Symbiosis—Is Collaboration the New Innovation? (Part 3 of 3), Mike Conlon
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This document discusses five trends in scholarship and collaboration that require open data approaches: 1. Molecular science problems have become more complex, as have other scientific problems 2. The rise of "big data" has resulted in extremely large datasets 3. The internet has impacted science by enabling new forms of collaboration 4. There is a growing emphasis on "team science" with collaboration between researchers 5. To study modern scholarship, open data is needed regarding scholarship activities, including persistent identifiers for people, publications, organizations, and the connections between them. Common data models and an engaged community are also important.
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Symbiosis—Is Collaboration the New Innovation? (Part 3 of 3), Mike Conlon
- 1. Scholarship, Collaboration and Data Michael Conlon, PhD Emeritus Faculty Member, University of Florida VIVO Project Director, Duraspace https://dx.doi.org/10.6084/m9.figshare.3187627
- 2. Five Trends
- 4. The Problems Got Hard 2. Increasing complexity of scientific problems
- 5. Data Got Big (IDR Slide) 3. Big Data
- 6. 4. Internet impact on science
- 8. Responding to the Trends
- 11. ScienceMap. Examine collections of publications for individuals, work groups, institutions
- 13. Co-Funded Network 2008 2012 Main Component: what changes have occurred between 2008 and 2012? • More of Health Science Center comes under the CTSI umbrella • The CTSI has a broader reach in the whole network • Increasingly the CTSI incorporates all researchers in relevant areas (areas not relevant to CTSI research fields naturally remain out of its network) Chris McCarty & Raffaele Vacca, UF Bureau of Business and Economic Research • Data source: UF Division of Sponsored Research award data • Each node represents one Contract PI, Project PI or Co-PI linked by a common PeopleSoft Contract number • Nodes are sized by Total Awarded in UF fiscal year (July-June)
- 14. To study scholarship we need open data regarding scholarship
- 15. 1. Open Persistent Identifiers
- 16. Everything has identifiers ORCiD Person identifier 0000-0002- 1304-8447 Profile identifier http://openvivo.o rg/a/orcid0000-0 002-1304-8447 Winecker, R. E., Goldberger, B. A., Tebbett, I. R., Behnke, M., Eyler, F. D., Karlix, J. L., Wobie, K., Conlon, M., Phillips, D, and Bertholf, R. L. “Detection of Cocaine and Its Metabolites in Breast Milk,” Journal of Forensic Science. 46(5), 2001, pp 1221-1223 Reference identifier http://openviv o.org/a /pmid115695 68 PubMed ID 11569568 (no DOI)
- 17. Persistent identifiers People: ORCiD Journals: ISSN Scholarly works: DOI Organizations: ISNI, GRID??
- 18. 2. Open, Common Data Models
- 19. … And the connections between them
- 21. CERIF Slide
- 22. 3. A community of vision
- 23. Community
- 24. Partners slide
- 25. DENVER, COLORADO | AUGUST 17-19, 2016 | www.vivoconference.org
Editor's Notes
- The rise of the molecular. Personalized medicine. Full base pair sequencing. Metabolomics.
- More difficult problems. We work to cure cancer. Risk factors, genetics, metabolomics, surgical procedures, radiology, chemotherapy, life style changes.
- Data got big. Terabytes, Petabytes, Exabytes.
- Internet speed and disintermediation. Expectations changing, openness, commonality, globalization, reuse, altmetrics (“downloads and citations”)
- Personalized medicine team. Lab, Hospital, Pathology, Pharmacy, Infomatics, Clinicians, Pharmacogenomics
- How do universities, research groups, professional societies, scholarly publishers respond to these trends?
- Use data to study science and scholarship itself. Science of Team Science. Who publishes with who? Who is funded with who? Who has written a proposal with who? Who has created data with who? Who has mentored who? Who did what on each project – beyond authorship – who did the data analysis, who designed the data capture, who created the illustrations, who managed the team?
- Profiles organize data in a familiar format. Provide a simple opportunity for discovery – for graduate students seeking advisors, for junior investigators seeking mentors, for senior investigators assembling grant teams.
- Tools for discovering research. Here, sample of University of Florida publications plotted on the UCSD science.
- So here’s the simple view – a faculty profile. Assembled by machines. Can be finished off by the faculty member. All links are to other objects in the semantic web. Positions, visualizations, organizations, people, web sites. Navigation via search, facets, link traversal. Note the RDF link for techno guys.
- Today we will focus on challenges in the data of scholarship. We want to draw those pictures. For everyone. All the time. So good decisions can be made about teams. Here are three big ideas for reshaping the scholarly ecosystem
- The scholarly ecosystem has made extraordinary strides over the past five years in creating, promoting, and disseminating open persistent identifiers, building them into publishing workflows. But there is more work to do.
- Common, shared data models for the things of science/scholarship and the connections between them. Many kinds of connections between each type of object. Objects have significant complexity. What do we mean by “project” – a human subject study? A clinical trial?
- Partners slide
- We are building a community of people who share a common interest in research discovery.