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 http://impactstory.it 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 [...]

At total-impact, we love data. So we get a lot of it, and we show a lot of it, like this: There’s plenty of data here. But we’re missing another thing we love: stories supported by data. The Wall Of Numbers approach tells much, but reveals little. One way to fix this is to Use [...]

In the previous post we assumed we had a list of 100 papers to use as baseline for our percentile calculations. But what papers should be on this list?  It matters: not to brag, but I’m probably a 90th-percentile chess player compared to a reference set of 3rd-graders. The news isn’t so good when I’m [...]

Let’s take the definitions from our last post for a test drive on tweeted percentiles for a hypothetical set of 100 papers, presented here in order of increasing readership with our assigned percentile ranges: 10 papers have 0 tweets (0-9th percentile) 40 papers have 1 tweet (10-49th) 10 papers have 2 tweets (50-59th) 20 papers have 5 tweets (60-79th) 1 paper [...]

Normalizing altmetrics seem by percentiles seems so easy!  And it is. except when it’s not. Our first clue that percentiles have tricky bits is that there is no standard definition for what percentile means.  When you get an 800/800 on your SAT test, the testing board announces you are in the 98th percentile (or whatever) [...]

Total-impact is in the process of incorporating as a non-profit, which means (among other things) we need to form a Board of Directors.  It was been a tough decision, since we are lucky to know tons of qualified people, and there’s not even a standard number of people to pick. After much discussion, though, we [...]

In the last post we talked about the need to give raw counts context on expected impact.  How should this background information be communicated?   Our favourite approach: percentiles.    Try it on for size: Your paper is in the 88th percentile of CiteULike bookmarks, relative to other papers like it.  That tells you something, [...]

How many tweets is a lot? Total-impact is getting pretty good at finding raw numbers of tweets, bookmarks, and other interactions. But these numbers are hard to interpret. Say I’ve got 5 tweets on a paper—am I doing well? To answer that, we must know how much activity we expect on a paper like this [...]