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Worldwide Universities Network (WUN) Web Observatory
Applying Lessons from the Web to Transform the Research Data Ecosystem
Simon PriceUniversity of Bristol, UK
Wendy Hall, Graeme Earl,Thanassis Tiropanis,
Ramine Tinati, Xin Wang,Eleonora Gandolfi
University of Southampton, UKwh, tt2, [email protected]
Jane GatewoodUniversity of Rochester, USA
Richard BoatengUniversity of Ghana, Ghana
David DenemarkThe University of Western
Australia, [email protected]
Alexander GroflinUniversity of Basel, [email protected]
Brian LoaderUniversity of York, UK
Maxine Schmidt,Marilyn Billings
University of MassachusettsAmherst, USA
Gerasimos SpanakisUniversiteit Maastricht,
Hussein SulemanUniversity of Cape Town,
South [email protected]
Kelvin TsoiThe Chinese University of
Hong Kong, [email protected]
Bridgette WesselsNewcastle University, UK
Jie Xu, Mark BirkinUniversity of Leeds, UK
j.xu, [email protected]
ABSTRACTThe ongoing growth in research data publication supportsglobal intra-disciplinary and inter-disciplinary research col-laboration but the current generation of archive-centric re-search data repositories do not address some of the key prac-tical obstacles to research data sharing and re-use, specifi-cally: 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 compli-cated; and, researchers are not guaranteed attribution forre-use of their own research data. These issues are keenlyfelt in an international network like the Worldwide Univer-sities Network (WUN) as it seeks to address major globalchallenges. In this paper we outline the WUN Web Obser-vatory project’s plan to overcome these obstacles and, giventhat these obstacles are not unique to WUN, we also pro-pose an ambitious, longer-term route to their solution atWeb-scale by applying lessons from the Web itself.
c©2017 International World Wide Web Conference Committee(IW3C2), published under Creative Commons CC BY 4.0 License.WWW’17 Companion, April 3–7, 2017, Perth, Australia.ACM 978-1-4503-4914-7/17/04.http://dx.doi.org/10.1145/3041021.3051691
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KeywordsResearch Data Management, Data Science, Social Machines.
1. INTRODUCTIONThe Worldwide Universities Network (WUN) is the most
active global higher education and research network with 90active research initiatives, engaging over 2,000 researchersand students collaborating on a diverse range of projects.These initiatives are committed to addressing some of theworld’s most urgent challenges and are supported by pro-lific partners such as the United Nations Foundation, WorldBank, OECD and World Health Organization. WUN re-search focuses on four themes that address the followingglobally significant challenges.
• Responding to Climate Change.Explores sustainable approaches to food and environ-ment security, addressing the scientific, cultural, healthand social issues that govern our response to a chang-ing climate.
• Public Health (Non-Communicable Disease).Emphasizes a life-course approach to addressing non-communicable diseases (NCDs), especially but not ex-clusively, in low and middle income countries and tran-sitioning populations.
• Global Higher Education and Research.Proposes policy reforms to address the sources, mech-
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anisms, and social structures that give rise to access,mobility and investment challenges for internationalresearch and education.
• Understanding Cultures.Facilitates the understanding of some of the princi-pal consequences of globalisation for cultural identi-ties, migration, population, trans-disciplinary indige-nous research, global digital cultures, and heritage.
Collaborative and multi-disciplinary research programmesare a crucial component of WUN’s response to these globalchallenges, which depends on experts from across the globeworking together, sharing both open and sensitive data be-tween and across disciplines.
The sharing and citation of research data that supportacademic publications is increasingly an expectation of jour-nals, funders and researchers. The proliferation of institu-tional, national and discipline-specific research data repos-itories provides researchers with a wide choice of venues inwhich to publish their research data - so much so that find-ing and accessing relevant research data without a directlink can be difficult. The Australian National Data Ser-vice runs a national discovery service for Australian researchdata, and the UK’s JISC is establishing a similar service forUK research data. So, while there are now a multitude ofrepositories publishing metadata descriptions of, and linksto, their research data, the global discovery and re-use ofdata requires researchers to search multiple metadata direc-tories within specific territories or disciplines.
This situation echoes the early days of the Web when itwas still feasible for a small number of“centralised”organisa-tions to curate usable directories describing all the websitesin an institution, a nation, a discipline or the world.
2. WUN WEB OBSERVATORYThe Web Observatory infrastructure [3, 4], developed un-
der the auspices of the Web Science Trust, brings the princi-ples of the early Web to this emerging research data ecosys-tem, with the same goal of transformational growth throughdecentralisation [1]. Below we describe three components tothis infrastructure that the WUN Web Observatory projectseeks to exploit.
2.1 Virtual Research Data RepositoriesAnyone can establish a Web Observatory to create their
own virtual research data repository with its own, possiblyhighly focused, scope and self-determined rules for accessand sharing. At a foundational level this enables researchersand organisations to create individualised or project-specificresearch data directories and to maintain control over the de-scription, ownership and access to their own research meta-data. Indeed, one of the objectives of the WUN Web Obser-vatory project is to create a WUN research data directoryby establishing a network of Web Observatories cataloguingthe institutional research data repository and other majorholdings of all the WUN member universities. The resultantself-updating and readily searchable directory of WUN re-search data will facilitate research data sharing within andbetween WUN universities and support interdisciplinary andinter-institutional research.
2.2 Management of ‘Live’ Research DataHowever, the greater significance of creating the WUN vir-
tual research data repository is that its constituent networkof Web Observatories provides the requisite critical massfor the realisation of the WUN Web Observatory project’stransformational vision: to position the WUN as global lead-ers in the exchange and re-use of research data betweendisciplines and institutions, driving forward research un-derpinning WUN’s priority Global Challenges and Cross-Cutting Themes while, at the same time, bootstrapping theworldwide development and adoption of the Web Observa-tory infrastructure envisioned and supported by the WebScience Trust. Establishing a network of Web Observa-tories facilitates the bottom-up adoption of Web Observa-tory infrastructure by WUN researchers for the creation andwidespread use of virtual research data repositories as thedefault means of research data discovery and exchange. Cru-cial to its appeal, and unlike the current generation of re-search data management software, Web Observatory infras-tructure supports the management of active, ‘live’, researchdata, not just archival post-publication research data.
2.3 Secure Access Control and AttributionUsing a technical trick, known as “reverse proxying”, Web
Observatories enable authentication and access control todecentralised research data - regardless of where that datais held, whether it is in an archival repository or in working,active research stores, digital asset management systems orin arbitrary cloud-hosted platforms or even in streamed real-time data feeds. This distinguishes a virtual research datarepository from a research data directory or a traditionalresearch data repository and is the core innovation of theWeb Observatory concept that the WUN Web Observatoryproject seeks to investigate and develop through a range ofresearch demonstrator activities, described below.
3. RESEARCH DEMONSTRATORSTo explore research questions across the four Global Chal-
lenges, the WUN Web Observatory project established fourcorresponding demonstrator activities that also support theCross-Cutting Themes as depicted by numbered circles andthemes in the annotated WUN research matrix of Figure 1.
Each of the four research demonstrator activities exploresdifferent aspects and configurations of the Web Observatoryinfrastructure to enable and support WUN research as out-lined below.
3.1 Demonstrator 1: Online LearningAggregates datasets from different online learning plat-
forms across the WUN and provides analytics on those datasets,quantifying the diverse usage patterns of Massive Open On-line Courses (MOOCs) to reveal success criteria for onlinelearning and to provide insights into future directions1.
3.2 Demonstrator 2: Ageing and Well-beingCombines data from established sources, such as cancer
registries, with newer (often personalised) sources, such aswearable sensors, mobile devices and crowdsourcing, to en-able visualisation and analysis of ageing patterns and poor
1Online Learning lead: Spanakis, Universiteit Maastricht.
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Online
Learning
Ageing and
Well-being
Disaster
Management
Youth and
Digital Media
21 43
21 43
21 43
4
21 43
Figure 1: Mapping to WUN Web Observatory demonstrators to Global Challenges and Cross-Cutting Themes
health, disease trend projection, and perceptions of ageingwell2.
3.3 Demonstrator 3: Disaster ManagementEnables and investigates social and technical mechanisms
for Citizen-Driven Disaster Management, where individualsprovide their own data in a decentralised system to pro-actively support emergency response and planning prior,during, and post natural and urban disasters, while retain-ing control and privacy of their own data3.
3.4 Demonstrator 4: Youth and Digital MediaLinks and analyses disparate WUN datasets to explore the
patterns and impact of youth reliance on social media anddigital information platforms for their political involvement,and the consequences for young citizens’ attitudes towardgovernment, citizenship, politics and civic engagement4.
4. SUMMARYBuilding on experience from these four research demon-
strators, the WUN Web Observatory project5 aims to in-stantiate a collection of virtual research data repositories,enable effective management of ‘live’ research data, and con-trol of its access and attribution; thus becoming the defaultpathway for WUN’s researchers working in pursuit of itsGlobal Challenges. Such widespread international adoption
2Ageing and Well-being lead: Tsoi, The Chinese Universityof Hong Kong.3Disaster Management lead: Tinati, University ofSouthampton.4Youth and Digital Media lead: Denemark, The Universityof Western Australia.5Project website – http://wun.ac.uk/wun/research/view/web-observatory-project
significantly advances the project’s longer-term ambition tobootstrap the worldwide development and adoption of WebObservatory infrastructure to the benefit of all research [2].
5. REFERENCES[1] W. Hall and T. Tiropanis. Web evolution and web
science. Computer Networks, 56(18):3859–3865, 2012.
[2] C. Phethean, E. Simperl, T. Tiropanis, R. Tinati, andW. Hall. The role of data science in web science. IEEEIntelligent Systems, 31(3):102–107, May 2016.
[3] T. Tiropanis, W. Hall, J. Hendler, and C. de Larrinaga.The web observatory: a middle layer for broad data.Big Data, 2(3):129–133, 2014.
[4] T. Tiropanis, W. Hall, N. Shadbolt, D. De Roure,N. Contractor, and J. Hendler. The web scienceobservatory. IEEE Intelligent Systems, 28(2):100–104,2013.
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