This information is over 2 years old. Information was current at time of publication.{"id":12418,"date":"2019-12-17T11:54:06","date_gmt":"2019-12-17T15:54:06","guid":{"rendered":"https:\/\/info.hsls.pitt.edu\/updatereport\/?p=12418"},"modified":"2020-01-08T11:52:20","modified_gmt":"2020-01-08T15:52:20","slug":"assess-article-influence-and-citation-data-with-nih-icite","status":"publish","type":"post","link":"https:\/\/info.hsls.pitt.edu\/updatereport\/assess-article-influence-and-citation-data-with-nih-icite\/","title":{"rendered":"Assess Article Influence and Citation Data with NIH iCite"},"content":{"rendered":"

iCite<\/em><\/a>, from the National Institutes of Health (NIH), is a powerful web-based tool that provides bibliometric information for journal articles included in PubMed. Users can analyze single or multiple articles via three modules:<\/p>\n

1. Influence<\/em>:<\/p>\n

\"RCR<\/a>
Visualization from iCite Influence<\/em><\/a><\/figcaption><\/figure>\n

Delivers scientific influence metrics using the Relative Citation Ratio (RCR). Developed by the NIH, the RCR is a metric that represents the citations\/year of a paper, normalized to the citations\/year received by NIH-funded papers in the same field and year. For example, a paper with an RCR of 1.0 has the same number of citations\/year as the median NIH-funded paper in its field, whereas a paper with an RCR of 2.0 has twice as many citations\/year. For more information about how RCR is calculated, read these two articles by B. Ian Hutchins, \u201cRelative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level<\/a>\u201d (2016) and \u201cAdditional Support for RCR: A Validated Article-level Measure of Scientific Influence<\/a>\u201d (2017).<\/p>\n

2. Translation<\/em>:<\/p>\n

\"Density<\/a>
Visualization from iCite Translation<\/em><\/a><\/figcaption><\/figure>\n

Measures a paper\u2019s research subject orientation (human, animal, or molecular\/cellular biology) to track and predict future citations by clinical articles using the Approximate Potential to Translate (APT), which is a machine learning-based estimate of the likelihood that a paper will later be cited in clinical trials\/guidelines. Read more about APT and predicting translational progress in biomedical research in B. Ian Hutchins’ article, \u201cPredicting Translational Progress in Biomedical Research<\/a>\u201d (2019).<\/p>\n

 <\/p>\n

3. Open Cites<\/em>:<\/p>\n

\"Pubs<\/a>
Visualization from iCite Open Cites<\/em><\/a><\/figcaption><\/figure>\n

Disseminates link-level, public-domain citation data from the NIH Open Citation Collection (NIH-OCC). The NIH-OCC is a public access database for biomedical research aggregating open citation data gathered from MEDLINE, PubMed Central, and CrossRef. Read more about the NIH-OCC in B. Ian Hutchins’ article, \u201cThe NIH Open Citation Collection: A Public Access, Broad Coverage Resource<\/a>\u201d (2019).<\/p>\n

 <\/p>\n

 <\/p>\n

All three modules are analyzed simultaneously with a given search, with multiple options for the search query: (1) Search PubMed by author name, title, MeSH keyword, etc., (2) Upload a spreadsheet of PubMed Identifiers (PMIDs), or (3) Input a list of PMIDs. A maximum of 10,000 PMIDs may be queried at a time, and the iCite database coverage is limited to 1980-current.<\/p>\n

Results are displayed in a tabbed format by module, with customization options that include limiting by publication year(s) and\/or by article type (research only, clinical only, and\/or cited by clinical articles only).\u00a0 All relevant articles (or the most recent 10,000) are also listed, including PMID links and RCR scores.\u00a0 Searches may be re-analyzed using the options \u201cCiting Papers\u201d (run a new analysis on the papers that cite these articles) and \u201cReferenced Papers\u201d (run a new analysis on the papers that are referenced by these articles).<\/p>\n

~ Carrie Iwema<\/p>\n","protected":false},"excerpt":{"rendered":"

iCite, from the National Institutes of Health (NIH), is a powerful web-based tool that provides bibliometric information for journal articles included in PubMed. Users can analyze single or multiple articles via three modules: 1. Influence: Delivers scientific influence metrics using the Relative Citation Ratio (RCR). Developed by the NIH, the RCR is a metric that […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"issue-archives","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[145],"tags":[-1],"class_list":["post-12418","post","type-post","status-publish","format-standard","hentry","category-january-2020","avhec_catgroup-issue-archives"],"_links":{"self":[{"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/posts\/12418","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/comments?post=12418"}],"version-history":[{"count":20,"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/posts\/12418\/revisions"}],"predecessor-version":[{"id":12459,"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/posts\/12418\/revisions\/12459"}],"wp:attachment":[{"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/media?parent=12418"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/categories?post=12418"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/info.hsls.pitt.edu\/updatereport\/wp-json\/wp\/v2\/tags?post=12418"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}