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Investigating reproducibility and tracking provenance – A genomic workflow case study

Overview of attention for article published in BMC Bioinformatics, July 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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
30 tweeters

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
80 Mendeley
citeulike
3 CiteULike
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Title
Investigating reproducibility and tracking provenance – A genomic workflow case study
Published in
BMC Bioinformatics, July 2017
DOI 10.1186/s12859-017-1747-0
Pubmed ID
Authors

Sehrish Kanwal, Farah Zaib Khan, Andrew Lonie, Richard O. Sinnott

Abstract

Computational bioinformatics workflows are extensively used to analyse genomics data, with different approaches available to support implementation and execution of these workflows. Reproducibility is one of the core principles for any scientific workflow and remains a challenge, which is not fully addressed. This is due to incomplete understanding of reproducibility requirements and assumptions of workflow definition approaches. Provenance information should be tracked and used to capture all these requirements supporting reusability of existing workflows. We have implemented a complex but widely deployed bioinformatics workflow using three representative approaches to workflow definition and execution. Through implementation, we identified assumptions implicit in these approaches that ultimately produce insufficient documentation of workflow requirements resulting in failed execution of the workflow. This study proposes a set of recommendations that aims to mitigate these assumptions and guides the scientific community to accomplish reproducible science, hence addressing reproducibility crisis. Reproducing, adapting or even repeating a bioinformatics workflow in any environment requires substantial technical knowledge of the workflow execution environment, resolving analysis assumptions and rigorous compliance with reproducibility requirements. Towards these goals, we propose conclusive recommendations that along with an explicit declaration of workflow specification would result in enhanced reproducibility of computational genomic analyses.

Twitter Demographics

The data shown below were collected from the profiles of 30 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 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 31%
Researcher 20 25%
Other 6 8%
Student > Bachelor 6 8%
Student > Master 6 8%
Other 8 10%
Unknown 9 11%
Readers by discipline Count As %
Computer Science 25 31%
Agricultural and Biological Sciences 16 20%
Biochemistry, Genetics and Molecular Biology 13 16%
Engineering 5 6%
Arts and Humanities 2 3%
Other 9 11%
Unknown 10 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 01 July 2018.
All research outputs
#1,202,596
of 15,921,004 outputs
Outputs from BMC Bioinformatics
#326
of 5,768 outputs
Outputs of similar age
#32,408
of 267,839 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 7 outputs
Altmetric has tracked 15,921,004 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 5,768 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 94% 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 267,839 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 87% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them