Article-level metrics incorporate traditional metrics, such as number of citations, and new metrics ('altmetrics'), such as number of downloads, to quantify the impact of an article. Article-level metrics provide a more complete picture of how an individual article is being discussed, shared and used within and beyond the scholarly community.
Researchers continue to investigate methods for using citation rates to measure article influence. Relative Citation Ratio (RCR), an algorithm developed by the National Institutes of Health, utilizes an expected citation rate, based on performance of articles in the same field, to quantify the influence of an article.
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The preceding statements were adapted from SPARC: Article Level Metrics, PLoS: A Comprehensive Assessment of Impact with Article-Level Metrics, and 'Relative Citation Ratio (RCR): A New Metric that Uses Citation Rates to Measure Influence at the Article Level'.
Article-level metrics for article published in Nature
Several databases and publishers provide article-level metrics. These metrics depend on the content that is searched. For example, the number of citations that an article has received in Web of Science will likely be different than the number it has received in either Scopus or Google Scholar.
Analysts at the National Institutes of Health (NIH) have developed an alternative measure of article influence, the Relative Citation Ratio (RCR). RCR can be used to compare the performance of articles in the same field.
RCR is calculated by dividing article citation rate (ACR) by an expected citation rate (ECR), which is based on the number of citations that similar articles in the same field have received.
RCR = ACR/ECR
iCite can be used to calculate RCRs for articles in PubMed.
Source: Hutchins BI, Yuan X, Anderson JM, Santangelo GM (2016) Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level. PLoS Biol 14(9): e1002541. doi:10.1371/journal.pbio.1002541