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ImageJ2: ImageJ for the next generation of scientific image data

Overview of attention for article published in BMC Bioinformatics, November 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 6,173)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
1 blog
twitter
189 tweeters
googleplus
2 Google+ users
video
1 video uploader

Citations

dimensions_citation
2140 Dimensions

Readers on

mendeley
2198 Mendeley
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Title
ImageJ2: ImageJ for the next generation of scientific image data
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1934-z
Pubmed ID
Authors

Curtis T. Rueden, Johannes Schindelin, Mark C. Hiner, Barry E. DeZonia, Alison E. Walter, Ellen T. Arena, Kevin W. Eliceiri

Abstract

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called "ImageJ2" in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ's development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

Twitter Demographics

The data shown below were collected from the profiles of 189 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 2,198 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2198 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 563 26%
Student > Master 352 16%
Student > Bachelor 297 14%
Researcher 262 12%
Student > Doctoral Student 135 6%
Other 252 11%
Unknown 337 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 402 18%
Biochemistry, Genetics and Molecular Biology 354 16%
Engineering 200 9%
Medicine and Dentistry 126 6%
Chemistry 99 5%
Other 546 25%
Unknown 471 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 122. 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 February 2020.
All research outputs
#195,064
of 17,475,439 outputs
Outputs from BMC Bioinformatics
#4
of 6,173 outputs
Outputs of similar age
#7,287
of 419,966 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 441 outputs
Altmetric has tracked 17,475,439 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,173 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 99% 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 419,966 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 441 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.