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NEUROBLOCKS STUDIO · Examples & Tutorials

Getting Started: Choose a Data Type

Start from the help panel and the data-type entry to understand the basic NeuroBlocks workflow.

Learn how to use NeuroBlocks Studio for Getting Started: Choose a Data Type with real, manually matched screenshots. This tutorial covers setup, execution, result interpretation and practical checks.

What this tutorial covers

Start from the help panel and the data-type entry to understand the basic NeuroBlocks workflow.

Before you start：You need access to the product and should ideally follow the tutorial using your own real parameters or data. The screenshots below were manually rematched to avoid the image/topic mismatches seen in the previous version.
Getting Started: Choose a Data Type - Open the relevant module and confirm the starting state.
Open the relevant module and confirm the starting state.
Getting Started: Choose a Data Type - Configure the key inputs that match the topic.
Configure the key inputs that match the topic.
Getting Started: Choose a Data Type - Run the workflow and inspect the result/status.
Run the workflow and inspect the result/status.

Step-by-step workflow

1Open the module

Open NeuroBlocks Studio and start from the help area on the left. Confirm that the visible page really matches the tutorial topic before changing any settings.

2Configure the input

Prepare and check the kind of data you want to work with, such as images or tables.

3Run and watch the status

Choose a data type and enter the corresponding workflow. Watch the visible status, plot or log area while the workflow is running.

4Inspect the result

Pay special attention to the starting point for the later model-building and training workflow.

5Verify and iterate

Understanding the data-type entry first makes later model building much easier. Change one meaningful parameter and rerun so you can verify whether the output changes in the expected direction.

How to read the result

  • The main output of this workflow is the starting point for the later model-building and training workflow.
  • Read the result together with the exact inputs used for the run; a screenshot alone rarely tells the whole story.
  • Understanding the data-type entry first makes later model building much easier.

Troubleshooting & practical tips

  • If nothing changes, first confirm that the correct module is open.
  • If the output looks unrealistic, check units, field mapping and input scale first.
  • For comparisons, change one key parameter at a time whenever possible.
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