The video is a bit schmaltzy, but the thing itself is beautiful. And it's also an excellent example of how sonification can be much better than #visualization to explain / convey certain kinds of phenomena. (And then there's the question of context: ambient presentations that don’t require focused visual attention, a la Natalie Jeremijenko Live Wire at Xerox PARC.)
Q for my research colleagues (all disciplines): have you ever run for an elected position in your professional society in order to push for more open access / open science from the inside? If so, I'd welcome a chance to chat - please DM me or I'm (still) gvwilson@third-bit.com. Thx
If you have any interest in data visualization, consider giving @infobeautiful a follow.
Founded by British journalist & designer David McCandless, they've been posting their high quality visual explorations of information here since September.
The fedi account has been flying a bit under the radar so far - they have many tens of thousands of followers on other platforms for good reason.
Drawing inspiration from Mark Fisher's writing about genre fiction, film, and music, I recently wrote (and spoke) about communicating the Weird and the Eerie in the context of data #visualization design. #ieeevis#altVIS
I found some interesting trends today while working with the simplify function from #Sympy as part of an experimental project for @formak. A large portion of the time is spent in list comprehensions and calling simplify recursively, which seems to “confuse” the flame graph representation.
In a nutshell, a Bland-Altman plot shows the differences between two measurements against their means. It's a powerful tool for quality control and validation, widely used in various industries, including healthcare.
@lonnibesancon and I will be running a meetup for folks interested in @jovi today (Wed) at 5pm at #ieeevis in Room 103! Come chat about the future of open access publishing of #visualization and #HCI work, including living documents and the publication of interactive papers! https://www.journalovi.org/
Step 1: Load your data.
Step 2: Perform Principal Component Analysis (PCA).
Step 3: Calculate the variance explained.
Step 4: Create a stunning scree plot.
Step 5: Interpret the plot to find the "elbow."
Step 6: Decide how many components to retain.
Step 7: Apply your decision and get insights!