NYU Data Science newsletter – March 17, 2016

GROUP CURATION: N/A

Nestle is conducting a two-year clinical study with data startup Medidata in China to assess the impact of physical activity and nutrition on adults with joint discomfort. The study will use Garmin fitness trackers and a mobile app.

“The research program, developed in collaboration with dedicated experts from Nestle research centers in Lausanne and Beijing, is extending Medidata and Nestle Clinical Development Unit’s well-established relationship beyond traditional clinical trials,” Nestle Head of Clinical Operations Stephane Collet said in a statement.

Asheville may be earning the new title of Climate City with the launch of a new climate science center. U.S. Secretary of Transportation Anthony Foxx was the keynote speaker at the grand opening. [video, pre-roll + 1:46]

The Centre for Internet and Society recently released a groundbreaking paper on the practical, legal and ethical implications of using mobile phone data (CDRs, or Call Detail Records) in emergencies, with Liberia’s experience of the recent Ebola epidemic as the case study. Written by Sean Martin McDonald, the paper is brilliant, insightful and well researched, and is creating a much-needed debate in the humanitarian and responsible data communities. This post is a contribution to that debate. It reflects on just one of the ethical issues surfaced by the analysis, that of consent to data-sharing in the context of humanitarian emergency.

… Researchers studying prime numbers at Stanford University have stumbled upon a new phenomenon. In their study, published on arXiv, they show that consecutive prime numbers try hard not to be similar. That is, they may not be as random as once thought.

Weber Shandwick [a public relations firm] has joined the Affiliates Program at Columbia University’s Data Science Institute to collaborate on data solutions and adopt capabilities that will help the global PR agency improve how it benchmarks audience engagement with brands.

… what we can clearly see, is that there is evolution in the methods, process and technology used by a Data Scientist. Many would contest the true beginnings of statistical modelling,but fewer would argue what that evolutionary lifecycle looks like. From the early days of scratching numbers into papyrus, up to the modern day punching of numbers into a keyboard, Data Science has come a long way.The technology may have changed, the methods may also have changed, but what hasn’t changed going as far back past the industrial revolutions of the 19th and 20th centuries, or past the Renaissance, as far back as the dawn of human kind, is that we’ve always sought to leverage mathematics and statistics to improve the world around us.

Until recently plaintiffs have faced difficulties demonstrating material injury caused by data breaches, however several recent high-profile cases look likely to make it easier for consumers to bring individual suits and class actions against companies in the event of such attacks.

Technology companies have welcomed the White House’s recently unveiled policy that seeks to make software code used by federal agencies more open, sharable and reusable, but they’re concerned about a few details.

Federal CIO Tony Scott announced a draft Federal Source Code policy on March 10 that would create a new set of rules for custom code developed by or for the federal government. The proposal is now open for comment on GitHub.

Pervasive information streams that document people and their routines have been a boon to social computing research. But the ethics of collecting and analyzing available—but potentially sensitive—online data present challenges to researchers. In response to increasing public and scholarly debate over the ethics of online data research, this paper analyzes the current state of practice among researchers using online data. Qualitative and quantitative responses from a survey of 263 online data researchers document beliefs and practices around which social computing researchers are converging, as well as areas of ongoing disagreement. The survey also reveals that these disagreements are not correlated with disciplinary, methodological, or workplace affiliations. The paper concludes by reflecting on changing ethical practices in the digital age, and discusses a set of emergent best practices for ethical social computing research.

The contest is aimed at the exploration of the visual representations to communicate, or to support the communication of, risks in Bayesian reasoning tasks. Previous studies have found that the ability of people to solve Bayesian problems depends on the way the information is conveyed [2]. In this contest, a special emphasis is laid on the design of visual aids.

For this challenge, we picked two Bayesian reasoning tasks: the classic mammography problem [3] and an example from a prenatal test brochure for Down Syndrome [2]. Participants are asked to design visual solutions for both tasks. We supply a scenario to provide a context to design your visual solutions, and a text and a target question for each task.

Deadline for submissions is Friday, September 23.

Junbo (Jake) Zhao is a MS in Data Science student from the class of ‘16. Prior to studying with us, Jake received a Bachelor’s degree in Electronic Engineering. He also has experience in Computer Vision and Music Information Retrieval. Jake is primarily interested in Large Scale Machine Learning and Big Data.

I [Peadar Coyle] interviewed one of the core members of the pandas Python Library Masaaki Horikoshi (sinhrks). I was really happy to interview him, and glad to show that Data-science and software development are really global things. 🙂 I lightly edited his answers at his request because English is not his native language.

In this Working with Elasticsearch training course, expert author Radu Gheorghe will teach you how to search, aggregate, analyze, and scale large volume datastores. This course is designed for users that already have a familiarity with the use of a UNIX-like terminal. [video, 2:42]

Figaro 4.0 has just been released, available from http://www.cra.com/figaro. The headline new feature is called “Structured Factored Inference”, or SFI. So I’d like to take this opportunity to explain what SFI is about and why we’re pursuing it.