March 4, 2020 — Jeffrey Bryant, Research Programmer, Wolfram|Alpha Scientific Content
One of the most enigmatic stars in our galaxy is η Carinae (Eta Carinae). However, in its first recorded observation several hundred years ago, Eta Carinae was a star of little note. Since then, it has become a source of astronomical interest due to dramatic brightness variations, which at one time made it the second-brightest star in the sky. In this post, we’ll investigate the star using the Wolfram Language and the Wolfram Function Repository to discover why it’s changed in such a relatively short period of time, both in its appearance and in our interest in it.
February 28, 2020 — The Wolfram Function Repository Team
In June 2019, Stephen Wolfram announced the Wolfram Function Repository, a curated repository of functions that can be employed immediately in the Wolfram Language. Since then, the Repository has grown to include more than 1,000 functions in over 20 categories.
Functions included in the Repository range from those that are more general and utilitarian in nature to others with very specific applications. As with all Wolfram Language functions, Repository documentation pages contain examples showing how to use the functions. We’re featuring a few of the functions submitted to the Repository so far that showcase the variety of functions our users have built.
February 6, 2020 — Brian Wood, Lead Technical Writer, Document and Media Systems
When 20 presidential candidates duke it out on the debate stage, who wins? Americans have been watching a crowded and contentious primary season for the 2020 Democratic nomination for president. After the debates, everyone’s talking about who got the most talk time or attention, which exchanges were most exciting or some other measure of who “won” the night—and who might ultimately clinch a victory at the caucuses. So I decided I’d do a little exploration of the debates using the entity framework, text analytics and graph capabilities of the Wolfram Language and see if I could come up with my own measure of a “win” for a debate, based on which candidate was most central to the conversation.
January 23, 2020 — Jofre Espigule-Pons, Document & Media Systems
Who has not encountered a stink bug? Perhaps the better question is not if, but when. I remember well my first interactions with stink bugs—partly because of their pungent, cilantro-like odor, but also because in my native Catalan language they are called Bernat pudent (“stinky Bernat”) and Bernat is my twin brother’s name.
So when I encountered the stink bug again when visiting Champaign, Illinois, for the 2019 Wolfram Technology Conference, it brought up a lot of fond childhood memories. This time, however, two things had changed: the frequency of encounters with the stink bug seemed exponentially greater, and I now had the Wolfram Language to more fully (and computationally) satisfy my curiosity about this reviled insect and its growing impact on our ecosystem. So to get a better picture of the arrival and spread of this invasive bug across the US, I used available observation data and the Wolfram Language to make a map of sightings over the past two decades.
January 21, 2020 — Chapin Langenheim, Editor & Web Project Coordinator, Project Management
We’ve gathered some of our favorite Wolfram Community posts showing the variety of applications made possible with the Wolfram Language.
January 16, 2020 — Jamie Peterson, Technical Programs Manager, Wolfram U
Looking to fulfill your New Year’s resolution of learning new data science skills? Join Wolfram U for Wolfram Technology in Action: Data Science, a three-part web series demonstrating a range of data science applications in the Wolfram Language. These 90-minute sessions feature recorded talks from the 2019 Wolfram Technology Conference, along with live presentations by Wolfram staff scientists, application developers, software engineers and Wolfram Language users who apply the technology every day to their business operations and research.
Newcomers to Wolfram technology are welcome, as are longtime users wanting to see the latest functionality in the language.
January 14, 2020 — Jeffrey Bryant, Research Programmer, Wolfram|Alpha Scientific Content
Yellowstone National Park has long been known for its active geysers. These geysers are a surface indication of subterranean volcanic activity in the park. In fact, Yellowstone is actually the location of the Yellowstone Caldera, a supervolcano: a volcano with an exceptionally large magma reservoir. The park has had a history of many explosive eruptions over the last two million years or so.
I’ve found that the United States Geological Survey (USGS) maintains data on the various volcanic calderas and related features, which makes it perfect for computational exploration with the Wolfram Language. This data is in the form of SHP files and related data stored as a ZIP archive. Thanks to the detail of this available data, we can use the Wolfram Language and, in particular, GeoGraphics to get a better picture of what this data is telling us.
December 18, 2019 — Daniel Bigham, Business Systems R&D
When people think about Wolfram technology, corporate enterprise resource management (ERP) isn’t the first thing that comes to mind. It certainly wasn’t our first thought when we started searching for a new solution to manage our own accounting, customer service, licensing and HR needs. But after looking at the current ERP offerings, we found that none of the existing buy-in options did what we wanted.
So we thought, why not build our own?
The resulting project has been a revelation. Not only have we built something to our taste, but something fundamentally different: a new architecture, new interfaces, a new approach. Using Wolfram technology has not only made development easier; it has given us a revolutionary new perspective. By leveraging our uniquely powerful technology stack—and integrating it tightly with the existing infrastructure—we’re redefining what an ERP system can be.
December 10, 2019 — Jon McLoone, Director, Technical Communication & Strategy
Much effort and money are spent trying to analyze whether political messages resonate with the electorate. With the UK in its final days before a general election, I thought I would see if I could gain such insight with minimal effort.
My approach is simple: track the sentiment of tweets that mention each party. Since the Wolfram Language has a built-in sentiment classifier and connections to external services, we can analyze these messages with only a few lines of code.
November 7, 2019 — Paritosh Mokhasi, Kernel Developer, Algorithms R&D
My student days learning fluid dynamics were all about studying complicated equations and various methods of simplifying and manipulating these equations to get some kind of a result. Unfortunately, this left very little to the imagination when it came to getting an intuitive feel for how a fluid would behave in different situations. When I took my first experimental fluid dynamics course, I got to see how one would use different visualization techniques to understand qualitatively the behavior of the flow. These visualizations gave me a way of creatively looking at a flow, and, as an added bonus, they looked stunning. All these experiments and visualizations were being carried out inside a wind tunnel.