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The Enzyme Portal: a case study in applying user-centred design methods in bioinformatics

Overview of attention for article published in BMC Bioinformatics, March 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
2 blogs
twitter
24 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
98 Mendeley
citeulike
4 CiteULike
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Title
The Enzyme Portal: a case study in applying user-centred design methods in bioinformatics
Published in
BMC Bioinformatics, March 2013
DOI 10.1186/1471-2105-14-103
Pubmed ID
Authors

Paula de Matos, Jennifer A Cham, Hong Cao, Rafael Alcántara, Francis Rowland, Rodrigo Lopez, Christoph Steinbeck

Abstract

User-centred design (UCD) is a type of user interface design in which the needs and desires of users are taken into account at each stage of the design process for a service or product; often for software applications and websites. Its goal is to facilitate the design of software that is both useful and easy to use. To achieve this, you must characterise users' requirements, design suitable interactions to meet their needs, and test your designs using prototypes and real life scenarios.For bioinformatics, there is little practical information available regarding how to carry out UCD in practice. To address this we describe a complete, multi-stage UCD process used for creating a new bioinformatics resource for integrating enzyme information, called the Enzyme Portal (http://www.ebi.ac.uk/enzymeportal). This freely-available service mines and displays data about proteins with enzymatic activity from public repositories via a single search, and includes biochemical reactions, biological pathways, small molecule chemistry, disease information, 3D protein structures and relevant scientific literature.We employed several UCD techniques, including: persona development, interviews, 'canvas sort' card sorting, user workflows, usability testing and others. Our hope is that this case study will motivate the reader to apply similar UCD approaches to their own software design for bioinformatics. Indeed, we found the benefits included more effective decision-making for design ideas and technologies; enhanced team-working and communication; cost effectiveness; and ultimately a service that more closely meets the needs of our target audience.

Twitter Demographics

The data shown below were collected from the profiles of 24 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
United States 2 2%
Denmark 2 2%
Uruguay 1 1%
France 1 1%
Brazil 1 1%
Unknown 89 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 18%
Researcher 18 18%
Student > Master 16 16%
Student > Bachelor 11 11%
Professor > Associate Professor 7 7%
Other 21 21%
Unknown 7 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 28%
Computer Science 18 18%
Engineering 7 7%
Design 6 6%
Biochemistry, Genetics and Molecular Biology 6 6%
Other 22 22%
Unknown 12 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 September 2013.
All research outputs
#911,810
of 21,334,388 outputs
Outputs from BMC Bioinformatics
#94
of 6,922 outputs
Outputs of similar age
#6,962
of 173,385 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 30 outputs
Altmetric has tracked 21,334,388 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,922 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 98% 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 173,385 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 30 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 99% of its contemporaries.