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SUMMARY:Accessibility best practices for authoring Jupyter notebooks - Ste
 phannie Jimenez Gacha\, Isabela Presedo-Floyd
DTSTART;TZID=America/Chicago:20230713T142000
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UID:pretalx-2023-VGAUQN@cfp.scipy.org
DESCRIPTION:So you’ve written the perfect notebook\, but do you know who
  can read it? As a notebook author you have great stories\, code\, and vis
 ualizations filling your work\, but how often do you consider accessibilit
 y? Jupyter notebooks seem like they are for everyone\, but how a notebook 
 gets written can greatly impact how usable it is for people with disabilit
 ies. We’ve curated authoring-focused best practices for notebook content
  to help your notebooks be more inclusive and reach a wider audience.
LOCATION:Amphitheater 204
URL:https://cfp.scipy.org/2023/talk/VGAUQN/
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SUMMARY:Python Array API Standard: Toward Array Interoperability in the Sc
 ientific Python Ecosystem - Ralf Gommers\, Stephannie Jimenez Gacha\, Leo 
 Fang\, Saul Shanabrook\, Travis Oliphant\, Matthew Barber\, Aaron Meurer\,
  Thomas J. Fan\, John Kirkham\, Stephan Hoyer\, Tyler Reddy\, Andreas Muel
 ler\, Athan Reines\, Mario\, Alexandre Passos
DTSTART;TZID=America/Chicago:20230714T112500
DTEND;TZID=America/Chicago:20230714T115500
DTSTAMP:20261017T031932Z
UID:pretalx-2023-T7DTX8@cfp.scipy.org
DESCRIPTION:The array API standard (https://data-apis.org/array-api/) is a
  common specification for Python array libraries\, such as NumPy\, PyTorch
 \, CuPy\, Dask\, and JAX. \n\nThis standard will make it straightforward f
 or array-consuming libraries\, like scikit-learn and SciPy\, to write code
  that uniformly supports all of these libraries. This will allow\, for ins
 tance\, running the same code on the CPU and GPU.\n\nThis talk will cover 
 the scope of the array API standard\, supporting tooling which includes a 
 library-independent test suite and compatibility layer\, what work has bee
 n completed so far\, and the plans going forward.
LOCATION:Amphitheater 204
URL:https://cfp.scipy.org/2023/talk/T7DTX8/
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