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Developing data interoperability using standards: A wheat community use case

Overview of attention for article published in F1000 Research, October 2017
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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Article details
Title
Developing data interoperability using standards: A wheat community use case
Published in
F1000 Research, October 2017
DOI 10.12688/f1000research.12234.1
Pubmed ID
Authors
Abstract

In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability Working Group (WDI-WG) surveyed the wheat research community about the use of data standards, then discussed and selected a set of recommendations based on consensual criteria. The recommendations promote standards for data types identified by the wheat research community as the most important for the coming years: nucleotide sequence variants, genome annotations, phenotypes, germplasm data, gene expression experiments, and physical maps. For each of these data types, the guidelines recommend best practices in terms of use of data formats, metadata standards and ontologies. In addition to the best practices, the guidelines provide examples of tools and implementations that are likely to facilitate the adoption of the recommendations. To maximize the adoption of the recommendations, the WDI-WG used a community-driven approach that involved the wheat research community from the start, took into account their needs and practices, and provided them with a framework to keep the recommendations up to date. We also report this approach's potential to be generalizable to other (agricultural) domains.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 6 30%
Researcher 5 25%
Professor > Associate Professor 3 15%
Other 2 10%
Professor 1 5%
Other 1 5%
Unknown 2 10%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 5 25%
Computer Science 5 25%
Engineering 2 10%
Arts and Humanities 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 2 10%
Unknown 4 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 17 January 2018.
All research outputs
#2,527,843
of 34,518,599 outputs
Outputs from F1000 Research
#711
of 7,246 outputs
Outputs of similar age
#36,893
of 366,656 outputs
Outputs of similar age from F1000 Research
#29
of 168 outputs
Altmetric has tracked 34,518,599 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,246 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has done particularly well, scoring higher than 90% 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 366,656 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 89% of its contemporaries.
We're also able to compare this research output to 168 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.