How can researchers, in the spirit of open science, move beyond their traditional networks to forge new connections? How can public infrastructures like OpenAIRE further enable open science by bringing the right organizations into partnership to stimulate innovation and address the grand societal challenges? To answer these questions, Know-Center releases Matchbook, a prototype recommender service to spark scientific collaboration, produced in partnership with OpenAIRE.
This novel recommender service builds upon the OpenAIRE scholarly graph to enable organizations to keyword search for organizations with the strongest records of funding and collaboration success from the ever-expanding range of funders included in OpenAIRE. OpenAIRE’s information on funders and projects, enhanced with links to open access publications and data, is an invaluable resource that has not yet been fully exploited. Drawing information from OpenAIRE on institutions, projects and funders, Matchbook can help scientific institutions interested in forming consortia to identify potential partners with the exact disciplinary strengths and competences they need, rank them according to their previous success in securing funding, and specify searches according to specific national or international funders or even individual funding streams.
The service uses Know-Center’s Scalable Recommender Framework to generate recommendations based on three different types of algorithms:
Matchbook works as a successful proof-of-concept but would require more development before being able to be offered as a standalone production service – in particular, the recommendations are currently made on a relatively scarce dataset (i.e., just project titles and keywords, collaboration histories of institutions but not always individual departments) which means that performance suffers in comparison to similar recommender services based on richer information. A next step in this research would be to add enriched information – perhaps including text-mining of full project descriptions (also to the work-package level) and linking the publications linked to projects.
In addition, more work could be done to:
This use case has demonstrated that: