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OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies

Overview of attention for article published in Frontiers in Pharmacology, June 2018
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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 (94th percentile)

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22 X users
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1 Facebook page
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1 YouTube creator

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49 Mendeley
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Title
OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies
Published in
Frontiers in Pharmacology, June 2018
DOI 10.3389/fphar.2018.00609
Pubmed ID
Authors

Pantelis Natsiavas, Richard D. Boyce, Marie-Christine Jaulent, Vassilis Koutkias

Abstract

Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.

X Demographics

X Demographics

The data shown below were collected from the profiles of 22 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 20%
Student > Master 7 14%
Researcher 6 12%
Student > Doctoral Student 3 6%
Student > Bachelor 2 4%
Other 10 20%
Unknown 11 22%
Readers by discipline Count As %
Computer Science 10 20%
Engineering 4 8%
Agricultural and Biological Sciences 4 8%
Biochemistry, Genetics and Molecular Biology 3 6%
Medicine and Dentistry 3 6%
Other 11 22%
Unknown 14 29%
Attention Score in Context

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 11 April 2020.
All research outputs
#2,033,452
of 24,288,533 outputs
Outputs from Frontiers in Pharmacology
#811
of 18,210 outputs
Outputs of similar age
#42,530
of 333,101 outputs
Outputs of similar age from Frontiers in Pharmacology
#21
of 402 outputs
Altmetric has tracked 24,288,533 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 18,210 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 95% 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 333,101 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 402 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 94% of its contemporaries.