A MATLAB Live Script with accompanying
to access and analyze Microscopy image data sets from the Image Data Resource database
Use this tutorial to get started with freely available microscopy data at Image Data Resource directly from MATLAB.
- No downloads, no installations
- Open directly in MATLAB Online™ by clicking this
- Step-by-step tutorial shows how to
- Re-use available data. Access a list of openly available projects on Open Microscopy
- Query and inspect the metadata associated with these projects using commands directly from MATLAB (RESTful API)
- Avoid downloads. Access specific data from within the database directly and avoid time-consuming downloads of large data
- Analyze image data to identify cells
- Let others run your code and reproduce your results quickly. Pubish the results on GitHub and make them accessible using Open With MATLAB Online
- Allow people to cite you! Generate a DOI® for your code by linking your GitHub repository to one of several DOI-generating sites.
- Live Script contains easy-to-use menus for user to click and select different datasets
- Available on File Exchange for directly installing onto your MATLAB path with one click using the Add-Ons button
- Accompanying Jupyter notebook (.ipynb) for use in a Jupyter environment. More information on MATLAB kernel here
- Accompanying Code Ocean® reproducible capsule (.m) for one-click reproducibility of the code by anyone, including reviewers.
The Image Data Resource (IDR) is a public repository of image datasets from published scientific studies, where the community can submit, search and access high-quality bio-image data. It can be accessed at https://idr.openmicroscopy.org/
For advanced users A detailed guide to the Image Data Resource API can be found here. To access the REST API use the MATLAB webread function
This tutorial uses the following products
- MATLAB
- Image Processing Toolbox ™
This code has been developed and tested using MATLAB 2023b
Note This tutorial works best when delivered by a tutor. It is important to highlight best practices when working with Open Data, publishing Open Code or making research output reproducible