[Special issue] Quantitative Science Studies (QSS) on "Scientific Knowledge Graphs and Research Impact Assessment"

Dear colleagues,

Please find attached below the details of the call for papers for the
Quantitative Science Studies (QSS) special issue on “Scientific Knowledge
Graphs and Research Impact Assessment”.

Aim and Scope

In the last decades, there has been a huge increase in the volume of
published scientific articles and related research objects (e.g., data
sets, software packages). This trend gives rise to important challenges.

On the one hand, we have challenges related to the representation and
organisation of such data. We urge for flexible, context-sensitive,
fine-grained, and machine-actionable representations of scholarly knowledge
that at the same time are structured, interlinked, and semantically rich,
such as Scientific Knowledge Graphs (SKGs).

On the other hand, we have challenges related to research impact
assessment. Due to the aforementioned growth in the volume of research
outputs, rigorous approaches to research assessment are now more valuable
than ever. In this context, we urge for reliable and comprehensive metrics
and indicators of the scientific impact and merit of publications, data
sets, research institutions, individual researchers, and other relevant
entities.

More details available here:
https://www.mitpressjournals.org/pb-assets/pdfs/Calls%20for%20Papers/QSS_CFP_2020.pdf
.

Topics of interest

   -

   Models:
   -

      Data models for the description of scholarly data and their
      relationships
      -

      Description and use of provenance information of scientific data
      -

   Methods:
   -

      Methods for extracting metadata, entities and relationships from
      scientific data
      -

      Methods for the (semi-)automatic annotation and enhancement of
      scientific data
      -

      Novel methods, indicators, and metrics for quality and impact
      assessment of scientific publications, datasets, software, and other
      relevant entities based on scholarly data
      -

      Methods and interfaces for the exploration, retrieval, and
      visualisation of scholarly data focussing on facilitating impact
assessment
      -

      Studies of scientific knowledge graphs and citation networks for
      scholarly articles, data and software


Important Dates

   -

   Full paper submission: 31 January 2021
   -

   Review reports and invitation to submit revised paper: 31 March 2021
   -

   Revised paper submission: 31 May 2021
   -

   Expected publication: September 2021


Best regards,

Paolo Manghi, ISTI-CNR, Italy

Andrea Mannocci, ISTI-CNR, Italy

Francesco Osborne, The Open University, UK

Dimitris Sacharidis, TU Wien, Austria

Angelo Salatino, The Open University, UK

Thanasis Vergoulis, “Athena” RC, Greece

-- Guest editors