Hi all!  Deadline coming up soon, Feb 15th!  We'd like to encourage
abstracts for the 27th Congress and General Assembly of the International
Union of Crystallography, August 11–18, 2026, in Calgary, Alberta, Canada.

Specifically, please consider session MS-58, all about data quality and
reuse!  We are particularly interested in stories about how you are
handling big data volumes while maintaining high standards of metadata
provenance, but all interested candidates working with crystallographic
data challenges are encouraged to submit.

We are especially excited about our invited speakers, Loes Kroon-Batenburg,
Natalie Johnson, and Kamil Dziubek!

As part of our commitment to equity, diversity, and inclusion, we
particularly encourage abstract submissions from women, early-career
researchers, and members of groups historically underrepresented, in order
to work together toward building a strong, inclusive, and well-balanced
Congress.  Please share widely with potentially interested colleagues.

Links:

   - Abstract submission:  https://event.fourwaves.com/iucr-2026/submission
   - Registration: https://event.fourwaves.com/iucr-2026/registration
   - Bursaries & Grants:
   
https://event.fourwaves.com/iucr-2026/pages/b64022f8-0494-4478-b60e-41f7e25c74d0

Thanks!
Your co-chairs,

Aaron Brewster
Staff Scientist, Lawrence Berkeley National Laboratory
and
Christine Beavers
Synchrotron Business Development Manager, Rigaku

Abstract
Data collection volume and velocity has increased vastly, even considering
the last 5-10 years. High throughput, serial and electron crystallography
are but a few examples of techniques which not only produce high volumes of
data and results quickly but also challenge our notions of quality and
definitive results.  These challenges with data processing and workup
relate to simply "keeping up," as well as how to generate the best possible
quality results and what they are suitable to be used for.  Knock-on
consequences also exist for reuse and data-driven science, with potential
to populate databases with variable results and suitability questions for
follow-on work such as training AI models. Driven by practical examples and
(evolving) best practices, this general interest microsymposium will
consider the data challenges we currently face ranging from raw data
processing through refinement, quality, raw data archiving & reuse to
validity in data science.

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