What´s driving dermatology: Contribution title analysis of the largest German Dermatology Congress 2019 (Preprint)

2020 ◽  
Author(s):  
Robert Kaczmarczyk ◽  
Felix Bauerdorf ◽  
Alexander Zink
Keyword(s):  
Network Analysis ◽  
Annual Meeting ◽  
Research Network ◽  
Graph Analysis ◽  
Network Graph ◽  
Research Topics ◽  
German Speaking ◽  
Specialist Groups ◽  
Community Meeting ◽  
High Diversity

BACKGROUND Every two years, German-speaking dermatologic specialist groups gather in Berlin to share the latest developments at Germany´s largest dermatologic conference, the Annual Meeting of the Germany Society of Dermatology (DDG). Because this conference has a lasting effect on dermatologic practice and research, understanding what is moving the specialist groups means understanding what is driving dermatology in Germany. OBJECTIVE The objective of the article is to introduce the medical scientific community to a data visualization method, which will help understand more sophisticated data analysis and processing approaches in the future. METHODS We used word network analysis to compile and visualize the information embedded in the contribution titles to the DDG Annual Meeting in 2019. We extracted words, contributing cities and inter-connections. The data was standardized, visualized using network graphs and analyzed using common network analysis parameters. RESULTS A total of 5509 words were extracted from 1150 contribution titles. The most frequently used words were “therapy”, “patients”, and “psoriasis”. The highest number of contributions came from Hamburg, Berlin and Munich. High diversity in research topics was found, as well as a well-connected research network. CONCLUSIONS Focus of the well-connected German-speaking dermatology community meeting 2019 was patient and therapy centered and lies especially on the diseases psoriasis and melanoma. Network graph analysis can provide helpful insights and help planning future congresses. It can facilitate the choice which contributors to include as imbalances become apparent. Moreover, it can help distributing the topics more evenly across the whole dermatologic spectrum.

scholarly journals What’s driving dermatology? Contribution title analysis of the largest German Dermatology Congress 2019

2021 ◽  
Vol 7 ◽  
pp. 205520762110121
Author(s):  
Robert Kaczmarczyk ◽  
Felix King ◽  
Tilo Biedermann ◽  
Alexander Zink
Keyword(s):  
Network Analysis ◽  
Annual Meeting ◽  
Research Network ◽  
Graph Analysis ◽  
Network Graph ◽  
Research Topics ◽  
German Speaking ◽  
Specialist Groups ◽  
Community Meeting ◽  
High Diversity

Background Every two years, German-speaking dermatologic specialist groups gather in Berlin to share the latest developments at Germanýs largest dermatologic conference, the Annual Meeting of the Germany Society of Dermatology (DDG). Because this conference has a lasting effect on dermatologic practice and research, understanding what is moving the specialist groups means understanding what is driving dermatology in Germany. Methods We used word network analysis to compile and visualize the information embedded in the contribution titles to the DDG Annual Meeting in 2019. We extracted words, contributing cities and inter-connections. The data was standardized, visualized using network graphs and analyzed using common network analysis parameters. Results A total of 5509 words were extracted from 1150 contribution titles. The most frequently used words were “therapy”, “patients”, and “psoriasis”. The highest number of contributions came from Hamburg, Berlin and Munich. High diversity in research topics was found, as well as a well-connected research network. Conclusions Focus of the well-connected German-speaking dermatology community meeting 2019 was patient and therapy centered and lies especially on the diseases psoriasis and melanoma. Network graph analysis can provide helpful insights and help planning future congresses. It can facilitate the choice which contributors to include as imbalances become apparent. Moreover, it can help distributing the topics more evenly across the whole dermatologic spectrum.

scholarly journals Visualization of clinical teaching citations using social network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hakimeh Hazrati ◽  
Shoaleh Bigdeli ◽  
Seyed Kamran Soltani Arabshahi ◽  
Vahideh Zarea Gavgani ◽  
Nafiseh Vahed
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Collaboration ◽  
Clinical Teaching ◽  
Clustering Coefficient ◽  
Knowledge Network ◽  
Research Network ◽  
Centrality Measures ◽  
The Usa

Abstract Background Analyzing the previous research literature in the field of clinical teaching has potential to show the trend and future direction of this field. This study aimed to visualize the co-authorship networks and scientific map of research outputs of clinical teaching and medical education by Social Network Analysis (SNA). Methods We Identified 1229 publications on clinical teaching through a systematic search strategy in the Scopus (Elsevier), Web of Science (Clarivate Analytics) and Medline (NCBI/NLM) through PubMed from the year 1980 to 2018.The Ravar PreMap, Netdraw, UCINet and VOSviewer software were used for data visualization and analysis. Results Based on the findings of study the network of clinical teaching was weak in term of cohesion and the density in the co-authorship networks of authors (clustering coefficient (CC): 0.749, density: 0.0238) and collaboration of countries (CC: 0.655, density: 0.176). In regard to centrality measures; the most influential authors in the co-authorship network was Rosenbaum ME, from the USA (0.048). More, the USA, the UK, Canada, Australia and the Netherlands have central role in collaboration countries network and has the vertex co-authorship with other that participated in publishing articles in clinical teaching. Analysis of background and affiliation of authors showed that co-authorship between clinical researchers in medicine filed is weak. Nineteen subject clusters were identified in the clinical teaching research network, seven of which were related to the expected competencies of clinical teaching and three related to clinical teaching skills. Conclusions In order to improve the cohesion of the authorship network of clinical teaching, it is essential to improve research collaboration and co-authorship between new researchers and those who have better closeness or geodisk path with others, especially those with the clinical background. To reach to a dense and powerful topology in the knowledge network of this field encouraging policies to be made for international and national collaboration between clinicians and clinical teaching specialists. In addition, humanitarian and clinical reasoning need to be considered in clinical teaching as of new direction in the field from thematic aspects.

scholarly journals A Brief Overview of Social Network Analysis and its Current State within Romanian Sociology

2013 ◽  
Vol 3 (3) ◽  
pp. 5-11
Author(s):  
Marian-Gabriel Hâncean
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Relations ◽  
Early Spring ◽  
Research Topics ◽  
Political Sciences ◽  
Current State ◽  
Wide Range ◽  
Network Studies

Abstract The field of social network studies has been growing within the last 40 years, gathering scholars from a wide range of disciplines (biology, chemistry, geography, international relations, mathematics, political sciences, sociology etc.) and covering diverse substantive research topics. Using Google metrics, the scientific production within the field it is shown to follow an ascending trend since the late 60s. Within the Romanian sociology, social network analysis is still in his early spring, network studies being low in number and rather peripheral. This note gives a brief overview of social network analysis and makes some short references to the current state of the network studies within Romanian sociology

A Social Network Analysis of Ski Research Topics in China

2020 ◽  
Vol 1 (3) ◽  
pp. 1-20
Author(s):  
Mingliang Zhang ◽  
Weiqi Chai ◽  
Wangda Guo ◽  
Bin Li
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Research Topics

Creating “(Social) Network Art” with NodeXL

2017 ◽  
pp. 342-393
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Network ◽  
Network Analysis ◽  
Web 2.0 ◽  
Data Visualization ◽  
Network Graph ◽  
Social Media Platforms ◽  
Machine Art ◽  
Social Sharing

If human-created objects of art are historically contingent, then the emergence of (social) network art may be seen as a product of several trends: the broad self-expression and social sharing on Web 2.0; the application of network analysis and data visualization to understand big data, and an appreciation for online machine art. Social network art is a form of cyborg art: it melds data from both humans and machines; the sensibilities of humans and machines; and the pleasures and interests of people. This chapter will highlight some of the types of (social) network art that may be created with Network Overview, Discovery and Exploration for Excel (NodeXL Basic) and provide an overview of the process. The network graph artwork presented here were all built from datasets extracted from popular social media platforms (Twitter, Flickr, YouTube, Wikipedia, and others). This chapter proposes some early aesthetics for this type of electronic artwork.

Network analysis as a method of assessing terms in a dataset comprising eating disorders

2018 ◽  
Vol 7 (2.7) ◽  
pp. 841
Author(s):  
Dr Adimulam Yesu Babu ◽  
Dr Deepak Nedunuri ◽  
T Venkata Sai Krishna
Keyword(s):  
Eating Disorders ◽  
Network Analysis ◽  
Clustering Algorithm ◽  
Agglomerative Hierarchical Clustering ◽  
Network Graph ◽  
Undirected Network ◽  
Ward Clustering ◽  
R Programming ◽  
Network Centralities ◽  
Word Frequencies

Eating disorders are central reason of physical and psycho-social morbidity. Several factors have been identified as being associated with the prevalence and progression of eating disorders in humans. Scientific investigation was carried out to assess the usage of terms in manuscript titles of nearly 900 published articles followed by network analysis and network centralities using R programming. The tm package, term document matrix function was utilized to create a term document matrix (TDM) from the corpus. A binary word matrix comprising 17 terms was created based on higher probability of occurring a term in a column. An agglomerative hierarchical clustering technique using ward clustering algorithm was presented. A data frame from the TDM was created to store data and used to plot word cloud based on word frequencies. An undirected network graph was plotted based on terms that appeared in the term matrix. Centralization measures such as Degree centrality, Closeness, Eigenvector and betweenness Centrality were reported.  

scholarly journals Using network analysis to explore cognitive domains in patients with unipolar versus bipolar depression: a prospective naturalistic study

CNS Spectrums ◽  
2019 ◽  
Vol 25 (3) ◽  
pp. 380-391 ◽  
Author(s):  
Cesare Galimberti ◽  
Monica Francesca Bosi ◽  
Valentina Caricasole ◽  
Riccardo Zanello ◽  
Bernardo Dell’Osso ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Network Analysis ◽  
Cognitive Performance ◽  
Bipolar Depression ◽  
Correlation Coefficients ◽  
Unipolar Depression ◽  
Executive Dysfunction ◽  
Network Graph ◽  
Significant Difference ◽  
Neurocognitive Profiles

Objective.Despite growing evidence in the field of cognitive function in mood disorders, the neurocognitive profiles of patients with unipolar and bipolar depression still need further characterization. In this study, we applied network analysis, hypothesizing this approach could highlight differences between major depressive disorder (MDD) and bipolar disorder (BD) from a cognitive perspective.Methods.The cognitive performance of 109 patients (72 unipolar and 37 bipolar depressed outpatients) was assessed through the Montreal Cognitive Assessment (MoCA), and a series of clinical variables were collected. Differences in cognitive performance between MDD and BD patients were tested using non-parametric tests. Moreover, a network graph representing MoCA domains as nodes and Spearman’s rho correlation coefficients between the domains as edges was constructed for each group.Results.The presence of mild cognitive impairment was observed in both MDD and BD patients during depression. No statistical significant difference was found between the two groups in terms of overall cognitive performance and across single domains. Nonetheless, network analytic metrics demonstrated different roles of memory and executive dysfunction in MDD versus BD patients: in particular, MDD network was more densely interconnected than BD network, and memory was the node with the highest betweenness and closeness centrality in MDD, while executive function was more central in BD.Conclusions.From a network analytic perspective, memory impairment displays a central role in the cognitive impairment of patients with unipolar depression, whereas executive dysfunction appears to be more central in bipolar depression. Further research is warranted to confirm our results.

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