Category: Data

Bibliometrics Finale: Age and Size

Today, we use our H-index Benchmarking of Academic Research (HiBAR) to look at the relationship between institutional characteristics and H-index scores. We’ve talked a lot this week about the positive correlation between a researcher’s age and his or her H-index score. But there’s another correlation to watch for: normalized institutional average H-index scores and institutional age. Check it out: Normalized Institutional Average H-Index Score as a Function of Institutional Age The result isn’t wholly clear cut: there are a lot

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Back to Bibliometrics

About two months ago, we did a series on bibliometrics (if you missed it the first time out, you can catch up here), and promised we’d be back shortly with some new results. Well, we’ll those results will be released Wednesday, and we think they’re so interesting that we’ll be spending all week telling you about them. For bibliometrics to be really useful, they need to (a) be able to capture information about both productivity and impact, (b) be easy

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Getting a Global Common Data Set Off the Ground

How could a global common data set (CDS) come into existence? Here are a few considerations: In addition to improving accuracy and comparability, common data sets come into existence for two reasons. The first is to save money by limiting the number of data requests flying in from every yahoo wanting to create his or her own ranking. The second, less obvious reason, is that the creation of an open-access data platform lowers the barriers to entry for new rankers.

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What Would a Global Common Data Set Look Like?

The discussion Kris Olds and I started a couple of days ago (see here and here) about a global common data set seems to have generated quite a bit of discussion, so I thought I would flesh out two sets of thoughts regarding what a Global CDS would need to look like – today: content; tomorrow: governance. Start with first principles on content: it needs to be common enough that most institutions around the world are able to produce it. That

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Counting Faculty, Counting Students

Though amateur higher education statisticians are addicted to it, there is virtually no statistic less useful than the student-staff ratio. There are basically two reasons why this is the case. The first is that not all students are alike. Some are full-time, some are part-time. This problem is reasonably easy to solve by creating a method for calculating full-time equivalency. But for this, the number of credit-hours students take must be transparent. The second is that not all professors are

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