Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe latent structure of national scientific development
72
0
0.0
(
0
)
Added by
Lili Miao
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Science is considered essential to innovation and economic prosperity. Understanding how nations build scientific capacity is therefore crucial to promote economic growth and national development. Although studies have shown that national scientific development is affected by geographic, historic, and economic factors, it remains unclear whether there are universal structures and trajectories behind national scientific development that can inform forecasting and policy making. By examining countries scientific exportation-the publications that are internationally indexed-we reveal a three-cluster structure in the relatedness network of disciplines that underpin national scientific development and the organization of global science. Tracing the evolution of national research portfolios reveals that while nations are proceeding to more diverse research profiles individually, scientific production is increasingly specialized in global science over the past decades. We further demonstrate that the revealed disciplinary clusters inform economic development, where the number of publications in applied research centered cluster significantly predicts economic growth. By uncovering the underlying structure of scientific development and connecting it with economic development, our results may offer a new perspective to study national scientific development and its relationships with economic development.
rate research
Read More
The practice of scientific research is often thought of as individuals and small teams striving for disciplinary advances. Yet as a whole, this endeavor more closely resembles a complex system of natural computation, in which information is obtained, generated, and disseminated more effectively than would be possible by individuals acting in isolation. Currently, the structure of this integrated and innovative landscape of scientific ideas is not well understood. Here we use tools from network science to map the landscape of interconnected research topics covered in the multidisciplinary journal PNAS since 2000. We construct networks in which nodes represent topics of study and edges give the degree to which topics occur in the same papers. The network displays small-world architecture, with dense connectivity within scientific clusters and sparse connectivity between clusters. Notably, clusters tend not to align with assigned article classifications, but instead contain topics from various disciplines. Using a temporal graph, we find that small-worldness has increased over time, suggesting growing efficiency and integration of ideas. Finally, we define a novel measure of interdisciplinarity, which is positively associated with PNASs impact factor. Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight.
The quest for a model that is able to explain, describe, analyze and simulate real-world complex networks is of uttermost practical as well as theoretical interest. In this paper we introduce and study a network model that is based on a latent attribute structure: each node is characterized by a number of features and the probability of the existence of an edge between two nodes depends on the features they share. Features are chosen according to a process of Indian-Buffet type but with an additional random fitness parameter attached to each node, that determines its ability to transmit its own features to other nodes. As a consequence, a nodes connectivity does not depend on its age alone, so also young nodes are able to compete and succeed in acquiring links. One of the advantages of our model for the latent bipartite node-attribute network is that it depends on few parameters with a straightforward interpretation. We provide some theoretical, as well experimental, results regarding the power-law behaviour of the model and the estimation of the parameters. By experimental data, we also show how the proposed model for the attribute structure naturally captures most local and global properties (e.g., degree distributions, connectivity and distance distributions) real networks exhibit. keyword: Complex network, social network, attribute matrix, Indian Buffet process
The rapid development of modern science and technology has spawned rich scientific topics to research and endless production of literature in them. Just like X-ray imaging in medicine, can we intuitively identify the development limit and internal evolution pattern of scientific topic from the relationship of massive knowledge? To answer this question, we collect 71431 seminal articles of topics that cover 16 disciplines and their citation data, and extracts the idea tree of each topic to restore the structure of the development of 71431 topic networks from scratch. We define the Knowledge Entropy (KE) metric, and the contribution of high knowledge entropy nodes to increase the depth of the idea tree is regarded as the basis for topic development. By observing X-ray images of topics, We find two interesting phenomena: (1) Even though the scale of topics may increase unlimitedly, there is an insurmountable cap of topic development: the depth of the idea tree does not exceed 6 jumps, which coincides with the classical Six Degrees of Separation! (2) It is difficult for a single article to contribute more than 3 jumps to the depth of its topic, to this end, the continuing increase in the depth of the idea tree needs to be motivated by the influence relay of multiple high knowledge entropy nodes. Through substantial statistical fits, we derive a unified quantitative relationship between the change in topic depth ${Delta D}^t(v)$ and the change in knowledge entropy over time ${KE}^tleft(vright)$ of the article $v$ driving the increase in depth in the topic: ${Delta D}^t(v) approx log frac{KE^{t}(v)}{left(t-t_{0}right)^{1.8803}}$ , which can effectively portray evolution patterns of topics and predict their development potential. The various phenomena found by scientific x-ray may provide a new paradigm for explaining and understanding the evolution of science and technology.
In many real-world scenarios, it is nearly impossible to collect explicit social network data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We consider contagions propagating over the edges of an unobserved social network, where we only observe the times when nodes became infected, but not who infected them. Given such node infection times, we then identify the optimal network that best explains the observed data. We present a maximum likelihood approach based on convex programming with a l1-like penalty term that encourages sparsity. Experiments on real and synthetic data reveal that our method near-perfectly recovers the underlying network structure as well as the parameters of the contagion propagation model. Moreover, our approach scales well as it can infer optimal networks of thousands of nodes in a matter of minutes.
The low surface brightness visible wavelength Universe below 29 mag arcsec$^{-2}$ is teeming with unexplored astrophysical phenomena. Structures fainter than this surface brightness are extremely difficult to image due to systematic errors of sky subtraction and scattered light in the atmosphere and in the telescope. In Chapter 1, I show how The Dragonfly Telephoto Array (Dragonfly for short) addresses these systematics via a combination of hardware and software and is able to image at a level of 30 mag arcsec$^{-2}$ or fainter. In Chapter 2, I describe the Dragonfly Pipeline and how it is optimized for low surface brightness imaging, how it automatically rejects problematic exposures, and its cloud-orchestration. In Chapter 3, I present a study of the outer disk of the nearby spiral galaxy NGC 2841 using Dragonfly as well as archival data in UV from the Galaxy Evolution Explorer Satellite and rest frame 21 cm data using the Very Large Array. While it is commonly accepted that gas dominates over stars in galaxy outer disks, I find that in NGC 2841, this is not the case. The stellar disk extends to five times R25, and there is more stellar than gas mass at all radii. Surprisingly there is a constant ratio of stellar to gas mass beyond 30 kpc, where the disk is also warped. I propose the most likely formation mechanism for this outer disk is co-planar satellite accretion. In Chapter 4, I present a study of thermally emitted and scattered light from dust in the optically thin regions of the Spider HI Cloud, using Dragonfly and Herschel Space Observatory data. In closing the thesis (Chapter 5), I look forward to further improvements in the Dragonfly Pipeline, a population study of the formation mechanisms of galaxy disks and to carrying out tests of dust models.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
