12,856 research outputs found
The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations
In recent years scholars have built maps of science by connecting the
academic fields that cite each other, are cited together, or that cite a
similar literature. But since scholars cannot always publish in the fields they
cite, or that cite them, these science maps are only rough proxies for the
potential of a scholar, organization, or country, to enter a new academic
field. Here we use a large dataset of scholarly publications disambiguated at
the individual level to create a map of science-or research space-where links
connect pairs of fields based on the probability that an individual has
published in both of them. We find that the research space is a significantly
more accurate predictor of the fields that individuals and organizations will
enter in the future than citation based science maps. At the country level,
however, the research space and citations based science maps are equally
accurate. These findings show that data on career trajectories-the set of
fields that individuals have previously published in-provide more accurate
predictors of future research output for more focalized units-such as
individuals or organizations-than citation based science maps
What's It Worth? The Economic Value of College Majors
Analyzes, by college major, gender and racial/ethnic distribution, median annual earnings, likelihood of unemployment and advanced degree attainment, and occupation and industry, as well as earnings differences within majors by race/ethnicity and gender
Networks of reader and country status: An analysis of Mendeley reader statistics
The number of papers published in journals indexed by the Web of Science core
collection is steadily increasing. In recent years, nearly two million new
papers were published each year; somewhat more than one million papers when
primary research papers are considered only (articles and reviews are the
document types where primary research is usually reported or reviewed).
However, who reads these papers? More precisely, which groups of researchers
from which (self-assigned) scientific disciplines and countries are reading
these papers? Is it possible to visualize readership patterns for certain
countries, scientific disciplines, or academic status groups? One popular
method to answer these questions is a network analysis. In this study, we
analyze Mendeley readership data of a set of 1,133,224 articles and 64,960
reviews with publication year 2012 to generate three different kinds of
networks: (1) The network based on disciplinary affiliations of Mendeley
readers contains four groups: (i) biology, (ii) social science and humanities
(including relevant computer science), (iii) bio-medical sciences, and (iv)
natural science and engineering. In all four groups, the category with the
addition "miscellaneous" prevails. (2) The network of co-readers in terms of
professional status shows that a common interest in papers is mainly shared
among PhD students, Master's students, and postdocs. (3) The country network
focusses on global readership patterns: a group of 53 nations is identified as
core to the scientific enterprise, including Russia and China as well as two
thirds of the OECD (Organisation for Economic Co-operation and Development)
countries.Comment: 26 pages, 6 figures (also web-based startable), and 2 table
Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network
The network of patents connected by citations is an evolving graph, which
provides a representation of the innovation process. A patent citing another
implies that the cited patent reflects a piece of previously existing knowledge
that the citing patent builds upon. A methodology presented here (i) identifies
actual clusters of patents: i.e. technological branches, and (ii) gives
predictions about the temporal changes of the structure of the clusters. A
predictor, called the {citation vector}, is defined for characterizing
technological development to show how a patent cited by other patents belongs
to various industrial fields. The clustering technique adopted is able to
detect the new emerging recombinations, and predicts emerging new technology
clusters. The predictive ability of our new method is illustrated on the
example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of
patents is determined based on citation data up to 1991, which shows
significant overlap of the class 442 formed at the beginning of 1997. These new
tools of predictive analytics could support policy decision making processes in
science and technology, and help formulate recommendations for action
Proximity of firms to scientific production
Following Bergeaud et al. (2022), we construct a new measure of proximity between industrial sectors and public research laboratories. Using this measure, we explore the underlying network of knowledge linkages between scientific fields and industrial sectors in France. We show empirically that there exists a significant negative correlation between the geographical distance between firms and laboratories and their scientific proximity, suggesting strongly localized spillovers. Moreover, we uncover some important differences by field, stronger than when using standard patent-based measures of proximity
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