ISCC - image analysis workflow example 1.0
Visit source
Download RO-Crate
Run on GalaxyAn example how ISCC codes can be used to verify image analysis steps
Associated Tutorial
This workflows is part of the tutorial Content Tracking and Verification in Galaxy Workflows with ISCC-SUM, available in the GTN
Features
- Includes Galaxy Workflow Tests
- Includes a Galaxy Workflow Report
Thanks to...
Workflow Author(s): Maarten W. Paul
Tutorial Author(s): Maarten Paul, Martin Etzrodt
Tutorial Contributor(s): Beatriz Serrano-Solano, Diana Chiang Jurado, Pavankumar Videm, Leonid Kostrykin, Maarten Paul
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| Channel selection | Channel selection | Select the channel to segment |
|
| original_image | original_image | Select an original image that will be processed |
|
| segmented image | segmented image | Select an image segmentation result |
|
Steps
| ID | Name | Description |
|---|---|---|
| 3 | Split image along axes | As the example image contains multiple channels toolshed.g2.bx.psu.edu/repos/imgteam/split_image/ip_split_image/2.3.5+galaxy0 |
| 4 | Generate ISCC-CODE | toolshed.g2.bx.psu.edu/repos/imgteam/iscc_sum/iscc_sum/0.1.0+galaxy1 |
| 5 | Extract dataset | __EXTRACT_DATASET__ |
| 6 | Parse parameter value | param_value_from_file |
| 7 | Otsu | toolshed.g2.bx.psu.edu/repos/imgteam/2d_auto_threshold/ip_threshold/0.25.0+galaxy0 |
| 8 | Verify ISCC-CODE | toolshed.g2.bx.psu.edu/repos/imgteam/iscc_sum_verify/iscc_sum_verify/0.1.0+galaxy1 |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| otsu_segmented | otsu_segmented | n/a |
|
| ISCC-code | ISCC-code | n/a |
|
Version History
master
Creators and Submitter
Views: 491 Downloads: 80 Runs: 0
Created: 16th Feb 2026 at 13:26
Tags AttributionsNone
Related items
Teams: Galaxy Training Network
Organizations: Galaxy
I'm a bot managed by @hexylena to upload workflows from the Galaxy Training Network to the WorkflowHub. If you have any issues with my behaviour please let her know by filing an issue.
Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational biological research.
- Accessible: Users can easily run tools without writing code or using the CLI; all via a user-friendly web interface.
- Reproducible: Galaxy captures all the metadata from an analysis, making it completely reproducible.
- Transparent: Users share and publish analyses via interactive pages that can enhance analyses with user annotations.
- Scalable: Galaxy can run ...
Teams: Galaxy Training Network, usegalaxy-eu, Intergalactic Workflow Commission (IWC)
Web page: https://galaxyproject.org/
The Galaxy Training Network (GTN) is a collection of hands-on tutorials that are designed to be interactive and are built around Galaxy.
These tutorials can be used for learning and teaching how to use Galaxy for general data analysis, as well as a wide array of hands-on tutorials covering specific domains such as assembly, RNA-Seq analysis, deep learning, climate analysis, and more!
