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Purpose - The purpose of this paper is to propose an approach for conceptualizing science based on collaboration and competences of researchers. Design/methodology/approach - The research is conducted by exploratory analysis of co...
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Purpose - The purpose of this paper is to propose an approach for conceptualizing science based on collaboration and competences of researchers. Design/methodology/approach - The research is conducted by exploratory analysis of collaboration and competences using case studies from humanistic, engineering, natural sciences and a general topic. Findings - The findings show that by applying the proposed approach on bibliographic data that readily exist for many national sciences as well as for international scientific communities, one can obtain useful new insights into the research. The approach is demonstrated with the following exploratory findings: identification of important connections and individual researchers that connect the community of anthropologists; collaboration of technical scientists in the community of anthropologists caused by an interdisciplinary research project; connectivity, interdisciplinary and structure of artificial intelligence, nanotechnology and a community based on a general topic; and identifying research interest shift described with concretization and topic-shift. Practical implications - As demonstrated with the practical implementation, users can obtain information of the most relevant competences of a researcher and his most important collaborators. It is possible to obtaining researchers, community structure and competences of an arbitrary research topic. Social implications - The map for collaboration and competences of a complete science can be a crucial tool for policy-making. Social scientists can use the results of the proposed approach to better understand and direct the development of science. Originality/value - Originality and value of the paper is in combining text (competences) and network (research collaboration and co-authoring) approaches for exploring science. Additional values give the results of analysis that demonstrate the approach.
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Introduction Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely off...
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Introduction Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.
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Urban energy metabolism reflects the energy transmission and exchange process to uncover the structure and functionality of urban activities. The literature on urban energy metabolism is overwhelming and still growing. However, fe...
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Urban energy metabolism reflects the energy transmission and exchange process to uncover the structure and functionality of urban activities. The literature on urban energy metabolism is overwhelming and still growing. However, few publications have attempted to provide a specific literature review of urban energy metabolism. Therefore, this study conducted a systematic bibliometric analysis on urban energy metabolism publications to explore research status and emerging trends. Results showed that the amount of co-cited literature on urban metabolism displayed an upward trend during the study period. After 2006, the number of urban metabolism publications implied an upsurge. Based on the co-word analysis of keywords, "energy" occupied much of the attention of urban metabolism research. The high citation keywords relating to network analytical methods in urban energy metabolism publications were analyzed via the co-word network, including ecological network analysis, input-output analysis, and the complex network. The combination of input-output analysis and ecological network analysis has been widely applied to urban energy metabolism studies worldwide, especially in the context of China. Consequently, future research opportunities were suggested from the following aspects: (1) the inclusion of both spatial and temporal dimensions of energy metabolic systems, and (2) the ability to unravel the interactions of components in a dynamic manner. The findings of this study were not only beneficial for scholars to detail a holistic picture of current research progress, remained questions and emerging research methods, but also valuable to assist practitioners to evaluate and monitor the urban energy metabolic performance with suitable network methods. (c) 2020 Elsevier Ltd. All rights reserved.
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We present a network analysis of a cross-sectional study of mild cognitive impairment (MCI). Network analysis, as opposed to univariate analysis, accounts for interactions among brain structures in explaining a clinical outcome. I...
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We present a network analysis of a cross-sectional study of mild cognitive impairment (MCI). Network analysis, as opposed to univariate analysis, accounts for interactions among brain structures in explaining a clinical outcome. In this context, we analyze structural magnetic resonance (MR) data based on a Bayesian network representation of variables in the problem domain. The Bayesian network resulting from this analysis reveals complex, nonlinear multivariate associations among morphological changes in the left hippocampus and in the right thalamus and the presence of mild cognitive impairment. This Bayesian network could be used to predict the presence of mild cognitive impairment from structural MR scans.
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Network Environ Analysis is a formal, quantitative methodology to describe an object's within system "environ"ment [Patten, B.C., 1978a. Systems approach to the concept of environment. Ohio Journal of Science 78, 206-222]. It prov...
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Network Environ Analysis is a formal, quantitative methodology to describe an object's within system "environ"ment [Patten, B.C., 1978a. Systems approach to the concept of environment. Ohio Journal of Science 78, 206-222]. It provides a perspective of the environment, based on general system theory and input-output analysis. This approach is one type of a more general conceptual approach called ecological network analysis. Application of Network Environ Analysis on ecosystem models has revealed several important and unexpected results [see e.g., Patten, B.C., 1982. Environs: relativistic elementary particles or ecology. American Naturalist 119, 179-219; Patten, B.C., 1985. Energy cycling in the ecosystem. Ecological Modelling 28, 1-71; Fath, B.D., Patten, B.C., 1999a. Review of the foundations of network environ analysis. Ecosystems 2, 167-179], which have been identified and summarized in the literature as network environ properties. To conduct the analysis one needs ecosystem data including the intercompartmental flows, compartmental storages, and boundary input and output flows. The software presented herein uses these data to perform the main network environ analyses and environ properties including unit environs, indirect effects ratio, network homogenization, network synergism, network mutualism, mode partitioning, and environ control. The software is available from The MathWorks MATLAB~® Central File Exchange website (http://www.mathworks.com/matlabcentral/fileexchange/loadCategory.do).
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This article demonstrates, by example, 2 approaches to the analysis of knowledge work. Both methods draw on network as a framework: a Latourian actor-network theory analysis and a network analysis. The shared object of analysis is...
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This article demonstrates, by example, 2 approaches to the analysis of knowledge work. Both methods draw on network as a framework: a Latourian actor-network theory analysis and a network analysis. The shared object of analysis is a digital humanities and digital media research lab that is the outcome of the collective and coordinated efforts of researchers and other stakeholders at North Carolina State University. The authors show how the two methods are drawn to different objects of study, different data sources, and different assumptions about how data can be reduced and made understandable. The authors conclude by arguing that although these methods yield different outlooks on the same object, their findings are mutually informing.
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Prior research documents associations between personal network characteristics and health, but establishing causation has been a long-standing research priority. To evaluate approaches to causal inference in egocentric network dat...
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Prior research documents associations between personal network characteristics and health, but establishing causation has been a long-standing research priority. To evaluate approaches to causal inference in egocentric network data, this article uses three waves from the University of California Berkeley Social Networks Study (N = 1,159) to investigate connections between nine network variables and two global health outcomes. We compare three modeling strategies: cross-sectional ordinary least squares regression, regression with lagged dependent variables (LDVs), and hybrid fixed and random effects models. Results suggest that cross-sectional and LDV models may overestimate the causal effects of networks on health because hybrid models show that network–health associations operate primarily between individuals, as opposed to network changes causing within-individual changes in health. These findings demonstrate uses of panel data that may advance scholarship on networks and health and suggest that causal effects of network support on health may be more limited than previously thought.
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Abstract Ecological associations between hosts and parasites are influenced by host exposure and susceptibility to parasites, and by parasite traits, such as transmission mode. Advances in network analysis allow us to answer quest...
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Abstract Ecological associations between hosts and parasites are influenced by host exposure and susceptibility to parasites, and by parasite traits, such as transmission mode. Advances in network analysis allow us to answer questions about the causes and consequences of traits in ecological networks in ways that could not be addressed in the past. We used a network‐based framework (exponential random graph models or ERGMs) to investigate the biogeographic, phylogenetic and ecological characteristics of hosts and parasites that affect the probability of interactions among nonhuman primates and their parasites. Parasites included arthropods, bacteria, fungi, protozoa, viruses and helminths. We investigated existing hypotheses, along with new predictors and an expanded host–parasite database that included 213 primate nodes, 763 parasite nodes and 2319 edges among them. Analyses also investigated phylogenetic relatedness, sampling effort and spatial overlap among hosts. In addition to supporting some previous findings, our ERGM approach demonstrated that more threatened hosts had fewer parasites, and notably, that this effect was independent of hosts also having a smaller geographic range. Despite having fewer parasites, threatened host species shared more parasites with other hosts, consistent with loss of specialist parasites and threat arising from generalist parasites that can be maintained in other, non‐threatened hosts. Viruses, protozoa and helminths had broader host ranges than bacteria, or fungi, and parasites that infect non‐primates had a higher probability of infecting more primate species. The value of the ERGM approach for investigating the processes structing host–parasite networks provided a more complete view on the biogeographic, phylogenetic and ecological traits that influence parasite species richness and parasite sharing among hosts. The results supported some previous analyses and revealed new associations that warrant future research, thus revealing how hosts and parasites interact to form ecological networks.
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For the evaluation of data from stimulus response experiments dynamic metabolic network models are generated. With an increase of reaction steps and regulatory interdependencies the amount of the simulation data becomes hard to ha...
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For the evaluation of data from stimulus response experiments dynamic metabolic network models are generated. With an increase of reaction steps and regulatory interdependencies the amount of the simulation data becomes hard to handle. In this paper, we present the application and extension of methods combining visualization and animation of dynamic models to facilitate the analysis of the complex system behaviour.
The dynamic changes of metabolite pools and fluxes are simultaneous visualized within the network structure. Depending on the scaling used, different focuses can be set, e.g. to observe local dynamics or global concentration balances. For the visualization of the present inhibition and activation state of certain reaction steps of a metabolic network model a novel quantification method is proposed.
The sensitivity analysis of dynamic metabolic network models leads to high-dimensional sensitivity matrices that vary over time. To process the enormous amount of data we use a colour scale transformation and the reorderable matrix method for the visual exploration of the time-varying matrices.
The benefits of our methods are illustrated with the help of a metabolic network model of the central carbon metabolism in Escherichia coli.
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