scholarly journals Impact network analysis and the INA R package: Decision support for regional management interventions

2021 ◽  
Author(s):  
K. A. Garrett
Keyword(s):  
Decision Support ◽  
Network Analysis ◽  
R Package ◽  
Management Interventions ◽  
Regional Management

scholarly journals Impact network analysis and the INA R package: Decision support for regional management interventions

2020 ◽  
Author(s):  
K. A. Garrett
Keyword(s):  
Network Analysis ◽  
R Package ◽  
Lessons Learned ◽  
Policy Makers ◽  
Management Interventions ◽  
Scientific Disciplines ◽  
System Components ◽  
Common Framework ◽  
Global Change Scenarios

AbstractThe success of intervention projects in ecological systems depends not only on the quality of management technologies, but also patterns of adoption among land managers. Impact network analysis (INA) is a new framework for evaluating the likely success of regional interventions before, during, and after projects, for project implementers, policy makers, and funders. INA integrates across three key system components in a multilayer network analysis: (a) the quality of a management technology and the quality of research supporting it, (b) the socioeconomic networks through which managers learn about management technologies and decide whether to use them, and (c) the linked biophysical network for target species success or failure in the management landscape that results from managers’ decisions.The specific objectives of this paper are (1) to introduce the INA framework and INA R package, (2) to illustrate identification of key nodes for smart surveillance, for networks where the likelihood of invasive species entry into the biophysical network at a given node may be based on information available to the corresponding node in the socioeconomic network, (3) to illustrate application of the INA framework for evaluating the likely degree of success of a project in intervention ecology, before, during and after an intervention, and (4) to illustrate the use of INA for evaluating adaptation strategies under global change scenarios with pulse and press stressors, introducing ‘adaptation functions’ for sustainability and resilience.Examples of use of the INA package show one of the key outcomes of analyses: identifying when systems may be non-responsive to the system components that are readily changed through management decisions, to explore what additional adaptations may be necessary for intervention success.The broader goal for the development of impact network analysis and the INA package is to provide a common framework that integrates across intervention ecology, to enhance opportunities for lessons learned across systems and scientific disciplines, to support the development of a community of practice, and to create a general platform for analysis of sustainability, resilience, and economic viability in intervention ecology applications.

scholarly journals mangal - making ecological network analysis simple

2014 ◽  
Author(s):  
Timothée E Poisot ◽  
Benjamin Baiser ◽  
Jennifer A Dunne ◽  
Sonia Kéfi ◽  
Francois Massol ◽  
...  
Keyword(s):  
Network Analysis ◽  
R Package ◽  
Ecological Networks ◽  
Ecological Interactions ◽  
Ecological Network ◽  
Ecological Data ◽  
Meta Data ◽  
Ecological Network Analysis ◽  
Analysis Process ◽  
Access Data

The study of ecological networks is severely limited by (i) the difficulty to access data, (ii) the lack of a standardized way to link meta-data with interactions, and (iii) the disparity of formats in which ecological networks themselves are represented. To overcome these limitations, we conceived a data specification for ecological networks. We implemented a database respecting this standard, and released a R package ( `rmangal`) allowing users to programmatically access, curate, and deposit data on ecological interactions. In this article, we show how these tools, in conjunctions with other frameworks for the programmatic manipulation of open ecological data, streamlines the analysis process, and improves eplicability and reproducibility of ecological networks studies.

Application of network analysis to identify interactive systems of eating disorder psychopathology

2016 ◽  
Vol 46 (12) ◽  
pp. 2667-2677 ◽  
Author(s):  
K. T. Forbush ◽  
C. S. Q. Siew ◽  
M. S. Vitevitch
Keyword(s):  
Network Analysis ◽  
Mental Disorders ◽  
Behavioral Therapy ◽  
Average Distance ◽  
R Package ◽  
Interactive Systems ◽  
Body Checking ◽  
Undirected Network ◽  
Eating Disorder Psychopathology ◽  
Key Players

BackgroundTraditional approaches for the classification of eating disorders (EDs) attribute symptoms to an underlying, latent disease entity. The network approach is an alternative model in which mental disorders are represented as networks of interacting, self-reinforcing symptoms. This project was the first to use network analysis to identify interconnected systems of ED symptoms.MethodAdult participants (n = 143; 77.6% women) with a Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) ED were recruited from the community to take part in a larger ongoing longitudinal study. The Structured Clinical Interview for DSM Disorders (SCID-I) was used to establish diagnoses. An undirected network of ED symptoms was created using items from the Eating Pathology Symptoms Inventory (EPSI) and the R package qgraph.ResultsBody checking emerged as the strongest and most important single symptom in the entire network by having the shortest average distance to other symptoms in the network, and by being the most frequent symptom on the path between any two other symptoms. Feeling the need to exercise every day and two symptoms assessing dietary restraint/restricting emerged as ‘key players’, such that their removal from the network resulted in maximal fracturing of the network into smaller components.ConclusionsAlthough cognitive–behavioral therapy for EDs focuses on reducing body checking to promote recovery, our data indicate that amplified efforts to address body checking may produce stronger (and more enduring) effects. Finally, results of the ‘key players analysis’ suggested that targeting interventions at these key nodes might prevent or slow the cascade of symptoms through the ‘network’ of ED psychopathology.

scholarly journals xMWAS: an R package for data-driven integration and differential network analysis

2017 ◽  
Author(s):  
Karan Uppal ◽  
Young-Mi Go ◽  
Dean P. Jones
Keyword(s):  
Network Analysis ◽  
Data Integration ◽  
R Package ◽  
Data Driven ◽  
Central Component ◽  
Differential Network Analysis ◽  
Differential Network ◽  
Meaningful Relationships ◽  
Integrative Network ◽  
Integrative Omics

AbstractSummaryIntegrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Further-more, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, an R package for data integration, network visualization, clustering, differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms.Availabilityhttps://sourceforge.net/projects/xmwas/[email protected]

scholarly journals Metabolic Syndrome and Nutrition: Text Mining and Network Analysis Using Abstracts of PubMed

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 211-211
Author(s):  
Hweesoo Jeong ◽  
Dongwook Lee ◽  
Donggyu Jeong
Keyword(s):  
Metabolic Syndrome ◽  
Data Analysis ◽  
Network Analysis ◽  
Text Mining ◽  
Mediterranean Diet ◽  
Dairy Products ◽  
Dairy Product ◽  
Search Term ◽  
Omega 3 ◽  
Management Interventions

Abstract Objectives Big data consisting of unstructured data such as documents, images, sound sources, etc. are difficult to apply to existing structured data analysis programs and analyzed using techniques such as text mining, web mining, opinion mining, and network analysis. In metabolic syndrome management, interventions such as lifestyle correction are more important than drug treatment. Therefore, this study studied nutrition that can help prevent metabolic syndrome through atypical data analysis using Medline data. Methods From 1977 to December 2019, a total of 992 abstracts were extracted among the papers available from the Pubmed using the Mesh words of metabolic syndrome, nutrition, and prevention as a search term. Text mining using the Netminer 4.0 program resulted in the extraction of 7846 nouns and the final nouns of nutrients or foods defined as the frequency of occurrence are more than 30 times. For the selected words, we constructed and analyzed a network using links that connectivity values of 0.05 or more. Results Of the 27 words related to nutrition in 992 papers, most five frequent nouns were Calcium, Magnesium, Mediterranean diet, Zinc, and Dairy product. In the network analysis, the five keywords in the centrality analysis were Dairy products, Fish, Vegetables, Fruit and Copper. The 27 words were grouped into eight groups, and four groups of one or more words were identified: A first group consisting of Calcium, Copper, Flavonoid, Iron, Magnesium and Selenium, and the second group of Zinc, DHEA, EPA, Fish and Omega 3. The third group consisting of Polyphenol, Prebiotics, Probiotics, and Yogurt, and the last group consisting of Dairy products, Fruits, Mediterranean diet, Milk, Nut, Sodium, Sugar, and Vegetables. Conclusions Numerous minerals, omega 3, Probiotics and vegetables, fruits and dairy products were identified in the nutrition papers related to the prevention of metabolic syndrome. Funding Sources This study has not supported by any funds.

A Social-Academic Network Analysis of the EURO Working Group on DSS

2010 ◽  
Vol 2 (4) ◽  
pp. 13-36 ◽  
Author(s):  
Rahma Bouaziz ◽  
Tiago Simas ◽  
Fátima Dargam ◽  
Rita Ribeiro ◽  
Pascale Zaraté
Keyword(s):  
Social Network ◽  
Decision Support ◽  
Network Analysis ◽  
Working Group ◽  
Analytical Data ◽  
The Social ◽  
Group Members ◽  
Academic Network ◽  
Collaboration Dynamics ◽  
New Research

This paper addresses aspects of the social network analysis (SNA) performed on the social-academic network implemented for the EURO Working Group on Decision Support Systems (EWG-DSS). The EWG-DSS network has more than 105 members and is defined with the objective of analysing and representing the various relationships that academically link the group members, as well as evaluating the group’s collaboration dynamics. This paper shows graphical representations and discusses their corresponding interpretation and analytical data. This work is part of the study carried out within the underlying project of the EWG-DSS social-academic network to understanding how the group interacts, as well as encouraging new research and promoting further collaboration among the EWG-DSS group members.

A Social-Academic Network Analysis of the EURO Working Group on DSS

2012 ◽  
pp. 229-251
Author(s):  
Rahma Bouaziz ◽  
Tiago Simas ◽  
Fátima Dargam ◽  
Rita Ribeiro ◽  
Pascale Zaraté
Keyword(s):  
Social Network ◽  
Decision Support ◽  
Network Analysis ◽  
Working Group ◽  
Analytical Data ◽  
The Social ◽  
Group Members ◽  
Academic Network ◽  
Collaboration Dynamics ◽  
New Research

This paper addresses aspects of the social network analysis (SNA) performed on the social-academic network implemented for the EURO Working Group on Decision Support Systems (EWG-DSS). The EWG-DSS network has more than 105 members and is defined with the objective of analysing and representing the various relationships that academically link the group members, as well as evaluating the group’s collaboration dynamics. This paper shows graphical representations and discusses their corresponding interpretation and analytical data. This work is part of the study carried out within the underlying project of the EWG-DSS social-academic network to understanding how the group interacts, as well as encouraging new research and promoting further collaboration among the EWG-DSS group members.

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