iCite User Guide


iCite is a powerful web application that provides a panel of bibliometric information for scientific publications within a defined analysis group (where an analysis group can consist of a single article or a very large group of articles). The data produced by iCite can be downloaded as a customized report from the dashboard and could be used to understand the influence of articles within an analysis group. An example application for iCite might be to compare how the influence of a portfolio of articles compares to the remaining articles that come out of grants funded by the NIH, and characterize the portfolio's translation into clinical science.

iCite is organized into three modules:

  • Influence: Delivers metrics of scientific influence, field-adjusted and benchmarked to NIH publications as the baseline
  • Translation: Measures how Human, Animal, or Molecular/Cellular Biology-oriented each paper is; tracks and predicts citation by clinical articles
  • Citations: Disseminates link-level, public-domain citation data from the NIH Open Citation Collection

In addition to traditional citation metrics, iCite provides metrics and visualizations developed by the NIH Office of Portfolio Analysis:

  • The Relative Citation Ratio, presented in the Influence module, is a metric developed within the Office of Portfolio Analysis (OPA) that represents a citation-based measure of scientific influence of one or more articles, described in detail in a publication. It is calculated as the cites/year of each paper, normalized to the citations per year received by NIH-funded papers in the same field and year. Fields are sampled for each article by using its co-citation network. This benchmarking process ensures that a paper with an RCR of 1.0 has received the same number of cites/year as the median NIH-funded paper in its field, while a paper with an RCR of 2.0 has received twice as many cites/year as the median NIH-funded paper in its field. Diagram of a co-citation network
  • Human, Animal, and Molecular/Cellular Biology scores, shown in the Translation module, are calculated for each article using Medical Subject Heading terms assigned by the National Library of Medicine. These represent the content focus of the paper along these dimensions. For example, more human-focused papers have higher Human scores, while cell biology-focused papers will have higher Molecular/Cellular Biology scores.
  • In the Translation module, articles are plotted in "translational space" on the Triangle of Biomedicine. Papers with high Human scores will appear closer to the Human vertex, while those with high Animal and Molecular/Cellular Biology scores will appear close to those respective vertices. Triangle of biomedicine visualization
  • Clinical Citations to each paper are shown, where they exist, in the Translation module. This measure can be used to track the flow of knowledge into clinical research.
  • Approximate Potential to Translate scores represent a machine-learning based prediction of the likelihood that a paper will eventually receive a clinical citation, described in detail in a publication. The predictions are based on the type of paper this is, as well as the type and rate of citations it has received.
  • Citation links in the Open Citation Collection, shown in the Citations module, are presently sourced from CrossRef, MedLine, PubMed Central, and Entrez. In addition to these, references are extracted and resolved with a machine learning pipeline from the full text of papers with open access version online, described in detail in a publication.


What browsers are supported?

We seek to support all modern browsers and platforms. Internet Explorer is supported by Microsoft for backwards compatibility but recommends using Microsoft Edge as the default browser on Windows. For this reason, Internet Explorer is not supported by iCite, and not all features will work. If you experience a problem using the current version of any of the following, please let us know.

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Feedback and questions can be sent via email to icite@mail.nih.gov