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Worldwide universities network (WUN) web observatory: applying lessons from the web to transform the research data ecosystem

Worldwide universities network (WUN) web observatory: applying lessons from the web to transform the research data ecosystem
Worldwide universities network (WUN) web observatory: applying lessons from the web to transform the research data ecosystem

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.

Data Science, Research Data Management, Social Machines
1665-1667
International World Wide Web Conferences Steering Committee
Price, Simon
cf81da3e-4acc-422c-af1b-f2dc8a962b9a
Hall, Wendy
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Earl, Graeme
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Tiropanis, Thanassis
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Tinati, Ramine
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Wang, Xin
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Gandolfi, Eleonora
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Gatewood, Jane
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Boateng, Richard
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Denemark, David
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Groflin, Alexander
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Loader, Brian
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Schmidt, Maxine
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Billings, Marilyn
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Spanakis, Gerasimos
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Suleman, Hussein
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Tsoi, Kelvin
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Wessels, Bridgette
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Xu, Jie
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Birkin, Mark
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Price, Simon
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Hall, Wendy
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Earl, Graeme
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Tiropanis, Thanassis
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Tinati, Ramine
f74a0556-6a04-40c5-8bcf-6f5235dbf687
Wang, Xin
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Gandolfi, Eleonora
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Gatewood, Jane
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Boateng, Richard
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Denemark, David
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Groflin, Alexander
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Loader, Brian
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Schmidt, Maxine
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Billings, Marilyn
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Spanakis, Gerasimos
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Suleman, Hussein
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Tsoi, Kelvin
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Wessels, Bridgette
5a237976-7858-443f-b496-6b35ac9334de
Xu, Jie
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Birkin, Mark
6f32f53d-7f37-4a90-bcdc-3f64cdc6f196

Price, Simon, Hall, Wendy, Earl, Graeme, Tiropanis, Thanassis, Tinati, Ramine, Wang, Xin, Gandolfi, Eleonora, Gatewood, Jane, Boateng, Richard, Denemark, David, Groflin, Alexander, Loader, Brian, Schmidt, Maxine, Billings, Marilyn, Spanakis, Gerasimos, Suleman, Hussein, Tsoi, Kelvin, Wessels, Bridgette, Xu, Jie and Birkin, Mark (2019) Worldwide universities network (WUN) web observatory: applying lessons from the web to transform the research data ecosystem. In WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee. pp. 1665-1667 . (doi:10.1145/3041021.3051691).

Record type: Conference or Workshop Item (Paper)

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.

Full text not available from this repository.

More information

Published date: 2019
Venue - Dates: 26th International World Wide Web Conference, WWW 2017 Companion, Perth, Australia, 2017-04-03 - 2017-04-07
Keywords: Data Science, Research Data Management, Social Machines

Identifiers

Local EPrints ID: 428069
URI: https://eprints.soton.ac.uk/id/eprint/428069
PURE UUID: b4190da7-2be6-4b16-b78a-6590de34642e
ORCID for Wendy Hall: ORCID iD orcid.org/0000-0003-4327-7811
ORCID for Graeme Earl: ORCID iD orcid.org/0000-0001-9077-4605
ORCID for Thanassis Tiropanis: ORCID iD orcid.org/0000-0002-6195-2852

Catalogue record

Date deposited: 08 Feb 2019 17:30
Last modified: 14 Mar 2019 01:57

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Contributors

Author: Simon Price
Author: Wendy Hall ORCID iD
Author: Graeme Earl ORCID iD
Author: Ramine Tinati
Author: Xin Wang
Author: Eleonora Gandolfi
Author: Jane Gatewood
Author: Richard Boateng
Author: David Denemark
Author: Alexander Groflin
Author: Brian Loader
Author: Maxine Schmidt
Author: Marilyn Billings
Author: Gerasimos Spanakis
Author: Hussein Suleman
Author: Kelvin Tsoi
Author: Bridgette Wessels
Author: Jie Xu
Author: Mark Birkin

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