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Mendeley readers
Attention Score in Context
| Title |
BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains
|
|---|---|
| Published in |
Journal of Biomedical Semantics, February 2014
|
| DOI | 10.1186/2041-1480-5-5 |
| Pubmed ID | |
| Authors |
Toshiaki Katayama, Mark D Wilkinson, Kiyoko F Aoki-Kinoshita, Shuichi Kawashima, Yasunori Yamamoto, Atsuko Yamaguchi, Shinobu Okamoto, Shin Kawano, Jin-Dong Kim, Yue Wang, Hongyan Wu, Yoshinobu Kano, Hiromasa Ono, Hidemasa Bono, Simon Kocbek, Jan Aerts, Yukie Akune, Erick Antezana, Kazuharu Arakawa, Bruno Aranda, Joachim Baran, Jerven Bolleman, Raoul JP Bonnal, Pier Luigi Buttigieg, Matthew P Campbell, Yi-an Chen, Hirokazu Chiba, Peter JA Cock, K Bretonnel Cohen, Alexandru Constantin, Geraint Duck, Michel Dumontier, Takatomo Fujisawa, Toyofumi Fujiwara, Naohisa Goto, Robert Hoehndorf, Yoshinobu Igarashi, Hidetoshi Itaya, Maori Ito, Wataru Iwasaki, Matúš Kalaš, Takeo Katoda, Taehong Kim, Anna Kokubu, Yusuke Komiyama, Masaaki Kotera, Camille Laibe, Hilmar Lapp, Thomas Lütteke, M Scott Marshall, Takaaki Mori, Hiroshi Mori, Mizuki Morita, Katsuhiko Murakami, Mitsuteru Nakao, Hisashi Narimatsu, Hiroyo Nishide, Yosuke Nishimura, Johan Nystrom-Persson, Soichi Ogishima, Yasunobu Okamura, Shujiro Okuda, Kazuki Oshita, Nicki H Packer, Pjotr Prins, Rene Ranzinger, Philippe Rocca-Serra, Susanna Sansone, Hiromichi Sawaki, Sung-Ho Shin, Andrea Splendiani, Francesco Strozzi, Shu Tadaka, Philip Toukach, Ikuo Uchiyama, Masahito Umezaki, Rutger Vos, Patricia L Whetzel, Issaku Yamada, Chisato Yamasaki, Riu Yamashita, William S York, Christian M Zmasek, Shoko Kawamoto, Toshihisa Takagi |
| Abstract |
The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed. |
X Demographics
The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 13% |
| Ghana | 1 | 7% |
| Switzerland | 1 | 7% |
| Unknown | 11 | 73% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 80% |
| Scientists | 3 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 5 | 4% |
| Netherlands | 4 | 4% |
| United States | 4 | 4% |
| France | 2 | 2% |
| Brazil | 2 | 2% |
| South Africa | 1 | <1% |
| United Kingdom | 1 | <1% |
| Germany | 1 | <1% |
| Mexico | 1 | <1% |
| Other | 3 | 3% |
| Unknown | 89 | 79% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 40 | 35% |
| Student > Ph. D. Student | 15 | 13% |
| Other | 11 | 10% |
| Professor > Associate Professor | 11 | 10% |
| Student > Master | 8 | 7% |
| Other | 18 | 16% |
| Unknown | 10 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 40 | 35% |
| Computer Science | 24 | 21% |
| Biochemistry, Genetics and Molecular Biology | 11 | 10% |
| Medicine and Dentistry | 8 | 7% |
| Engineering | 5 | 4% |
| Other | 10 | 9% |
| Unknown | 15 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 04 April 2019.
All research outputs
#2,522,548
of 25,394,764 outputs
Outputs from Journal of Biomedical Semantics
#30
of 368 outputs
Outputs of similar age
#28,727
of 322,779 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 11 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% 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 particularly well, scoring higher than 91% 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 322,779 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 11 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 90% of its contemporaries.