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The flora phenotype ontology (FLOPO): tool for integrating morphological traits and phenotypes of vascular plants

Overview of attention for article published in Journal of Biomedical Semantics, November 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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6 tweeters
facebook
2 Facebook pages
wikipedia
1 Wikipedia page

Citations

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29 Dimensions

Readers on

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51 Mendeley
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Title
The flora phenotype ontology (FLOPO): tool for integrating morphological traits and phenotypes of vascular plants
Published in
Journal of Biomedical Semantics, November 2016
DOI 10.1186/s13326-016-0107-8
Pubmed ID
Authors

Robert Hoehndorf, Mona Alshahrani, Georgios V. Gkoutos, George Gosline, Quentin Groom, Thomas Hamann, Jens Kattge, Sylvia Mota de Oliveira, Marco Schmidt, Soraya Sierra, Erik Smets, Rutger A. Vos, Claus Weiland

Abstract

The systematic analysis of a large number of comparable plant trait data can support investigations into phylogenetics and ecological adaptation, with broad applications in evolutionary biology, agriculture, conservation, and the functioning of ecosystems. Floras, i.e., books collecting the information on all known plant species found within a region, are a potentially rich source of such plant trait data. Floras describe plant traits with a focus on morphology and other traits relevant for species identification in addition to other characteristics of plant species, such as ecological affinities, distribution, economic value, health applications, traditional uses, and so on. However, a key limitation in systematically analyzing information in Floras is the lack of a standardized vocabulary for the described traits as well as the difficulties in extracting structured information from free text. We have developed the Flora Phenotype Ontology (FLOPO), an ontology for describing traits of plant species found in Floras. We used the Plant Ontology (PO) and the Phenotype And Trait Ontology (PATO) to extract entity-quality relationships from digitized taxon descriptions in Floras, and used a formal ontological approach based on phenotype description patterns and automated reasoning to generate the FLOPO. The resulting ontology consists of 25,407 classes and is based on the PO and PATO. The classified ontology closely follows the structure of Plant Ontology in that the primary axis of classification is the observed plant anatomical structure, and more specific traits are then classified based on parthood and subclass relations between anatomical structures as well as subclass relations between phenotypic qualities. The FLOPO is primarily intended as a framework based on which plant traits can be integrated computationally across all species and higher taxa of flowering plants. Importantly, it is not intended to replace established vocabularies or ontologies, but rather serve as an overarching framework based on which different application- and domain-specific ontologies, thesauri and vocabularies of phenotypes observed in flowering plants can be integrated.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Belgium 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 22%
Student > Ph. D. Student 9 18%
Student > Master 6 12%
Professor > Associate Professor 5 10%
Student > Doctoral Student 4 8%
Other 10 20%
Unknown 6 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 43%
Computer Science 9 18%
Environmental Science 3 6%
Earth and Planetary Sciences 2 4%
Engineering 2 4%
Other 7 14%
Unknown 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 19 February 2020.
All research outputs
#3,221,637
of 17,020,562 outputs
Outputs from Journal of Biomedical Semantics
#68
of 352 outputs
Outputs of similar age
#61,188
of 244,434 outputs
Outputs of similar age from Journal of Biomedical Semantics
#6
of 43 outputs
Altmetric has tracked 17,020,562 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 352 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 80% 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 244,434 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 74% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.