The tutorial will cover four hours with the following topics
History of scientific societies and publications
Leeuwenhoek was the Man !
The Invisible College
Nullius in Verba
Replication of the early microscope experiments by Leeuwenhoek[a][b]
Image Acquisition (15 min)
Hands on: Cell camera phone microscope
With drop of water
Hands on: Each pair acquires images
Data Sharing (45min)
Image gathering, storage, and sharing (15min)
Hands on: Upload the images
Metadata Identifiers (15 min)
Hands on: Create data citation and machine readable metadata
Hands on: Download data via RESTful API (15min)
Hands on: Download the data via HTTP
Local processing (60min)
Replication Enablement (20min)
Create a virtualenv
Run our tutorial package verification script
Revision Control with Git (20min)
Keeping track of changes
Forking a repository in GitHub
Cloning a repository
Creating a branch
Making a commit
Pushing a branch
Create pull request
Python scripts (20min)
Data analysis, particle counting.
Run scripts on new data
Generate histogram for the data
Unit testing with known data
Regression testing with known data
Add coverage for another method to the unit tests
Publication Tools (30min)
RST to HTML
GitHub replication and sharing
Run dexy to generate the document
Reproducibility Verification (30min)
Publication of Positive and Negative results
Create Open Science Framework (OSF) project
Connect Figshare and Github to OSF project
Fork or link another group’s project in the OSF to run dexy on their work
Attendees will use software installed in their laptops to gather and process data, then publish and share a reproducible report.
They will access repositories in GitHub, upload data to a repository and publish materials necessary to replicate their data analysis.
We expect that wireless network will be have moderate bandwidth to allow all attendees to move data, source code and publications between their laptops and hosting servers.
Ben Golub and Solomon Hykes speech giving a thank you speech for the 1 year of Docker at the Docker HQ.
What is yt?
"Lingua-franca for astrophysical simulations"
Some selections from the gallery
105 citations to the yt method paper
Examples of large-scale calculations and visualizations performed with yt
Usage data on XSEDE visualization resources
Volumetric data analysis beyond astrophysics
Neurodome, Whole-earth seismic wave data, Weather simulation data, Nuclear engineering, Radio astronomy
??? (insert your field here!)
What's new in yt-3.0?
Rewrite of data selection, i/o, and field detection and creation
Octree and particle support (i.e., discrete points)
Unit conversions and dimensional analysis baked into the codebase
Rethinking the API, 'rebranding' the project
Advanced volume rendering
Growing the Community
New data styles
Finite element analysis
New domain-specific functionality (beyond astrophysics)
Browser GUIs powered by IPython
We say things like “don’t block the event loop”, “make sure your code runs at 60 frames-per-second”, “well of course, it won’t work, that function is an asynchronous callback!”
We present a functional take on front-end data-binding. MVC, MVP, MVVM, just the V – there are many useful architectures for data binding when data changes in discrete chunks. For data that changes continuously in time (animations, gesture controlled UI, responsive layout, etc), these are not the best tools. By treating continuously changing layout data in a functional way, and updating discrete changes to a model’s data in an MV* way, we can split the data-binding problem into two cleanly separated problems. In so doing we make all our tools better at what they do best.
Oh no! You have a bug in your app, but you have no idea where it is. I’ll walk you through how we found and squashed a gnarly bug in socket.io using wireshark, chrome’s developer tools, lots of logging, and pretty graphs. I’ll also show you some good tips and tricks for tracking down and squashing bugs of your own.
Open your issue trackers, get your pull requests ready, and join John-David Dalton, co-maintainer of jsperf.com and creator of Lo-Dash, to perf the web forward as he discusses commonly overlooked performance issues, rethinks established code patterns, and shares tips you can apply to your own projects and favorite libraries.