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Re-Imaging or Reformatting a PC Running EthoVision – Reinstallation After System Refresh

Last updated: Nov 01, 2026

Problem

A lab needs to re-image (wipe and reinstall Windows on) a computer running EthoVision XT due to system problems, aging hardware, or IT requirements. Users are unsure whether this is permitted, what steps to take to preserve their license, and what to expect in terms of performance.

Details

Re-imaging a PC that runs EthoVision XT is permitted by Noldus. After re-imaging, EthoVision must be reinstalled and the license reactivated. For SBLK (software-based) licenses, the license should be deactivated before re-imaging if possible, or Noldus support can reset the activation. For dongle-based licenses, the dongle simply needs to be reconnected after reinstallation. Performance after reinstallation may differ if the underlying hardware is old — newer hardware (particularly for EthoVision XT 19 with its GPU/deep learning requirements) may be needed for optimal performance. Affects any EthoVision XT version on Windows.

Solution

1. Before re-imaging, if you have an SBLK license, deactivate it within EthoVision (go to Help > License > Deactivate) to free the activation for reuse after reinstallation. If this is not possible (e.g., system is already unresponsive), contact Noldus support to reset the license activation remotely.
2. Back up your EthoVision experiment files and data to an external drive or network location before re-imaging.
3. Proceed with re-imaging the PC according to your IT department's procedure.
4. After re-imaging and Windows reinstallation, download the appropriate version of EthoVision XT from MyNoldus (noldus.com/app/home).
5. Install EthoVision XT following the standard installation procedure.
6. Reactivate your license: for SBLK, enter your activation code again; for dongle licenses, insert the USB dongle and install the dongle driver if prompted.
7. Restore your experiment files from backup and verify that they open correctly.
8. Note: If using EthoVision XT 19 on older hardware, performance (especially for deep learning features) may be reduced compared to newer systems. Consider hardware upgrades if performance is inadequate.

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