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Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics network

Overview of attention for article published in PLoS Computational Biology, June 2017
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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 (76th percentile)
  • Average Attention Score compared to outputs of the same age and source

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blogs
1 blog
twitter
1 X user

Citations

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10 Dimensions

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70 Mendeley
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1 CiteULike
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Title
Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics network
Published in
PLoS Computational Biology, June 2017
DOI 10.1371/journal.pcbi.1005419
Pubmed ID
Authors

C. Victor Jongeneel, Ovokeraye Achinike-Oduaran, Ezekiel Adebiyi, Marion Adebiyi, Seun Adeyemi, Bola Akanle, Shaun Aron, Efejiro Ashano, Hocine Bendou, Gerrit Botha, Emile Chimusa, Ananyo Choudhury, Ravikiran Donthu, Jenny Drnevich, Oluwadamila Falola, Christopher J. Fields, Scott Hazelhurst, Liesl Hendry, Itunuoluwa Isewon, Radhika S. Khetani, Judit Kumuthini, Magambo Phillip Kimuda, Lerato Magosi, Liudmila Sergeevna Mainzer, Suresh Maslamoney, Mamana Mbiyavanga, Ayton Meintjes, Danny Mugutso, Phelelani Mpangase, Richard Munthali, Victoria Nembaware, Andrew Ndhlovu, Trust Odia, Adaobi Okafor, Olaleye Oladipo, Sumir Panji, Venesa Pillay, Gloria Rendon, Dhriti Sengupta, Nicola Mulder

Abstract

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exercises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a framework for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exercise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 1%
Unknown 69 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 21%
Student > Master 15 21%
Student > Ph. D. Student 10 14%
Professor > Associate Professor 4 6%
Other 3 4%
Other 12 17%
Unknown 11 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 24%
Biochemistry, Genetics and Molecular Biology 15 21%
Social Sciences 5 7%
Computer Science 5 7%
Engineering 4 6%
Other 12 17%
Unknown 12 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 06 October 2017.
All research outputs
#4,660,989
of 25,382,440 outputs
Outputs from PLoS Computational Biology
#3,728
of 8,960 outputs
Outputs of similar age
#76,213
of 330,503 outputs
Outputs of similar age from PLoS Computational Biology
#78
of 153 outputs
Altmetric has tracked 25,382,440 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 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 58% 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 330,503 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 76% of its contemporaries.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.