[matplotlib-devel] [ANN, x-post] Creating a space for scientific open source at Berkeley (with UW and NYU)

[Fernando P. <fpe...@gm...>]

Hi folks,

forgive me for the x-post to a few lists and the semi off-topic nature of
this post, but I think it's worth mentioning this to our broader community.
 To keep the SNR of each list high, I'd prefer any replies to happen on the
numfocus list.

Yesterday, during an event at the White House OSTP, an announcement was
made about a 5-year, $37.8M initative funded by the Moore and Sloan
foundations to create a collaboration between UC Berkeley, the University
of Washington and NYU on Data Science environments:

- Press release:
http://www.moore.org/newsroom/press-releases/2013/11/12/%20bold_new_partnership_launches_to_harness_potential_of_data_scientists_and_big_data
- Project description:
http://www.moore.org/programs/science/data-driven-discovery/data-science-environments


We worked in private on this for a year, so it's great to be able to
finally engage the community in an open fashion. I've provided some
additional detail in my blog:

http://blog.fperez.org/2013/11/an-ambitious-experiment-in-data-science.html

At Berkeley, we are using this as an opportunity to create the new Berkeley
Institute for Data Science (BIDS):

http://newscenter.berkeley.edu/2013/11/13/new-data-science-institute-to-help-scholars-harness-big-data

and from the very start, open source and the scientific Python ecosystem
have been at the center of our thinking.  In the team of co-PIs we have, in
addition to me, a bunch of Python supporters:

- Josh Bloom leads our Python bootcamps and graduate seminar)
- Cathryn Carson founded the DLab (dlab.berkeley.edu), which runs
python.berkeley.edu.
- Philip Stark: Stats Chair, teaches reproducible research with Python
tools.
- Kimmen Sjolander: comp. biologist whose tools are all open source Python.
- Mike Franklin and Ion Stoica: co-directors of AMPLab, whose Spark
framework has Python support.
- Dave Culler: chair of CS, which now uses Python for its undergraduate
intro courses.

We will be working very hard to basically make BIDS "a place for people
like us" (and by that I mean open source scientific computing, not just
Python: Juila, R, etc. are equally welcome). This is a community that has a
significant portion of academic scientists who struggle with all the issues
I list in my post, and solving that problem is an explicit goal of this
initiative (in fact, it was the key point identified by the foundations
when they announced the competition for this grant).

Beyond that, we want to create a space where the best of academia, the
power of a university like Berkeley, and the best of our open source
communities, can come together.  We are just barely getting off the ground,
deep in more mundane issues like building renovations, but over the next
few months we'll be clarifying our scientific programs, starting to have
open positions, etc.

Very importantly, I want to thank everyone who, for the last decade+, has
been working like mad to make all of this possible. It's absolutely clear
to me that the often unrewarded work of many of you was essential in this
process, shaping the very existence of "data science" and the recognition
that it should be done in an open, collaborative, reproducible fashion.
Consider this event an important victory along the way, and hopefully a
starting point for much more work in slightly better conditions.

Here are some additional resources for anyone interested:

http://bitly.com/bundles/fperezorg/1

-- 
Fernando Perez (@fperez_org; http://fperez.org)
fperez.net-at-gmail: mailing lists only (I ignore this when swamped!)
fernando.perez-at-berkeley: contact me here for any direct mail