scholarly journals Social network data of Swiss farmers related to agricultural climate change mitigation

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106898
Author(s):  
Cordelia Sophie Kreft ◽  
Mario Angst ◽  
Robert Huber ◽  
Robert Finger
2020 ◽  
Vol 66 (No. 7) ◽  
pp. 265-279 ◽  
Author(s):  
Alessandro Paletto ◽  
Ilaria Biancolillo ◽  
Jacques Bersier ◽  
Michael Keller ◽  
Manuela Romagnoli

Over the last couple of decades, many peer-reviewed publications focused on the bioeconomy, which it is frequently argued to be a key part of the solution to global challenges (climate change, ecosystem degradation). This study investigates the scientific literature on forest bioeconomy by applying a social network analysis to the bibliometric science. The bibliometric network analysis was performed over the time-frame of 2003–2020 to provide an overview on the main aspects characterising the forest bioeconomy issue. The results show that 225 documents on forest bioeconomy were published by 567 organisations from 44 countries. Finland and Canada are the two most productive countries with 32.8% and 12.7% of forest bioeconomy documents respectively. The co-occurrence network map of the keywords shows that the forest bioeconomy is related to three main concepts: sustainable development, bioenergy production, climate change mitigation.


2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.


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