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Towards exergaming commons: composing the exergame ontology for publishing open game data

Overview of attention for article published in Journal of Biomedical Semantics, February 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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8 X users

Citations

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Title
Towards exergaming commons: composing the exergame ontology for publishing open game data
Published in
Journal of Biomedical Semantics, February 2016
DOI 10.1186/s13326-016-0046-4
Pubmed ID
Authors

Giorgos Bamparopoulos, Evdokimos Konstantinidis, Charalampos Bratsas, Panagiotis D. Bamidis

Abstract

It has been shown that exergames have multiple benefits for physical, mental and cognitive health. Only recently, however, researchers have started considering them as health monitoring tools, through collection and analysis of game metrics data. In light of this and initiatives like the Quantified Self, there is an emerging need to open the data produced by health games and their associated metrics in order for them to be evaluated by the research community in an attempt to quantify their potential health, cognitive and physiological benefits. We have developed an ontology that describes exergames using the Web Ontology Language (OWL); it is available at http://purl.org/net/exergame/ns#. After an investigation of key components of exergames, relevant ontologies were incorporated, while necessary classes and properties were defined to model these components. A JavaScript framework was also developed in order to apply the ontology to online exergames. Finally, a SPARQL Endpoint is provided to enable open data access to potential clients through the web. Exergame components include details for players, game sessions, as well as, data produced during these game-playing sessions. The description of the game includes elements such as goals, game controllers and presentation hardware used; what is more, concepts from already existing ontologies are reused/repurposed. Game sessions include information related to the player, the date and venue where the game was played, as well as, the results/scores that were produced/achieved. These games are subsequently played by 14 users in multiple game sessions and the results derived from these sessions are published in a triplestore as open data. We model concepts related to exergames by providing a standardized structure for reference and comparison. This is the first work that publishes data from actual exergame sessions on the web, facilitating the integration and analysis of the data, while allowing open data access through the web in an effort to enable the concept of Open Trials for Active and Healthy Ageing.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 <1%
Spain 1 <1%
France 1 <1%
Unknown 131 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 20%
Student > Ph. D. Student 18 13%
Student > Bachelor 17 13%
Researcher 10 7%
Student > Postgraduate 8 6%
Other 21 16%
Unknown 33 25%
Readers by discipline Count As %
Computer Science 40 30%
Nursing and Health Professions 13 10%
Sports and Recreations 10 7%
Medicine and Dentistry 8 6%
Social Sciences 6 4%
Other 18 13%
Unknown 39 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 16 October 2016.
All research outputs
#4,823,998
of 25,373,627 outputs
Outputs from Journal of Biomedical Semantics
#70
of 368 outputs
Outputs of similar age
#78,719
of 409,533 outputs
Outputs of similar age from Journal of Biomedical Semantics
#3
of 7 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 80% 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 409,533 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 80% 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 4 of them.