| Title |
SciData: a data model and ontology for semantic representation of scientific data
|
|---|---|
| Published in |
Journal of Cheminformatics, October 2016
|
| DOI | 10.1186/s13321-016-0168-9 |
| Pubmed ID | |
| Authors |
Stuart J. Chalk |
| Abstract |
With the move toward global, Internet enabled science there is an inherent need to capture, store, aggregate and search scientific data across a large corpus of heterogeneous data silos. As a result, standards development is needed to create an infrastructure capable of representing the diverse nature of scientific data. This paper describes a fundamental data model for scientific data that can be applied to data currently stored in any format, and an associated ontology that affords semantic representation of the structure of scientific data (and its metadata), upon which discipline specific semantics can be applied. Application of this data model to experimental and computational chemistry data are presented, implemented using JavaScript Object Notation for Linked Data. Full examples are available at the project website (Chalk in SciData: a scientific data model. http://stuchalk.github.io/scidata/, 2016). |
X Demographics
The data shown below were collected from the profiles of 12 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 | 4 | 33% |
| United Kingdom | 2 | 17% |
| Sweden | 1 | 8% |
| India | 1 | 8% |
| Mexico | 1 | 8% |
| Unknown | 3 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 42% |
| Scientists | 5 | 42% |
| Science communicators (journalists, bloggers, editors) | 2 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 1% |
| Unknown | 81 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 20% |
| Researcher | 16 | 20% |
| Student > Master | 8 | 10% |
| Other | 6 | 7% |
| Student > Bachelor | 4 | 5% |
| Other | 16 | 20% |
| Unknown | 16 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 21 | 26% |
| Chemistry | 11 | 13% |
| Agricultural and Biological Sciences | 8 | 10% |
| Biochemistry, Genetics and Molecular Biology | 4 | 5% |
| Engineering | 4 | 5% |
| Other | 17 | 21% |
| Unknown | 17 | 21% |
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 10 March 2017.
All research outputs
#4,341,706
of 24,143,470 outputs
Outputs from Journal of Cheminformatics
#419
of 891 outputs
Outputs of similar age
#69,082
of 324,450 outputs
Outputs of similar age from Journal of Cheminformatics
#11
of 26 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 891 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has gotten more attention than average, scoring higher than 53% of its peers.
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We're also able to compare this research output to 26 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 61% of its contemporaries.