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Recommending plant taxa for supporting on-site species identification

Overview of attention for article published in BMC Bioinformatics, May 2018
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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 (80th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters
facebook
1 Facebook page

Citations

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119 Dimensions

Readers on

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34 Mendeley
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Title
Recommending plant taxa for supporting on-site species identification
Published in
BMC Bioinformatics, May 2018
DOI 10.1186/s12859-018-2201-7
Pubmed ID
Authors

Hans Christian Wittich, Marco Seeland, Jana Wäldchen, Michael Rzanny, Patrick Mäder

Abstract

Predicting a list of plant taxa most likely to be observed at a given geographical location and time is useful for many scenarios in biodiversity informatics. Since efficient plant species identification is impeded mainly by the large number of possible candidate species, providing a shortlist of likely candidates can help significantly expedite the task. Whereas species distribution models heavily rely on geo-referenced occurrence data, such information still remains largely unused for plant taxa identification tools. In this paper, we conduct a study on the feasibility of computing a ranked shortlist of plant taxa likely to be encountered by an observer in the field. We use the territory of Germany as case study with a total of 7.62M records of freely available plant presence-absence data and occurrence records for 2.7k plant taxa. We systematically study achievable recommendation quality based on two types of source data: binary presence-absence data and individual occurrence records. Furthermore, we study strategies for aggregating records into a taxa recommendation based on location and date of an observation. We evaluate recommendations using 28k geo-referenced and taxa-labeled plant images hosted on the Flickr website as an independent test dataset. Relying on location information from presence-absence data alone results in an average recall of 82%. However, we find that occurrence records are complementary to presence-absence data and using both in combination yields considerably higher recall of 96% along with improved ranking metrics. Ultimately, by reducing the list of candidate taxa by an average of 62%, a spatio-temporal prior can substantially expedite the overall identification problem.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 24%
Student > Master 7 21%
Student > Ph. D. Student 5 15%
Student > Bachelor 3 9%
Professor 2 6%
Other 3 9%
Unknown 6 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 32%
Environmental Science 6 18%
Computer Science 2 6%
Earth and Planetary Sciences 2 6%
Nursing and Health Professions 1 3%
Other 3 9%
Unknown 9 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 29 April 2021.
All research outputs
#2,393,041
of 18,439,562 outputs
Outputs from BMC Bioinformatics
#900
of 6,391 outputs
Outputs of similar age
#56,408
of 291,852 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 22 outputs
Altmetric has tracked 18,439,562 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,391 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 85% 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 291,852 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 80% of its contemporaries.
We're also able to compare this research output to 22 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 95% of its contemporaries.