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The impact of preprint servers in the formation of novel ideas

Satish, S.; Yao, Z.; Drozdov, A.; Veytsman, B.

2020-10-08 scientific communication and education
10.1101/2020.10.08.330696 bioRxiv
Show abstract

We study whether novel ideas in biomedical literature appear first in preprints or traditional journals. We develop a Bayesian method to estimate the time of appearance for a phrase in the literature, and apply it to a number of phrases, both automatically extracted and suggested by experts. We see that presently most phrases appear first in the traditional journals, but there is a number of phrases with the first appearance on preprint servers. A comparison of the general composition of texts from bioRxiv and traditional journals shows a growing trend of bioRxiv being predictive of traditional journals. We discuss the application of the method for related problems.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 1%
18.8%
Similar papers in this journal
2
eLife
● 5828 papers in training set
Top 13%
8.0%
Similar papers in this journal
3
PLOS Computational Biology
● 1863 papers in training set
Top 4%
8.0%
Similar papers in this journal
4
Patterns
○ 78 papers in training set
Top 0.1%
6.4%
Similar papers in this journal
5
PLOS ONE
● 5266 papers in training set
Top 26%
5.6%
Similar papers in this journal
6
BioData Mining
○ 22 papers in training set
Top 0.1%
5.6%
50% of probability mass above
7
eneuro
● 439 papers in training set
Top 2%
4.4%
8
Frontiers in Bioinformatics
○ 49 papers in training set
Top 0.1%
4.1%
9
PeerJ
◐ 308 papers in training set
Top 2%
3.6%
10
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
2.8%
11
GigaScience
◐ 212 papers in training set
Top 1%
2.8%
12
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.2%
2.4%
13
F1000Research
○ 88 papers in training set
Top 1.0%
2.2%
14
Wellcome Open Research
○ 67 papers in training set
Top 0.3%
2.2%
15
Heliyon
○ 152 papers in training set
Top 3%
1.9%
16
Scientific Reports
○ 3612 papers in training set
Top 51%
1.8%
17
PLOS Biology
● 486 papers in training set
Top 7%
1.1%
18
Ecological Informatics
○ 33 papers in training set
Top 0.5%
1.1%
19
Bulletin of Mathematical Biology
○ 92 papers in training set
Top 1%
1.1%
20
BMC Medical Research Methodology
○ 47 papers in training set
Top 1%
1.1%
21
BMC Bioinformatics
○ 457 papers in training set
Top 5%
1.1%
22
Ecology and Evolution
○ 267 papers in training set
Top 5%
1.1%
23
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 2%
1.0%
24
Frontiers in Systems Biology
○ 10 papers in training set
Top 0.1%
1.0%
25
Journal of Cheminformatics
○ 29 papers in training set
Top 0.7%
0.9%

"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.