FAIR discovery - Open Knowledge Maps - Kraker Peter
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Open Knowledge Maps aims to enhance the visibility of scientific knowledge through an open access framework, targeting the issue of uncitedness in research. The organization focuses on improving data discoverability and usability across disciplines while advocating for open science and innovative user interfaces. Their work plan includes assessing current use cases, defining open ecosystem standards, and implementing this ecosystem to facilitate easier research access.
FAIR discovery - Open Knowledge Maps - Kraker Peter
- 1. @OK_Maps
- 2. Uncitedness (publications): 12% - 82% (Larivière & Gingras 2009) Uncitedness (data): 85% (Peters et al. 2016) Transfer to practice (medicine): 14%, taking 17 years (Balas 1998)
- 3. Who we are Open Knowledge Maps is a charitable non-profit organization dedicated to dramatically improving the visibility of scientific knowledge for science and society alike
- 4. Overview of heart diseases
- 7. Advantages
- 8. Open science, all the way open sourceopen content open data open roadmap
- 9. Largest visual search engine for research First 2.5 years: ½ million visits on the site 100,000+ maps created 1000+ participants in workshops
- 11. Core Team
- 12. Advisors
- 13. Enthusiasts
- 14. Partners
- 15. Networks
- 16. Findability vs. discoverability Findability = attribute of the (meta-)data Discoverability = attribute of the infrastructure FAIR is a precondition to discoverability
- 17. The open discovery infrastructure Libraries, Archives, Repositories, Aggregators Meta aggregators Institutions, Researchers, Publishers Value added services A cycle of continuous innovation
- 18. DISCOVERY
- 19. Discovery IN – Rationale Discoverability is a key challenge: up to 85% of research data is not reused Lack of adequate user interfaces for data discovery Many market entrants following a closed/proprietary model o Prevents reuse o Takes away control and governance from the researchers and research institutions
- 20. Discovery IN – Purpose & Objective Provide user interfaces and other user-facing services for data discovery across disciplines Explore new and innovative ways of enabling discovery Apply user involvement and participatory design, going beyond academia Create FAIR and open infrastructures
- 21. Discovery IN – Workplan Stocktaking of relevant use cases as well as indices, interfaces and services Structuring: Defining the standards and structure of an open ecosystem for discovery that fulfils the use cases Implementation: Working towards implementation of the ecosystem