Worldwide Universities Network (WUN) Web Observatory: Applying Lessons from the Web to Transform the Research Data Ecosystem

Simon Price*, Wendy Hall, Graeme Earl, Thanassis Tiropanis, Ramine Tinati, Xin Wang, Eleonora Gandolfi, Jane Gatewood, Richard Boateng, David Denemark, Alexander Groflin, Brian Loader, Maxine Schmidt, Marilyn Billings, Gerasimos Spanakis, Hussein Suleman, Kelvin Tsoi, Bridgette Wessels

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

The ongoing growth in research data publication supports global intra-disciplinary and inter-disciplinary research collaboration but the current generation of archive-centric research data repositories do not address some of the key practical obstacles to research data sharing and re-use, specifically: discovering relevant data on a global scale is time-consuming; sharing "live" and streaming data is non-trivial; managing secure access to sensitive data is overly complicated; and, researchers are not guaranteed attribution for re-use of their own research data. These issues are keenly felt in an international network like the Worldwide Universities Network (WUN) as it seeks to address major global challenges. In this paper we outline the WUN Web Observatory project's plan to overcome these obstacles and, given that these obstacles are not unique to WUN, we also propose an ambitious, longer-term route to their solution at Web-scale by applying lessons from the Web itself.
Original languageEnglish
Title of host publicationInternational World Wide Web Conference: (WOW'17) Web Observatory Workshop 2017
Pages1665-1667
DOIs
Publication statusPublished - Apr 2017

Keywords

  • Research Data Management
  • Data Science
  • Social Machines

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