Data Analysis

WVU responds to data revolution with new major

The world is in the midst of a data revolution. From how we shop to how we vote and all decisions in between, there is a growing need for professionals trained to use modern data analysis to solve everyday problems. To meet these 21st century workforce demands, WVU is launching a new undergraduate data science major.

Four Rutgers Professors Named AAAS Fellows

Four Rutgers professors have been named fellows of the American Association for the Advancement of Science (AAAS), an honor given to AAAS members by their peers. They join 485 other new AAAS fellows as a result of their scientifically or socially distinguished efforts to advance science or its applications. A virtual induction ceremony is scheduled for Feb. 13, 2021.

With Digital Phenotyping, Smartphones May Play a Role in Assessing Severe Mental Illness

Digital phenotyping approaches that collect and analyze Smartphone-user data on locations, activities, and even feelings – combined with machine learning to recognize patterns and make predictions from the data – have emerged as promising tools for monitoring patients with psychosis spectrum illnesses, according to a report in the September/October issue of Harvard Review of Psychiatry. The journal is published in the Lippincott portfolio by Wolters Kluwer.

The University of Chicago is awarded a major federal contract to host a new COVID-19 medical imaging resource center

A new center hosted at the University of Chicago — co-led by the largest medical imaging professional organizations in the country — will help tackle the ongoing COVID-19 pandemic by curating a massive database of medical images to help better understand and treat the disease. The work is supported by a $20 million, two-year federal contract that could be renewable to $50 million over five years.

Countries Group into Clusters as COVID-19 Outbreak Spreads

Mathematicians based in Australia and China have developed a method to analyze the large amount of data accumulated during the COVID-19 pandemic. The technique, described in the journal Chaos, can identify anomalous countries — those that are more successful than expected at responding to the pandemic and those that are particularly unsuccessful. The investigators analyzed the data with a variation of a statistical technique known as a cluster analysis.

Modeling COVID-19 Data Must Be Done With Extreme Care

As the virus causing COVID-19 began its devastating spread, an international team of scientists was alarmed by the lack of uniform approaches by various countries’ epidemiologists. Data modeling to predict the numbers of likely infections varied widely and revealed a high degree of uncertainty. In the journal Chaos, the group describes why modeling and extrapolating the evolution of COVID-19 outbreaks in near real time is an enormous scientific challenge that requires a deep understanding of the nonlinearities underlying the dynamics of epidemics.

Researchers work on early warning system for COVID-19

To better understand early signs of coronavirus and the virus’ spread, physicians around the country and data scientists at UC San Diego are working together to use a wearable device to monitor more than 12,000 people, including thousands of healthcare workers. The effort has started at hospitals in the San Francisco Bay Area and at the University of West Virginia.

New ORNL software improves neutron spectroscopy data resolution

Neutron spectroscopy is an important tool for studying magnetic and thermoelectric properties in materials. But often the resolution, or the ability of the instrument to see fine details, is too coarse to clearly observe features identifying novel phenomena in new advanced materials. To solve this problem, researchers at Oak Ridge National Laboratory, developed a new super-resolution software, called SRINS, that makes it easier for scientists to better understand materials’ dynamical properties using neutron spectroscopy.