National Cancer Institute scientific production scientometric analysis
Scientometrics analyzes scientific publications through bibliometric and computational techniques, whereby productivity and impact indicators are generated. To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and ra...
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| Veröffentlicht in: | Gaceta médica de México 2020-01, Vol.156 (1), p.4 |
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| container_title | Gaceta médica de México |
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| creator | Ruiz-Coronel, Alí Andrade, José Luis Jiménez Carrillo-Calvet, Humberto |
| description | Scientometrics analyzes scientific publications through bibliometric and computational techniques, whereby productivity and impact indicators are generated.
To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and rank it with regard to other national health institutions.
Using the LabSOM software and the ViBlioSOM methodology based on artificial neural networks, the INCan scientific production indexed in the Web of Science from 2007 to 2017 was analyzed. The multidimensional scientometric profile of the Institute was obtained and compared with that of other national health institutions.
In terms of productivity, INCan ranks fourth among the 10 Mexican public health institutions indexed in the Web of Science; in the normalized impact ranking, it ranks sixth. Although out of 1323 articles 683 (51.62 %) did not receive citations, 11 articles classified as excellent (0.83 %) obtained 24 % of 11,932 citations and, consequently, INCan normalized impact rate showed a mean productivity higher than the world mean.
Multidimensional analysis with the proposed neural network enables obtaining a more reliable and comprehensive absolute and relative institutional scientiometric profile than that derived from measuring isolated variables. |
| doi_str_mv | 10.24875/GMM.M19000315 |
| format | Article |
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To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and rank it with regard to other national health institutions.
Using the LabSOM software and the ViBlioSOM methodology based on artificial neural networks, the INCan scientific production indexed in the Web of Science from 2007 to 2017 was analyzed. The multidimensional scientometric profile of the Institute was obtained and compared with that of other national health institutions.
In terms of productivity, INCan ranks fourth among the 10 Mexican public health institutions indexed in the Web of Science; in the normalized impact ranking, it ranks sixth. Although out of 1323 articles 683 (51.62 %) did not receive citations, 11 articles classified as excellent (0.83 %) obtained 24 % of 11,932 citations and, consequently, INCan normalized impact rate showed a mean productivity higher than the world mean.
Multidimensional analysis with the proposed neural network enables obtaining a more reliable and comprehensive absolute and relative institutional scientiometric profile than that derived from measuring isolated variables.</description><identifier>ISSN: 0016-3813</identifier><identifier>DOI: 10.24875/GMM.M19000315</identifier><identifier>PMID: 32026874</identifier><language>eng</language><publisher>Mexico</publisher><subject>Abstracting and Indexing - statistics & numerical data ; Academies and Institutes - classification ; Academies and Institutes - statistics & numerical data ; Bibliometrics ; Biomedical Research - statistics & numerical data ; Efficiency, Organizational - statistics & numerical data ; Medical Oncology - statistics & numerical data ; Mexico ; Neural Networks, Computer</subject><ispartof>Gaceta médica de México, 2020-01, Vol.156 (1), p.4</ispartof><rights>Copyright: © 2019 Permanyer.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c302t-ce0eee7c5a6966f22b54e943cf50c3e2cc6a175a6a541cc767f744797578a9453</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32026874$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ruiz-Coronel, Alí</creatorcontrib><creatorcontrib>Andrade, José Luis Jiménez</creatorcontrib><creatorcontrib>Carrillo-Calvet, Humberto</creatorcontrib><title>National Cancer Institute scientific production scientometric analysis</title><title>Gaceta médica de México</title><addtitle>Gac Med Mex</addtitle><description>Scientometrics analyzes scientific publications through bibliometric and computational techniques, whereby productivity and impact indicators are generated.
To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and rank it with regard to other national health institutions.
Using the LabSOM software and the ViBlioSOM methodology based on artificial neural networks, the INCan scientific production indexed in the Web of Science from 2007 to 2017 was analyzed. The multidimensional scientometric profile of the Institute was obtained and compared with that of other national health institutions.
In terms of productivity, INCan ranks fourth among the 10 Mexican public health institutions indexed in the Web of Science; in the normalized impact ranking, it ranks sixth. Although out of 1323 articles 683 (51.62 %) did not receive citations, 11 articles classified as excellent (0.83 %) obtained 24 % of 11,932 citations and, consequently, INCan normalized impact rate showed a mean productivity higher than the world mean.
Multidimensional analysis with the proposed neural network enables obtaining a more reliable and comprehensive absolute and relative institutional scientiometric profile than that derived from measuring isolated variables.</description><subject>Abstracting and Indexing - statistics & numerical data</subject><subject>Academies and Institutes - classification</subject><subject>Academies and Institutes - statistics & numerical data</subject><subject>Bibliometrics</subject><subject>Biomedical Research - statistics & numerical data</subject><subject>Efficiency, Organizational - statistics & numerical data</subject><subject>Medical Oncology - statistics & numerical data</subject><subject>Mexico</subject><subject>Neural Networks, Computer</subject><issn>0016-3813</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNo9kLtOw0AQRbcAkRBoKZF_wGH2bZcoIiFSDA3U1mYylhbFdrS7LvL3GPKoRrr3nikOY08c5kIVVr-sqmpe8RIAJNc3bArATS4LLifsPsYfAKENlHdsIgUIU1g1ZcsPl3zfuX22cB1SyNZdTD4NibKInrrkG4_ZIfS7Af-G57RvKYWxcCN5jD4-sNvG7SM9nu-MfS_fvhbv-eZztV68bnKUIFKOBERkUTtTGtMIsdWKSiWx0YCSBKJx3I6t04ojWmMbq5QtrbaFK5WWMzY__cXQxxioqQ_Bty4caw71v4R6lFBfJYzA8wk4DNuWdtf5xYD8BeVYWo8</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Ruiz-Coronel, Alí</creator><creator>Andrade, José Luis Jiménez</creator><creator>Carrillo-Calvet, Humberto</creator><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20200101</creationdate><title>National Cancer Institute scientific production scientometric analysis</title><author>Ruiz-Coronel, Alí ; Andrade, José Luis Jiménez ; Carrillo-Calvet, Humberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c302t-ce0eee7c5a6966f22b54e943cf50c3e2cc6a175a6a541cc767f744797578a9453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Abstracting and Indexing - statistics & numerical data</topic><topic>Academies and Institutes - classification</topic><topic>Academies and Institutes - statistics & numerical data</topic><topic>Bibliometrics</topic><topic>Biomedical Research - statistics & numerical data</topic><topic>Efficiency, Organizational - statistics & numerical data</topic><topic>Medical Oncology - statistics & numerical data</topic><topic>Mexico</topic><topic>Neural Networks, Computer</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ruiz-Coronel, Alí</creatorcontrib><creatorcontrib>Andrade, José Luis Jiménez</creatorcontrib><creatorcontrib>Carrillo-Calvet, Humberto</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><jtitle>Gaceta médica de México</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ruiz-Coronel, Alí</au><au>Andrade, José Luis Jiménez</au><au>Carrillo-Calvet, Humberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>National Cancer Institute scientific production scientometric analysis</atitle><jtitle>Gaceta médica de México</jtitle><addtitle>Gac Med Mex</addtitle><date>2020-01-01</date><risdate>2020</risdate><volume>156</volume><issue>1</issue><spage>4</spage><pages>4-</pages><issn>0016-3813</issn><abstract>Scientometrics analyzes scientific publications through bibliometric and computational techniques, whereby productivity and impact indicators are generated.
To propose a multidimensional methodology in order to obtain the scientometric profile of the National Cancer Institute (INCan), Mexico, and rank it with regard to other national health institutions.
Using the LabSOM software and the ViBlioSOM methodology based on artificial neural networks, the INCan scientific production indexed in the Web of Science from 2007 to 2017 was analyzed. The multidimensional scientometric profile of the Institute was obtained and compared with that of other national health institutions.
In terms of productivity, INCan ranks fourth among the 10 Mexican public health institutions indexed in the Web of Science; in the normalized impact ranking, it ranks sixth. Although out of 1323 articles 683 (51.62 %) did not receive citations, 11 articles classified as excellent (0.83 %) obtained 24 % of 11,932 citations and, consequently, INCan normalized impact rate showed a mean productivity higher than the world mean.
Multidimensional analysis with the proposed neural network enables obtaining a more reliable and comprehensive absolute and relative institutional scientiometric profile than that derived from measuring isolated variables.</abstract><cop>Mexico</cop><pmid>32026874</pmid><doi>10.24875/GMM.M19000315</doi><oa>free_for_read</oa></addata></record> |
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| language | eng |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Abstracting and Indexing - statistics & numerical data Academies and Institutes - classification Academies and Institutes - statistics & numerical data Bibliometrics Biomedical Research - statistics & numerical data Efficiency, Organizational - statistics & numerical data Medical Oncology - statistics & numerical data Mexico Neural Networks, Computer |
| title | National Cancer Institute scientific production scientometric analysis |
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