Available Training Resources modules are dedicated to providing a condense overview of available training resources and e-learning platforms dedicated to light microscopy, electron microscopy, preclinical and medical imaging, and core facility management & operation.
This module provides an overview of light microscopy training resources and e-learning platforms suitable for researchers and staff working in the field of light microscopy.
MyScope is an online learning platform developed by Microscopy Australia.
MyScope provides in-depth information into topics focused on light microscopy and electron microscopy. In addition, MyScope provides an opportunity to operate a virtual high-end microscope mimicking real life experience.
MyScope is designed for researchers interested in learning microscopy technologies and for staff of imaging core facilities to assit with the user training.
Presenter: Jenny Whiting, Microscopy Australia
BioImaging North America (BINA) is a network of imaging communities in Canada, United States, and Mexico.
BINA hosts a section dedicated to training and education that contains a list of training resources in light and electron microscopy.
BINA Training Resource is powered by MicroscopyDB database.
iBiology provides open access to free video content including the content that focuses on the topic of light microscopy.
Over 70 high quality videos that cover many topics including introduction to microscopy, large number of microscopy technologies from laser scanning confocal microscopy to super-resolution imaging and light sheet microscopy to image analysis are delivered by world leading instructors from all over the globe.
Full table of contents along with video descriptions can be accessed here.
Andor Microscopy Training consists of free video lectures that cover essential light microscopy topics.
A large number of free online resources are available in the field of light microscopy:
This module provides an overview of image analysis resources and e-learning platforms suitable for researchers and staff working in the field of light microscopy.
Introduction to Bioimage Analysis is a book that is written primarily for biologists and focuses on teaching image analysis concepts.
Developed and managed by: Peter Bankhead, University of Edinburgh, United Kingdom
Image Analysis Training Resource is a resource for instructors who teach image analysis.
The modules available can be used as a ready to go material for teaching image analysis to researchers. Modules cover large number of image analysis concepts and include theoretical basis of each concept, practical activities in a particular software to understand the concept as well as assessment along with a solution to test one's knowledge. In addition, follow up material is provided for each concept.
This resource is built on Carpentries Style platform.
Bioimage Data Analysis is a book for life science researchers providing detailed information on bioimage analysis tools available for reserachers, ImageJ macro language, introduction to Matlab and context specific image analysis.
Bioimage Data Analysis Workflows is an open access book for life science researchers that provides practical information on how to quantitatively analyze data images. This book contains practical examples of image analysis using image analysis tools such as ImageJ, MatLab and Python.
Presenter: Robert Haase, Dresden University of Technology, Germany
This Jupyter book contains training resources for scientists who want to dive into image processing with Python.
This book is specifically aimed at students and scientists working with microscopy images in the life sciences. The book starts with basics of Python programming. The images will be processed using numpy, scipy, scikit-image, SimpleITK and clEsperanto. Napari will be explored for interactive image data analysis and the Napari-Assistant for generating Jupyter Notebooks from interactively designed image processing workflows.
Prerequisites: basic programming and image analysis knowledge.
This project has been made possible in part by a grant from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation.
