|Nature Blogs||PMC Europe Citations|
|ScienceSeeker||PMC Europe Database Citations|
Views and downloads of a research article in PubMed Central are collected. (Page views and PDF downloads at each publisher's site is independently aggregated and may be available as a private source.)
Online usage data should be interpreted with caution. In general, it is dependent on the age of the article and its subject area. Robot activity may also impact usage data. COUNTER 3 maintains a defined list of robots that should be excluded in each publisher's usage reports.
PMC provides monthly reports of the prior month's usage, and so there may be a lag in the display of data up to one month's time. PMC statistics are COUNTER 3- compliant to the extent that they exclude internal use and crawlers/bots, and do not count duplicate requests for HTML pages or PDFs that are made within the limits specified by the standard. They are not compliant in that NLM does not provide usage data by specific IP, user, or organization. PMC began to make their usage data available to publishers upon request at various dates, and so articles published prior that time may not have PMC data .
Citation data is computed by third-party citation measuring services. The following public indices are open and available to all: PubMed Central, Europe PMC and CrossRef. Each displays a single number (article citations) and links to a landing page containing information related to the citing articles.
Citation counts will vary between services, as each draws upon a different database of journals that they index. To attain the most comprehensive view of citations, consult all lists and "de-duplicate" the results. If there are missing citations from one of the sources, please contact the appropriate vendor for more information.
CrossRef citations are provided by the CrossRef Cited-by Linking service. The data are limited to journals participating in CrossRef's Cited-by Linking service.
Online reference management services - CiteULike and Mendeley - have become common ways for researchers to bookmark papers, collate references, and share sources with their community. The ALM application captures the number of times the research article in question has been bookmarked by individual researchers or research groups. Each is linked to a landing page that allows users to navigate to other services such as subject tags and other bookmarked articles.
The CiteULike landing page captures total number of individuals and groups who have added the article to their CiteULike bookmarking account. There may be multiple users attached to each posting on this landing page, and they are found hyperlinked by the article listing. For example, the listing with the description: "posted by UserX along with 2 people and 1 group" will have a total count of 4. The Mendeley count is an aggregate of the number of individuals and groups who have added the article.
With the establishment of a networked landscape in research, researchers today employ a host of tools from which to share and discuss each other's work. The ALM application has integrated the leading channels to offer a more comprehensive view of the discussions surrounding a paper after publication.
Blog posts serve as a common dissemination channel for articles published in PLOS journals. To identify and link to them from each article, third party blog aggregators are used: Nature Blogs, ScienceSeeker, ResearchBlogging, Wordpress.com and OpenEdition. For each service, the count reflects the number of blog articles which have discussed the paper and depends on the method of aggregation specific to each service. To attain the most comprehensive picture of how many (and which) blogs cite the article in question, consult all the constitutive lists and de-duplicate repeated entries.
The system also tracks the dissemination activity of articles through Twitter, Facebook, and Reddit. Given the ease and scope of digital propagation, researchers increasingly employ this social channel to recommend and discuss articles. This activity thus represents interest in the article, in a similar manner as usage data and provides insight into the reach of the article.
- Twitter: a social networking and microblogging service. The application tracks the sharing of articles on Twitter and provides the total number of tweets.
- Facebook: the largest social network. The Facebook count reflects the aggregate number of Facebook Likes, "shares," "posts," and "comments" on an article.
- Reddit: a user-generated news links site. The system aggregates the number of posts and comments. It also collects the Reddit score based on the number of upvotes and downvotes for the post, which contains the article mention.
PLOS also collects data for articles discussed in Wikipedia encyclopedia entries. We include references found across the 25 largest Wikipedia language sites and Wikimedia Commons.
Social media: appropriate use of the social network data types will aid the discovery of related papers as well as reveal the article's readership reach. In collaboration with Cameron Neylon, this informational video discusses the power of such metrics as a research and discovery tool.
Blogs: in many cases, blog authors do not reference the article in a way that allows for automated aggregation, and the aggregating services we link to cover only a selection of all possible blogs. Therefore, there will potentially be many more blogs about an article than these aggregators are able to identify.
Currently the recommendations service included in the ALM application is a private source, F1000Prime, which requires a contract. See the respective publisher or provider specific pages for details on this source.