This document discusses visualizing and analyzing the PUBMED dataset. It describes creating co-authorship networks showing nodes, edges, and properties over time. It also analyzes researchers' academic throughput by looking at years of first/last publication, number of publications, and duration of activity. Finally, it examines terminology evolution by analyzing word terms in 5-year title corpora. The analysis uses Pandas for data manipulation, NetworkX for network analysis, NLTK for natural language processing, and Matplotlib for plotting.

1. PUBMED
dataset
visualisa1on
George
Gkotsis
Knowledge
Media
Ins1tute
The
Open
University
21.5
million
cita1ons
10.8
million
authors

2. Visualisa1on
• XKCD-style
• Infographic-­‐style
Ver1cal
scrolling

3. Data
analysis
1.
Co-­‐authorship
network
2.
Academic
reten1on
and
produc1vity
3.
Terminology
&
evolu1on

4. 1.
Co-­‐authorship
network
• For
each
year,
a
co-­‐authorship
graph
is
constructed
• Visualise
graph
proper1es:
– Nodes
– Edges
– Clustering
coefficient
– Entropy:
Rowe
&
Strohmaier,
WWW2014

5. 1.
Co-­‐authorship
network
(cont.)

6. 2.
Academic
throughput
and
reten1on
• Researcher
profile
– 4
aYributes:
[1]
Year
of
first
publica1on
[2]
Year
of
last
publica1on
[3]
Number
of
publica1ons
[4]
Dura1on
of
research
ac1vity
([2]-­‐[1])
1966
-­‐
2001

7. 2.
Academic
throughput
and
reten1on
(cont.)

8. 3.
Terminology
&
evolu1on
w:
1-­‐gram
word-­‐term
M:
5-­‐year
;tles’
corpus

9. 3.
Terminology
&
evolu1on
(cont.)

10. Development
• Pandas
Data
analysis
and
manipula1on
• NetworkX
Network
analysis
• NLTK
Natural
Language
processing
• Matplotlib
Plobng