Timetable Timezone: Please select from dropdown menu to upper-right of page. Default is "Europe/Berlin".

This event is part of the Laser-Plasma Accelerator Seminars. Click here for more information, including data protection.

Sitzung

Big data storage, handling and access

26.01.2022, 16:00

Vorsitzende der Sitzung

Big data storage, handling and access

  • Andreas Döpp (LMU Munich)

Präsentationsmaterialien

Es gibt derzeit keine Materialien.

  1. 37. openPMD - A Scientific, Community Meta-Data Standard
    Dr. Axel Huebl (LBNL)
    26.01.22, 16:15

    The open standard for particle-mesh data (openPMD) is a community meta-data standard. openPMD adds scientific self-description in a machine-actionable format to data sets, which allows sharing data processing frameworks, chaining data through simulations and designing long analysis pipelines.
    Developed in the open as a FAIR data standard, openPMD is based on portable and scalable file formats...

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  2. 38. Data Pipelines for Small Teams and High-Rep-Rates
    Prof. Scott Feister
    26.01.22, 16:45

    The smallest laboratories in our field often have the highest repetition-rate experiments and the opportunity for the fattest data pipelines. One shortcut to achieving high-repetition-rate data analysis and storage, while retaining high-fidelity of datasets for post-analysis, is to implement an automated global shot counter in the lab and stamp it on every bit of scientific data. Data can then...

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  3. 39. Designing Flexible Data Structures for High-Rep-Rate Laser Experiments at UCLA
    Derek Schaeffer (Princeton University)
    26.01.22, 17:20

    Flexible data structures are critical when designing control systems and data storage for high-repetition-rate experiments, and they must take into account the full lifetime of the experiment from facility preparation to data analysis. We present lessons learned from our experience creating software to control high-rep-rate laser experiments at UCLA. We overview the main motivations and key...

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  4. 26. A scalable cloud-based platform for laser plasma experimental data collection, sharing, and machine-learning
    Dr. Archis S. Joglekar (Center for Ultrafast Optical Sciences, University of Michigan - Ann Arbor)
    26.01.22, 17:40

    We show a cloud-based platform for experimental data storage, management, and sharing. The platform has a UI that runs on a containerized Pythonic web application hosted by a container service. It is fronted by a lightweight authentication portal for a username and password. The experimental data is stored on a database service and object storage service. The container server, database, object...

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  5. 2. FAIR data storage for user experiments: Challenges and Opportunities
    Hans-Peter Schlenvoigt (HZDR)
    26.01.22, 18:00

    Experiments have per definition an unknown outcome and need adaption and improvement during their runtime. Especially user experiments are frequently changed or even just temporarily set up. On the other hand, policies develop towards open science what is undeniably useful in our field since drive lasers/facilities and experiments are cutting-edge but always have their peculiarities, hence...

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