Data from: Metacommunity theory review and its application in community assembly of soil animals

We were interested in community assembly of soil animals and performed a literature study to find out what is known about soil metacommunities. We aimed to study keywords co-occurrence relationships of scientific journal articles in the field of "metacommunity" research from 1992-2017 as a whole. We also investigated the co-occurrence of the top 20 most frequent keywords in five 5-year time periods.

In September 2017 we searched the Web of Science with ‘metacommunity’ as the only keyword, and found 1226 English papers published in international journals between January 1992 and September 2017. And we exported these papers from Web of Science as a plain text file (.txt) with the option ‘Full Record and Cited References’, which is archived here. The text file thus contains full records and cited references (in a concise format, so without the titles of the cited papers), and each field is prefaced by a two-character field tag. Afterwards we used the software ‘Citespace’ to create Table 1 and Figure 2 in Guo et al. (2018). We created that Figure 2 using the following settings in Citespace: we choose the “co-occurrence” function and “keyword” as node types, then created keywords co-occurrence relationships (Figure 2). Next, we created lists of the top 20 keywords in different periods, which can reveal the research hotspots (Table 1). The method for extracting the top 20 most frequent keywords was as follows: We use the Web of Science database to retrieve scientific papers from 1992 to 2017, taking into account the relationship between citation and publication time. We calculated the percentage of 200 most cited papers published in each of the 5-year periods. To do so we followed the following steps:

Step1: we selected the 200 most cited papers from the entire datset (1226 in the 1992-2017 period).

Step 2: we calculated the percentage of those 200 papers published each 5-year period.

Step 3: we also calculated an correction coefficient by taking, for each 5-year period, the number of top-200 most cited papers published in that period, and dividing that number by the total number of papers (out of the 1226 selected papers) published in that period.

Step 4 Last, we calculate the real keyword frequency by multiplying the keyword frequency in specific 5-year periods with the correction coefficient.