WPI-NanoLSI Special Computational Workshop on Digital Solutions on Feb. 8, 2023
Digital data management, advanced analysis, and machine learning
Modern research efforts benefit from a rapidly increasing ability to generate a wealth of experimental and modelling data. Recent developments in data science, machine learning, and automation provide powerful opportunities to leverage this research data. To use scientific data as a resource beyond its initial publication, database infrastructures and digital workflows must be established to collect, store, organise and analyse the data, with records of how it was produced, and how it was processed or transformed afterwards. Good management of research data is of critical importance to knowledge-led discovery and innovation, and is increasingly embedded in the requirements of research journals and funding bodies. It is a prerequisite step in the application of data science and machine learning techniques in research. In this workshop, we highlight some examples of modern data management and discuss case studies involving the application of machine learning to scientific data. Emphasis is placed on applications to Scanning Probe Microscopy (SPM) in general and Atomic Force Microscopy (AFM) in particular.
Date & Time
Wednesday, February 8, 2023 9:30 am – 4:30 pm
Dr. Damien Hall, Computational Science, Nano Life Science Institute (WPI-NanoLSI)
Hybrid (In person & Online)
- Venue: Main Conference Room, 4th floor of NanoLSI bldg.
- Online: Zoom link will be provided
Registration has been closed. From now on, we’ll accept on-site participation only. Please come to the venue directly on the day without registration.
*Registration deadline: 10:00 am of February 6
February 8th – Wednesday 9:30am – 12:30pm
Adam Foster: Workshop overview, digital workflows, open data, advanced analysis
David Gao: Introduction to digital data and machine learning tools
Filippo Canova: Highly optimized data management tools
February 8th – Wednesday 1:30pm – 4:30 pm
Adam Foster: Advanced analysis of AFM images
Niko Oinonen: Disease recognition from AFM adhesion measurements
Damien Hall: Computational AFM tools for biophysical measurements of dynamic surfaces
Nano Life Science Institute, Kanazawa University