Public involvement in setting a national research agenda

<p>(A) Graphical map of the BLAST results showing nucleotide identity between <i>A</i>. <i>fasciata</i> mitogenome and 15 related species listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136297#pone.0136297.t001" target="_blank">Table 1</a>, as generated by the CGView comparison tool (CCT). CCT arranges BLAST result in an order where sequence that is most similar to the reference (<i>A</i>. <i>fasciata</i>) is placed closer to the outer edge of the map. The rings labelled 1 to17 indicate BLAST results of <i>A</i>. <i>fasciata</i> mitogenome against <i>A</i>. <i>chrysaetos</i>, <i>N</i>. <i>nipalensis</i>, <i>N</i>. <i>alboniger</i>, <i>S</i>. <i>cheela</i>, <i>A</i>. <i>monachus</i>, <i>B</i>. <i>lagopus</i>, <i>B</i>. <i>buteo</i>, <i>B</i>. <i>buteo burmanicus</i>, <i>A</i>. <i>soloensis</i>, <i>A</i>. <i>virgatus</i>, <i>A</i>. <i>gentilis</i>, <i>A</i>. <i>nisus</i>, <i>P</i>. <i>haliaetus</i>, <i>S</i>. <i>serpentarius</i>, <i>C</i>. <i>aura</i>, <i>P</i>. <i>badius</i>, and <i>S</i>. <i>leptogrammica</i>, respectively. (B) Nucleotide-based phylogenetic tree of 16 Accipitriformes species, with two Strigiformes birds as outgroups. This analysis is based on 13PCGs. Both ML and Bayesian analyses produced identical tree topologies. The ML bootstrap and Bayesian posterior probability values for each node are indicated.</p

Supplementary Data for risk analysis

The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network. The files consist of: Reservoir data and river flood routing parameters Code and results of the Monte Carlo simulations Code and results of the Bayesian network </ul

QAP correlation analysis.

Green development, an essential part of sustainable development transformation, is spatially correlated intra- and inter-regionally. However, previous research has not fully addressed the spatial characteristics of green development. This study investigates the spatial correlation structures, core–peripheral positions, and factors impacting the spatial network formation of China’s green development. Based on the green development evaluation index system modified by the entropy method, this study applies social network analysis, block model analysis, and quadratic assignment procedure analysis to data from 30 provinces in China. The results confirm the spatial spillover effect is overwhelmingly present in China’s green development. The findings further distinguish the core roles of provinces including Hunan, Tianjin, Zhejiang, Henan, and Xinjiang, and underline factors of green economic growth, governmental policy support, spatial adjacency, and geographic distance as significantly affecting the spatial network formation of China’s green development. Policy recommendations for green development are then put forward.</div

Four blocks and their spillover relations.

Green development, an essential part of sustainable development transformation, is spatially correlated intra- and inter-regionally. However, previous research has not fully addressed the spatial characteristics of green development. This study investigates the spatial correlation structures, core–peripheral positions, and factors impacting the spatial network formation of China’s green development. Based on the green development evaluation index system modified by the entropy method, this study applies social network analysis, block model analysis, and quadratic assignment procedure analysis to data from 30 provinces in China. The results confirm the spatial spillover effect is overwhelmingly present in China’s green development. The findings further distinguish the core roles of provinces including Hunan, Tianjin, Zhejiang, Henan, and Xinjiang, and underline factors of green economic growth, governmental policy support, spatial adjacency, and geographic distance as significantly affecting the spatial network formation of China’s green development. Policy recommendations for green development are then put forward.</div

Result of nonlinear Granger causal test.

Green development, an essential part of sustainable development transformation, is spatially correlated intra- and inter-regionally. However, previous research has not fully addressed the spatial characteristics of green development. This study investigates the spatial correlation structures, core–peripheral positions, and factors impacting the spatial network formation of China’s green development. Based on the green development evaluation index system modified by the entropy method, this study applies social network analysis, block model analysis, and quadratic assignment procedure analysis to data from 30 provinces in China. The results confirm the spatial spillover effect is overwhelmingly present in China’s green development. The findings further distinguish the core roles of provinces including Hunan, Tianjin, Zhejiang, Henan, and Xinjiang, and underline factors of green economic growth, governmental policy support, spatial adjacency, and geographic distance as significantly affecting the spatial network formation of China’s green development. Policy recommendations for green development are then put forward.</div

Relationships between blocks: Gross analysis and intensity analysis.

Relationships between blocks: Gross analysis and intensity analysis.</p

Ego network characteristics of China’s green development.

Ego network characteristics of China’s green development.</p

Evaluation index system of China’s green development.

Evaluation index system of China’s green development.</p

Whole network characteristics of China’s green development.

Whole network characteristics of China’s green development.</p

QAP regression analysis about the adjacency matrix.

QAP regression analysis about the adjacency matrix.</p