Introducing ImpactStory

ImpactStory has launched!  We’ve refocused and renamed Total-Impact: this first release of ImpactStory is a quantum step forward, debuting badges, normalization, and categorization.

To try out ImpactStory, start by visiting and point to the scholarly products you’ve made.  Articles can be easily imported from Google Scholar Profiles, DOIs, and PubMed IDs.  We also have importers for software on GitHub, presentations on SlideShare, and datasets on Dryad (and we’ve got more importers on the way).

ImpactStory searches over a dozen Web APIs to learn where your stuff is making an impact. Instead of a Wall Of Numbers, we categorize your impacts along two dimensions: audience (scholars or the public) and type of engagement with research (view, discuss, save, cite, and recommend).

As you drill into the details of an item in your report, you can see a graph of the percentile score for each metric compared to a baseline.  In the case of articles, the baseline is “articles indexed in Web of Science that year.” If your 2009 paper has 17 Mendeley readers, for example, that puts you in the 87th-98th percentile of all WoS-indexed articles published in 2009 (we report percentiles as a range expressing the 95% confidence interval). Since it’s above the 75th percentile, the article is also tagged with a “highly saved by scholars” badge. Scanning the badges helps you get a sense of your collection’s overall strengths, while also letting  you easily spot success stories.

Interested?  Have a look at this sample collection, or even better, go create your own report!

We’re excited for folks to try out ImpactStory, and excited to get feedback; it’s a beta release, and want to listen to the community as we prioritize new features. Working together, we can build something that helps reseachers tell data driven stories that push us beyond the Impact Factor and beyond the article.

For more information:

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