  Collaborator finding systems are a special type of expert finding models.
There is a long-lasting challenge for research in the collaborator recommending
research area, which is the lack of the structured dataset to be used by the
researchers. We introduce two datasets to fill this gap. The first dataset is
prepared for designing a consistent, collaborator finding system. The next one,
called a co-author finding model, models an academic social network as a table
that contains different relations between the pair of users. Both of them
provide an opportunity for introducing potential collaborators to each other.
These two models have been extracted from ResearchGate (RG) data set and are
available publicly. RG dataset has been collected from Jan. 2019 to April 2019
and includes raw data of 3980 RG users. The dataset consists of almost complete
information about users. In the preprocessing phase, the well-known Elmo was
used for analyzing textual data. We call this as ResearchGate dataset for
Recommending Systems (RGRS). For assessing the validity of data, we analyze
each layer of data separately, and the results are reported. After preparing
data and evaluating the collaborator finding models, we have done some
assessments on RGRS. Some of these assessments are co-author,
following-follower, and question answering relations. The outcomes indicate
that it is the best relation in propagating knowledge in the network. To the
best of our knowledge, there is no processed and analyzed dataset of this size.
