<｜begin▁of▁sentence｜>### Instruction:
As a Construction Manager, imagine you're overseeing a project that involves laying down a network of pipelines connecting different facilities, symbolized by the junction points 1, 2, 3, and 4 with links [(1, 2), (2, 3), (3, 4)]. To effectively communicate the layout to your colleagues and digital systems, you're seeking a way to represent this network in a digital format that's easily shareable and editable.

Your task is to explore the idea of portraying this pipeline network as a JSON object, a well-known format that's compatible with a broad range of software. To achieve that, think about utilizing the adjacency_data feature from the json_graph toolbox provided by NetworkX. This approach would allow you to transcribe the details of the network, including its nodes and connecting lines, into a structured JSON format, enabling other team members and computer applications to analyze and visualize the system effortlessly.

Could you reframe the process of transforming this pipeline network data into a JSON format using the mentioned NetworkX function, keeping in mind that the fundamental structure and meaning of the data should remain consistent?
### Response:
from networkx.readwrite import json_graph
import networkx as nx
import json

# Create a NetworkX graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4)])

# Use the adjacency_data function to get the graph data
adj_data = json_graph.adjacency_data(G)

# Serialize with json
json_data = json.dumps(adj_data)
print(json_data)
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