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Consolidating drug data on a global scale using Linked Data

Overview of attention for article published in Journal of Biomedical Semantics, January 2017
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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 (#18 of 356)
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

twitter
18 tweeters
facebook
1 Facebook page
googleplus
2 Google+ users
reddit
1 Redditor

Readers on

mendeley
55 Mendeley
citeulike
1 CiteULike
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Title
Consolidating drug data on a global scale using Linked Data
Published in
Journal of Biomedical Semantics, January 2017
DOI 10.1186/s13326-016-0111-z
Pubmed ID
Authors

Milos Jovanovik, Dimitar Trajanov

Abstract

Drug product data is available on the Web in a distributed fashion. The reasons lie within the regulatory domains, which exist on a national level. As a consequence, the drug data available on the Web are independently curated by national institutions from each country, leaving the data in varying languages, with a varying structure, granularity level and format, on different locations on the Web. Therefore, one of the main challenges in the realm of drug data is the consolidation and integration of large amounts of heterogeneous data into a comprehensive dataspace, for the purpose of developing data-driven applications. In recent years, the adoption of the Linked Data principles has enabled data publishers to provide structured data on the Web and contextually interlink them with other public datasets, effectively de-siloing them. Defining methodological guidelines and specialized tools for generating Linked Data in the drug domain, applicable on a global scale, is a crucial step to achieving the necessary levels of data consolidation and alignment needed for the development of a global dataset of drug product data. This dataset would then enable a myriad of new usage scenarios, which can, for instance, provide insight into the global availability of different drug categories in different parts of the world. We developed a methodology and a set of tools which support the process of generating Linked Data in the drug domain. Using them, we generated the LinkedDrugs dataset by seamlessly transforming, consolidating and publishing high-quality, 5-star Linked Drug Data from twenty-three countries, containing over 248,000 drug products, over 99,000,000 RDF triples and over 278,000 links to generic drugs from the LOD Cloud. Using the linked nature of the dataset, we demonstrate its ability to support advanced usage scenarios in the drug domain. The process of generating the LinkedDrugs dataset demonstrates the applicability of the methodological guidelines and the supporting tools in transforming drug product data from various, independent and distributed sources, into a comprehensive Linked Drug Data dataset. The presented user-centric and analytical usage scenarios over the dataset show the advantages of having a de-siloed, consolidated and comprehensive dataspace of drug data available via the existing infrastructure of the Web.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
North Macedonia 2 4%
France 1 2%
Unknown 52 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 27%
Student > Master 13 24%
Researcher 10 18%
Professor > Associate Professor 4 7%
Student > Bachelor 3 5%
Other 5 9%
Unknown 5 9%
Readers by discipline Count As %
Computer Science 27 49%
Medicine and Dentistry 7 13%
Engineering 5 9%
Agricultural and Biological Sciences 3 5%
Social Sciences 2 4%
Other 4 7%
Unknown 7 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 27 March 2020.
All research outputs
#1,361,236
of 17,363,630 outputs
Outputs from Journal of Biomedical Semantics
#18
of 356 outputs
Outputs of similar age
#38,415
of 365,971 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 4 outputs
Altmetric has tracked 17,363,630 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 356 research outputs from this source. They receive a mean Attention Score of 4.5. 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 365,971 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 4 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