Fwd: Announcement: Workshop on exploiting open data for machine learning training: can the Photon and Neutron community do it?

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Simon Billinge

Aug 23, 2023, 4:37:39 AM8/23/23
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This is for those of you interested in materials characterization usin gscattering/diffraction and/or AI machine-learning

---------- Forwarded message ---------
From: Martin Etter <martin...@desy.de>
Date: Fri, Aug 18, 2023 at 4:42 PM
Subject: Announcement: Workshop on exploiting open data for machine learning training: can the Photon and Neutron community do it?
To: <rietv...@ill.fr>

Dear colleagues,

I was asked if I can distribute this in the Powder Diffraction and Total
Scattering community. Therefore I wanted to send it to this mailing
list, since I think some of the large scale facility colleagues or other
colleagues from the field might be interested in this.

With best wishes,


Workshop on exploiting open data for machine learning training: can the
Photon and Neutron community do it?

During the last decade, most European Photon and Neutron (PaN)
facilities have adopted open data policies, making data available for
the benefit of the entire scientific community. At the same time,
machine learning (ML) is seen as an essential tool to address the
exponential growth of data volumes from PaN facilities.

Exploitation of experimental training datasets is a key component of
machine learning. The combination of ML algorithms and open data can
therefore be seen as an ideal marriage that would ultimately help the
entire community to tackle ‘big data’ challenges with more automation.

However, finding the right data to train machine learning algorithms is
a challenge and one of the motivations for making data FAIR is exactly
that: to provide scientists working on AI applications with quality
training datasets.

But what does 'quality' mean to PaN science communities? What metadata
fields are needed to find the data, to understand if it is suitable for
our research, and ultimately to be able to ingest it in our training
models? How can we provide sufficiently rich metadata? What would be the
enablers for more machine learning applications? How can we improve the
collaboration between data producers (domain scientists) and data
consumers (ML experts)?

Duration: from 2023-10-17 12:00 to 2023-10-18 14:00

Location: Synchrotron SOLEIL (~Paris) and online


More details: https://indico.synchrotron-soleil.fr/event/67/


Dr. Martin Etter
Notkestr. 85, Bldg. 47c, Rm. L118
22607 Hamburg, Germany
phone: +49-40-8998-5648
E-mail: martin...@desy.de
ORCID: http://orcid.org/0000-0002-5183-3585
ResearcherID: http://www.researcherid.com/rid/H-9715-2018

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Simon Billinge
Professor, Columbia University

Fedlu Kedir

Aug 23, 2023, 10:19:37 AM8/23/23
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Dear Prof.Billinge
Thank you for sharing workshop announcement.

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With Best Regards!!!
Fedlu Kedir Sabir (PhD), Associate Professor
Department of Applied Chemistry (Physical Chemistry Stream)
Associate Dean, for Research Project Management,ASTU
Associate Editor-in-Chief of EJSSD
Academic Editor of Journal of Nanomaterials
Guest Editor of Special Issue (Crystals, MDPI)
Former Head of Department of Applied Chemistry
Adama Science and Technology University, 
Cell Phone: +251911301849
P.O.Box: 1888
Submit a paper into the Special Issue of Crystals:

aserese alula

Aug 23, 2023, 10:48:51 AM8/23/23
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Dear Prof Simon,
Thank you for sharing this information.

With best regards,
Aseres Alula

Postgraduate student :Materials Science
Addis Ababa University

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