Multi-query center: Aggregation

Note: This tutorial was originally written for Publish or Perish version 4 and all screenshots come from this version. Publish or Perish versions 5 and 6 show the multi-query center at the top of every screen for easier access. Its basic functions are similar to PoP4, however.

The Publish or Perish Multi-query center also makes it possible to aggregate queries at a higher level of aggregation in just two simple steps and less than 20 seconds. There might be a number of key scenarios in which you might want to aggregate queries

  • Aggregate academic metrics across a research group or department. Calculating metrics at the level of a research group, department, or school after searching for individual academics in that entity. As Google Scholar does not have a reliable affiliation search, this is the only way to assess the collective performance of a group of researchers.
  • Aggregate article metrics across a research group or department. Calculating metrics at the level of a research group, department, or school after searching for individual articles published by that entity.
  • Metrics for a collection of articles. Calculating the metrics for any collection of articles of interest. For instance one might want to assess the collective impact of a specific set of articles in a particular field of research.
  • Combine results of split-year searches for one journal. Combining the results of a number of identical journal queries split up by year to address the Google Scholar 1,000 results limit. This would allow you to get a comprehensive record of publications in most journals as few have more than 1,000 publications a year.
  • Combine results of different journal searches. Combining the results of a number of different queries that could not be combined into a single query because of Google Scholar limitations in field size. For instance one might want to search for articles on a specific topic in a range of journals, but Google Scholar’s journal field only allows a certain number of characters.

Worked example: Institutional aggregation

Let’s assume we want to assess the performance of the Department of Management & Marketing at the University of Melbourne (my previous employer). The screenshot below show the query results for all 15 full professors in the department, sorted by h-index. We then select all queries and right-click to get the pop-up menu (see Using PoP). Select Save as CSV… and give the file a meaningful name.

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Subsequently, simply re-import this file into PoP. The screenshot below shows the resulting data, sorted by hI,annual. Combined, the department’s professors have an h-index of 119, and more than 56,000 citations.

Find the Department's most cited articles

Having the complete list of the Department’s academic articles also allows one to assess which are the Department’s most highly cited articles, both overall and per year, which journals its academics publish in etc.

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Worked example: Aggregation of collection of articles

This screenshot shows the results of an aggregation of 230 articles related to the role of language in international business. We used this for a review article on this topic.

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Worked example: Journal aggregation

A journal like Scientometrics publishes a large number of articles per year. Hence a search without year limitations will always run into the Google Scholar limitation of 1,000 results. Thus low cited articles will not show up in your searches. If you wanted to have a complete record of articles published in Scientometrics for the last 10 years, you could run five searches for 2006-2007, 2008-2009, 2010-2011, 2012-2013 and 2014-2015 and then combine them into one file (see screenshot).

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This allows you to compare the more than 2,300 papers published in the journal in the last decade, rather than focusing only on the 1,000 most cited ones. This is especially important for more recent articles, as these are likely to fall outside the top 1,000 if one conducts an aggregate search for 2006-2015.

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