Japan Geoscience Union Meeting 2022

Presentation information

Print

[E] Oral

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI30] Open Science with FAIR Science Data Sharing and Management and e-Infrastructures

Tue. May 24, 2022 3:30 PM - 5:00 PM 201A (International Conference Hall, Makuhari Messe)

convener:Yasuhiro Murayama(NICT Knowldge Hub, National Institute of Information and Communications Technology), convener:Baptiste Cecconi(LESIA, Observatoire de Paris, CNRS, PSL Research University), Yasuhisa Kondo(Research Institute for Humanity and Nature), convener:Shelley Stall(American Geophysical Union), Chairperson:Yasuhiro Murayama(NICT National Institute of Information and Communications Technology), Baptiste Cecconi(LESIA, Observatoire de Paris, CNRS, PSL Research University)

4:15 PM - 4:30 PM

[MGI30-10] Practical Problems in Quality Management of Science Data (What is the role of Data Repositories?)

*Takashi Watanabe1 (1.National Institute of Information and Communications Technology)

Keywords:Open Science, Repository, Management

The FAIR principle has been well recognized to be a guideline of data provision in the Open-Science era. It seems that, however, the provision of quality-controlled data by data repositories is tentatively assumed. The quality management of data will be an essential work of a data repository but the current status of the quality control (QC) of data shows a vast range of the level, from “none of controlled” to “re-edited by repositories”. The final judgment of data quality is expected to be done by individual data users, without suitable information on QC in some cases. In this presentation, examples of practical problems encountered in day-to-day data-management works of worldwide cosmic-ray neutron monitor data at the World Data Center for Cosmic Rays, Nagoya University (https://cidas.isee.nagoya-u.ac.jp/WDCCR/), will be discussed addressing the establishment of a guide-line of QC of science data, particularly for time-series data, in the Open-Science era.