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Structuring research methods and data with the research object model: genomics workflows as a case study

Overview of attention for article published in Journal of Biomedical Semantics, September 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#48 of 364)
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
11 X users
wikipedia
3 Wikipedia pages

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
87 Mendeley
citeulike
2 CiteULike
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Title
Structuring research methods and data with the research object model: genomics workflows as a case study
Published in
Journal of Biomedical Semantics, September 2014
DOI 10.1186/2041-1480-5-41
Pubmed ID
Authors

Kristina M Hettne, Harish Dharuri, Jun Zhao, Katherine Wolstencroft, Khalid Belhajjame, Stian Soiland-Reyes, Eleni Mina, Mark Thompson, Don Cruickshank, Lourdes Verdes-Montenegro, Julian Garrido, David de Roure, Oscar Corcho, Graham Klyne, Reinout van Schouwen, Peter A C ‘t Hoen, Sean Bechhofer, Carole Goble, Marco Roos

Abstract

One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows.

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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 3%
Brazil 2 2%
United States 2 2%
Spain 2 2%
Canada 1 1%
France 1 1%
Netherlands 1 1%
Russia 1 1%
Unknown 74 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 26%
Student > Ph. D. Student 11 13%
Student > Master 9 10%
Student > Bachelor 8 9%
Professor 7 8%
Other 16 18%
Unknown 13 15%
Readers by discipline Count As %
Computer Science 36 41%
Biochemistry, Genetics and Molecular Biology 10 11%
Agricultural and Biological Sciences 7 8%
Business, Management and Accounting 3 3%
Medicine and Dentistry 3 3%
Other 12 14%
Unknown 16 18%
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 15 July 2018.
All research outputs
#3,203,885
of 24,447,003 outputs
Outputs from Journal of Biomedical Semantics
#48
of 364 outputs
Outputs of similar age
#34,489
of 254,609 outputs
Outputs of similar age from Journal of Biomedical Semantics
#2
of 5 outputs
Altmetric has tracked 24,447,003 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 364 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 87% 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 254,609 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 86% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.