Semantic Web Technologies for Open Science

Dear Colleagues,

Open science and the adoption of the FAIR principles have led to the development of tools and methodologies for enabling faster and reliable research across scientific disciplines.

As research becomes a key factor in addressing key social and environmental issues (disease outbreaks, climate change, etc.), it is crucial to ensure that scientific products, including research data, software, and methods, become understandable and reusable by other scientists. Semantic web technologies have emerged as a means to provide more transparent descriptions for open science that can be consumed both by humans and machines.

The goal of this Special Issue is to showcase novel tools, applications, methodologies, and best practices that use semantic technologies for enabling open science. Topics include, but are not limited to, the following:

a. Semantic open science technologies (SOST) and methodologies for easing the usability and understandability of scientific data, scientific software, and scientific methods:

• Algorithms, methods, and tools to find, interpret, describe, annotate, augment, and share scientific data;
• Algorithms, methods, and tools to find, describe, annotate, document, and reuse scientific software and protocols;
• Algorithms, methods, and tools to help compare scientific data, software, and methods;
• Algorithms, methods, and tools to help interrelate similar research products;
• Novel approaches for scientific data representation and visualization;
• Vocabularies and best practices for enabling trust in scientific results.

b. SOST for improving the science of science:

• New approaches for automating scientific analyses (hypothesis evaluation, meta-analysis, etc.);
• Algorithms, methods, and tools to help with reproducing, explaining, summarizing, or abstracting scientific results;
• New approaches for helping scientific collaboration in multidisciplinary domains.

c. Applications of SOST in domain-specific domains:

• SOST for enabling faster emergency response;
• SOST for creating collaborative learning resources;
• SOST for improving sustainable development (sensor data, citizen science, etc.);
• SOST for multidisciplinary domains.

d. SOST for knowledge management:

• SOST for mining scientific products (data, software, methods, papers) from distributed repositories;
• SOST for creating, maintaining, and curating scientific knowledge graphs.

Dr. Daniel Garijo
Dr. Idafen Santana Pérez
Guest Editors

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