A Citation Count Prediction Model for STEM Publishing Domains
Goals
I attempt to tackle the task of citation count prediction using existing and new features. Looking at multiple domains, I identify differences both in the ability to predict citation counts as well as the nature of features that contribute to the prediction. For instance, the phenomenon of famous authors attracting more citations is more apparent in Biology and Medicine compared with other domains. Additionally, while the popularity of a paper’s references is predictive of the paper’s success in most domains, this is clearly not the case in Engineering and Physics. The following is a model that can be used to predict citations 5 years in the future (using data from 2005
…show more content…
Table 1. Domain-specific Statistics
Domain Affiliations Papers – 2005 Papers – 2015 Authors per paper – 2005 Authors per paper - 2015
CS 4,851 59,116 110,506 2.43 2.75
Biology 2,082 59,395 93,792 3.58 4.04
Chemistry 811 26,496 50,381 3.56 3.99
Medicine 5,524 125,113 214,854 3.52 3.67
Engineering 2,589 43,440 77,664 3.20 3.53
Mathematics 581 11,057 17,317 1.75 1.90
Physics 688 25,393 42,955 4.41 5.05
Methods & Techniques
Feature Engineering - I consider four groups of features: Authors, Institutions, Affiliations, References Network. The first three (group 1)—Authors, Institutions and Affiliations—describe the reputation of the paper’s venue, of its authors and of its author’s institutions. I start by calculating the following features for each venue, author and institution in the dataset: the sum of citation counts of papers published by the entity, mean citations over papers published by the entity, and max citations, e.g. the citation count of the most cited work by the entity. I also calculate the h-index and g-index of these entities. The h-index is defined as the largest h such that at least h papers by the entity received at least h citations. The g-index is defined as the largest g such that the top g papers by the entity received together at least g2 citations. Both h-index and g-index numbers are easily calculable using the capabilities in the Scopus database. For each paper I aggregate the features of the entities (authors, institutions and
Bi et al \cite{Rec:Bi} provides ranked related entities to the user query along with the results of the main entity. In order to do this, this articles makes use of user's search history, click history and knowledge base. A matrix is created comprising of the user information which connects to the entities along with the ranking, click results. A tri-linear function\cite{Rec:Bi} is defined mapping these details and which will be used to rank the related entities
Part One: A big argument that is made in “Where the Jobs Are: STEM Fields” by Linda Rosen and “Many With New College Degree Find the Job Market Humbling” by Catherine Rampell, is that employment rates for college grads are much lower than they were before, as are the starting salaries. According to a study released by the John J. Heldrich Center for Workforce Development at Rutgers University, “The median starting salary for students graduating from four-year colleges in 2009 and 2010 was $27,000, down from $30,000 for those who entered the work force in 2006 to 2008.” Along with lower employment rates and starting salaries, those without a college degree are even less fortunate than those who graduated from college. On the other hand, STEM
In Machine Learning with Baseball Hall of Fame, portrays 3 figures comparing the Hall of Fame Ballots vs. the predictions. Moreover, this differentiation is recorded every year by a machine: the Artificial Neural Network(ANNs). However, these college students at George Fox University utilize the Fast Artificial Neural Network(FANN) toolkit for their research on predicting which players have the potential to be elected to the Hall of Fame for Baseball.
Cuebed wrote an article on July 21, 2017 about how free parking and parking minimums make living on big cities much more expensive. The article showed how it is a waste of space to have so much parking and there are more effective and faster ways to move around without a car. The article also showed an example of this with mexico city and how they cut down on parking spaces.
My study proposes to examine the New York Times sports pages between 1997 and 2017 as a way of testing some ideas about the nature of the changes in the discourse about baseball as that discourse has evolved over the last 20 years. Although these ideas did not necessarily take hold in professional baseball circles until the 21st century, outsiders like Bill James have been promoting non-traditional baseball statistics as more accurate ways of describing the game since the 1970s, while in the 1990s Baseball Prospectus, a publication which debuted the PECOTA predictive baseball model developed by eventual data celebrity Nate Silver, began to spread these ideas to increasingly wide groups of baseball fans. Today, these ideas have widespread popularity, and the yearly Bill James Baseball Abstracts and Baseball Prospectus anthologies both have high circulations, while websites like FanGraphs, which approach baseball journalism from a statistical point of view, have significant daily readership (among them, yours truly).
I am reviewing the scholarly article written by Graham Keith that is called Justin Martyr and Religious Exclusivism. The article conveys the message of Christians and the journey to exclusivism. Justin Martyr is considered one of the most influential Greek apologists of his time and studying his thinking helps the reader understand exclusivism better especially in the Christian religion. In this reading we learn of the basis for exclusivism based on the idea that no one can reach God or deserved salvation through their own efforts. The story of Justin Martyr opinion has no exception to the exclusivism of Christianity of this period as we learn of different forms of exclusivism regarding the pagan culture
The importance of relying on scholarly - peer reviewed - articles and journals become clearer with each passing day; it is difficult to discern fact from fiction.
system known as a Gini index, named after an Italian statistician, Corrado Gini. The United
In Michael Teitelbaum’s 2014 article on the Myth of STEM shortage he informs us that American students need to improve in math and science, but not just because there’s a surplus of jobs in those fields. Teitelbaum states that a compiling body of research is now available. Completed by academic researchers and other respected research organizations. These organizations report there has been no evidence found that would indicate a current widespread shortage among the science and engineering labor market or nothing that indicates hiring difficulties in those STEM
A recently, much publicized, outbreak of measles in multiple states illustrate an evolving community health problem in America. The Centers for Disease Control and Prevention reports that 2014 had a significant increase in the number of measles case over when compared to the previous 15 years (Centers for Disease Control and Prevention, 2015). This largely being the result of a debunked correlation between immunization shots and autism (Recame, 2012). The goal of this paper to identify the target population at risk, a collation needed to make changes within that population, interventions that could be taken, implementation of those interventions, and finally evaluation of interventions.
As a student that values the ability to make my own choices and opportunities to conduct research, UChicago appeals to my sense of learning. Unlike other universities, UChicago’s Department of Computer Science provides some of the best education in the world and allows students to build strength in an additional field due to the program’s flexibility. Not only that, UChicago’s Center for Scholarly Advancement encourages undergraduate research and would allow me to continue the scientific research that I am currently conducting, as well as allowing me to be mentored by great professors who are researching similar topics. In particular, Professor Ben Zhao and Professor Heather Zheng’s research on data-driven models of user behavior appeals to
Isolated and other journal or magazines, Scholarly journals' scholars are educators, graduate understudies, or powers who have had a not too dreadful measure of direct relationship in making academic journals. While the subject may have all the earmarks of being stupefied, they use their vocabulary and their industry understanding how to sufficiently differentiate and unmistakable get-togethers of spectators. Their inclination makes insightful journals a remarkable resource for understudies and diverse experts
Growing up in a family of doctors influenced me to pursue the knowledge of health and diseases. I long for devoting myself to public health. With data explosion, I realized that data-driven science is indispensable to meet the increasing demand in medical and biological improvement. Therefore, I want to apply tools of statistics to address human health problems and become an excellent biostatistician. My goal in entering in the M.S. program at Johns Hopkins University (JHU) is to develop expertise in Biostatistics. At JHU, I am able to obtain an interdisciplinary perspective, combining approaches and knowledge derived from statistics, computing and biology. In the future, I plan to pursue Ph.D. study and one day lead my own research group. To meet these objectives, I wish to carry on advanced study at JHU.
I think everything we learned in life at one point we might use it. Everything we are learning now in class will be handy not only in our professional field but most likely in all aspects of our live. I been noticing there is a couple of us that have problem with citations. We need to work hard to learn how to do them properly. I think we are going to be using them a lot in future projects. Anyway giving up it is not an option.
As people know, college rankings can provide more informations to student and their parents. Also, college rankings make students be able to consider their future school based on the facts they need. Those data of college rankings are collected by trustable survey agencies, independent third parties and etc. Therefore, the resources of the based informations are trustworthy. According to College and University Ranking Systems,