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WP8: Knowledge Sharing

WP8: Knowledge Sharing. Lead: UDE Partners: UMA, CICE, MTA KSZI, FR, RICYT Month 1 - Month 36. Overview. Social dimensions and models related with scientific processes with respect to knowledge sharing and knowledge flows Research question: H ow the new knowledge is generated,

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WP8: Knowledge Sharing

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  1. WP8: Knowledge Sharing Lead: UDE Partners: UMA, CICE, MTA KSZI, FR, RICYT Month1 - Month36

  2. Overview • Social dimensions and models related with scientific processes with respect to knowledge sharing and knowledge flows • Research question: How the new knowledge • is generated, • can be identified by patterns, • is spread within scientific communities, • can be transferred to other areas of society • Will provide and exemplify tools and indicators (based on WP 6) to measure the social appropriation of knowledge among different actors

  3. Specific objectives • Definition of indicators for knowledge flow and sharing in scientific communities • Identification and characterisation of examples • "Deep" (software-supported) analysis of selected examples • Provision of guidelines for decision makers. WP8 will be aware of possible effects between knowledge sharing and mobility, productivity and development of new disciplinary fields to make results comparable with the other case studies.

  4. T8.1: Taxonomy of indicators for knowledge flow and sharing • Critical review of existing approaches to modeling knowledge flow and sharing in scientific communities • Identification of the main types of • actors and relationships, • communication channels, • "knowledge objects" (documents or data types) • Integration in a taxonomy of specific indicators Additional aspects: distinction formal/informal knowledge; including meta knowledge “who is doing what” R: RICYT (2PM) C: UDE (2PM), MTA (2PM), FR (2PM), CICE (1PM)

  5. T8.2: Ident. and first charact. of example communities • Identify interesting communities for case study by: • innovativeness • social and scientific relevance • accessibility to data and results • Description of the communities by the taxonomy (8.1) baseline for systematic theoretical sampling of the cases for the deeper analysis Preferred: life sciences and nanotechnology, e.g. CENIDE UDE and Gene Ontology R: UDE (2PM). C: RICYT (2PM), FR (1PM)

  6. T8.3: Software-based "deep" analysis • Identify relevant data sources • Access data by inventory from WP6 • SNA and semantic analysis of data considering dynamic (time dimension) • Characterizeby structural and process patterns • Adapt and verify data format (semantic, structural, dynamic) • Evaluate and interpret considering indicators from WP4 R: UDE (2PM) C: UMA (2PM), CICE (1PM), FR (1PM), RICYT (1PM)

  7. T8.4: Best practices and recommendations Characterize best practices in the evolution of scientific communities to support: • strategic decisions around scientific innovations • right balance between informal and formal science • right sense of with whom one should share knowledge at which phase of research or growth of the field R: UDE (2PM) C: FR (2PM), RICYT (2PM), CICE (1PM), MTA (1PM)

  8. Deliverables • 8.1 Report on data collection and specification of needs for the construction of mobility indicators (RP:UDE,RV:CICE, C: all inv. / M8) • 8.2 Preliminary report - taxonomy and examples (RP: UDE, RV: RICYT, C: all inv. / M30) • 8.3 Analysis report (RP: UDE, RV: FR, C: all inv. / M26) • 8.4 Guideline for decision makers for knowledge sharing (RP: UDE, RV: CICE, C: all inv. / M30)

  9. WP dependencies

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