You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Implementation of linked data in the life sciences at BioHackathon 2011
|
|---|---|
| Published in |
Journal of Biomedical Semantics, January 2015
|
| DOI | 10.1186/2041-1480-6-3 |
| Pubmed ID | |
| Authors |
Kiyoko F Aoki-Kinoshita, Akira R Kinjo, Mizuki Morita, Yoshinobu Igarashi, Yi-an Chen, Yasumasa Shigemoto, Takatomo Fujisawa, Yukie Akune, Takeo Katoda, Anna Kokubu, Takaaki Mori, Mitsuteru Nakao, Shuichi Kawashima, Shinobu Okamoto, Toshiaki Katayama, Soichi Ogishima |
| Abstract |
Linked Data has gained some attention recently in the life sciences as an effective way to provide and share data. As a part of the Semantic Web, data are linked so that a person or machine can explore the web of data. Resource Description Framework (RDF) is the standard means of implementing Linked Data. In the process of generating RDF data, not only are data simply linked to one another, the links themselves are characterized by ontologies, thereby allowing the types of links to be distinguished. Although there is a high labor cost to define an ontology for data providers, the merit lies in the higher level of interoperability with data analysis and visualization software. This increase in interoperability facilitates the multi-faceted retrieval of data, and the appropriate data can be quickly extracted and visualized. Such retrieval is usually performed using the SPARQL (SPARQL Protocol and RDF Query Language) query language, which is used to query RDF data stores. For the database provider, such interoperability will surely lead to an increase in the number of users. This manuscript describes the experiences and discussions shared among participants of the week-long BioHackathon 2011 who went through the development of RDF representations of their own data and developed specific RDF and SPARQL use cases. Advice regarding considerations to take when developing RDF representations of their data are provided for bioinformaticians considering making data available and interoperable. Participants of the BioHackathon 2011 were able to produce RDF representations of their data and gain a better understanding of the requirements for producing such data in a period of just five days. We summarize the work accomplished with the hope that it will be useful for researchers involved in developing laboratory databases or data analysis, and those who are considering such technologies as RDF and Linked Data. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 29% |
| Japan | 1 | 14% |
| France | 1 | 14% |
| Unknown | 3 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 86% |
| Scientists | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 4 | 10% |
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 35 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 32% |
| Student > Master | 5 | 12% |
| Student > Ph. D. Student | 4 | 10% |
| Other | 3 | 7% |
| Student > Doctoral Student | 2 | 5% |
| Other | 7 | 17% |
| Unknown | 7 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 29% |
| Agricultural and Biological Sciences | 11 | 27% |
| Social Sciences | 3 | 7% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Medicine and Dentistry | 2 | 5% |
| Other | 3 | 7% |
| Unknown | 8 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 02 September 2015.
All research outputs
#5,532,261
of 22,776,824 outputs
Outputs from Journal of Biomedical Semantics
#82
of 364 outputs
Outputs of similar age
#73,793
of 352,357 outputs
Outputs of similar age from Journal of Biomedical Semantics
#7
of 12 outputs
Altmetric has tracked 22,776,824 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 76% 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 352,357 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 78% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.