TY - GEN
T1 - Worldwide universities network (WUN) web observatory:Applying lessons from the web to transform the research data ecosystem
AU - Price, Simon
AU - Boateng, Richard
AU - Loader, Brian
AU - Suleman, Hussein
AU - Hall, Wendy
AU - Earl, Graeme
AU - Tiropanis, Thanassis
AU - Tinati, Ramine
AU - Wang, Xin
AU - Gandolfi, Eleonora
AU - Denemark, David
AU - Schmidt, Maxine
AU - Billings, Marilyn
AU - Tsoi, Kelvin
AU - Xu, Jie
AU - Birkin, Mark
AU - Gatewood, Jane
AU - Groflin, Alexander
AU - Spanakis, Gerasimos
AU - Wessels, Bridgette
N1 - Publisher Copyright:
© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Data Science
KW - Research Data Management
KW - Social Machines
UR - http://www.scopus.com/inward/record.url?scp=85048407525&partnerID=8YFLogxK
U2 - 10.1145/3041021.3051691
DO - 10.1145/3041021.3051691
M3 - Conference contribution
AN - SCOPUS:85048407525
T3 - 26th International World Wide Web Conference 2017, WWW 2017 Companion
SP - 1665
EP - 1667
BT - 26th International World Wide Web Conference 2017, WWW 2017 Companion
PB - International World Wide Web Conferences Steering Committee
T2 - 26th International World Wide Web Conference, WWW 2017 Companion
Y2 - 3 April 2017 through 7 April 2017
ER -