ICSTI General Assembly & Workshops in the Library of Congress in Washington D. C.

Workshops: "Next generation metrics for open science" and "Machine Learning and Its Applications to Scientific and Technical Information"

The International Council for Scientific and Technical Information (ICSTI) organizes two workshops in Washington D. C. (USA) as part of the annual General Assembly on 26 October 2017.

The workshop "Next generation metrics for open science", organized by the ICSTI Information Trends and Opportunities Committee (ITOC), will focus on how to determine the impact and value of research. As science shifts towards collaborative endeavour, transparency of process and increasing significance of data driven research, new modes of work and expertise are emerging in academia. However, common metrics which aim to benchmark the impact and value of research mostly emphasize traditional scientific outputs (publications in high impact journals). Novel bases and methods of research and forms of scholarly communication are not included, e.g. data curation, data publication, new modes of scientific output including video abstracts, blogs, micropublications and the sharing of scientific tools and software. To develop new modes of scholarly communication and activity that ensure transparency, reproducibility and reusability, additional systems are necessary to recognize and value new scientific roles (e.g. data experts) and new incentive/accreditation processes for science researchers. This workshop presents thoughts on how to address these imperatives for change. Margret Plank, Head of the Competence Centre for Non-Textual Materials at the Technical Information Library (TIB) and Chair of the ITOC, presents the workshop.

The ICSTI workshop "Machine Learning and Its Applications to Scientific and Technical Information", which is organized by the Technical Activities Coordinating Committee (TACC), focuses on the subject of "Machine Learning", which may be the next great innovation in knowledge search and discovery. Machine learning describes what happens in machines that get trained to perform a task by exposure to examples of what they’re supposed to learn.  It’s already happening all around us in the development of facial and object recognition; self-driving cars; instant language translation; and speech recognition. This workshop will explore machine learning and its applications relevant to science and, more specifically, to various forms of scientific and technical information, including images, data, and text.

More information about the workshops

Date: 26 October 2017
Location: Library of Congress, Washington D. C. (USA)