Crowdsourcing Analytics – Final Manuscript.pdf ShareDownloadViewRevisions

Twenty-nine teams involving 61 analysts used the same dataset to address the same research question: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. Analytic approaches varied widely across teams, and estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. Twenty teams (69%) found a statistically significant positive effect and nine teams (31%) observed a non-significant relationship. Crowdsourcing data analysis, a strategy by which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how variations in analytical choices affect results.

Source: OSF | Crowdsourcing Analytics – Final Manuscript.pdf

Molecular biologists and neuroscientists are tweeting with the hashtag #ASAPbio in protest of a system that keeps research from being shared with the public, typically for more than six months.

On Feb. 29, Carol Greider of Johns Hopkins University became the thirdNobel Prize laureate biologist in a month to do something long considered taboo among biomedical researchers: She posted a report of her recent discoveries to a publicly accessible website, bioRxiv, before submitting it to a scholarly journal to review for “official’’ publication.

It was a small act of information age defiance, and perhaps also a bit of a throwback, somewhat analogous to Stephen King’s 2000 self-publishing an e-book or Radiohead’s 2007 release of a download-only record without a label. To commemorate it, she tweeted the website’s confirmation under the hashtag #ASAPbio, a newly coined rallying cry of a cadre of biologists who say they want to speed science by making a key change in the way it is published.

Source: Handful of Biologists Went Rogue and Published Directly to Internet

The era of predictable improvement in computer hardware is ending. What comes next?

IN 1971 the fastest car in the world was the Ferrari Daytona, capable of 280kph (174mph). The world’s tallest buildings were New York’s twin towers, at 415 metres (1,362 feet). In November that year Intel launched the first commercial microprocessor chip, the 4004, containing 2,300 tiny transistors, each the size of a red blood cell.

Since then chips have improved in line with the prediction of Gordon Moore, Intel’s co-founder. According to his rule of thumb, known as Moore’s law, processing power doubles roughly every two years as smaller transistors are packed ever more tightly onto silicon wafers, boosting performance and reducing costs. A modern Intel Skylake processor contains around 1.75 billion transistors—half a million of them would fit on a single transistor from the 4004—and collectively they deliver about 400,000 times as much computing muscle. This exponential progress is difficult to relate to the physical world. If cars and skyscrapers had improved at such rates since 1971, the fastest car would now be capable of a tenth of the speed of light; the tallest building would reach half way to the Moon.

The impact of Moore’s law is visible all around us. Today 3 billion people carry smartphones in their pockets: each one is more powerful than a room-sized supercomputer from the 1980s. Countless industries have been upended by digital disruption. Abundant computing power has even slowed nuclear tests, because atomic weapons are more easily tested using simulated explosions rather than real ones. Moore’s law has become a cultural trope: people inside and outside Silicon Valley expect technology to get better every year.

But now, after five decades, the end of Moore’s law is in sight (see Technology Quarterly). Making transistors smaller no longer guarantees that they will be cheaper or faster. This does not mean progress in computing will suddenly stall, but the nature of that progress is changing. Chips will still get better, but at a slower pace (number-crunching power is now doubling only every 2.5 years, says Intel). And the future of computing will be defined by improvements in three other areas, beyond raw hardware performance.

Source: The future of computing | The Economist

As someone who creates open-source software, I spend a lot of time thinking about how to make software better.

This is unavoidable: there’s an unending stream of pleas for help on Stack Overflow, in GitHub issues and Slack mentions, in emails and direct messages. Fortunately, you also see people succeed and make fantastic things beyond your imagination, and knowing you helped is a powerful motivation to keep at it.

So you wonder: what qualities of software lead people to succeed or fail? How can I improve my software and empower more people to be successful? Can I articulate any guiding principles, or do I just have intuition that I apply on a case-by-case basis? (Thinking about something and externalizing — articulating — that thought are two very different activities.) Perhaps something like Dieter Ram’s principles for good design, tailored for software?

Good design is innovative.
Good design makes a product useful.
Good design is aesthetic.
Good design makes a product understandable.
Good design is unobtrusive.
Good design is honest.
Good design is long-lasting.
Good design is thorough down to the last detail.
Good design is environmentally-friendly.
Good design is as little design as possible.

Source: What Makes Software Good? — Medium

R is decidedly the most popular statistical/data analysis language in use today. R Tools for Visual Studio brings together the power of R and Visual Studio in a convenient and easy to use plug-in that’s free and Open Source. When combined with Visual Studio Community Edition, you get a multi-lingual IDE that is perpetually free (for small teams). Today we’re releasing this as a public preview for evaluation and testing by developers.

Here are the exciting features of this preview release:

• Editor – complete editing experience for R scripts and functions, including detachable/tabbed windows, syntax highlighting, and much more.
• IntelliSense – (aka auto-completion) available in both the editor and the Interactive R window.
• R Interactive Window – work with the R console directly from within Visual Studio.
• History window – view, search, select previous commands and send to the Interactive window.
• Variable Explorer – drill into your R data structures and examine their values.
• Plotting – see all of your R plots in a Visual Studio tool window.
• Debugging – breakpoints, stepping, watch windows, call stacks and more.
• R Markdown – R Markdown/knitr support with export to Word and HTML.
• Git – source code control via Git and GitHub.
• Extensions – over 6,000 Extensions covering a wide spectrum from Data to Languages to Productivity.
• Help – use ? and ?? to view R documentation within Visual Studio.
• A polyglot IDE – VS supports R, Python, C++, C#, Node.js, SQL, etc. projects simultaneously.

Other features requested by the R developer community, including a Package Manager GUI, Visual Studio Code (cross-plat), etc. will be part of one of our future updates.
Source: Announcing R Tools for Visual Studio | Machine Learning Blog

Neural Networks and Deep Learning is a free online book. The book will teach you about:

• Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
• Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

For more details about the approach taken in the book, see here. Or you can jump directly to Chapter 1 and get started.

Source: Neural networks and deep learning