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Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry

Overview of attention for article published in Giga Science, May 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
1 blog
twitter
13 X users
patent
1 patent
peer_reviews
1 peer review site
facebook
1 Facebook page
googleplus
2 Google+ users

Citations

dimensions_citation
55 Dimensions

Readers on

mendeley
127 Mendeley
citeulike
1 CiteULike
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Title
Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry
Published in
Giga Science, May 2015
DOI 10.1186/s13742-015-0059-4
Pubmed ID
Authors

Janina Oetjen, Kirill Veselkov, Jeramie Watrous, James S McKenzie, Michael Becker, Lena Hauberg-Lotte, Jan Hendrik Kobarg, Nicole Strittmatter, Anna K Mróz, Franziska Hoffmann, Dennis Trede, Andrew Palmer, Stefan Schiffler, Klaus Steinhorst, Michaela Aichler, Robert Goldin, Orlando Guntinas-Lichius, Ferdinand von Eggeling, Herbert Thiele, Kathrin Maedler, Axel Walch, Peter Maass, Pieter C Dorrestein, Zoltan Takats, Theodore Alexandrov

Abstract

Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 127 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Brazil 1 <1%
Unknown 125 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 22%
Student > Ph. D. Student 25 20%
Student > Master 17 13%
Student > Doctoral Student 9 7%
Other 9 7%
Other 21 17%
Unknown 18 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 19 15%
Agricultural and Biological Sciences 19 15%
Chemistry 16 13%
Computer Science 14 11%
Engineering 10 8%
Other 24 19%
Unknown 25 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 06 January 2021.
All research outputs
#1,759,793
of 25,394,764 outputs
Outputs from Giga Science
#322
of 1,168 outputs
Outputs of similar age
#21,893
of 279,145 outputs
Outputs of similar age from Giga Science
#3
of 15 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,168 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one has gotten more attention than average, scoring higher than 72% 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 279,145 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 92% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.