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A bibliometric analysis on tobacco regulation investigators
BACKGROUND: To facilitate the implementation of the Family Smoking Prevention and Tobacco Control Act of 2009, the Federal Drug Agency (FDA) Center for Tobacco Products (CTP) has identified research priorities under the umbrella of tobacco regulatory science (TRS). As a newly integrated field, the c...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4432889/ https://www.ncbi.nlm.nih.gov/pubmed/25984237 http://dx.doi.org/10.1186/s13040-015-0043-7 |
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author | Li, Dingcheng Okamoto, Janet Liu, Hongfang Leischow, Scott |
author_facet | Li, Dingcheng Okamoto, Janet Liu, Hongfang Leischow, Scott |
author_sort | Li, Dingcheng |
collection | PubMed |
description | BACKGROUND: To facilitate the implementation of the Family Smoking Prevention and Tobacco Control Act of 2009, the Federal Drug Agency (FDA) Center for Tobacco Products (CTP) has identified research priorities under the umbrella of tobacco regulatory science (TRS). As a newly integrated field, the current boundaries and landscape of TRS research are in need of definition. In this work, we conducted a bibliometric study of TRS research by applying author topic modeling (ATM) on MEDLINE citations published by currently-funded TRS principle investigators (PIs). RESULTS: We compared topics generated with ATM on dataset collected with TRS PIs and topics generated with ATM on dataset collected with a TRS keyword list. It is found that all those topics show a good alignment with FDA’s funding protocols. More interestingly, we can see clear interactive relationships among PIs and between PIs and topics. Based on those interactions, we can discover how diverse each PI is, how productive they are, which topics are more popular and what main components each topic involves. Temporal trend analysis of key words shows the significant evaluation in four prime TRS areas. CONCLUSIONS: The results show that ATM can efficiently group articles into discriminative categories without any supervision. This indicates that we may incorporate ATM into author identification systems to infer the identity of an author of articles using topics generated by the model. It can also be useful to grantees and funding administrators in suggesting potential collaborators or identifying those that share common research interests for data harmonization or other purposes. The incorporation of temporal analysis can be employed to assess the change over time in TRS as new projects are funded and the extent to which new research reflects the funding priorities of the FDA. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-015-0043-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4432889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44328892015-05-16 A bibliometric analysis on tobacco regulation investigators Li, Dingcheng Okamoto, Janet Liu, Hongfang Leischow, Scott BioData Min Research BACKGROUND: To facilitate the implementation of the Family Smoking Prevention and Tobacco Control Act of 2009, the Federal Drug Agency (FDA) Center for Tobacco Products (CTP) has identified research priorities under the umbrella of tobacco regulatory science (TRS). As a newly integrated field, the current boundaries and landscape of TRS research are in need of definition. In this work, we conducted a bibliometric study of TRS research by applying author topic modeling (ATM) on MEDLINE citations published by currently-funded TRS principle investigators (PIs). RESULTS: We compared topics generated with ATM on dataset collected with TRS PIs and topics generated with ATM on dataset collected with a TRS keyword list. It is found that all those topics show a good alignment with FDA’s funding protocols. More interestingly, we can see clear interactive relationships among PIs and between PIs and topics. Based on those interactions, we can discover how diverse each PI is, how productive they are, which topics are more popular and what main components each topic involves. Temporal trend analysis of key words shows the significant evaluation in four prime TRS areas. CONCLUSIONS: The results show that ATM can efficiently group articles into discriminative categories without any supervision. This indicates that we may incorporate ATM into author identification systems to infer the identity of an author of articles using topics generated by the model. It can also be useful to grantees and funding administrators in suggesting potential collaborators or identifying those that share common research interests for data harmonization or other purposes. The incorporation of temporal analysis can be employed to assess the change over time in TRS as new projects are funded and the extent to which new research reflects the funding priorities of the FDA. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-015-0043-7) contains supplementary material, which is available to authorized users. BioMed Central 2015-03-21 /pmc/articles/PMC4432889/ /pubmed/25984237 http://dx.doi.org/10.1186/s13040-015-0043-7 Text en © Li et al.; licensee BioMed Central. 2015 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Li, Dingcheng Okamoto, Janet Liu, Hongfang Leischow, Scott A bibliometric analysis on tobacco regulation investigators |
title | A bibliometric analysis on tobacco regulation investigators |
title_full | A bibliometric analysis on tobacco regulation investigators |
title_fullStr | A bibliometric analysis on tobacco regulation investigators |
title_full_unstemmed | A bibliometric analysis on tobacco regulation investigators |
title_short | A bibliometric analysis on tobacco regulation investigators |
title_sort | bibliometric analysis on tobacco regulation investigators |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4432889/ https://www.ncbi.nlm.nih.gov/pubmed/25984237 http://dx.doi.org/10.1186/s13040-015-0043-7 |
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