<｜begin▁of▁sentence｜>### Instruction:
Imagine that we're working on a project to enhance the digital accessibility for individuals with disabilities, and part of this involves creating an efficient network of resources. We have a set of connections, each with its own capacity for data flow (which can be thought of as 'weight'). These connections are as follows: between Node 0 and Node 1 the weight is 5, between Node 0 and Node 2 it's 3, between Node 1 and Node 2 it's 4, between Node 2 and Node 3 it's 7, and lastly between Node 1 and Node 3 it's 6.

Our goal is to organize these resources in a manner that allows for the maximum potential data flow, optimizing the connectivity for users with different needs. To do this using NetworkX, we want to apply Kruskal's algorithm to construct a 'maximum spanning tree' from these connections. Could you walk us through the process of determining this optimized tree and list out which edges are included in it using NetworkX? This would enable us to streamline our accessibility network, ensuring that our services are delivered in the most effective way possible.
### Response:
import networkx as nx

# Create an undirected graph
G = nx.Graph()

# Add edges with weights
data_edges = [(0, 1, 5), (0, 2, 3), (1, 2, 4), (2, 3, 7), (1, 3, 6)]
G.add_weighted_edges_from(data_edges)

# Generate the maximum spanning tree using Kruskal's algorithm
T = nx.maximum_spanning_tree(G, algorithm='kruskal')

# Print the edges of the maximum spanning tree
print(sorted(T.edges(data=True)))
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