Enthought proud to announce that the SciPy Conference organization will transition to NumFocus beginning in 2023

Since founding the SciPy Conference in 2001, Enthought has been an institutional sponsor and organizer of the popular annual event. 

To build on the twenty years of success and growth of the conference, Enthought will be transitioning the institutional organization of the SciPy Conference to NumFocus. This change will make the conference formally part of a not-for-profit organization, a more appropriate home for it, and enable the future direction and success of the conference to be even more community-driven.

NumFocus is an organization dedicated to the promotion of open practices in research, data, and scientific computing by serving as a fiscal sponsor for open source projects and organizing community-driven educational programs. In addition to its fiscal support of open source projects, it has been a long-time sponsor of the SciPy Conference. NumFocus has extensive experience in planning and operating educational programs and conferences.

Enthought remains deeply committed to the SciPy community and the SciPy Conference, and will continue to be an institutional sponsor, continuing its long-running financial support and participation. Enthought and NumFocus both view this as a great direction for the conference and how our organizations can best support the SciPy Conference and its long-term success going forward.

 

About Enthought

Enthought, Inc. powers digital transformation for science. Enthought’s technology and deep scientific expertise enable faster discovery and continuous innovation, building a digitally enabled workforce and arming them with analytics-ready scientific data to be catalysts of value creation in science and business. Enthought specializes in transforming organizations in the electronic, semiconductor, materials design, manufacturing, pharmaceutical, biotechnology, energy and consumer goods markets. Enthought is headquartered in Austin, Texas, with additional offices in Houston, Texas; Cambridge, United Kingdom; Zürich, Switzerland; and Tokyo, Japan. Connect with Enthought: Email List | LinkedInTwitter.  

Share this article:

Related Content

Utilizing LLMs Today in Industrial Materials and Chemical R&D

Leveraging large language models (LLMs) in materials science and chemical R&D isn't just a speculative venture for some AI future. There are two primary use...

Read More

Top 10 AI Concepts Every Scientific R&D Leader Should Know

R&D leaders and scientists need a working understanding of key AI concepts so they can more effectively develop future-forward data strategies and lead the charge...

Read More

Why A Data Fabric is Essential for Modern R&D

Scattered and siloed data is one of the top challenges slowing down scientific discovery and innovation today. What every R&D organization needs is a data...

Read More

Jupyter AI Magics Are Not ✨Magic✨

It doesn’t take ✨magic✨ to integrate ChatGPT into your Jupyter workflow. Integrating ChatGPT into your Jupyter workflow doesn’t have to be magic. New tools are…

Read More

Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More

By Mike Heiber, Ph.D., Materials Informatics Manager Enthought, Materials Science Solutions The American Chemical Society (ACS) is a premier scientific organization with members all over…

Read More

Real Scientists Make Their Own Tools

There’s a long history of scientists who built new tools to enable their discoveries. Tycho Brahe built a quadrant that allowed him to observe the…

Read More

How IT Contributes to Successful Science

With the increasing importance of AI and machine learning in science and engineering, it is critical that the leadership of R&D and IT groups at...

Read More

From Data to Discovery: Exploring the Potential of Generative Models in Materials Informatics Solutions

Generative models can be used in many more areas than just language generation, with one particularly promising area: molecule generation for chemical product development.

Read More

7 Pro-Tips for Scientists: Using LLMs to Write Code

Scientists gain superpowers when they learn to program. Programming makes answering whole classes of questions easy and new classes of questions become possible to answer….

Read More

The Importance of Large Language Models in Science Even If You Don’t Work With Language

OpenAI's ChatGPT, Google's Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language....

Read More