| Title |
Cited references and Medical Subject Headings (MeSH) as two different knowledge representations: clustering and mappings at the paper level
|
|---|---|
| Published in |
Scientometrics, October 2016
|
| DOI | 10.1007/s11192-016-2119-7 |
| Pubmed ID | |
| Authors |
Loet Leydesdorff, Jordan A. Comins, Aaron A. Sorensen, Lutz Bornmann, Iina Hellsten |
| Abstract |
For the biomedical sciences, the Medical Subject Headings (MeSH) make available a rich feature which cannot currently be merged properly with widely used citing/cited data. Here, we provide methods and routines that make MeSH terms amenable to broader usage in the study of science indicators: using Web-of-Science (WoS) data, one can generate the matrix of citing versus cited documents; using PubMed/MEDLINE data, a matrix of the citing documents versus MeSH terms can be generated analogously. The two matrices can also be reorganized into a 2-mode matrix of MeSH terms versus cited references. Using the abbreviated journal names in the references, one can, for example, address the question whether MeSH terms can be used as an alternative to WoS Subject Categories for the purpose of normalizing citation data. We explore the applicability of the routines in the case of a research program about the amyloid cascade hypothesis in Alzheimer's disease. One conclusion is that referenced journals provide archival structures, whereas MeSH terms indicate mainly variation (including novelty) at the research front. Furthermore, we explore the option of using the citing/cited matrix for main-path analysis as a by-product of the software. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| Spain | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Sweden | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 71 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 18% |
| Student > Ph. D. Student | 11 | 15% |
| Librarian | 7 | 9% |
| Student > Bachelor | 6 | 8% |
| Professor | 4 | 5% |
| Other | 12 | 16% |
| Unknown | 21 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 10 | 14% |
| Social Sciences | 8 | 11% |
| Medicine and Dentistry | 7 | 9% |
| Agricultural and Biological Sciences | 6 | 8% |
| Business, Management and Accounting | 5 | 7% |
| Other | 13 | 18% |
| Unknown | 25 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 06 January 2017.
All research outputs
#14,273,624
of 22,890,496 outputs
Outputs from Scientometrics
#1,856
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Outputs of similar age
#182,552
of 320,638 outputs
Outputs of similar age from Scientometrics
#35
of 51 outputs
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