Every week we will be grappling with data collection, management, and/or analysis. Using R we will explore topics from data science, statistics, and machine learning. R is widely used in statistics and data science and has a lot of modules for traditional statistics and modern machine learning algorithms. Find out more here.
The Laubichler Lab
The Manfred Laubichler Lab at ASU studies evolutionary novelties from genomes to knowledge systems, the structure of evolutionary theory and the evolution of knowledge by means of computational approaches. Projects in the lab create new methods, tools and digital infrastructures for the history and philosophy of science.
On November 7, 2018, Ken Aiello successfully defended his dissertation titled “Systematic Analysis of the Factors Contributing to the Variation and Change of the Microbiome Concept.” He will continue his research as a postdoctoral researcher in the Global Biosocial Complexity Initiative. The Laubichler Lab congratulates Dr. Aiello on a job well done!
Want to know what Aiello’s dissertation is about? Read the abstract!
Mankind finds itself in the Anthropocene, the current geological epoch generally accepted by scholars and denoted by one species, our own, ascending to the role of major driver on Earth. The term Anthropocene was first-coined in the eponymous article by Paul Crutzen and Eugene Stoermer in the IGBP Global Newsletter . Later, Crutzen argues in "Geology of Mankind"  that human effects on the global ecosystem have accelerated and are now the primary influence on the global ecosystem. This human-domination over nature necessitates a new epochal designation; in contrast to the previous epoch, the Holocene, that designated the post-glacial geological period proposed by Sir Charles Lyell in Principles of Geology  in 1833 and adopted in 1885 by the International Geological Congress (IGC). Read the story here.
A research team led by Bryan Daniels with the direction of Sara Walker of the School of Earth and Space Exploration just published the paper "Criticality Distinguishes the Ensemble of Biological Regulatory Networks". Read the story here.
Tethne provides a variety of methods for working with text corpora and the output of modeling tools like MALLET. This tutorial focuses on parsing, modeling, and visualizing a Latent Dirichlet Allocation topic model, using data from the JSTOR Data-for-Research portal.
In this tutorial, we will use Tethne to prepare a JSTOR DfR corpus for topic modeling in MALLET, and then use the results to generate a semantic network like the one shown above.
In this visualization, words are connected if they are associated with the same topic; the heavier the edge, the more strongly those words are associated with that topic. Each topic is represented by a different color. The size of each word indicates the structural importance (betweenness centrality) of that word in the semantic network.