Back

Evaluating institutional open access performance: Sensitivity analysis

Huang, C.-K.; Neylon, C.; Hosking, R.; Montgomery, L.; Wilson, K.; Ozaygen, A.; Brookes-Kenworthy, C.

2020-03-21 scientific communication and education
10.1101/2020.03.19.998542 bioRxiv
Show abstract

In the article “Evaluating institutional open access performance: Methodology, challenges and assessment” we develop the first comprehensive and reproducible workflow that integrates multiple bibliographic data sources for evaluating institutional open access (OA) performance. The major data sources include Web of Science, Scopus, Microsoft Academic, and Unpaywall. However, each of these databases continues to update, both actively and retrospectively. This implies the results produced by the proposed process are potentially sensitive to both the choice of data source and the versions of them used. In addition, there remain the issue relating to selection bias in sample size and margin of error. The current work shows that the levels of sensitivity relating to the above issues can be significant at the institutional level. Hence, the transparency and clear documentation of the choices made on data sources (and their versions) and cut-off boundaries are vital for reproducibility and verifiability.View Full Text

Matching journals

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

1
PLOS ONE
● 5266 papers in training set
Top 5%
27.4%
Similar papers in this journal
2
Ecology and Evolution
○ 267 papers in training set
Top 1%
5.7%
Similar papers in this journal
3
Ecological Informatics
○ 33 papers in training set
Top 0.2%
4.5%
Similar papers in this journal
4
Heliyon
○ 152 papers in training set
Top 0.7%
4.5%
Similar papers in this journal
5
PeerJ
◐ 308 papers in training set
Top 1%
4.2%
Similar papers in this journal
6
Frontiers in Bioinformatics
○ 49 papers in training set
Top 0.1%
4.2%
50% of probability mass above
7
eneuro
● 439 papers in training set
Top 2%
4.2%
8
Wellcome Open Research
○ 67 papers in training set
Top 0.2%
2.7%
9
Sustainability
○ 10 papers in training set
Top 0.1%
2.5%
10
Bioinformatics
◐ 1204 papers in training set
Top 6%
2.5%
11
Royal Society Open Science
● 214 papers in training set
Top 2%
2.5%
12
Scientific Reports
○ 3612 papers in training set
Top 40%
2.5%
13
PLOS Digital Health
● 106 papers in training set
Top 2%
2.2%
14
Behavior Research Methods
○ 30 papers in training set
Top 0.2%
2.2%
15
FACETS
● 14 papers in training set
Top 0.1%
2.2%
16
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.2%
1.8%
17
eLife
● 5828 papers in training set
Top 50%
1.7%
18
BMC Medical Research Methodology
○ 47 papers in training set
Top 0.8%
1.4%
19
F1000Research
○ 88 papers in training set
Top 2%
1.2%
20
PLOS Computational Biology
● 1863 papers in training set
Top 16%
1.2%
21
ICES Journal of Marine Science
◐ 11 papers in training set
Top 0.1%
1.2%
22
GigaScience
◐ 212 papers in training set
Top 3%
1.2%
23
Frontiers in Marine Science
○ 62 papers in training set
Top 1.0%
0.9%
24
BMC Research Notes
○ 33 papers in training set
Top 1%
0.9%
25
FASEB BioAdvances
○ 18 papers in training set
Top 0.5%
0.6%
26
BioData Mining
○ 22 papers in training set
Top 0.8%
0.6%
27
PLANTS, PEOPLE, PLANET
○ 27 papers in training set
Top 0.7%
0.6%
28
Animals
○ 23 papers in training set
Top 0.9%
0.6%
29
European Journal of Neuroscience
○ 189 papers in training set
Top 4%
0.5%
30
Frontiers in Microbiology
○ 427 papers in training set
Top 9%
0.5%

"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.