Dataset Open Access
Background and methodology:
The subject co-occurrence matrix represents the pairs of All Science Journal Classification (ASJC) disciplines that co-occur in journals represented in the SHAPE-ID Literature Review dataset, prepared for the purposes of quantitative analysis.
We take disciplinary affiliations of journals as a proxy of disciplinary characteristics of the journal articles in the Literature Review dataset, mindful of the fact that a particular article might deviate from the disciplinary affiliation of the journal in which it was published. However, since there was no data readily available on item level, and manual disciplinary encoding of all the items in the bibliography was beyond the scope of this study, the method used is the best approximation of the presence of discourse on interdisciplinarity and transdisciplinarity, in and between disciplines.
In the matrix, each co-occurrence value is weighted by the number of journals that feature the given pair of disciplines, and by the number of articles represented in the dataset that feature in these journals. E.g. if Journals J1 and J2 each featured disciplines D1 and D2, and if 4 articles from J1 and 7 articles from J2 are represented in the SHAPE-ID Literature Review dataset, the co-occurrence value is 11.
The pairings cross-referencing a single discipline (e.g. 1202 History in both first row and first column) correspond to the co-occurence value of mono-disciplinary journals.
Description of the file:
This is a csv file containing a 308x308 cell matrix with ASJC disciplines in first rows and columns, and co-occurrence value in the remaining cells.