About this series: Through August and September we are publishing a series of posts authored by some of the team at Altmetric, a data science company who provide such attention data to authors, publishers, institutions and funders. The posts will discuss, amongst other topics, using altmetrics within your C.V.s and grant applications, and how journal editors can make use of the tools. Learn more about the series by starting with the first post here. This particular post is authored by guest bloggers Cat Chimes and Fran Davies.
Altmetric provide data for researchers, publishers, universities and funders. We track a wide variety of non-traditional sources, including mainstream news outlets, public policy documents, online reference managers, blogs, social media networks such as Facebook, Twitter, Google + and Sina Weibo, research highlight platforms, post-publication peer-review forums, and Wikipedia, looking for mentions and shares of published research.
A full list of our sources can be found here. All of the attention data we collate from this carefully curated list of sources is then disambiguated for each piece of research (sometimes the same article is hosted many places) and displayed on our colourful details pages (see below).
For each research output we assign a score of attention and create a unique donut visualisation in order to provide an easy-to-read summary of the online attention it has received.
The score is derived from an algorithm, and represents a weighted count of all the attention data we’ve picked up for that research output. For more detailed information about the score and how it’s calculated, please see this blog post. It’s important to remember that the score is only an indicator of the amount of attention a research output has received. It can’t tell you anything about the quality of the research output, or the researcher, and you need to click through to the original mentions on the details page to understand why a particular piece of research has got a lot of attention.
The Altmetric donut, which can be found on the articles pages of many publisher sites, including SpringerLink, changes colour depending on the sources we have picked up attention for the research from:
So, for example, if the donut is mostly light blue it probably means we have seen a lot of Twitter or LinkedIn attention for that research. Lots of red would indicate a high percentage of mainstream media coverage, and a purple or grey streak would indicate mentions in public policy documents and on Wikipedia.
The details pages are visual representations of who has been sharing and discussing a piece of research, what they’ve been saying about it, and where that data came from. Accessible via the colourful donut visualisation displayed on many publisher and institutional repository sites, you can click on the tabs on the details pages to filter the data by source, and click through to the original mentions.
For example, we can see that this research output has been mentioned in news outlets, blogs, on social media, and in a policy document.
The maps on the summary tab of each details page can also give you a geographical breakdown of Twitter and Mendeley readers who have shared or saved the work – simply hover over a country to see how much of the attention from these sources came from that location. We also text mine people’s Twitter bios to determine how much of the Twitter attention for a research output is from the academic community, and how much is from members of the general public – you can find this in the demographic breakdown section.
Sometimes the score can seem a little like an arbitrary number, so it’s useful to be able to contextualise the data. Looking at the “score in context” section on the summary tab of the details page will tell you how highly the research output has scored in relation to all the research outputs tracked by Altmetric. You can also see how highly the research output has scored in the context of the journal it was published in, and in comparison to other outputs that were published at around the same time. This data is useful for benchmarking and “normalising” the data as well as for finding anomalies. This research output has done particularly well and is the highest-scoring Altmetric article from the European Journal of Nutrition.
We hope this post has provided a brief but informative introduction to altmetrics, and Altmetric. Over the next few months, we’ll be publishing some more posts on the different use cases for Altmetric data, and on the ever-changing landscape of altmetrics in general. The best way to get started with Altmetric data is to download the free bookmarklet, or click on the Altmetric donuts when you see them on publisher platforms.
If you have any questions please leave them in the comments below and we’ll get back to you – coming up in our next post we’ll be providing some tips and tricks on using altmetrics data in your CV and grant applications, so stay tuned!