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      Decision support systems based on scientific evidence: bibliometric networks of invasive Lantana camara

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          Abstract

          Extraction and analysis of useful knowledge from the vast amount of relevant published literature can add valuable insights to any research theme or area of interest. We introduce a simplified bibliometric data analysis protocol for gaining substantial insights into research thematics, which can also serve as a handy practical skill for researchers while working from home. In this paper, we provide ways of developing a holistic research strategy using bibliometric-data driven approaches that integrate network analysis and information management, without the need for full paper access. This protocol is a comprehensive multi-modular pathway for analysis of metadata obtained from major scientific publishing houses by the use of a Decision Support System (DSS). A simple case study on the invasive species Lantana camara has been presented as a proof-of-concept to show how one can implement this DSS based protocol. Some perspectives are also provided on how the outcomes can be used directly or scaled up for long term research interventions. We hope that this work will simplify exploratory literature review, and enable rational design of research objectives for scholars, as well as the development of comprehensive grant proposals that address gaps in research.

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          Most cited references14

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Is Open Access

            Software survey: VOSviewer, a computer program for bibliometric mapping

            We present VOSviewer, a freely available computer program that we have developed for constructing and viewing bibliometric maps. Unlike most computer programs that are used for bibliometric mapping, VOSviewer pays special attention to the graphical representation of bibliometric maps. The functionality of VOSviewer is especially useful for displaying large bibliometric maps in an easy-to-interpret way. The paper consists of three parts. In the first part, an overview of VOSviewer’s functionality for displaying bibliometric maps is provided. In the second part, the technical implementation of specific parts of the program is discussed. Finally, in the third part, VOSviewer’s ability to handle large maps is demonstrated by using the program to construct and display a co-citation map of 5,000 major scientific journals.
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              Finding scientific topics.

              A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.
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                Author and article information

                Contributors
                preetmishra8@gmail.com
                abhishek1992@nipgr.ac.in
                suresh@aud.ac.in
                gy@nipgr.ac.in
                Journal
                Proc.Indian Natl. Sci. Acad.
                Proceedings of the Indian National Science Academy. Part A, Physical Sciences
                Indian National Science Academy (New Delhi )
                0370-0046
                2454-9983
                10 June 2021
                : 1-6
                Affiliations
                [1 ]GRID grid.10706.30, ISNI 0000 0004 0498 924X, School of Computational and Integrative Sciences, , Jawaharlal Nehru University, ; New Delhi, India
                [2 ]GRID grid.419632.b, ISNI 0000 0001 2217 5846, National Institute of Plant Genome Research, ; New Delhi, India
                [3 ]GRID grid.448818.9, ISNI 0000 0004 1765 2312, School of Human Ecology, , Ambedkar University, ; New Delhi, India
                Author information
                https://orcid.org/0000-0002-1840-7883
                http://orcid.org/0000-0002-3004-3066
                https://orcid.org/0000-0001-6591-9964
                Article
                16
                10.1007/s43538-021-00016-7
                8190976
                9853f5a8-89a6-40c1-927b-13d6073fdafa
                © Indian National Science Academy 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 3 May 2020
                : 2 September 2020
                Categories
                Original Paper

                decision support system (dss),text data mining (tdm),community detection,bibliometric analysis,complex networks,invasive species,lantana camara

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