From data portal to knowledge portal: Leveraging semantic technologies to support interdisciplinary studies
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The document discusses the Deep Carbon Virtual Observatory (DCvO), which aims to provide integrated access to scientific data and information related to deep carbon science. It describes the DCvO's architecture, which leverages semantic technologies and ontologies to link different types of data and entities. The DCvO provides visualization tools to discover information by clicking through linked concepts, facilitates potential collaborations, and acts as an integrated portal for diverse scientific content. It also discusses some of the DCvO's boundary activities, like linking datasets to publications and integrating with external data repositories.
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From data portal to knowledge portal: Leveraging semantic technologies to support interdisciplinary studies
- 1. deepcarbon.net Xiaogang Ma, Patrick West, John Erickson, Stephan Zednik, Yu Chen, Han Wang, Hao Zhong, Peter Fox Tetherless World Constellation Rensselaer Polytechnic Institute From data portal to knowledge portal: Leveraging semantic technologies to support interdisciplinary studies
- 2. Outline • Deep Carbon Observatory • Deep Carbon Virtual Observatory (DCvO) – Architecture of DCvO – DCO Ontologies – Boundary activities – Discovering information by clicking through • Summary 2
- 3. A 10-year (2009-2019) initiative to intensify global attention and scientific effort in the burgeoning field of deep carbon science 3
- 4. • Faculty, staff and students from the Tetherless World Constellation (TWC) at Rensselaer Polytechnic Institute (RPI) • Responsible for – DCO Architecture and technology infrastructure – DCO Computer Cluster – The Deep Carbon Virtual Observatory DCvO Deep Carbon Observatory – Data Science 4
- 5. Deep Carbon Virtual Observatory Scientists – actually ANYONE - should be able to access a global, distributed knowledge base of scientific data and information that: • appears to be integrated • appears to be locally available • is in a language (written, programming, or science) that is understandable and can be shared Data intensive – volume, complexity, mode, scale, heterogeneity, … in an OPEN WORLD 5
- 6. Deep Carbon Virtual Observatory • A vision of the DCvO: – A conceptual model of the interplay between data, people, publication, instruments, models, organizations, etc. – Identify, annotate and link all key entities, agents and activities – A repository for datasets and associated metadata – Unique and powerful data and metadata visualization for dissemination of information – Facilitates the discovery of potential collaborations – An integrated portal for diverse content and applications (Fox et al., 2014) 6
- 8. vivo.cornell.edu VIVO - represents academic research communities DCO ontology: a model for concept types and relationships DCO ontologies extend each other and the VIVO ontology 8
- 9. Ontologies and schemas used in the DCO web portal 9 Name Prefix Dublin Core Metadata Element Set dc DCMI Metadata Terms dct VIVO Core vivo VIVO Scientific Research Ontology scires Data Catalog Vocabulary dcat Bibliographic Ontology bibo Citation Counting and Context Characterization Ontology c4o Citation Typing Ontology cito FRBR-Aligned Bibliographic Ontology fabio Event Ontology event Friend of a Friend foaf vCard Ontology vcard Geopolitical Ontology geo Simple Knowledge Organization System skos DCO Ontology dco PROV Ontology prov
- 10. Ontologies and schemas used in the DCO web portal 10 DCO Boundary Activities are driving the extensions within the DCO Ontologies
- 11. DCO Extension for Project Updates 11
- 12. 12 Dynamically generated list of Grants that are part of the Deep Carbon Observatory. Users can click through to learn more, and members can create reports to be sent to funding orgs
- 13. 13 Grant page lists all projects and reporting updates for each of the projects and field studies
- 14. DCO Extension for Data Types 14
- 15. 15 A Few Boundary Activities • Given a DOI pull publication information from CrossRef and/or Web of Science • DCO IGSN Allocation Agent to work with the IGSN Registry • Integration with existing data portals and repositories • Data Rescue activities
- 16. Modern informatics enables a new scale-free framework approach • Use cases • Stakeholders • Modeling • Ontologies • Evaluation 16
- 17. What does a DCO data publication look like? 17
- 18. 18 Identification and annotation Information on the landing page of a dataset
- 19. 19 Linking to enable forward and backward tracking Landing page of Helium Concept
- 20. 20 Landing page of a person Linking to build Collaborations
- 21. 21 Landing page of a research area Linking to build Collaborations
- 22. 22 DCO Knowledge Graph Analytics
- 23. Thus… progress… • Integrative – semantics • Transparent – semantics • Collaborative – semantics • Application integration – Yep – semantics 23
- 24. Thank you! 24 Patrick West, westp@rpi.edu, https://deepcarbon.net, http://tw.rpi.edu
- 25. 25 An integrated portal: deepcarbon.net
- 27. Repository for archiving datasets Archived datasets of ‘Noble gas isotope abundances in terrestrial fluids’ 27
- 28. Collaboration tools Group Based CollaborationGroup data deposit and reporting Listings of group content Group management and messaging 28
- 29. 29 RDA DTR and PIT adoption The DTR primitives are comparable to a list of BASIC DATA TYPE CLASSES in the DCO ontology, e.g. Dataset, Image, Video, Audio, etc. A registered DCO dataset is asserted as an instance of one of those basic data type classes. It is possible to further annotate the dataset with the SPECIFIC DATA TYPES defined within a DTR, and each data type has a unique PID. A Few Boundary Activities
- 30. Results of data type specification • Updates to the DCO Ontology: – A new class dco:DataType. Each specific data type is an instance of it – An object property dco:hasDataType linking a dataset and a data type – A collection of other classes and properties associated with dco:DataType 30
- 31. 31 • New datasets available via dataset browser • Includes citations to the originating publication • Data files accessible through dataset repository Thermodynamic Data Rescue
- 32. 32 DCO Knowledge Store Analytics
- 33. 33 DCO Knowledge Store Visualizations
- 34. All information is linked and traceable! 34
- 35. Mediation From: C. Borgman, 2008, NSF Cyberlearning Report, Illustration by Roy Pea and Jillian C. Wallis 6th Generation All these generations of mediation are in effect as we collaborate 35
Editor's Notes
- Scientific research practices regularly adopt new technologies and platforms in an effort to increase information timeliness, sharing and discoverability. There are many initiatives related to open data, open code, open access, open collections, composing the topic of Open Science in academia. Being open has two levels of meanings. The first is to make the data, code, sample collections and publications, etc. freely accessible online. The other is the annotation and connection between those resources to establish linked open data, a web of data. Moving from a data portal, adding context and experience using semantic technology in order to create a knowledge portal.
- We present here our work on Deep Carbon Observatory
- The Alfred P. Sloan Foundation pledged $50 million over the duration of the initiative to fund infrastructure development, scientific workshops, novel technology development, and preliminary research and fieldwork. The project spans four different scientific communities, shown here. Deep Life, Reservoirs and Fluxes, Deep Energy and Extreme Physics and Chemistry. A wide range of science domains are covered, Earth and Environmental science, ocean science, volcanology and more. There are close to a thousand members from hundreds of organizations all across the world. Diversity of research, diversity of people, diversity of information.
- There are a lot of initiatives out there creating their own data portals. Unfortunately in a lot of cases they are data silos. Information is obtained by different means (instruments, models, analysis) using various (often opaque) protocols, in differing vocabularies, using (sometimes unstated) assumptions, with inconsistent (or non-existent) meta-data. It may be inconsistent, incomplete, evolving, and distributed and created and organized in a manner to facilitate its generation, over its use And… there exist(ed) significant levels of semantic heterogeneity, large-scale data, complex data types, legacy systems, inflexible and unsustainable implementation technology…
- Specifically the use cases generated as part of the boundary activity development, is driving the extensions and additions found within the DCO ontology Boundary activities are independent collaborations with organizations in an effort to build additional features and integrate additional data for the virtual observatory
- Ability for projects collaborators to enter information stating the progress of their projects, updating their project information for use in reports to funding agencies
- Dynamically generating a page in Drupal showing all of the Grants that are and have been funded by the Alfred P. Sloan Foundation. The example shown here is the grant for the DCO-DS work. Shows different information about the grant, can click through to find additional information, and you can click through to a reporting year to …
- See the list of projects, field studies and any instruments associated with the grant. With the projects and field studies you can see a textual representation of the progress that has been made in the projet. What we were able to do is print out this page as a PDF and send that PDF to the funding organization as the report for 2015.
- Stems from our work to implement the Data Type Registry as defined by the Research Data Alliance
- International Geo-Sample Number. For example taking a core sample from the Arctic, that sample gets assigned an IGSN. Take slices of that core to run experiments, each of those slices gets an IGSN with provenance to the original core sample Many portals already exist with their own representation of information, if any. The goal here isn’t to smash the existing models into the DCO model but to extend the DCO ontology with concepts and relationships specific to that portal Data Rescue, pulling data using OCR software from PDF documents from the 70s, 80s and 90s, creating new datasets and using provenance to tie it back to the original publications.
- Look at scale free network definitions in wikipedia, web, semantic networks.
- Boundary activity working with the DCO communities to pull publication information from CrossRef and Web of Science given a Digital Object ID
- Here we can see the dataset registered within CKAN, and the two files that are a part of the distribution of the dataset. This gives you the access to the data. Discovering the data is done through the community portal and the data portal.
- We’ve installed Drupal Organic Groups to facilitate teams of people working on a common initiative within DCO. Many of the collaboration tools are outside of DCO, such as Google Docs, Webex, Skype, Dropbox, Slack and more. Here a team can coordinate their efforts, links to these various collaboration tools, broadcast messages to team members, and provide the links to create content for the group, such as publications and datasets.
- RDA: Research Data Alliance; DTR: Data Type Registry; and PIT: Persistent Identifier Type The primitives in a DTR are comparable to a list of BASIC DATA TYPE CLASSES in the DCO ontology, such as Dataset, Image, Video, and Audio, etc. A registered DCO dataset is asserted as an instance of one of those basic data type classes. It is possible to further annotate a registered dataset with the SPECIFIC DATA TYPES defined within a DTR, and each data type has a unique PID.
- Illustration by Roy Pea and Jillian C. Wallis, from C. L. Borgman, H. Abelson, L. Dirks, R. Johnson, K. R. Koedinger, M. C. Linn, C. A. Lynch, D. G. Oblinger, R. D. Pea, K. Salen, M. S. Smith, and A. Szalay, “Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge. A 21st Century Agenda for the National Science Foundation. Report of the NSF Task Force on Cyberlearning,” Office of Cyberinfrastructure and Directorate for Education and Human Resources. National Science Foundation, Washington, D.C., 2008. ?=smart agents. Open world, semantic agents, with rules… it is notable that these capabilities NEVER made it into the top row of capabilities… ?=data agents. Ones that can find data for you, and perhaps even convert it to the right format, find contextual information, etc.