Prolegomenon¶
This is the publication of all data used analysing the Nationale Forschungsdateninfrastruktur (NFDI) with its different consortia. The source of the data are the binding Letters of Intent (LoI) of the consortia in which they name their collaboration partners.1 The following analysis only includes documents that were submitted to the DFG as binding pre-applications for 2019 and 2020 (binding Letters of Intent). Consortia that did not submit a binding Letter of Intent in 2019 or 2020 were not included. We gathered all the data at the point when the LoI have been turned in. Consortia which have not turned in a binding Letter of Intent are not considered at all.
Have a look at the GitHub repository of Dorothea Strecker (https://github.com/dorothearrr/NFDI_Netzwerk), where the data has been destilled from the various LoI.↩
Presettings¶
Making sure that we have a common seed.
nfdi_seed <- function() {
set.seed(1234)
}
We define colors we will use for grouping consortia.
nfdi_conference_colors <- function() {
nfdi_color_code <<- c("#f5ac9f", # Medizin
"#e43516", # Lebenswissenschaften
"#f9b900", # Geisteswissenschaften
"#007aaf", # Ingenieurwissenschaften
"#6ca11d" # Chemie und Physik
)
nfdi_color_groups <<- nfdi_color_code[as.numeric(as.factor(V(nfdi_network_year)$group))]
}
Some presettings for plotting the networks.
nfdi_plot_settings <- function(){
nfdi_conference_colors()
graph_attr(nfdi_network_year, "layout") <<- norm_coords(layout.graphopt(nfdi_network_year),
ymin = -1, ymax = 1, xmin = -1, xmax = 1)*2
vertex_attr(nfdi_network_year, "label.cex") <<- .7
vertex_attr(nfdi_network_year, "label.color") <<- "black"
vertex_attr(nfdi_network_year, "label.font") <<- 1
vertex_attr(nfdi_network_year, "label.family") <<- "Helvetica"
vertex_attr(nfdi_network_year, "color") <<- nfdi_color_groups
vertex_attr(nfdi_network_year, "frame.color") <<- nfdi_color_groups
vertex_attr(nfdi_network_year, "size") <<- rescale_vertices(degree(nfdi_network_year,
mode="total"))*20
edge_attr(nfdi_network_year, "color") <- "#808080"
edge_attr(nfdi_network_year, "curved") <<- 0.1
edge_attr(nfdi_network_year, "arrow.size") <<- .5
edge_attr(nfdi_network_year, "arrow.width") <<- .5
rescale = F
}
We can define a function for rescaling the vertex size. This makes plots of the 2019 and 2020 networks visually comparable.
rescale_vertices <- function(x){(x - 3)/(20 - 3)} # min-max normalization; min = (min - 1) to omit zeroes
Next we are setting up a function to get the same presettings for all different data frames.
nfdi_presettings <- function(nfdi_edges_year,nfdi_directed_graph){
nfdi_seed()
nfdi_edges <<- get(paste0("nfdi_edges_",nfdi_edges_year))
nfdi_nodes <<- get(paste0("nfdi_nodes_",nfdi_edges_year))
nfdi_network_year <<- graph_from_data_frame(
d = nfdi_edges,
vertices = nfdi_nodes,
directed = nfdi_directed_graph)
nfdi_plot_settings()
}
Data sets¶
The core of the this publication are the sets of edges i.e. the connections for collaborations between the consortia. So far there are only two data sets available, for 2019 and for 2020.
2019¶
Now follows the information regarding the consortia.
Precisly the allocated group according to the NFDI-conference system (group),
and since at the time of turning in the LoI none of the consortia had been funded the column (funded) has 0 as value for all the consortia.
nfdi_nodes_2019 <- read.table(
header=TRUE,
sep=",",
text="
name,group,funded
Astro-NFDI,5,0
BERD\\@NFDI,3,0
DAPHNE,5,0
DataPLANT,2,0
FAIRmat,5,0
ForumX,3,0
GHGA,1,0
KonsortSWD,3,0
MaRDI,4,0
NFDI4Agri,2,0
NFDI4BioDiversity,2,0
NFDI4Cat,5,0
NFDI4Chem,5,0
NFDI4Crime,2,0
NFDI4Culture,3,0
NFDI4Earth,2,0
NFDI4Health,1,0
NFDI4Ing,4,0
NFDI4MSE,5,0
NFDI4Medicine,1,0
NFDI4MobilTech,4,0
PAHN-PaN,5,0
Text+,3,0
")
2020¶
What follows is the data for the year 2020 and the collaboration intentions between new and already funded consortia.
nfdi_nodes_2020 <- read.table(
header=TRUE,
sep=",",
text="
name,group,funded
BERD\\@NFDI,3,0
DAPHNE4NFDI,5,0
DataPLANT,2,1
FAIRmat,5,0
GHGA,1,1
KonsortSWD,3,1
MaRDI,4,0
NFDI-MatWerk,4,0
NFDI-Neuro,1,0
NFDI4Agri,2,0
NFDI4BioDiversity,2,1
NFDI4Cat,5,1
NFDI4Chem,5,1
NFDI4Culture,3,1
NFDI4DataScience,4,0
NFDI4Earth,2,0
NFDI4Health,1,1
NFDI4Immuno,1,0
NFDI4Ing,4,1
NFDI4Memory,3,0
NFDI4Microbiota,2,0
NFDI4Objects,3,0
NFDI4SD,3,0
NFDIxCS,4,0
PUNCH4NFDI,5,0
Text+,3,0
")
Edges¶
Let us now display the edges for the data sets we have so far. We do this using a particular function.
nfdi_edges <- function(nfdi_edges_year) {
df <- get(paste0("nfdi_edges_",nfdi_edges_year))
# df
print(tbl_df(df), n=Inf)
}
# nfdi_edges(2019)
# nfdi_edges(2020)
2019¶
nfdi_edges(2019)
# A tibble: 111 × 2
from to
<fctr> <fctr>
1 Astro-NFDI PAHN-PaN
2 Astro-NFDI DAPHNE
3 Astro-NFDI NFDI4Earth
4 BERD\\@NFDI KonsortSWD
5 BERD\\@NFDI ForumX
6 BERD\\@NFDI Text+
7 DAPHNE FAIRmat
8 DAPHNE NFDI4Chem
9 DAPHNE NFDI4Ing
10 DAPHNE NFDI4MSE
11 DAPHNE NFDI4Cat
12 DAPHNE PAHN-PaN
13 DAPHNE Astro-NFDI
14 DataPLANT NFDI4BioDiversity
15 DataPLANT NFDI4Agri
16 DataPLANT NFDI4Chem
17 FAIRmat DAPHNE
18 FAIRmat MaRDI
19 FAIRmat NFDI4Chem
20 FAIRmat NFDI4Cat
21 FAIRmat NFDI4Ing
22 FAIRmat NFDI4MSE
23 ForumX NFDI4Medicine
24 ForumX KonsortSWD
25 ForumX BERD\\@NFDI
26 ForumX NFDI4Culture
27 ForumX Text+
28 GHGA NFDI4Medicine
29 GHGA NFDI4Health
30 KonsortSWD BERD\\@NFDI
31 KonsortSWD NFDI4BioDiversity
32 KonsortSWD NFDI4Earth
33 KonsortSWD NFDI4Health
34 KonsortSWD Text+
35 MaRDI NFDI4MSE
36 MaRDI PAHN-PaN
37 MaRDI FAIRmat
38 MaRDI NFDI4Ing
39 MaRDI NFDI4Chem
40 MaRDI NFDI4Culture
41 NFDI4Agri NFDI4Health
42 NFDI4Agri DataPLANT
43 NFDI4Agri NFDI4BioDiversity
44 NFDI4Agri KonsortSWD
45 NFDI4Agri NFDI4Earth
46 NFDI4BioDiversity NFDI4Earth
47 NFDI4BioDiversity NFDI4Agri
48 NFDI4BioDiversity NFDI4Chem
49 NFDI4BioDiversity NFDI4Health
50 NFDI4BioDiversity KonsortSWD
51 NFDI4BioDiversity NFDI4Crime
52 NFDI4BioDiversity DataPLANT
53 NFDI4BioDiversity NFDI4Medicine
54 NFDI4Cat FAIRmat
55 NFDI4Cat NFDI4Chem
56 NFDI4Cat NFDI4Ing
57 NFDI4Cat DAPHNE
58 NFDI4Chem FAIRmat
59 NFDI4Chem NFDI4Ing
60 NFDI4Chem NFDI4Cat
61 NFDI4Chem DAPHNE
62 NFDI4Chem PAHN-PaN
63 NFDI4Chem NFDI4Health
64 NFDI4Chem NFDI4BioDiversity
65 NFDI4Crime NFDI4BioDiversity
66 NFDI4Crime NFDI4Medicine
67 NFDI4Crime Text+
68 NFDI4Culture Text+
69 NFDI4Culture MaRDI
70 NFDI4Culture NFDI4Ing
71 NFDI4Earth Astro-NFDI
72 NFDI4Earth KonsortSWD
73 NFDI4Earth NFDI4Agri
74 NFDI4Earth NFDI4BioDiversity
75 NFDI4Earth NFDI4Ing
76 NFDI4Health NFDI4Medicine
77 NFDI4Health GHGA
78 NFDI4Health KonsortSWD
79 NFDI4Health NFDI4Chem
80 NFDI4Health NFDI4Agri
81 NFDI4Health NFDI4Earth
82 NFDI4Health NFDI4BioDiversity
83 NFDI4Ing NFDI4MSE
84 NFDI4Ing FAIRmat
85 NFDI4Ing NFDI4MobilTech
86 NFDI4Ing NFDI4Chem
87 NFDI4Ing NFDI4Earth
88 NFDI4Ing MaRDI
89 NFDI4Ing NFDI4Medicine
90 NFDI4Ing Text+
91 NFDI4Ing NFDI4Culture
92 NFDI4Medicine GHGA
93 NFDI4Medicine NFDI4Health
94 NFDI4Medicine NFDI4Ing
95 NFDI4Medicine NFDI4Crime
96 NFDI4Medicine ForumX
97 NFDI4Medicine KonsortSWD
98 NFDI4Medicine NFDI4Agri
99 NFDI4MobilTech NFDI4Ing
100 NFDI4MobilTech ForumX
101 NFDI4MobilTech NFDI4Earth
102 NFDI4MSE FAIRmat
103 NFDI4MSE NFDI4Ing
104 NFDI4MSE MaRDI
105 NFDI4MSE NFDI4Chem
106 NFDI4MSE DAPHNE
107 Text+ NFDI4Culture
108 Text+ KonsortSWD
109 Text+ NFDI4Ing
110 Text+ NFDI4Earth
111 Text+ NFDI4BioDiversity
2020¶
nfdi_edges(2020)
# A tibble: 125 × 2
from to
<fctr> <fctr>
1 BERD\\@NFDI KonsortSWD
2 BERD\\@NFDI MaRDI
3 BERD\\@NFDI NFDI4Memory
4 BERD\\@NFDI Text+
5 DAPHNE4NFDI FAIRmat
6 DAPHNE4NFDI NFDI-MatWerk
7 DAPHNE4NFDI NFDI4Cat
8 DAPHNE4NFDI NFDI4Chem
9 DAPHNE4NFDI NFDI4Health
10 DAPHNE4NFDI NFDI4Ing
11 DAPHNE4NFDI NFDI4Objects
12 DAPHNE4NFDI PUNCH4NFDI
13 FAIRmat DAPHNE4NFDI
14 FAIRmat DataPLANT
15 FAIRmat MaRDI
16 FAIRmat NFDI-MatWerk
17 FAIRmat NFDI4Cat
18 FAIRmat NFDI4Chem
19 FAIRmat NFDI4DataScience
20 FAIRmat NFDI4Ing
21 FAIRmat NFDIxCS
22 FAIRmat PUNCH4NFDI
23 MaRDI BERD\\@NFDI
24 MaRDI FAIRmat
25 MaRDI NFDI-MatWerk
26 MaRDI NFDI-Neuro
27 MaRDI NFDI4Cat
28 MaRDI NFDI4Chem
29 MaRDI NFDI4Ing
30 MaRDI PUNCH4NFDI
31 NFDI-MatWerk DAPHNE4NFDI
32 NFDI-MatWerk DataPLANT
33 NFDI-MatWerk FAIRmat
34 NFDI-MatWerk MaRDI
35 NFDI-MatWerk NFDI4Chem
36 NFDI-MatWerk NFDI4DataScience
37 NFDI-MatWerk NFDI4Ing
38 NFDI-MatWerk NFDIxCS
39 NFDI-Neuro DataPLANT
40 NFDI-Neuro GHGA
41 NFDI-Neuro NFDI4BioDiversity
42 NFDI-Neuro NFDI4Culture
43 NFDI-Neuro NFDI4Earth
44 NFDI-Neuro NFDI4Health
45 NFDI-Neuro NFDI4Ing
46 NFDI-Neuro NFDI4Microbiota
47 NFDI4Agri DataPLANT
48 NFDI4Agri KonsortSWD
49 NFDI4Agri NFDI4BioDiversity
50 NFDI4Agri NFDI4Earth
51 NFDI4Agri NFDI4Health
52 NFDI4Agri NFDI4Immuno
53 NFDI4Agri NFDI4Microbiota
54 NFDI4DataScience KonsortSWD
55 NFDI4DataScience MaRDI
56 NFDI4DataScience NFDI-MatWerk
57 NFDI4DataScience NFDI4BioDiversity
58 NFDI4DataScience NFDI4Cat
59 NFDI4DataScience NFDI4Chem
60 NFDI4DataScience NFDI4Culture
61 NFDI4DataScience NFDI4Health
62 NFDI4DataScience NFDI4Ing
63 NFDI4DataScience NFDI4Microbiota
64 NFDI4DataScience NFDIxCS
65 NFDI4Earth DataPLANT
66 NFDI4Earth GHGA
67 NFDI4Earth KonsortSWD
68 NFDI4Earth NFDI4Agri
69 NFDI4Earth NFDI4BioDiversity
70 NFDI4Earth NFDI4Cat
71 NFDI4Earth NFDI4Chem
72 NFDI4Earth NFDI4Culture
73 NFDI4Earth NFDI4Health
74 NFDI4Earth NFDI4Ing
75 NFDI4Earth NFDI4Objects
76 NFDI4Immuno GHGA
77 NFDI4Immuno NFDI4Agri
78 NFDI4Immuno NFDI4Health
79 NFDI4Immuno NFDI4Microbiota
80 NFDI4Memory BERD\\@NFDI
81 NFDI4Memory KonsortSWD
82 NFDI4Memory MaRDI
83 NFDI4Memory NFDI4Culture
84 NFDI4Memory NFDI4Objects
85 NFDI4Memory Text+
86 NFDI4Microbiota DataPLANT
87 NFDI4Microbiota GHGA
88 NFDI4Microbiota NFDI4Agri
89 NFDI4Microbiota NFDI4BioDiversity
90 NFDI4Microbiota NFDI4Chem
91 NFDI4Microbiota NFDI4DataScience
92 NFDI4Microbiota NFDI4Health
93 NFDI4Microbiota NFDI4Immuno
94 NFDI4Microbiota NFDI4Ing
95 NFDI4Objects KonsortSWD
96 NFDI4Objects NFDI4Agri
97 NFDI4Objects NFDI4BioDiversity
98 NFDI4Objects NFDI4Culture
99 NFDI4Objects NFDI4Earth
100 NFDI4Objects NFDI4Memory
101 NFDI4Objects Text+
102 NFDI4SD NFDI4Culture
103 NFDI4SD NFDI4DataScience
104 NFDI4SD NFDI4Memory
105 NFDI4SD NFDI4Objects
106 NFDIxCS FAIRmat
107 NFDIxCS MaRDI
108 NFDIxCS NFDI4Chem
109 NFDIxCS NFDI4DataScience
110 NFDIxCS NFDI4Earth
111 NFDIxCS NFDI4Ing
112 PUNCH4NFDI DAPHNE4NFDI
113 PUNCH4NFDI FAIRmat
114 PUNCH4NFDI GHGA
115 PUNCH4NFDI MaRDI
116 PUNCH4NFDI NFDI4Earth
117 PUNCH4NFDI NFDI4Ing
118 PUNCH4NFDI NFDIxCS
119 Text+ KonsortSWD
120 Text+ NFDI4BioDiversity
121 Text+ NFDI4Culture
122 Text+ NFDI4Earth
123 Text+ NFDI4Ing
124 Text+ NFDI4Memory
125 Text+ NFDI4Objects
Data vectors¶
Following we present and explain the data and its origin.
Funded¶
The values whether a consortia is funded or not cannot be calculated. Therefore we already passed the information as a column to the consortia above.
The column funded has either the value 0 (consortium has not been funded) or 1 (consortium has been funded).
Since we look at the consortia at the time of turning in binding Letters of Intent,
for 2019 there is no value at all.
nfdi_funded <- function(nfdi_edges_year) {
df <- data.frame(get(paste0("nfdi_nodes_",nfdi_edges_year)))
names(df)[1] <- "Name"
rownames(df) <- 1:nrow(df)
df %>% select(1,3)
}
# nfdi_funded(2019)
# nfdi_funded(2020)
Now we can plot the networks and for 2020 we will highlight the funded consortia.
nfdi_plot<- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,FALSE)
colrs <- c("#ffcc66","#acd69a")
nfdi_local_network <- function() {
plot(nfdi_network_year,
vertex.color = colrs[V(nfdi_network_year)$funded + 1 ],
vertex.frame.color = colrs[V(nfdi_network_year)$funded + 1 ]
)
}
pdf(paste0("network_",nfdi_edges_year,".pdf"))
nfdi_local_network()
dev.off()
nfdi_local_network()
}
nfdi_plot(2019)
nfdi_plot(2020)
NFDI conference system (group)¶
Each consortium has been allocated to a group which is known as the NFDI conference system.
In the first column you see the consortia’s name in the second column (group) the number of the allocated group.
There are five groups and each has a special color code.
| No. | Conference group | Color code |
|---|---|---|
| (1) | Medizin | #f5ac9f |
| (2) | Lebens- und Geowissenschaften | #e43516 |
| (3) | Geistes- und Sozialwissenschaften | #f9b900 |
| (4) | Ingenieurwissenschaften und Mathemaik | #007aaf |
| (5) | Chemie und Physik | #6ca11d |
: NFDI Conference system. Each consortia is allocated to one specific group.
nfdi_group <- function(nfdi_edges_year) {
df <- data.frame(get(paste0("nfdi_nodes_",nfdi_edges_year)))
names(df)[1] <- "Name"
rownames(df) <- 1:nrow(df)
df %>% select(1,2)
}
# nfdi_group(2019)
# nfdi_group(2020)
Plotting grouped network¶
First we are defining a common legend for the networks, since we want to be able to differentiate the various groups the consortia are colored in.
nfdi_plot_legend <- function(){
legend("bottomright", # x-position
title = "NFDI-Konferenzsystematik", # title
legend = c(
"(1)",
"(2)",
"(3)",
"(4)",
"(5)"
), # the text of the legend
col = nfdi_color_code, # colors of lines and points beside the legend text
pch = 20, # the plotting symbols appearing in the legend
bty = "n", # no frame, the type of box to be drawn around the legend (n=no frame)
cex = .75, # character expansion factor relative to current par("cex").
pt.cex = 2, # expansion factor(s) for the points
ncol = 5,
)
}
Now we can go on and plot the network with colored consortia nodes.
nfdi_plot_group <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,FALSE)
pdf(paste0("network_group_",nfdi_edges_year,".pdf"))
plot(nfdi_network_year)
nfdi_plot_legend()
dev.off()
plot(nfdi_network_year)
nfdi_plot_legend()
}
# nfdi_plot_group(2019)
nfdi_plot_group(2020)
Cluster¶
The value of the column cluster has been calculated by the function cluster_optimal. Each consortia is allocated to a cluster.
nfdi_list_cluster <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
liste <- list(y=c(membership(cluster_optimal(nfdi_network_year))))
df = with(data=liste,
expr=data.frame(y))
df <- cbind(Name = rownames(df), df)
rownames(df) <- 1:nrow(df)
names(df) <- c("Name","cluster")
df
}
# nfdi_list_cluster(2019)
# nfdi_list_cluster(2020)
Plotting clustered network¶
With the algorithm cluster_optimal we can do a community detection.
nfdi_plot_cluster <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,FALSE)
nfdi_network_year_cluster <- cluster_optimal(nfdi_network_year)
nfdi_local_network <- function() {
plot(nfdi_network_year_cluster,
nfdi_network_year,
edge.color = NA,
col = nfdi_color_groups, #color of nodes
mark.col = "grey", # color groups
mark.border = NA, # no border color
)
nfdi_plot_legend()
}
pdf(paste0("network_cluster_",nfdi_edges_year,".pdf"))
nfdi_local_network()
dev.off()
nfdi_local_network()
}
# nfdi_plot_cluster(2019)
nfdi_plot_cluster(2020)
Now we are hightlighting only the connections of consortia between different clusters.
nfdi_plot_cluster_trans <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,FALSE)
nfdi_network_year_cluster <- cluster_optimal(nfdi_network_year)
nfdi_local_network <- function() {
plot(nfdi_network_year_cluster,
nfdi_network_year, # loading data frame
col = nfdi_color_groups, #color of nodes
mark.col = "grey", # color groups
mark.border = NA, # no border color
edge.color = c(NA, "#bf4040")[crossing(nfdi_network_year_cluster, nfdi_network_year) + 1], # color of edges
)
nfdi_plot_legend()
}
pdf(paste0("network_cluster_trans_",nfdi_edges_year,".pdf"))
nfdi_local_network()
dev.off()
nfdi_local_network()
}
# nfdi_plot_cluster_trans(2019)
nfdi_plot_cluster_trans(2020)
Amount of edges¶
There are three different ways of counting edges to a node: all, incoming ones and outgoing ones.
All edges (degree.total)
We get all the edges with the function
degree(<GRAPH-OF-DATA-FRAME>, mode="total")and receive the following table.
nfdi_degree_total_filtered <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
df <- data.frame(degree(nfdi_network_year,
mode = "total"))
names(df)[1] <- "degree.total"
df <- cbind(Name = rownames(df), df)
rownames(df) <- 1:nrow(df)
df
}
#nfdi_degree_total_filtered(2019)
#nfdi_degree_total_filtered(2020)
Incoming edges (degree.in)
For counting incomgin edges a directed network is necessary.
Then the function degree with a different value for mode is applied.
degree(<GRAPH-OF-DATA-FRAME>, mode="in")nfdi_degree_in <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
df <- data.frame(degree(nfdi_network_year,
mode = "in"))
names(df)[1] <- "degree.in"
df <- cbind(Name = rownames(df), df)
rownames(df) <- 1:nrow(df)
df
}
# nfdi_degree_in(2019)
# nfdi_degree_in(2020)
Outgoing edges (degree.out)
As before the function degree with a different value for mode is applied.
degree(<GRAPH-OF-DATA-FRAME>, mode="out")nfdi_degree_out <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
df <- data.frame(degree(nfdi_network_year,
mode = "out"))
names(df)[1] <- "degree.out"
df <- cbind(Name = rownames(df), df)
rownames(df) <- 1:nrow(df)
df
}
#nfdi_degree_out(2019)
#nfdi_degree_out(2020)
Vertex and edge betweenness centrality (betweenness)¶
The vertex betweenness has been calculated by the function betweenness()^[https://igraph.org/r/doc/betweenness.html].
nfdi_betweenness <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
df <- data.frame(betweenness(nfdi_network_year,
v=V(nfdi_network_year)))
names(df)[1] <- "betweenness"
df <- cbind(Name = rownames(df), df)
rownames(df) <- 1:nrow(df)
df
}
#nfdi_betweenness(2019)
#nfdi_betweenness(2020)
Closeness centrality¶
Clo[se]ness centrality measures how many steps is required to access every other vertex from a given vertex.^[https://igraph.org/r/doc/closeness.html]
Thereby we can differentiate between three different ways of closeness centrality.
closeness.total
closeness(<GRAPH-OF-DATA-FRAME>, mode="total")nfdi_closeness_total <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
df <- data.frame(closeness(nfdi_network_year,
mode="total"))
names(df)[1] <- "closeness.total"
df <- cbind(Name = rownames(df), df)
rownames(df) <- 1:nrow(df)
df
}
#nfdi_closeness_total(2019)
#nfdi_closeness_total(2020)
closeness.in
closeness(<GRAPH-OF-DATA-FRAME>, mode="in")nfdi_closeness_in <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
df <- data.frame(closeness(nfdi_network_year,
mode="in"))
names(df)[1] <- "closeness.in"
df <- cbind(Name = rownames(df), df)
rownames(df) <- 1:nrow(df)
df
}
#nfdi_closeness_in(2019)
#nfdi_closeness_in(2020)
closeness.out
closeness(<GRAPH-OF-DATA-FRAME>, mode="out")nfdi_closeness_out <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
df <- data.frame(closeness(nfdi_network_year,
mode="out"))
names(df)[1] <- "closeness.out"
df <- cbind(Name = rownames(df), df)
rownames(df) <- 1:nrow(df)
df
}
#nfdi_closeness_out(2019)
#nfdi_closeness_out(2020)
Data Collection¶
Here is an overview of all the data we have gathered so far.
nfdi_data_set <- function(nfdi_edges_year) {
d1 <- nfdi_funded(nfdi_edges_year)
d2 <- nfdi_group(nfdi_edges_year)
d3 <- nfdi_list_cluster(nfdi_edges_year)
d4 <- nfdi_degree_in(nfdi_edges_year)
d5 <- nfdi_degree_out(nfdi_edges_year)
d6 <- nfdi_degree_total_filtered(nfdi_edges_year)
d7 <- nfdi_betweenness(nfdi_edges_year)
d8 <- nfdi_closeness_in(nfdi_edges_year)
d9 <- nfdi_closeness_out(nfdi_edges_year)
d10 <-nfdi_closeness_total(nfdi_edges_year)
df <- d1 %>% inner_join(d2, by = "Name") %>%
full_join(d3, by = "Name") %>%
full_join(d4, by = "Name") %>%
full_join(d5, by = "Name") %>%
full_join(d6, by = "Name") %>%
full_join(d7, by = "Name") %>%
full_join(d8, by = "Name") %>%
full_join(d9, by = "Name") %>%
full_join(d10, by = "Name") %>%
arrange(Name) %>%
replace(., is.na(.), "")
write.csv(df, paste0("nfdi_data_set",nfdi_edges_year,".csv"))
df
}
2019¶
nfdi_data_set(2019)
| Name | funded | group | cluster | degree.in | degree.out | degree.total | betweenness | closeness.in | closeness.out | closeness.total |
|---|---|---|---|---|---|---|---|---|---|---|
| Astro-NFDI | 0 | 5 | 1 | 2 | 3 | 5 | 5.975000 | 0.01408451 | 0.018518519 | 0.02083333 |
| BERD\@NFDI | 0 | 3 | 2 | 2 | 3 | 5 | 2.000000 | 0.01250000 | 0.017543860 | 0.01923077 |
| DAPHNE | 0 | 5 | 1 | 5 | 7 | 12 | 16.019048 | 0.01388889 | 0.022222222 | 0.02325581 |
| DataPLANT | 0 | 2 | 3 | 2 | 3 | 5 | 1.416667 | 0.01333333 | 0.020000000 | 0.02127660 |
| FAIRmat | 0 | 5 | 1 | 6 | 6 | 12 | 10.063889 | 0.01538462 | 0.021739130 | 0.02272727 |
| ForumX | 0 | 3 | 2 | 3 | 5 | 8 | 11.720635 | 0.01333333 | 0.020000000 | 0.02173913 |
| GHGA | 0 | 1 | 3 | 2 | 2 | 4 | 0.000000 | 0.01315789 | 0.018518519 | 0.01851852 |
| KonsortSWD | 0 | 3 | 3 | 8 | 5 | 13 | 33.279762 | 0.01612903 | 0.021739130 | 0.02380952 |
| MaRDI | 0 | 4 | 1 | 4 | 6 | 10 | 9.225000 | 0.01428571 | 0.021276596 | 0.02325581 |
| NFDI4Agri | 0 | 2 | 3 | 5 | 5 | 10 | 8.885714 | 0.01492537 | 0.020833333 | 0.02222222 |
| NFDI4BioDiversity | 0 | 2 | 3 | 8 | 8 | 16 | 57.990476 | 0.01754386 | 0.025000000 | 0.02857143 |
| NFDI4Cat | 0 | 5 | 1 | 3 | 4 | 7 | 0.000000 | 0.01333333 | 0.020833333 | 0.02173913 |
| NFDI4Chem | 0 | 5 | 1 | 9 | 7 | 16 | 75.065079 | 0.01724138 | 0.025641026 | 0.02777778 |
| NFDI4Crime | 0 | 2 | 3 | 2 | 3 | 5 | 1.616667 | 0.01388889 | 0.020000000 | 0.02040816 |
| NFDI4Culture | 0 | 3 | 2 | 4 | 3 | 7 | 7.630556 | 0.01492537 | 0.019607843 | 0.02222222 |
| NFDI4Earth | 0 | 2 | 3 | 8 | 5 | 13 | 48.296429 | 0.01754386 | 0.023809524 | 0.02777778 |
| NFDI4Health | 0 | 1 | 3 | 6 | 7 | 13 | 32.397222 | 0.01612903 | 0.024390244 | 0.02631579 |
| NFDI4Ing | 0 | 4 | 1 | 11 | 9 | 20 | 123.969841 | 0.01851852 | 0.027027027 | 0.03030303 |
| NFDI4Medicine | 0 | 1 | 3 | 6 | 7 | 13 | 54.310317 | 0.01666667 | 0.024390244 | 0.02631579 |
| NFDI4MobilTech | 0 | 4 | 2 | 1 | 3 | 4 | 5.461905 | 0.01351351 | 0.020833333 | 0.02222222 |
| NFDI4MSE | 0 | 5 | 1 | 4 | 5 | 9 | 2.880556 | 0.01428571 | 0.021276596 | 0.02222222 |
| PAHN-PaN | 0 | 5 | 1 | 4 | 0 | 4 | 0.000000 | 0.02000000 | 0.001976285 | 0.02000000 |
| Text+ | 0 | 3 | 2 | 6 | 5 | 11 | 32.795238 | 0.01612903 | 0.022727273 | 0.02631579 |
2020¶
nfdi_data_set(2020)
| Name | funded | group | cluster | degree.in | degree.out | degree.total | betweenness | closeness.in | closeness.out | closeness.total |
|---|---|---|---|---|---|---|---|---|---|---|
| BERD\@NFDI | 0 | 3 | 1 | 2 | 4 | 6 | 9.9523810 | 0.003663004 | 0.012500000 | 0.01923077 |
| DAPHNE4NFDI | 0 | 5 | 2 | 3 | 8 | 11 | 9.7119048 | 0.003571429 | 0.014285714 | 0.02325581 |
| DataPLANT | 1 | 2 | 3 | 6 | 0 | 6 | 0.0000000 | 0.004184100 | 0.001538462 | 0.02127660 |
| FAIRmat | 0 | 5 | 2 | 5 | 10 | 15 | 14.0928571 | 0.003663004 | 0.014705882 | 0.02439024 |
| GHGA | 1 | 1 | 3 | 5 | 0 | 5 | 0.0000000 | 0.004149378 | 0.001538462 | 0.02040816 |
| KonsortSWD | 1 | 3 | 1 | 7 | 0 | 7 | 0.0000000 | 0.004255319 | 0.001538462 | 0.02325581 |
| MaRDI | 0 | 4 | 2 | 7 | 8 | 15 | 77.0333333 | 0.003802281 | 0.014492754 | 0.02439024 |
| NFDI4Agri | 0 | 2 | 3 | 4 | 7 | 11 | 28.5000000 | 0.003759398 | 0.012195122 | 0.02325581 |
| NFDI4BioDiversity | 1 | 2 | 3 | 7 | 0 | 7 | 0.0000000 | 0.004255319 | 0.001538462 | 0.02325581 |
| NFDI4Cat | 1 | 5 | 2 | 5 | 0 | 5 | 0.0000000 | 0.004201681 | 0.001538462 | 0.02173913 |
| NFDI4Chem | 1 | 5 | 2 | 8 | 0 | 8 | 0.0000000 | 0.004273504 | 0.001538462 | 0.02380952 |
| NFDI4Culture | 1 | 3 | 1 | 7 | 0 | 7 | 0.0000000 | 0.004237288 | 0.001538462 | 0.02272727 |
| NFDI4DataScience | 0 | 4 | 2 | 5 | 11 | 16 | 66.3333333 | 0.003745318 | 0.015151515 | 0.02702703 |
| NFDI4Earth | 0 | 2 | 3 | 6 | 11 | 17 | 51.9023810 | 0.003846154 | 0.012658228 | 0.02857143 |
| NFDI4Health | 1 | 1 | 3 | 7 | 0 | 7 | 0.0000000 | 0.004219409 | 0.001538462 | 0.02222222 |
| NFDI4Immuno | 0 | 1 | 3 | 2 | 4 | 6 | 0.3333333 | 0.003636364 | 0.011111111 | 0.01785714 |
| NFDI4Ing | 1 | 4 | 2 | 11 | 0 | 11 | 0.0000000 | 0.004329004 | 0.001538462 | 0.02564103 |
| NFDI4Memory | 0 | 3 | 1 | 4 | 6 | 10 | 29.0595238 | 0.003663004 | 0.013157895 | 0.02083333 |
| NFDI4Microbiota | 0 | 2 | 3 | 4 | 9 | 13 | 46.4166667 | 0.003745318 | 0.013333333 | 0.02380952 |
| NFDI4Objects | 0 | 3 | 1 | 5 | 7 | 12 | 41.5833333 | 0.003773585 | 0.013157895 | 0.02439024 |
| NFDI4SD | 0 | 3 | 1 | 0 | 4 | 4 | 0.0000000 | 0.001538462 | 0.018867925 | 0.02000000 |
| NFDI-MatWerk | 0 | 4 | 2 | 4 | 8 | 12 | 18.1428571 | 0.003717472 | 0.014084507 | 0.02272727 |
| NFDI-Neuro | 0 | 1 | 3 | 1 | 8 | 9 | 11.0595238 | 0.003623188 | 0.012658228 | 0.02439024 |
| NFDIxCS | 0 | 4 | 2 | 4 | 6 | 10 | 6.6166667 | 0.003663004 | 0.014084507 | 0.02325581 |
| PUNCH4NFDI | 0 | 5 | 2 | 3 | 7 | 10 | 10.7023810 | 0.003636364 | 0.014084507 | 0.02272727 |
| Text+ | 0 | 3 | 1 | 3 | 7 | 10 | 8.5595238 | 0.003649635 | 0.012658228 | 0.02325581 |
Further stats¶
Wilcoxon rank sum test¶
nfdi_count_edges_out <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
df <- as.data.frame(table(nfdi_edges$from))
df <- as.data.frame(df)
names(df)[1] <- "Name"
names(df)[2] <- "count"
rownames(df) <- 1:nrow(df)
df
}
#nfdi_count_edges_out(2019)
#nfdi_count_edges_out(2020)
wilcox.test(nfdi_count_edges_out(2019)$count,
nfdi_count_edges_out(2020)$count,
alternative="two.sided",
exact=F,
correct=T
)
Wilcoxon rank sum test with continuity correction data: nfdi_count_edges_out(2019)$count and nfdi_count_edges_out(2020)$count W = 80, p-value = 0.002314 alternative hypothesis: true location shift is not equal to 0
Amount of Letters of Intent with collaborations mentioned¶
nfdi_count_LoI <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
sum(table(nfdi_count_edges_out(nfdi_edges_year)))
}
# nfdi_count_LoI(2019)
# nfdi_count_LoI(2020)
print(paste0(2019,": ",nfdi_count_LoI(2019)))
print(paste0(2020,": ",nfdi_count_LoI(2020)))
[1] "2019: 22" [1] "2020: 17"
Nodes and edges in the network¶
Nodes
We can easily count all the nodes in a network
of a particular year by using the function gorder.^[https://igraph.org/r/doc/gorder.html]
nfdi_count_nodes <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
gorder(nfdi_network_year)
}
#nfdi_count_nodes(2019)
#nfdi_count_nodes(2020)
print(paste0(2019,": ",nfdi_count_nodes(2019)))
print(paste0(2020,": ",nfdi_count_nodes(2020)))
[1] "2019: 23" [1] "2020: 26"
Edges
We can easily count all the nodes in a network of a particular year by
using the function gsize.^[https://igraph.org/r/doc/gsize.html]
nfdi_count_edges <- function(nfdi_edges_year) {
nfdi_presettings(nfdi_edges_year,TRUE)
gsize(nfdi_network_year)
}
#nfdi_count_edges(2019)
#nfdi_count_edges(2020)
print(paste0(2019,": ",nfdi_count_edges(2019)))
print(paste0(2020,": ",nfdi_count_edges(2020)))
[1] "2019: 111" [1] "2020: 125"