Describing the Difference Research Has Made to the World

Here is an interesting new blog post by Heather Piwowar about the different ways research can impact the world and the importance of telling them apart. Good food for thought as we think about ways to help researchers analyze how people are reading, bookmarking, sharing, discussing, and citing research online.

I think Anirvan made a great point to think about ways how we can integrate “altmetrics” data with UCSF Profiles. Some of the metrics mentioned below may be a great starting point.

Here’s what Heather writes:

Figuring out the flavors of science impact. CC-BY-NC by maniacyak on flickr

We have clustered all PLoS ONE papers published before 2010 using five metrics that are fairly distinct from one another: HTML article page views, number of Mendeley reader bookmarks, Faculty of 1000 score, Web of Science citation counts as of 2011, and a combo count of twitter, Facebook, delicious, and blog discussion.

We normalized the metrics to account for differences due to publication date and service popularity, transformed them, and standardized to a common scale. We tried lots of cluster possibilities; it seems that five clusters fit this particular sample the best.

Here is a taste of the clusters we found.  Bright blue in the figure below means that the metric has high values in that cluster, dark grey means the metric doesn’t have much activity.  For example, papers in “flavour E” in the first column have fairly low scores on all five metrics, whereas papers in “flavour C” on the far right have a lot of HTML page views and Sharing (blog posts, tweeting, facebook clicking, etc) activity. View image and read on

Further reading:

Crowdsourcing the Analysis and Impact of Scholarly Tweets

“Twitter is one of the fastest tools to discover newly published scholarly papers”, Martin Fenner wrote in one of his earlier posts. Now Fenner and Euan Adie finished the first phase of an interesting new experiment, the CrowdoMeter project.

In the last 2 months, they used crowdsourcing to analyze the semantic content of almost 500 tweets linking to scholarly papers (953 classifications by 105 users for 467 tweets). Their preliminary results show: 

  • 3 predominant subject areas: Medicine and Health, Life Sciences, and Social Sciences and Economics.
  • Most tweets (88%) discussed the papers, 10% were in agreement, 3% disagreed.
  • Most papers are not tweeted by their authors or publishers

In his recent guest post on Impact of Social Sciences, Fenner makes the argument that “social media, and Twitter in particular, provide almost instant, relevant recommendations as opposed to traditional citations.

A few years from now the ‘personalized journal’ will have replaced the traditional journal as the primary means to discover new scholarly papers with impact to our work.

What is still missing are better tools that integrate social media with scholarly content, in particular personalized recommendations based on the content you are interested in (your Mendeley or CiteULike library are a good approximation) and the people you follow on Twitter and other social media.

Fenner’s view is also based on Gunther Eysenbach’s study from 2011 that showed “highly tweeted papers were more likely to become highly cited (but the numbers were to small for any firm conclusions; 12 out of 286 papers were highly tweeted)”.

Fenner and Adie are using to track the scholarly impact of your research. Other tools – some of which we wrote about – include  ReaderMeterTotal ImpactPLoS Article-Level Metrics, and ScienceCard.