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Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer

Overview of attention for article published in BioMed Research International, January 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
54 Mendeley
Title
Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer
Published in
BioMed Research International, January 2017
DOI 10.1155/2017/8327980
Pubmed ID
Authors

Pedro Sernadela, Lorena González-Castro, Claudio Carta, Eelke van der Horst, Pedro Lopes, Rajaram Kaliyaperumal, Mark Thompson, Rachel Thompson, Núria Queralt-Rosinach, Estrella Lopez, Libby Wood, Agata Robertson, Claudia Lamanna, Mette Gilling, Michael Orth, Roxana Merino-Martinez, Manuel Posada, Domenica Taruscio, Hanns Lochmüller, Peter Robinson, Marco Roos, José Luís Oliveira

Abstract

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges. Usually, these systems are developed as closed data silos, with independent formats and models, lacking comprehensive mechanisms to enable data sharing. To tackle these challenges, we developed a Semantic Web based solution that allows connecting distributed and heterogeneous registries, enabling the federation of knowledge between multiple independent environments. This semantic layer creates a holistic view over a set of anonymised registries, supporting semantic data representation, integrated access, and querying. The implemented system gave us the opportunity to answer challenging questions across disperse rare disease patient registries. The interconnection between those registries using Semantic Web technologies benefits our final solution in a way that we can query single or multiple instances according to our needs. The outcome is a unique semantic layer, connecting miscellaneous registries and delivering a lightweight holistic perspective over the wealth of knowledge stemming from linked rare disease patient registries.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 54 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 19%
Student > Master 8 15%
Other 7 13%
Researcher 5 9%
Student > Bachelor 5 9%
Other 10 19%
Unknown 9 17%
Readers by discipline Count As %
Computer Science 9 17%
Medicine and Dentistry 7 13%
Biochemistry, Genetics and Molecular Biology 6 11%
Agricultural and Biological Sciences 6 11%
Pharmacology, Toxicology and Pharmaceutical Science 4 7%
Other 13 24%
Unknown 9 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 July 2018.
All research outputs
#3,675,186
of 13,292,231 outputs
Outputs from BioMed Research International
#931
of 5,828 outputs
Outputs of similar age
#124,734
of 385,302 outputs
Outputs of similar age from BioMed Research International
#30
of 270 outputs
Altmetric has tracked 13,292,231 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 5,828 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 83% 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 385,302 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 270 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.