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JAMI: a Java library for molecular interactions and data interoperability

Overview of attention for article published in BMC Bioinformatics, April 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
24 Mendeley
citeulike
2 CiteULike
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Title
JAMI: a Java library for molecular interactions and data interoperability
Published in
BMC Bioinformatics, April 2018
DOI 10.1186/s12859-018-2119-0
Pubmed ID
Authors

M. Sivade, M. Koch, A. Shrivastava, D. Alonso-López, J. De Las Rivas, N. del-Toro, C. W. Combe, B. H. M. Meldal, J. Heimbach, J. Rappsilber, J. Sullivan, Y. Yehudi, S. Orchard

Abstract

A number of different molecular interactions data download formats now exist, designed to allow access to these valuable data by diverse user groups. These formats include the PSI-XML and MITAB standard interchange formats developed by Molecular Interaction workgroup of the HUPO-PSI in addition to other, use-specific downloads produced by other resources. The onus is currently on the user to ensure that a piece of software is capable of read/writing all necessary versions of each format. This problem may increase, as data providers strive to meet ever more sophisticated user demands and data types. A collaboration between EMBL-EBI and the University of Cambridge has produced JAMI, a single library to unify standard molecular interaction data formats such as PSI-MI XML and PSI-MITAB. The JAMI free, open-source library enables the development of molecular interaction computational tools and pipelines without the need to produce different versions of software to read different versions of the data formats. Software and tools developed on top of the JAMI framework are able to integrate and support both PSI-MI XML and PSI-MITAB. The use of JAMI avoids the requirement to chain conversions between formats in order to reach a desired output format and prevents code and unit test duplication as the code becomes more modular. JAMI's model interfaces are abstracted from the underlying format, hiding the complexity and requirements of each data format from developers using JAMI as a library.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 25%
Student > Ph. D. Student 5 21%
Other 3 13%
Professor 2 8%
Student > Bachelor 2 8%
Other 3 13%
Unknown 3 13%
Readers by discipline Count As %
Computer Science 7 29%
Agricultural and Biological Sciences 6 25%
Biochemistry, Genetics and Molecular Biology 2 8%
Social Sciences 2 8%
Medicine and Dentistry 2 8%
Other 2 8%
Unknown 3 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 17 April 2018.
All research outputs
#6,938,018
of 12,813,846 outputs
Outputs from BMC Bioinformatics
#2,577
of 4,759 outputs
Outputs of similar age
#123,232
of 271,394 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 24 outputs
Altmetric has tracked 12,813,846 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,759 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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 271,394 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.