Call for Papers: Special Issue on Semantic eScience: Methods, tools and applications

Special Issue on Semantic eScience: Methods, tools and applications


In the past few years, a push for open reproducible research has led to a proliferation of community efforts for publicly publishing datasets, software and methods described in scientific publications. These efforts underpin research outcomes much more explicitly accessible. However, the actual time and effort required to achieve this new form of scientific communication remains a key barrier to reproducibility. Furthermore, scientific experiments are becoming increasingly complex, and ensuring that research outcomes become understandable, interpretable, reusable and reproducible is still a challenge. Semantic Web technologies provide a promising means for achieving this goal, enabling more transparent and well-defined descriptions for all scientific objects required for this envisioned form of science and communication.

The goal of this special issue is to collect the most recent and advanced research solutions to bridge the gap between existing scientific communication methods and the vision of a reproducible and accountable open science. Topics include, but are not limited to:

  • Tools, methods and use cases/applications for helping linking existing papers to their research products: data, software, methods and execution traces.
  • New methods for helping linking scientific papers to other papers (e.g., papers that use similar approaches, similar methods, common software, common data, etc.)
  • New methods for helping visualizing and presenting scientific information to scientists (e.g., provenance-based visualizations, summaries, presenting results at different levels of granularity, etc.)
  • New approaches for extracting the specific steps used in a method described expressed in a scientific paper.
  • New methods for generating automated explanations of scientific results.
  • New approaches for comparing methods, protocols and methodologies expressed in scientific papers.
  • New methods to highlight the differences between execution runs of a scientific experiment (based on their configuration, performance, results, etc.)
  • Tools and methods for discovering data and software used in similar publications or to address similar problems.
  • Vocabularies and ontologies that help relate and describe software, data, methods and provenance used in a scientific publication.
  • Vocabularies and ontologies that help capturing and presenting experiment information to scientists.
  • Automatic annotation of scientific research
  • Provenance, quality, privacy and trust of scientific information
  • Novel visualizations of scientific data
  • Novel approaches to apply Linked Data and Semantic Web techniques to scientific workflows


  • Initial submission deadline: November 16th, 2018
  • First round notifications: December 31st, 2018

Guest Editors

  • Tomi Kauppinen, Aalto University, Finland
  • Daniel Garijo, University of Southern California, USA
  • Natalia Villanueva, The University of Texas at El Paso, USA

Guest Editorial Board

  • Federica Cena, Università degli Studi di Torino
  • Carsten Kessler, Aalborg University Copenhagen, Denmark
  • Maxime Lefrançois, Ecole des Mines de Saint-Etienne
  • Tobias Kuhn, VU University Amsterdam
  • Gully Burns, University of Southern California, USA
  • Anita de Waard, Elsevier Labs
  • Amrapali Zaveri, Maastricht University, Netherlands
  • Khalid Belhajjame, University Paris-Dauphine
  • Oscar Corcho, Universidad Politécnica de Madrid, Spain
  • Idafen Santana Pérez, Universidad Politécnica de Madrid, Spain
  • Alexander Garcia Castro, Universidad Politécnica de Madrid, Spain
  • Olga Ximena Giraldo, Universidad Politecnica de Madrid, Spain
  • Jeff Pan, University of Aberdeen, UK
  • Paul Groth, Elsevier Labs, the Netherlands
  • Richard Boyce, University of Pittsburgh
  • Alasdair Gray, Heriot-Watt University, UK
  • Marieke van Erp, VU University Amsterdam