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Nanomaterial datasets to advance tomography in scanning transmission electron microscopy

Overview of attention for article published in Scientific Data, June 2016
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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 (84th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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11 X users
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3 Wikipedia pages
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1 Google+ user
reddit
1 Redditor

Citations

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54 Dimensions

Readers on

mendeley
106 Mendeley
Title
Nanomaterial datasets to advance tomography in scanning transmission electron microscopy
Published in
Scientific Data, June 2016
DOI 10.1038/sdata.2016.41
Pubmed ID
Authors

Barnaby D.A. Levin, Elliot Padgett, Chien-Chun Chen, M.C. Scott, Rui Xu, Wolfgang Theis, Yi Jiang, Yongsoo Yang, Colin Ophus, Haitao Zhang, Don-Hyung Ha, Deli Wang, Yingchao Yu, Hector D. Abruña, Richard D. Robinson, Peter Ercius, Lena F. Kourkoutis, Jianwei Miao, David A. Muller, Robert Hovden

Abstract

Electron tomography in materials science has flourished with the demand to characterize nanoscale materials in three dimensions (3D). Access to experimental data is vital for developing and validating reconstruction methods that improve resolution and reduce radiation dose requirements. This work presents five high-quality scanning transmission electron microscope (STEM) tomography datasets in order to address the critical need for open access data in this field. The datasets represent the current limits of experimental technique, are of high quality, and contain materials with structural complexity. Included are tomographic series of a hyperbranched Co2P nanocrystal, platinum nanoparticles on a carbon nanofibre imaged over the complete 180° tilt range, a platinum nanoparticle and a tungsten needle both imaged at atomic resolution by equal slope tomography, and a through-focal tilt series of PtCu nanoparticles. A volumetric reconstruction from every dataset is provided for comparison and development of post-processing and visualization techniques. Researchers interested in creating novel data processing and reconstruction algorithms will now have access to state of the art experimental test data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 106 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Bulgaria 1 <1%
Unknown 105 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 30%
Researcher 17 16%
Student > Master 11 10%
Student > Doctoral Student 9 8%
Student > Bachelor 8 8%
Other 10 9%
Unknown 19 18%
Readers by discipline Count As %
Materials Science 31 29%
Engineering 13 12%
Physics and Astronomy 11 10%
Chemistry 10 9%
Biochemistry, Genetics and Molecular Biology 5 5%
Other 14 13%
Unknown 22 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 04 March 2017.
All research outputs
#3,037,972
of 23,881,329 outputs
Outputs from Scientific Data
#1,064
of 2,721 outputs
Outputs of similar age
#53,621
of 344,683 outputs
Outputs of similar age from Scientific Data
#17
of 28 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,721 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.1. This one has gotten more attention than average, scoring higher than 60% 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 344,683 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 84% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.