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Evolution of computational models in BioModels Database and the Physiome Model Repository

Overview of attention for article published in BMC Systems Biology, April 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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13 X users

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Title
Evolution of computational models in BioModels Database and the Physiome Model Repository
Published in
BMC Systems Biology, April 2018
DOI 10.1186/s12918-018-0553-2
Pubmed ID
Authors

Martin Scharm, Tom Gebhardt, Vasundra Touré, Andrea Bagnacani, Ali Salehzadeh-Yazdi, Olaf Wolkenhauer, Dagmar Waltemath

Abstract

A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable. We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database. We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub https://github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at https://hub.docker.com/r/binfalse/bives-statsgenerator/ . The website https://most.bio.informatik.uni-rostock.de/ provides interactive access to model versions and their evolutionary statistics. The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model's provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 23%
Student > Ph. D. Student 8 23%
Other 4 11%
Student > Master 3 9%
Student > Bachelor 2 6%
Other 4 11%
Unknown 6 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 17%
Computer Science 6 17%
Biochemistry, Genetics and Molecular Biology 3 9%
Medicine and Dentistry 3 9%
Unspecified 2 6%
Other 7 20%
Unknown 8 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 September 2018.
All research outputs
#4,536,924
of 25,425,223 outputs
Outputs from BMC Systems Biology
#114
of 1,132 outputs
Outputs of similar age
#82,249
of 343,435 outputs
Outputs of similar age from BMC Systems Biology
#7
of 44 outputs
Altmetric has tracked 25,425,223 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 343,435 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.