Skip to content

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • 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
Original languageEnglish
Title of host publicationWWW '17 Companion
Subtitle of host publicationProceedings of the 26th International Conference on World Wide Web Companion
EditorsRick Barrett, Rick Cummings
Place of PublicationPerth, Australia
Number of pages3
DateAccepted/In press - 7 Feb 2017
DatePublished (current) - 3 Apr 2017
EventWorkshop on Web Observatories, Social Machines and Decentralisation - PCEC, Perth, Australia
Duration: 3 Apr 2017 → …


WorkshopWorkshop on Web Observatories, Social Machines and Decentralisation
Abbreviated titleWOW17
Period3/04/17 → …
Internet address


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.

    Structured keywords

  • WUN - Big Data, Data Science

    Research areas

  • Research Data Management, Data Science, Social Machines


Workshop on Web Observatories, Social Machines and Decentralisation

Abbreviated titleWOW17
Duration3 Apr 2017 → …
Location of eventPCEC
Web address (URL)
Degree of recognitionInternational event

Event: Workshop

Download statistics

No data available



  • Full-text PDF (final published version)

    Rights statement: This is the final published version of the article (version of record). It first appeared online via International World Wide Web Conference Committee at Please refer to any applicable terms of use of the publisher.

    Final published version, 1 MB, PDF document

    Licence: CC BY


View research connections

Related faculties, schools or groups