Posts Tagged ‘bigdata’

Reading by the Numbers: When Big Data Meets Literature

Sunday, November 12th, 2017

Reading by the Numbers: When #BigData Meets Literature
https://www.NYTimes.com/2017/10/30/arts/franco-moretti-stanford-literary-lab-big-data.html Distant reading as a complement to close reading for literary texts. Perhaps a useful dichotomy for biosequences too!

QT:{{”
“Literary criticism typically tends to emphasize the singularity of exceptional works that have stood the test of time. But the canon, Mr. Moretti argues, is a distorted sample. Instead, he says, scholars need to consider the tens of thousands of books that have been forgotten, a task that computer algorithms and enormous digitized databases have now made possible.

“We know how to read texts,” he wrote in a much-quoted essay included in his book “Distant Reading,” which won the 2014 National Book Critics Circle Award for Criticism. “Now let’s learn how to not read them.””

“}}

Public v. Private Polling – PredictWise

Sunday, November 27th, 2016

Public v Private Polling
http://PredictWise.com/blog/2016/11/public-v-private-polling Meta-prediction from extrapolating group characteristics limited; need raw individual data

Big Data’s Mathematical Mysteries | Quanta Magazine

Friday, December 18th, 2015

#BigData’s Mathematical Mysteries https://www.quantamagazine.org/20151203-big-datas-mathematical-mysteries/ Nice description of unsupervised analysis as ink diffusing from drops

QT:{{"
“In the last 15 years or so, researchers have created a number of tools to probe the geometry of these hidden structures. For example, you might build a model of the surface by first zooming in at many different points. At each point, you would place a drop of virtual ink on the surface and watch how it spread out. Depending on how the surface is curved at each point, the ink would diffuse in some directions but not in others. If you were to connect all the drops of ink, you would get a pretty good picture of what the surface looks like as a whole. And with this information in hand, you would no longer have just a collection of data points. Now you would start to see the connections on the surface, the interesting loops, folds and kinks. This would give you a map for how to explore it.”
"}}

Most Hyped Tech: Big Data Out, IoT In

Friday, July 24th, 2015

http://www.datanami.com/2014/08/20/hyped-tech-big-data-iot/

Core services: Reward bioinformaticians

Saturday, May 9th, 2015

QT:{{"The research system does not recognize bioinformaticians for doing what the scientific community needs most. “People realize the importance, but currently there are no real solutions,” says Xiaole Liu, a bioinformatician at the Dana-Farber Cancer Institute in Boston, Massachusetts, and at Tongji University in Shanghai, China. This is why it can take more than six months to fill positions at a core, why many of biology’s brightest are leaving science for technology companies, and why conventional biologists wait nine months to get help to dissect their data.
"}}

Reward bioinformaticians [for collaboration] http://www.nature.com/news/core-services-reward-bioinformaticians-1.17251 Despite #bigdata boom, biomedical analysis could be made more appealing

My public notes from the Yale Day of Data (#ydod2014, i0dataday)

Tuesday, September 30th, 2014

http://linkstream2.gerstein.info/tag/i0dataday
http://elischolar.library.yale.edu/dayofdata/2014/
https://storify.com/markgerstein/tweets-related-to-the-yale-day-of-data-2014-ydod20

The Institute for Data Intensive Engineering and Science – The Data-Scope

Tuesday, September 30th, 2014

Coppi mentions: JHU’s Data-scope (http://idies.jhu.edu/datascope ), which has a specialized architecture for astronomical computation #ydod2014

4 PB / yr

What Big Data means to me — Bourne 21 (2): 194 — Journal of the American Medical Informatics Association

Tuesday, September 30th, 2014

Bourne mentions “What Big Data means to me”
(http://jamia.bmj.com/content/21/2/194.extract ) in connection with the creation of a digital ecosystem #ydod2014

The Parable of Google Flu: Traps in Big Data Analysis

Monday, September 29th, 2014

Parable of #Google Flu: Traps in #BigData Analysis http://www.sciencemag.org/content/343/6176/1203.summary Replicating results is hard, w/ an ever-changing search algorithm

Mentions http://www.google.com/trends/correlate

For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights

Monday, September 29th, 2014

For #BigData Scientists, Janitor Work Is Key http://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html Is a #datascientist a digital maid or a data priest? Perhaps a hybrid.

O’Neil talk