See the technical program schedule for workshop location and time.
A new wave of knowledge technologies is sparking innovation in eScience, including the emergence of large knowledge graphs created from text extraction and crowdsourcing, the rise of Wikidata as a nexus for core entities and resources in science, the proposed open knowledge networks of scientific content that include provenance and natural interfaces, and the advent of web-scale semantic dataset search using standard schemas. Given that semantics and ontologies have enabled many scientific advances, these new knowledge technologies offer exciting possibilities that will be discussed at this workshop.
Research addressing global challenges federates a growing diversity of disciplines, requires sustained contributions from many autonomous organizations and builds on heterogeneous evolving computational platforms. Scientific knowledge is scattered across cloud-based services, local storage, and in source code targeting specific architectures and computational contexts. Concepts reflected in disparate sources are hardly computer-communicable and computer-actionable across or even within disciplines. This workshop focuses on platform-driven and domain-specific developments that aim to unify underlying platforms, clouds, data, computational resources and concepts in order to empower research developers to deliver increasingly complex eScience systems.
The emergence of Data Science technologies that combine Cloud Computing, Big Data and Data Analytics technologies as specialized fields in computing is motivating development of new teaching methods in course design to provide education in the techniques and technologies needed to extract knowledge from large datasets in virtualized environment. In current literature there is a lack of well-articulated learning resource for beginners that would integrate administrative, programing, and algorithm design aspects of related domains. We believe it is important to allow students, researchers, and professionals to understand cross-domain aspects of these challenges before they embark on further exploration of these fields. A small number of high-quality contributions will be presented during the workshop. At the end of the workshop, a forum discussion is planned to debate on future directions of curricula and teaching methods in Data Science, Big Data, and Cloud Computing.
Addressing emerging grand challenges in scientific research, health, engineering or global consumer services necessitates dramatic increases in responsive supercomputing and extreme data capacities. The half-day workshop Platform-driven e-Infrastructure Innovations (EINFRA) addresses projects and use cases that deal with extreme data or computing challenges. They will benefit from this workshop by discussing commonalities and differences among their different approaches.
Scholarly Communication has evolved significantly, with increasing focus on Open Research, FAIR data sharing and community-developed open source methods. The concepts of authorship and citation are changing, as researchers are increasingly reusing and evolving common software tools and datasets. Yet with a growing amount of cloud compute power and open platforms available, reproducibility of computational analyses becomes more challenging, and not yet commonly included in peer review.
While recent advances in scientific workflows and provenance capture systems have improved on this situation, Research Objects propose a way to package, describe, publish, archive, explore and understand digital research outputs by reusing existing Web standards and formats. In this workshop we will explore recent advancements in Research Objects and research data packaging, and attempt to address the challenges remaining to increase Research Object uptake with data providers, researchers, infrastructures, publishers and other stakeholders.