Data card review feedback: commercial-use designation, add Version v1.0, simplify Ethical Considerations

README.md

@@ -30,7 +30,7 @@ tags:
 
 PhysicalAI SDG-Warehouse is a synthetic, fully-annotated video dataset of staged industrial-safety events captured in a simulated warehouse environment. It contains approximately 123k video clips, totaling roughly 412 hours of footage at 1920x1080 resolution and 30 frames per second, organized across four scenarios: a forklift near-miss with a human worker, a warehouse fire with worker evacuation, a forklift collision with a storage shelf, and a routine box-pickup action. Every multi-camera simulation run is filmed from 5 to 10 synchronized viewpoints, and the entire pipeline is reproducible end-to-end from a single random seed.
 
-This dataset is described in Appendix A.1.5 of the Cosmos3 technical report (citation [below](#citation)). This dataset is for research and development only.
+This dataset is described in Appendix A.1.5 of the Cosmos3 technical report (citation [below](#citation)). This dataset is ready for commercial or non-commercial uses.
 
 ## Overview
 
@@ -49,6 +49,11 @@ Two small Parquet indexes — `metadata/runs.parquet` (one row per run) and `met
 
 **Dataset creation date.** 2026-05.
 
+**Version.**
+v1.0 <br>
+
+Previous Version(s): None. <br>
+
 **License / terms of use.** Released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
 
 **Intended usage.** SDG-Warehouse is intended for research and development of physical-AI models — including but not limited to video understanding (action recognition, anomaly and incident detection, multi-camera person re-identification, worker activity recognition), pixel-level perception (monocular depth estimation, instance segmentation, edge prediction, 2D and 3D object detection and tracking), video generation and world modeling (text-to-video, conditional video generation, long-horizon prediction), and policy or planning research that benefits from controllable, reproducible safety-event footage. It is also a useful resource for studying sim-to-real transfer in warehouse and industrial settings, and as a controlled benchmark for evaluating model robustness across viewpoints, lighting, and agent appearance.
@@ -363,8 +368,4 @@ Please reach out to the main author Nalin Dadhich ([ndadhich@nvidia.com](mailto:
 
 ## Ethical considerations
 
-NVIDIA believes trustworthy AI is a shared responsibility, and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this dataset meets the requirements for their relevant industry and use case, and addresses any unforeseen product misuse.
-
-SDG-Warehouse is fully synthetic. It contains no real people, no real workplaces, and no real surveillance footage, and it depicts safety-critical events — near-misses, collisions, fires, and evacuations — only in simulation. Models trained on it should still be carefully evaluated on real footage before being deployed in any safety-critical setting, and operators should be aware of the sim-to-real gap noted above.
-
-Please report quality, risk, security vulnerabilities, or NVIDIA AI concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.