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X Demographics
Mendeley readers
Attention Score in Context
| Title |
BioCarian: search engine for exploratory searches in heterogeneous biological databases
|
|---|---|
| Published in |
BMC Bioinformatics, October 2017
|
| DOI | 10.1186/s12859-017-1840-4 |
| Pubmed ID | |
| Authors |
Nazar Zaki, Chandana Tennakoon |
| Abstract |
There are a large number of biological databases publicly available for scientists in the web. Also, there are many private databases generated in the course of research projects. These databases are in a wide variety of formats. Web standards have evolved in the recent times and semantic web technologies are now available to interconnect diverse and heterogeneous sources of data. Therefore, integration and querying of biological databases can be facilitated by techniques used in semantic web. Heterogeneous databases can be converted into Resource Description Format (RDF) and queried using SPARQL language. Searching for exact queries in these databases is trivial. However, exploratory searches need customized solutions, especially when multiple databases are involved. This process is cumbersome and time consuming for those without a sufficient background in computer science. In this context, a search engine facilitating exploratory searches of databases would be of great help to the scientific community. We present BioCarian, an efficient and user-friendly search engine for performing exploratory searches on biological databases. The search engine is an interface for SPARQL queries over RDF databases. We note that many of the databases can be converted to tabular form. We first convert the tabular databases to RDF. The search engine provides a graphical interface based on facets to explore the converted databases. The facet interface is more advanced than conventional facets. It allows complex queries to be constructed, and have additional features like ranking of facet values based on several criteria, visually indicating the relevance of a facet value and presenting the most important facet values when a large number of choices are available. For the advanced users, SPARQL queries can be run directly on the databases. Using this feature, users will be able to incorporate federated searches of SPARQL endpoints. We used the search engine to do an exploratory search on previously published viral integration data and were able to deduce the main conclusions of the original publication. BioCarian is accessible via http://www.biocarian.com . We have developed a search engine to explore RDF databases that can be used by both novice and advanced users. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 40% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 80% |
| Members of the public | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Postgraduate | 4 | 14% |
| Professor | 4 | 14% |
| Student > Bachelor | 4 | 14% |
| Student > Ph. D. Student | 2 | 7% |
| Researcher | 2 | 7% |
| Other | 6 | 21% |
| Unknown | 7 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 28% |
| Agricultural and Biological Sciences | 5 | 17% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Unspecified | 1 | 3% |
| Social Sciences | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 8 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 October 2017.
All research outputs
#13,515,676
of 23,318,744 outputs
Outputs from BMC Bioinformatics
#4,092
of 7,384 outputs
Outputs of similar age
#158,893
of 323,712 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 107 outputs
Altmetric has tracked 23,318,744 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,384 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 323,712 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.