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Measures for interoperability of phenotypic data: minimum information requirements and formatting

Overview of attention for article published in Plant Methods, November 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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

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25 tweeters
facebook
1 Facebook page

Citations

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86 Dimensions

Readers on

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128 Mendeley
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2 CiteULike
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Title
Measures for interoperability of phenotypic data: minimum information requirements and formatting
Published in
Plant Methods, November 2016
DOI 10.1186/s13007-016-0144-4
Pubmed ID
Authors

Hanna Ćwiek-Kupczyńska, Thomas Altmann, Daniel Arend, Elizabeth Arnaud, Dijun Chen, Guillaume Cornut, Fabio Fiorani, Wojciech Frohmberg, Astrid Junker, Christian Klukas, Matthias Lange, Cezary Mazurek, Anahita Nafissi, Pascal Neveu, Jan van Oeveren, Cyril Pommier, Hendrik Poorter, Philippe Rocca-Serra, Susanna-Assunta Sansone, Uwe Scholz, Marco van Schriek, Ümit Seren, Björn Usadel, Stephan Weise, Paul Kersey, Paweł Krajewski

Abstract

Plant phenotypic data shrouds a wealth of information which, when accurately analysed and linked to other data types, brings to light the knowledge about the mechanisms of life. As phenotyping is a field of research comprising manifold, diverse and time-consuming experiments, the findings can be fostered by reusing and combining existing datasets. Their correct interpretation, and thus replicability, comparability and interoperability, is possible provided that the collected observations are equipped with an adequate set of metadata. So far there have been no common standards governing phenotypic data description, which hampered data exchange and reuse. In this paper we propose the guidelines for proper handling of the information about plant phenotyping experiments, in terms of both the recommended content of the description and its formatting. We provide a document called "Minimum Information About a Plant Phenotyping Experiment", which specifies what information about each experiment should be given, and a Phenotyping Configuration for the ISA-Tab format, which allows to practically organise this information within a dataset. We provide examples of ISA-Tab-formatted phenotypic data, and a general description of a few systems where the recommendations have been implemented. Acceptance of the rules described in this paper by the plant phenotyping community will help to achieve findable, accessible, interoperable and reusable data.

Twitter Demographics

The data shown below were collected from the profiles of 25 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 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 <1%
Italy 1 <1%
Canada 1 <1%
Unknown 125 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 30%
Student > Ph. D. Student 20 16%
Student > Master 15 12%
Other 11 9%
Student > Bachelor 7 5%
Other 23 18%
Unknown 14 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 64 50%
Computer Science 20 16%
Biochemistry, Genetics and Molecular Biology 9 7%
Engineering 4 3%
Environmental Science 2 2%
Other 8 6%
Unknown 21 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 10 February 2019.
All research outputs
#1,179,292
of 14,269,298 outputs
Outputs from Plant Methods
#57
of 655 outputs
Outputs of similar age
#36,179
of 285,838 outputs
Outputs of similar age from Plant Methods
#3
of 24 outputs
Altmetric has tracked 14,269,298 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 655 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one has done particularly well, scoring higher than 91% 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 285,838 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 87% 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 done well, scoring higher than 87% of its contemporaries.