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FAIRsoft - A practical implementation of FAIR principles for research software

Martin del Pico, E.; Gelpi, J. L.; Capella-Gutierrez, S.

2022-10-10 bioinformatics
10.1101/2022.05.04.490563 bioRxiv
Show abstract

Software plays a crucial and growing role in research. Unfortunately, the computational component in Life Sciences research is challenging to reproduce and verify most of the time. It could be undocumented, opaque, may even contain unknown errors that affect the outcome, or be directly unavailable, and impossible to use by others. These issues are detrimental to the overall quality of scientific research. One step to address this problem is the formulation of principles that research software in the domain should meet to ensure its quality and sustainability, resembling the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. Within the ELIXIR infrastructure, OpenEBench aims to be an open platform providing both support for scientific benchmarking and an active observatory of software quality for the universe of Life Sciences research software. We present our initial proposal to instantiate a FAIR-like framework for assessing software quality as the first step toward the implementation of such an observatory in OpenEBench. Supplementary Material: FAIRsoft - Supplementary materials FAIRsoft.SupplementaryTables FAIRsoft.SupplementaryTables-Landscape Other Figures: figures draft Repository: https://gitlab.bsc.es/inb/elixir/software-observatory/FAIRsoft_ETL

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 0.5%
31.4%
Similar papers in this journal
2
GigaScience
◐ 212 papers in training set
Top 0.3%
7.4%
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3
BMC Bioinformatics
○ 457 papers in training set
Top 2%
4.9%
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4
PeerJ
◐ 308 papers in training set
Top 1%
4.9%
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5
Peer Community Journal
● 281 papers in training set
Top 1%
4.1%
Similar papers in this journal
50% of probability mass above
6
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
3.6%
7
Frontiers in Bioinformatics
○ 49 papers in training set
Top 0.2%
2.8%
8
PLOS ONE
● 5266 papers in training set
Top 39%
2.7%
9
Scientific Reports
○ 3612 papers in training set
Top 41%
2.4%
10
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 3%
2.1%
11
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.1%
12
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
2.1%
13
F1000Research
○ 88 papers in training set
Top 1%
1.7%
14
PLOS Computational Biology
● 1863 papers in training set
Top 16%
1.4%
15
Frontiers in Genetics
○ 230 papers in training set
Top 4%
1.1%
16
Database
◐ 61 papers in training set
Top 0.6%
1.1%
17
Journal of Molecular Biology
○ 232 papers in training set
Top 3%
1.0%
18
Computer Methods and Programs in Biomedicine
○ 28 papers in training set
Top 0.9%
0.9%
19
Journal of Open Source Software
● 25 papers in training set
Top 0.4%
0.9%
20
Neuroinformatics
○ 46 papers in training set
Top 0.8%
0.9%
21
DIGITAL HEALTH
○ 17 papers in training set
Top 0.7%
0.9%
22
FACETS
● 14 papers in training set
Top 0.5%
0.6%
23
BioData Mining
○ 22 papers in training set
Top 0.8%
0.6%
24
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.8%
0.6%
25
Methods in Ecology and Evolution
○ 176 papers in training set
Top 2%
0.6%
26
PLOS Biology
● 486 papers in training set
Top 13%
0.6%
27
Computers in Biology and Medicine
○ 128 papers in training set
Top 5%
0.6%
28
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 3%
0.6%

"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.