ISBN:
9781608459940
Language:
English
Pages:
1 Online-Ressource (xvii, 160 Seiten)
,
Illustrationen
Edition:
Also available in print
Series Statement:
Synthesis lectures on the semantic web, theory and technology #2
Series Statement:
Synthesis lectures on the semantic web: theory and technology
Parallel Title:
Print version VIVO
DDC:
302.30285
Keywords:
VIVO (Computer file)
;
Scholars Databases
;
Web applications
;
Research Databases
;
Online social networks
;
Universities and colleges Research
;
Databases
;
Semantic Web
Abstract:
The world of scholarship is changing rapidly. Increasing demands on scholars, the growing size and complexity of questions and problems to be addressed, and advances in sophistication of data collection, analysis, and presentation require new approaches to scholarship. A ubiquitous, open information infrastructure for scholarship, consisting of linked open data, open-source software tools, and a community committed to sustainability are emerging to meet the needs of scholars today. This book provides an introduction to VIVO, http://vivoweb.org/, a tool for representing information about research and researchers--their scholarly works, research interests, and organizational relationships. VIVO provides an expressive ontology, tools for managing the ontology, and a platform for using the ontology to create and manage linked open data for scholarship and discovery. Begun as a project at Cornell and further developed by an NIH funded consortium, VIVO is now being established as an open-source project with community participation from around the world. By the end of 2012, over 20 countries and 50 organizations will provide information in VIVO format on more than one million researchers and research staff, including publications, research resources, events, funding, courses taught, and other scholarly activity. The rapid growth of VIVO and of VIVO-compatible data sources speaks to the fundamental need to transform scholarship for the 21st century
Abstract:
1. Scholarly networking needs and desires / Michael Conlon -- 1.1 The world of the scholar today -- 1.2 Research discovery and expert identification -- 1.3 The semantic web -- 1.4 VIVO: a semantic web information infrastructure for scholarship -- 1.5 How VIVO addresses the needs of the scholar today and tomorrow -- References --
Abstract:
2. The VIVO ontology / Jon Corson-Rikert ... [et al.] -- 2.1 Introduction -- 2.2 Semantic technologies -- 2.2.1 Rationale for using semantic standards -- 2.2.2 RDF and OWL -- 2.2.3 Linked data -- 2.2.4 Features of semantic modeling -- 2.3 Design of the ontology -- 2.3.1 Goals -- 2.3.2 Independence -- 2.3.3 The class hierarchy -- 2.3.4 Modeling principles -- 2.4 Relationship to the application -- 2.4.1 Ontology as data model -- 2.4.2 Reasoning -- 2.4.3 Common identifiers for shared individuals -- 2.4.4 External controlled vocabulary references -- 2.4.5 Migrating instance data -- 2.4.6 Integrated ontology editor -- 2.5 Extending the ontology -- 2.5.1 Modeling guidelines -- 2.5.2 Case studies -- 2.6 VIVO ontology community effort -- 2.7 Looking ahead -- 2.7.1 International partnerships -- 2.7.2 Future directions -- References --
Abstract:
3. Implementing VIVO and filling it with life / Valerie Davis ... [et al.] -- 3.1 Preparation for implementation -- 3.1.1 Create your project plan -- 3.1.2 Create your one-pager -- 3.2 The importance of stakeholders -- 3.2.1 Identifying stakeholders -- 3.2.2 What motivates stakeholders? -- 3.2.3 Engaging stakeholders -- 3.3 Identifying sources and negotiating data access -- 3.4 Filling VIVO with life -- 3.4.1 Manual editing -- 3.4.2 Automated ingest -- 3.4.3 Tools for ingest -- 3.4.4 Updating data -- 3.5 Making VIVO a local success -- 3.5.1 Outreach and marketing to community -- 3.5.2 Value-added services sharing data -- 3.5.3 Theme elements and customization of interface -- 3.5.4 Conclusion and general tips for success -- References --
Abstract:
4. Case study : University of Colorado Boulder / Liz Tomich and Alex Viggio -- 4.1 How CU-Boulder chose VIVO -- 4.2 Faculty demographics and reporting tools at CU-Boulder -- 4.2.1 FIS applications -- 4.2.2 FIS team -- 4.3 Implementation strategy -- 4.4 Source data from enterprise systems rather than building new profiles -- 4.5 Our technical environment and getting work done -- 4.5.1 Values and principles -- 4.5.2 Process -- 4.5.3 Practices -- 4.5.4 Tools -- 4.5.5 It partners and VIVO hosting -- 4.5.6 Continuous improvement -- 4.6 Challenges -- 4.7 The current and future value of VIVO to the CU-Boulder campus -- 4.8 Contributing to the VIVO community -- 4.8.1 National implementation support -- 4.8.2 University of Colorado semantic web incubator -- 4.8.3 Implementation workshop --
Abstract:
5. Case study : Weill Cornell Medical College / Paul J. Albert ... [et al.] -- 5.1 Multi-institutional environment -- 5.2 Legacy researcher profiling system -- 5.2.1 Advantages of the legacy system as compared with VIVO -- 5.2.2 Shortcomings of the legacy system as compared with VIVO -- 5.3 Preparation for ingest -- 5.3.1 Setting up the environment -- 5.3.2 Negotiating with system owners and operators -- 5.3.3 Finding and cleaning authoritative data -- 5.3.4 Manual entry vs. automated ingest -- 5.4 Policies and procedures -- 5.4.1 Inaccurate data -- 5.4.2 Sensitive data -- 5.4.3 Preference data -- 5.4.4 What constitutes a minimally populated profile -- 5.4.5 Inclusion criteria -- 5.4.6 Representing data across institutions -- 5.4.7 Federated authentication -- 5.5 Data ingest -- 5.5.1 Data mapping and modeling -- 5.5.2 Harvester and google refine + VIVO -- 5.5.3 Publications metadata -- 5.5.4 Testing -- 5.6 SPARQL query builder -- 5.7 Custom extensions to the ontology -- 5.8 Successes -- 5.9 Select remaining issues -- 5.9.1 Clinical specialty and expertise -- 5.9.2 Self-editing -- 5.9.3 Transition to operations --
Abstract:
6. Extending VIVO / Chris Barnes ... [et al.] -- 6.1 VIVO application overview: functions, components, and tools -- 6.1.1 Key application functions -- 6.1.2 VIVO open-source components -- 6.1.3 Tools developed for VIVO -- 6.2 VIVO application architecture -- 6.2.1 Vitro -- 6.2.2 VIVO: the first extension -- 6.3 Typical VIVO modifications -- 6.3.1 Theme changes -- 6.3.2 Ontology extensions -- 6.3.3 Custom list views -- 6.3.4 Custom templates -- 6.3.5 Menus and pages -- 6.3.6 Logging activity in VIVO -- 6.3.7 Alternative authentication protocols -- 6.3.8 Extensions achieved through VIVO mini-grants -- 6.3.9 Modularity -- 6.4 Tools for data -- 6.4.1 VIVO harvester -- 6.4.2 Data sharing and reuse -- 6.4.3 VIVO multi-institutional search -- 6.5 The VIVO open-source community -- References--
Abstract:
7. Analyzing and visualizing VIVO data / Chintan Tank ... [et al.] -- 7.1 Visualization design philosophy and development goals -- 7.1.1 User friendly and informative -- 7.1.2 Gracefully degrading -- 7.1.3 Modular and robust software -- 7.1.4 Extendible and well-documented software -- 7.2 Social network visualizations -- 7.2.1 Sparkline -- 7.2.2 Temporal graph -- 7.2.3 Map of science -- 7.2.4 Network visualization -- 7.3 VIVO visualization system architecture -- 7.3.1 Front-end visualization libraries -- 7.3.2 Client-server architecture -- 7.3.3 Client-server architecture in action -- 7.4 Accessing, mining, and visualizing VIVO data -- 7.4.1 Data provided by VIVO visualizations -- 7.4.2 Data access and visualization using visualization templates -- 7.4.3 Data retrieval via SPARQL queries or dumps -- 7.5 Insightful visualizations of IRN data -- 7.5.1 Collaboration patterns for medical institutions -- 7.5.2 Top MeSH disease concepts appearing in PubMed publications -- 7.5.3 Identification of collaboration networks in support of funding proposals -- 7.5.4 Inter-institutional collaboration explorer -- 7.6 Discussion and outlook -- 7.6.1 Open social containers and gadgets -- 7.6.2 Federated search and visualizations -- References --
Abstract:
8. The future of VIVO : growing the community / Dean B. Krafft ... [et al.] -- 8.1 Introduction -- 8.2 Upcoming research and development -- 8.2.1 Developing the VIVO application -- 8.2.2 Supporting VIVO collaboration and discovery networks -- 8.3 Integrating VIVO into the researcher ecosystem -- 8.4 Encouraging adoption -- 8.5 Creating an open-source community -- 8.6 A standard for exchanging information about researchers -- 8.7 Summary: VIVO's challenges and opportunities --
Abstract:
Appendix A: VIVO ontology classes, object properties, and data type properties -- Authors' biographies
Description / Table of Contents:
Preface; Structure of the Book; Acknowledgments; Scholarly Networking Needs and Desires; The World of the Scholar Today; Research Discovery and Expert Identification; The Semantic Web; VIVO: A Semantic Web Information Infrastructure for Scholarship; How VIVO Addresses the Needs of the Scholar Today and Tomorrow; References; The VIVO Ontology; Introduction; Semantic Technologies; Rationale for Using Semantic Standards; RDF and OWL; Linked Data; Features of Semantic Modeling; Design of the Ontology; Goals; Independence; The Class Hierarchy; Modeling Principles; Relationship to the Application
Description / Table of Contents:
Ontology as Data ModelReasoning; Common Identifiers for Shared Individuals; External Controlled Vocabulary References; Migrating Instance Data; Integrated Ontology Editor; Extending the Ontology; Modeling Guidelines; Case Studies; VIVO Ontology Community-Effort; Looking Ahead; International Partnerships; Future Directions; References; Implementing VIVO and Filling It with Life; Preparation for Implementation; Create Your Project Plan; Create Your One-Pager; The Importance of Stakeholders; Identifying Stakeholders; What Motivates Stakeholders?; Engaging Stakeholders
Description / Table of Contents:
Identifying Sources and Negotiating Data AccessFilling VIVO with Life; Manual Editing; Automated Ingest; Tools for Ingest; Updating Data; Making VIVO a Local Success; Outreach and Marketing to Community; Value-Added Services Sharing Data; Theme Elements and Customization of Interface; Conclusion and General Tips for Success; References; Case Study: University of Colorado Boulder; How CU-Boulder Chose VIVO; Faculty Demographics and Reporting Tools at CU-Boulder; FIS Applications; FIS Team; Implementation Strategy; Source Data from Enterprise Systems Rather than Building New Profiles
Description / Table of Contents:
Our Technical Environment and Getting Work DoneValues and Principles; Process; Practices; Tools; IT Partners and VIVO Hosting; Continuous Improvement; Challenges; The Current and Future Value of VIVO to the CU-Boulder Campus; Contributing to the VIVO Community; National Implementation Support; University of Colorado Semantic Web Incubator; Implementation Workshop; Case Study: Weill Cornell Medical College; Multi-institutional Environment; Legacy Researcher Profiling System; Advantages of the Legacy System as Compared with VIVO; Shortcomings of the Legacy System as Compared with VIVO
Description / Table of Contents:
Preparation for IngestSetting Up the Environment; Negotiating with System Owners and Operators; Finding and Cleaning Authoritative Data; Manual Entry vs. Automated Ingest; Policies and Procedures; Inaccurate Data; Sensitive Data; Preference Data; What Constitutes a Minimally Populated Profile; Inclusion Criteria; Representing Data Across Institutions; Federated Authentication; Data Ingest; Data Mapping and Modeling; Harvester and Google Refine+VIVO; Publications Metadata; Testing; SPARQL Query Builder; Custom Extensions to the Ontology; Successes; Select Remaining Issues
Description / Table of Contents:
Clinical Specialty and Expertise
Note:
Part of: Synthesis digital library of engineering and computer science
,
Includes bibliographical references
,
Abstract freely available; full-text restricted to subscribers or individual document purchasers
,
1. Scholarly networking needs and desires
,
6. Extending VIVO
,
1.1 The world of the scholar today ; 1.2 Research discovery and expert identification ; 1.3 The semantic web ; 1.4 VIVO: a semantic web information infrastructure for scholarship ; 1.5 How VIVO addresses the needs of the scholar today and tomorrow ; References ; 2. The VIVO ontology
,
2.1 Introduction ; 2.2 Semantic technologies ; 2.2.1 Rationale for using semantic standards ; 2.2.2 RDF and OWL ; 2.2.3 Linked data ; 2.2.4 Features of semantic modeling ; 2.3 Design of the ontology ; 2.3.1 Goals ; 2.3.2 Independence ; 2.3.3 The class hierarchy ; 2.3.4 Modeling principles ; 2.4 Relationship to the application ; 2.4.1 Ontology as data model ; 2.4.2 Reasoning ; 2.4.3 Common identifiers for shared individuals ; 2.4.4 External controlled vocabulary references ; 2.4.5 Migrating instance data ; 2.4.6 Integrated ontology editor ; 2.5 Extending the ontology ; 2.5.1 Modeling guidelines ; 2.5.2 Case studies ; 2.6 VIVO ontology community effort ; 2.7 Looking ahead ; 2.7.1 International partnerships ; 2.7.2 Future directions ; References ; 3. Implementing VIVO and filling it with life
,
3.1 Preparation for implementation ; 3.1.1 Create your project plan ; 3.1.2 Create your one-pager ; 3.2 The importance of stakeholders ; 3.2.1 Identifying stakeholders ; 3.2.2 What motivates stakeholders? ; 3.2.3 Engaging stakeholders ; 3.3 Identifying sources and negotiating data access ; 3.4 Filling VIVO with life ; 3.4.1 Manual editing ; 3.4.2 Automated ingest ; 3.4.3 Tools for ingest ; 3.4.4 Updating data ; 3.5 Making VIVO a local success ; 3.5.1 Outreach and marketing to community ; 3.5.2 Value-added services sharing data ; 3.5.3 Theme elements and customization of interface ; 3.5.4 Conclusion and general tips for success ; References ; 4. Case study : University of Colorado Boulder
,
4.1 How CU-Boulder chose VIVO ; 4.2 Faculty demographics and reporting tools at CU-Boulder ; 4.2.1 FIS applications ; 4.2.2 FIS team ; 4.3 Implementation strategy ; 4.4 Source data from enterprise systems rather than building new profiles ; 4.5 Our technical environment and getting work done ; 4.5.1 Values and principles ; 4.5.2 Process ; 4.5.3 Practices ; 4.5.4 Tools ; 4.5.5 It partners and VIVO hosting ; 4.5.6 Continuous improvement ; 4.6 Challenges ; 4.7 The current and future value of VIVO to the CU-Boulder campus ; 4.8 Contributing to the VIVO community ; 4.8.1 National implementation support ; 4.8.2 University of Colorado semantic web incubator ; 4.8.3 Implementation workshop ; 5. Case study : Weill Cornell Medical College
,
5.1 Multi-institutional environment ; 5.2 Legacy researcher profiling system ; 5.2.1 Advantages of the legacy system as compared with VIVO ; 5.2.2 Shortcomings of the legacy system as compared with VIVO ; 5.3 Preparation for ingest ; 5.3.1 Setting up the environment ; 5.3.2 Negotiating with system owners and operators ; 5.3.3 Finding and cleaning authoritative data ; 5.3.4 Manual entry vs. automated ingest ; 5.4 Policies and procedures ; 5.4.1 Inaccurate data ; 5.4.2 Sensitive data ; 5.4.3 Preference data ; 5.4.4 What constitutes a minimally populated profile ; 5.4.5 Inclusion criteria ; 5.4.6 Representing data across institutions ; 5.4.7 Federated authentication ; 5.5 Data ingest ; 5.5.1 Data mapping and modeling ; 5.5.2 Harvester and google refine + VIVO ; 5.5.3 Publications metadata ; 5.5.4 Testing ; 5.6 SPARQL query builder ; 5.7 Custom extensions to the ontology ; 5.8 Successes ; 5.9 Select remaining issues ; 5.9.1 Clinical specialty and expertise ; 5.9.2 Self-editing ; 5.9.3 Transition to operations
,
6.1 VIVO application overview: functions, components, and tools ; 6.1.1 Key application functions ; 6.1.2 VIVO open-source components ; 6.1.3 Tools developed for VIVO ; 6.2 VIVO application architecture ; 6.2.1 Vitro ; 6.2.2 VIVO: the first extension ; 6.3 Typical VIVO modifications ; 6.3.1 Theme changes ; 6.3.2 Ontology extensions ; 6.3.3 Custom list views ; 6.3.4 Custom templates ; 6.3.5 Menus and pages ; 6.3.6 Logging activity in VIVO ; 6.3.7 Alternative authentication protocols ; 6.3.8 Extensions achieved through VIVO mini-grants ; 6.3.9 Modularity ; 6.4 Tools for data ; 6.4.1 VIVO harvester ; 6.4.2 Data sharing and reuse ; 6.4.3 VIVO multi-institutional search ; 6.5 The VIVO open-source community ; References ; 7. Analyzing and visualizing VIVO data
,
7.1 Visualization design philosophy and development goals ; 7.1.1 User friendly and informative ; 7.1.2 Gracefully degrading ; 7.1.3 Modular and robust software ; 7.1.4 Extendible and well-documented software ; 7.2 Social network visualizations ; 7.2.1 Sparkline ; 7.2.2 Temporal graph ; 7.2.3 Map of science ; 7.2.4 Network visualization ; 7.3 VIVO visualization system architecture ; 7.3.1 Front-end visualization libraries ; 7.3.2 Client-server architecture ; 7.3.3 Client-server architecture in action ; 7.4 Accessing, mining, and visualizing VIVO data ; 7.4.1 Data provided by VIVO visualizations ; 7.4.2 Data access and visualization using visualization templates ; 7.4.3 Data retrieval via SPARQL queries or dumps ; 7.5 Insightful visualizations of IRN data ; 7.5.1 Collaboration patterns for medical institutions ; 7.5.2 Top MeSH disease concepts appearing in PubMed publications ; 7.5.3 Identification of collaboration networks in support of funding proposals ; 7.5.4 Inter-institutional collaboration explorer ; 7.6 Discussion and outlook ; 7.6.1 Open social containers and gadgets ; 7.6.2 Federated search and visualizations ; References ; 8. The future of VIVO : growing the community
,
8.1 Introduction ; 8.2 Upcoming research and development ; 8.2.1 Developing the VIVO application ; 8.2.2 Supporting VIVO collaboration and discovery networks ; 8.3 Integrating VIVO into the researcher ecosystem ; 8.4 Encouraging adoption ; 8.5 Creating an open-source community ; 8.6 A standard for exchanging information about researchers ; 8.7 Summary: VIVO's challenges and opportunities ; Appendix A: VIVO ontology classes, object properties, and data type properties ; Authors' biographies.
,
Also available in print.
,
System requirements: Adobe Acrobat Reader.
,
Mode of access: World Wide Web.
DOI:
10.2200/S00428ED1V01Y201207WEB002
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