VISA - Virtual Infrastructure for Scientific Analysis

VISA (Virtual Infrastructure for Scientific Analysis) makes it simple to create compute instances on the data analysis infrastructure to analyse your experimental data. Easily create compute instances on the data analysis infrastructure and analyse your experimental data directly in your web browser.

This learning path introduces the PaNOSC (Photon and Neutron Open Science Cloud) services and demonstrates how to use VISA to create and manage compute instances for data analysis directly from your web browser. Participants will learn how VISA connects experimental facilities with cloud-based analysis environments, enabling reproducible and efficient scientific workflows.

Licence: Creative Commons Attribution 4.0 International

Keywords: VISA, compute hardware, remote analysis

Status: Active

Prerequisites:

Participants should:
• Have basic knowledge of experimental data workflows in photon or neutron science (or similar research domains).
• Be familiar with concepts of remote computing or cloud-based environments.
• Have a user account for VISA or access credentials provided by their facility.
• (Optional) Have some experience using Jupyter Notebooks or Python for data analysis.

Learning objectives:

After completing this learning path, participants will be able to:
1. Understand the role of PaNOSC services in supporting open and FAIR data principles across research infrastructures.
2. Access and navigate the VISA platform through a web browser.
3. Create and manage compute instances on the VISA infrastructure for scientific analysis.
4. Use VISA’s integrated tools (such as JupyterLab) to analyse experimental data efficiently.
5. Connect VISA to data sources from their experimental facility and store or share analysis results.
6. Recognise how VISA contributes to reproducible and collaborative data analysis workflows.

1

Module 1: PaNOSC Services

• beginner 2 materials

This module provides an overview of the Photon and Neutron Open Science Cloud (PaNOSC) and its core services that enable open and FAIR data practices across European research facilities. Participants will learn how PaNOSC integrates data catalogues, remote analysis environments, and APIs to create a connected ecosystem for scientific data management and analysis. The module highlights how these services—such as the PaN Data Portal, VISA, and training platforms—support researchers in accessing, analysing, and reusing experimental data across facilities.

2

Module 2: First Steps in VISA

•• intermediate 3 materials

This module guides participants through the initial steps of using VISA (Virtual Infrastructure for Scientific Analysis). It introduces the platform’s interface, key functionalities, and access options. Participants will learn how to log in, navigate the VISA dashboard, and launch their first compute instance within the secure PaNOSC analysis environment. The module also explains how VISA connects to facility data sources and how its browser-based interface simplifies access to high-performance computing resources without complex setup or installation.

Learning Outcomes:

After completing this module, participants will be able to:
1. Access the VISA platform and understand authentication and user roles.
2. Navigate the VISA interface and locate essential features and tools.
3. Launch and configure a compute instance for data analysis.
4. Understand how VISA integrates with data services from PaNOSC facilities.
5. Identify where to find help, documentation, and support resources.

3

Module 3: Using VISA for Data Analysis

••• advanced 0 materials

In this module, participants will learn how to perform data analysis within VISA (Virtual Infrastructure for Scientific Analysis) using integrated tools such as JupyterLab and other supported environments. The module demonstrates how to connect VISA to experimental datasets stored at photon and neutron facilities, run analysis workflows, and visualize results directly in the browser. Participants will also explore how to save, share, and reproduce their analysis within the VISA ecosystem, fostering open and collaborative research practices.

Learning Outcomes:

After completing this module, participants will be able to:
1. Use VISA’s integrated environments (e.g., JupyterLab) to run data analysis workflows.
2. Access and load experimental data from facility repositories into their compute instance.
3. Execute, visualize, and interpret analysis results within VISA.
4. Manage files, outputs, and environments to ensure reproducibility.
5. Save and share analysis sessions or results with collaborators.
6. Understand best practices for efficient and sustainable use of VISA resources.

Activity log