Recommending Scientific Datasets Using Author Networks in Ensemble Methods

Tracking #: 720-1700

Responsible editor: 

Stephen Pettifer

Submission Type: 

Research Paper


Open access to datasets is increasingly driving modern science. Consequently, discovering such datasets is becoming an important functionality for scientists in many different fields. We investigate methods for dataset recommendation: the task of recommending relevant datasets given a dataset that is already known to be relevant. Previous work has used meta-data descriptions of datasets and interest profiles of authors to support dataset recommendation. In this work, we are the first to investigate the use of co-author networks to drive the recommendation of relevant datasets. We also investigate the combination of such co-author networks with existing methods, resulting in three different algorithms for dataset recommendation. We obtain experimental results on a realistic corpus which show that only the ensemble combination of all three algorithms achieves sufficiently high precision for the dataset recommendation task.



  • Reviewed

Data repository URLs: 

The data, python implementation code, and sample experiment could be found at this link.

Some of the RDF/HDT datasets could be found at this link.

Date of Submission: 

Tuesday, February 22, 2022

Date of Decision: 

Friday, April 22, 2022

Nanopublication URLs:



Solicited Reviews:

1 Comment

Meta-Review by Editor

We are pleased to inform you that your paper has provisionally been accepted for publication, under the condition that you address the various issues raised by the reviewers. Many of these are minor corrections or suggestions for improvements, but I would draw your attention in particular to the comments by R1 regarding a comparison with SoA, and the comments by the others regarding FAIRness / openness.

Stephen Pettifer (