Why You Shouldn’t Recycle Historical Summary Data in Your Charts

Fast Facts:

It is not good data reporting practice to harvest data points from charts created by someone else for use as historical data on a new chart you are creating. This is because it is usually impossible to be sure how they filtered or cleaned the data.

Data for editorial reports often come from several data tables in the manuscript handling system. How you link these tables when building extraction reports can affect the resulting dataset you create.

Why You Shouldn’t Recycle Historical Summary Data in Your Charts

 

June 2, 2021

By: Jason Roberts & Sherrie Hill

When creating a new chart, you should not ‘harvest’ data from previous charts created by someone else. Unless it was explicitly documented, it is not possible to know what they counted, included, or excluded. When performing data calculations, it can be difficult to match someone else’s summary data.

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If you created the chart above using historical data for years 2017-2019, would you be able to explain why it appears that there was a significant increase in submission in 2020? Was this increase expected based on what the editorial office saw in 2020? Did the previous managing editor calculate submissions differently? For instance, they may have excluded editorials or other commissioned materials.

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A better practice is to start over, using raw data from that time period and calculating all of the values yourself so you can be sure all reported data points have been obtained in the same way.

Additionally, most manuscript handling systems store data relating to the submissions, editors, and reviewers in a number of different data tables.  (Databases are made up of data tables.  Data tables are two dimensional objects [rows and columns of data], which is what you are accessing regardless of the system.)  When extracting your data, you may need to pull from multiple tables.  Depending on the manuscript handling system that you are using, it may not be obvious to you that is what you are doing.  How you select and connect your data tables can produce different outcomes.  This can potentially confound your results and may show false trends.

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 Imagine your database as an old fashioned telephone switchboard.  You can see how making the wrong connection will give you the wrong result!  If you link your manuscript data table to the reviewer data table with the wrong connection, you might be told that the manuscript doesn’t exist because it does not have any reviewers associated with it if it did not go through peer review.

It may well be that someone at your journal has been inadvertently counting data incorrectly for years.  That is one of the reasons that we created Origin Reports. Everyone on your team can produce consistent, accurate charts, year after year, regardless of who is extracting the data and generating the charts.

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