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The Size of Fields in Biomedical Sciences

Dragotakes, Q.; Casadevall, A.

2023-06-14 systems biology
10.1101/2023.06.13.544650 bioRxiv
Show abstract

Scientific research output has increased exponentially over the past few decades, but not equally across all fields of study, and we lack clear methods for estimating the size of any given field of research. Understanding how fields grow, change, and are organized is essential to understanding how human resources are allocated to the investigation of scientific problems. In this study we estimated the size of certain biomedical fields from the number of unique author names appearing in field relevant publications in the PubMed database. Focusing on microbiology, where the size of fields is often associated with those who work on a particular microbe, we find large differences in the size of its subfields. We found that plotting the number of unique investigators as a function of time can show changes consistent with growing or shrinking fields. We envision using unique author count to measure the strength of a workforce in any given field, analyze the overlap of workforce between fields, and compare how workforce correlates to available research funds and public health burden of a field.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 17%
10.7%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 10%
6.7%
Similar papers in this journal
3
iScience
○ 1154 papers in training set
Top 1%
6.7%
Similar papers in this journal
4
Cell Reports Methods
○ 165 papers in training set
Top 0.3%
4.9%
Similar papers in this journal
5
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 10%
4.9%
Similar papers in this journal
6
eLife
● 5828 papers in training set
Top 30%
4.0%
7
Proceedings of the Royal Society B: Biological Sciences
● 393 papers in training set
Top 2%
3.2%
8
Cell Systems
○ 201 papers in training set
Top 2%
2.8%
9
Wellcome Open Research
○ 67 papers in training set
Top 0.2%
2.6%
10
mSphere
● 302 papers in training set
Top 3%
2.6%
11
Journal of Fungi
○ 32 papers in training set
Top 0.2%
2.4%
50% of probability mass above
12
Patterns
○ 78 papers in training set
Top 0.9%
2.4%
13
PLOS Computational Biology
● 1863 papers in training set
Top 13%
2.1%
14
iMeta
○ 10 papers in training set
Top 0.1%
2.1%
15
mBio
● 833 papers in training set
Top 7%
1.9%
16
PLOS Biology
● 486 papers in training set
Top 4%
1.9%
17
BMC Bioinformatics
○ 457 papers in training set
Top 4%
1.7%
18
npj Systems Biology and Applications
○ 125 papers in training set
Top 1%
1.5%
19
Nature
○ 645 papers in training set
Top 8%
1.1%
20
Nature Communications
○ 5641 papers in training set
Top 50%
1.1%
21
GeoHealth
● 12 papers in training set
Top 0.1%
1.1%
22
mSystems
● 394 papers in training set
Top 5%
1.1%
23
Microbiology Spectrum
● 469 papers in training set
Top 9%
1.1%
24
Cell Reports
○ 1498 papers in training set
Top 24%
1.0%
25
G3: Genes, Genomes, Genetics
◐ 252 papers in training set
Top 4%
1.0%
26
Philosophical Transactions of the Royal Society B: Biological Sciences
● 72 papers in training set
Top 1%
1.0%
27
PeerJ
◐ 308 papers in training set
Top 9%
1.0%
28
Phytobiomes Journal
● 27 papers in training set
Top 0.4%
0.9%
29
Frontiers in Microbiology
○ 427 papers in training set
Top 8%
0.8%
30
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
0.8%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.