Key integrating concepts Groups
Formal Community Groups Ad-hoc special purpose/ interest groups Fine-grained access control and membership
Linked All content can be tagged at a variety of level of detail
Incentives to contribute content Content is visible in key reports – accepted by sponsors
Progress in Open-World, Integrative, Collaborative Science Data Platforms
Peter Fox1 ([email protected])
(1Rensselaer Polytechnic Institute 110 8th St., Troy, NY, 12180 United States – see Acknowledgements)
Glossary:RPI – Rensselaer Polytechnic InstituteTWC – Tetherless World Constellation at Rensselaer Polytechnic InstituteCKAN – Comprehensive Knowledge Archive NetworkVIVO - Research Collaboration Network model and softwareGHS – Global Handle SystemPROV – W3 Provenance Model and Ontology
Acknowledgments:DCO Data Science TeamW3C Provenance Working Group
Sponsors:AP Sloan FoundationNational Science FoundationTetherless World Constellation
ABSTRACT
As collaborative, or network science spreads into more Earth and space science fields, both the participants and their funders have expressed a very strong desire for highly functional data and information capabilities that are a) easy to use, b) integrated in a variety of ways, c) leverage prior investments and keep pace with rapid technical change, and d) are not expensive or time-consuming to build or maintain.
At a conceptual level, science networks (even small ones) deal with people, and many intellectual artifacts produced or consumed in research, organizational and/our outreach activities, as well as the relations among them. Increasingly these networks are modeled as knowledge networks, i.e. graphs with named and typed relations among the 'nodes'. Nodes can be people, organizations, datasets, events, presentations, publications, videos, meetings, reports, groups, and more. In this heterogeneous ecosystem, it is also important to use a set of common informatics approaches to co-design and co-evolve the needed science data platforms based on what real people want to use them for.
Community Data and Groups
Integrate Semantic Applications into a Data Science Platform
Information Models and Leveraged Ontology
Community Dashboard and Reporting Progress
Solution: Leverage
Open-Source
Semantic
Technologies
Triples tie it together
Integrate terms from VIVO, FoaF, BIBO, O&M,
VSTO, BCO-DMO, TWC and others Starting with their information models where
possible
Progress = integration at the application level
to bring several key functions together in a
Web-based environment oovering most
research needs
HIDE ALL OF “IT” FROM THE USERS!!
Identify Everything• DCO-ID
• Content negotiation
• Respect other locations, names
USE: W3C Provenance Ontology (PROV-
O)• Developed by W3C Provenance Working Group
• W3C 2013 Recommendation - http://www.w3.org/TR/prov-o/
AGUFM13 - IN11A-1509 (MS Hall A-C)
CKAN VIVO
GHS – Handle.net
VIVO - represents academic research communities• Every person, organization, or other data entity in
VIVO has a unique identifier• VIVO enables the discovery of research and
scholarship across disciplines at one institution or across many
• Records are both human-readable and machine-readable
• We’ve extended (yes, ontologies) VIVO to the science network – datasets, instruments, sites, etc.
• Feeding this back to VIVO
Knowledge network – implements
both the collaboration and the
integration Many means of population
User generation Machine generation
Substantially contributing these
enhancements back to open-source
community (CKAN, VIVO, GHS)
Information models provide domain level view
and logical models implemented in ontology
leverage a wide variety of vocabularies and
encoding schemes Alignment performed using …
RDFS subClassOf assertions SKOS broadMatch statements
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