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Sci-K 2021 : 1st International Workshop on Scientific Knowledge Representation, Discovery, and Assessment

Ljubljana, Slovenia
Event Date: Apr 19, 2021 - Apr 23, 2021
Submission Deadline: Jan 25, 2021
Notification of Acceptance: Feb 15, 2021
Camera Ready Version Due : Mar 01, 2021

About

In the last decades, we have experienced a substantial increase in the volume of published scientific articles and related research objects (e.g., data sets, software packages); a trend that is expected to continue. This opens up fundamental challenges including generating large-scale machine-readable representations of scientific knowledge, making scholarly data discoverable and accessible, and designing reliable and comprehensive metrics to assess scientific impact. The main objective of Sci-K is to provide a forum for researchers and practitioners from different disciplines to present, educate from, and guide research related to scientific knowledge. Specifically, we foresee three main themes that cover the most important challenges in the field: representation, discoverability, and assessment.

Call For Paper

Sci-K is calling for high-quality submissions around the three main themes of research related to scientific knowledge: representation, discoverability, and assessment. Topics of interest include, but are not limited to:

  • Representation
    • Data models for the description of scholarly data and their relationships.
    • Description and use of provenance information of scientific data.
    • Integration and interoperability models of different data sources.
  • Discoverability
    • Methods for extracting metadata, entities and relationships from scientific data.
    • Methods for the (semi-)automatic annotation and enhancement of scientific data.
    • Methods and interfaces for the exploration, retrieval, and visualisation of scholarly data.
  • Assessment
    • Novel methods, indicators, and metrics for quality and impact assessment of scientific publications, datasets, software, and other relevant entities based on scholarly data.
    • Uses of scientific knowledge graphs and citation networks for the facilitation of research assessment.
    • Studies regarding the characteristics or the evolution of scientific impact or merit.

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