STAM 20th Anniversary Symposium

Open Science and Materials Research

November 1, 2019 (Fri.) 13:00~17:25
The University of Tokyo, Sanjo Conference Hall
Language: Japanese / English (no simultaneous interpretation)

NIMS WEEK 2019 Top > STAM Symposium Top

Forecasting the frontiers of materials science
based on analysis of open data

Science and Technology of Advanced Materials (STAM) celebrates its 20th anniversary in 2019. Now, inspired by two decades of publishing premier articles on research in materials science the editors are focusing on the importance of sharing discovery and innovation based on “Data Science” for the advancement of next-generation research on materials science.
In this symposium internationally renowned experts will share their insights into approaches for Open Data—the source of Date Science—and STAM editors will describe their views on recent trends and future publication of materials research in STAM.

PROGRAM

The symposium commemorates the 20th anniversary of STAM, an open access journal for materials science. The editors will describe STAM’s approach towards creating a data-driven journal and global trends in materials development using open data.

STAM 20th Anniversary Commemorative Symposium

STAM is an open-access journal ranked 78 out of /285 journals in the category of materials science/multidisciplinary worldwide. The editors will discuss future plans for STAM.

STAM Awards Ceremony/
Commemorative Lecture

The STAM Best Paper Award recognizes research that conveys the appeal of materials science to a wide cross-section of society and made a significant impact on materials research. A commemorative speech will be given for this award this year.

Invited Lectures

The symposium will include invited talks on US / EU / Japan open data strategies, such as the current status and activities of materials R&D based on AI and big data around Silicon Valley, and beyond.

Academic Lectures

Six researchers will present their activities on advanced materials research related to open data and machine learning.

UPDATE

2019.09.09
“Day 4” special site released.