The Episciences.org project is involved in the open access movement #epijournals
The main idea is to provide a technical platform of peer-reviewing; its purpose is to promote the emergence of epijournals, namely open access electronic journals taking their contents from preprints deposited in open archives such as arXiv or HAL, that have not been published elsewhere.
The editorial boards of such epijournals organize peer reviewing and scientific discussion of selected or submitted preprints. Epijournals can thus be considered as “overlay journals” built above the open archives; they add value to these archives by attaching a scientific caution to the validated papers.
Johns Hopkins adopts statement on academic freedom principles, philosophy | Hub
#Reminderto come back to Moulton's comment here on tenure faculty versus other levels. Check to see how the tenure ratio fits into rankings of universities and colleges.
.@JohnsHopkins has an @Coursera sequence of 7 courses on Genomic Data Science starting soon.
Introduction to Genomic Technologies
Genomic Data Science with Galaxy
Python for Genomic Data Science
Command Line Tools for Genomic Data Science
Algorithms for DNA Sequencing
Bioconductor for Genomic Data Science
Statistics for Genomic Data Science
Genomic Data Science Capstone
"When is one thing equal to some other thing?" by Barry Mazur #categorytheory
Great essay on equality and category theory
Harbingers of Failure | Are there people you DON'T want to buy your product?
This is kind of like the polar opposite of the "mavens" Malcolm Gladwell talks about in Tipping Point. This is also much like the concept of negative results in science - few pay attention to what not to do or look at the "negative image" to give them a better view of the "image" they'd like to see.
Abstract: We show that some customers, whom we call ‘Harbingers’ of failure, systematically purchase new products that flop. Their early adoption of a new product is a strong signal that a product will fail - the more they buy, the less likely the product will succeed. Firms can identify these customers either through past purchases of new products that failed, or through past purchases of existing products that few other customers purchase. We discuss how these insights can be readily incorporated into the new product development process. Our findings challenge the conventional wisdom that positive customer feedback is always a signal of future success.
Using Fisher Information In Big Data via arXiv [1507.00389]
In this era of Big Data, proficient use of data mining is the key to capture useful information from any dataset. As numerous data mining techniques make use of information theory concepts, in this paper, we discuss how Fisher information (FI) can be applied to analyze patterns in Big Data. The main advantage of FI is its ability to combine multiple variables together to inform us on the overall trends and stability of a system. It can therefore detect whether a system is seeking/loosing stability and whether any regime shifts have occurred. In this work, we first provide a brief overview of Fisher information theory and then present a simple step-by-step numerical example on how to compute FI. Finally, as a numerical demonstration, we calculate the evolution of FI for GDP per capita (current US Dollar) and total population of the USA from 1960 to 2013.
The Evolution of Information Gathering: Operational Constraints by Cynthia F. Kurtz
The Evolution of Information Gathering: Operational Constraints
Cynthia F. Kurtz
1991 Master's Thesis, SUNY Stony Brook, Ecology & Evolution
Abstract: I present two new approaches to the study of information in foraging theory. First, rather than determine the cost a forager should pay to obtain information, I concentrate on the consequences of information use in an interacting population. I describe a density-dependent model which tracks genotypes with high and low information access through evolutionary time. Stable polymorphisms result. I suggest that the value of information is not monotonically increasing. Second, I present a scheme for partitioning the information used in the decision making process. Three types of information are recognized: internal information, or an individual's internal state; external information, or environmental factors; and relational information, or rules for predicting transformations of internal state. Interactions between the three types are examined in an extension of the basic model.
Getting started with schema.org using Microdata
Most webmasters are familiar with HTML tags on their pages. Usually, HTML tags tell the browser how to display the information included in the tag. For example,
Avatar
tells the browser to display the text string "Avatar" in a heading 1 format. However, the HTML tag doesn't give any information about what that text string means—"Avatar" could refer to the hugely successful 3D movie, or it could refer to a type of profile picture—and this can make it more difficult for search engines to intelligently display relevant content to a user.
Schema.org provides a collection of shared vocabularies webmasters can use to mark up their pages in ways that can be understood by the major search engines: Google, Microsoft, Yandex and Yahoo!
You use the schema.org vocabulary along with the Microdata, RDFa, or JSON-LD formats to add information to your Web content. This guide will help get you up to speed with Microdata and schema.org so that you can start adding markup to your web pages.
Although this guide focusses on Microdata, most examples on the schema.org site show examples in RDFa and JSON-LD too. The basic ideas (types, properties etc.) introduced here are relevant beyond Microdata - take a look at the examples to see how the details compare. #indieweb #microformats