<｜begin▁of▁sentence｜>### Instruction:
In the realm of nuclear physics, when we venture into the analysis of isotopic networks or collaborations between research facilities and universities, we often come across datasets that are essentially bipartite graphs representing relationships with associated intensities, such as the frequency of collaborative publications or the quantity of isotopic exchanges. Suppose we are given a dataset that defines such a network, where the connections are listed as edge-lists along with the corresponding affinity weights linking two categories of nodes.

For the purpose of our exploratory data analysis, how would we proceed to construct a representative bipartite graph using NetworkX, ensuring that we systematically translate the textual node identifiers into a standardized numerical format—integers, to be precise—while simultaneously casting the affinity weights to a numeric floating-point representation? This transformation is crucial to facilitate the application of certain algorithms that require numerical inputs.

Let's consider this task in the context of a practical application by assuming that the dataset is provided in the form of raw text data, as follows:

```
'ResearchLab1 UniversityA 0.75'
'ResearchLab2 UniversityB 0.50'
'ResearchLab1 UniversityB 0.60'
'ResearchLab3 UniversityA 0.85'
```

Here each line represents an edge in the network, and it spells out the two parties involved in the collaboration accompanied by the weight of their interaction. How should one proceed to properly encode this information into a NetworkX bipartite graph while adhering to the prerequisites of node integer-encoding and edge weight float conversion?
### Response:
from networkx.algorithms import bipartite
lines = ["1 2 {'weight':3}", "2 3 {'weight':27}", "3 4 {'weight':3.0}"]
G = bipartite.parse_edgelist(lines, nodetype=int, data=(('weight', float),))
for edge in sorted(G.edges(data=True)):
    print(edge)
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