By: Matt Murbach, University of Washington

Co-organizer David Beck led a hack session during the ECS Data Sciences Hack Day.

The full vibrancy of the electrochemical community was on show during the recent 232nd ECS Meeting in National Harbor, MD. Adding to the diversity of ideas and excitement for electrochemistry were the 30 participants of the inaugural ECS Data Sciences Hack Day on Wednesday, October 4. The participants in the hack day traveled from around the globe and represented varying stages of careers in both academic and industry roles.

The day-long event was kicked off with a short series of informational sessions covering some of the essential tools in any data scientist’s toolbox. During lunch, participants pitched their ideas for projects, and teams for the afternoon session organically formed around common interests. The remaining time during the afternoon was reserved as open “hacking” time for working on the project ideas. Excitingly, good progress was made in this four-hour block with teams working on a wide variety of projects, including:

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Researchers have developed a new way to alleviate many of the issues that make magnetic data storage for computer hard disks and other data storage hardware problematic, including speed and energy use.

For almost seventy years now, magnetic tapes and hard disks have been used for data storage in computers. In spite of many new technologies that have arisen in the meantime, the controlled magnetization of a data storage medium remains the first choice for archiving information because of its longevity and low price.

As a means of realizing random access memories (RAMs), however, which are used as the main memory for processing data in computers, magnetic storage technologies have long been considered inadequate. That is mainly due to its low writing speed and relatively high energy consumption.

Pietro Gambardella, professor at the materials department of ETH Zurich, and his colleagues, have now shown that using a novel technique, faster magnetic storage is possible without wasting energy.

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By: Rand Wilcox, University of Southern California – Dornsife College of Letters, Arts and Sciences

No matter the field, if a researcher is collecting data of any kind, at some point he is going to have to analyze it. And odds are he’ll turn to statistics to figure out what the data can tell him.

A wide range of disciplines – such as the social sciences, marketing, manufacturing, the pharmaceutical industry and physics – try to make inferences about a large population of individuals or things based on a relatively small sample. But many researchers are using antiquated statistical techniques that have a relatively high probability of steering them wrong. And that’s a problem if it means we’re misunderstanding how well a potential new drug works, or the effects of some treatment on a city’s water supply, for instance.

As a statistician who’s been following advances in the field, I know there are vastly improved methods for comparing groups of individuals or things, as well as understanding the association between two or more variables. These modern robust methods offer the opportunity to achieve a more accurate and more nuanced understanding of data. The trouble is that these better techniques have been slow to make inroads within the larger scientific community.

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