Tone down attribution: keep author only in Citation and Contact sections

README.md

@@ -318,7 +318,7 @@ for art in artifacts_ds:
 
 **Dataset description.** PhysicalAI SDG-Warehouse is a synthetic, fully-annotated multi-view video dataset of staged industrial-safety events captured in a simulated warehouse environment using NVIDIA Isaac Sim. It contains 122,967 video clips drawn from 29,195 distinct multi-camera simulation runs across four scenarios: forklift–human near-miss, warehouse fire with worker evacuation, forklift–shelf collision, and warehouse box pickup. Each run is filmed from five to ten synchronized cameras at 1920 × 1080 resolution and 30 frames per second, and every RGB frame is paired with deterministic ground truth (metric depth, instance and shaded segmentation, Canny edges, 2D and 3D bounding boxes, and per-frame camera intrinsics and extrinsics) rendered natively by the simulator. This dataset is for research and development only.
 
-**Dataset owner(s).** NVIDIA Corporation. Lead author and primary point of contact: **Nalin Dadhich** (ndadhich@nvidia.com), NVIDIA Corporation.
+**Dataset owner(s).** NVIDIA Corporation.
 
 **Dataset creation date.** 2026-05.
 
@@ -357,10 +357,6 @@ Agent motion is driven by procedural navigation and behavior, so reactions — a
 
 The current release is concentrated on a single warehouse layout family. Future work will broaden environment diversity to additional warehouse, retail, and factory floor plans, will add additional incident types such as spills, dropped pallets, and shelf collapses without forklift involvement, and will broaden variation in worker attire and personal protective equipment.
 
-## Author
-
-**Nalin Dadhich** — Lead author and primary point of contact, NVIDIA Corporation, ndadhich@nvidia.com.
-
 ## Citation
 
 If you use SDG-Warehouse in your research, please cite the dataset itself as well as the Cosmos3 technical report it accompanies:
@@ -371,8 +367,7 @@ If you use SDG-Warehouse in your research, please cite the dataset itself as wel
   author       = {Dadhich, Nalin and {NVIDIA}},
   year         = {2026},
   publisher    = {NVIDIA Corporation},
-  howpublished = {\url{https://huggingface.co/datasets/nvidia/PhysicalAI-SDG-WareHouse}},
-  note         = {Lead author and corresponding author: Nalin Dadhich (ndadhich@nvidia.com)}
+  howpublished = {\url{https://huggingface.co/datasets/nvidia/PhysicalAI-SDG-WareHouse}}
 }
 
 @techreport{nvidia2026cosmos3,
@@ -386,11 +381,7 @@ If you use SDG-Warehouse in your research, please cite the dataset itself as wel
 
 ## Contact
 
-For questions about this dataset, requests for clarification, bug reports, or collaboration enquiries, please contact:
-
-**Nalin Dadhich** — Lead author and primary point of contact, NVIDIA Corporation. Email: [ndadhich@nvidia.com](mailto:ndadhich@nvidia.com).
-
-Nalin Dadhich is the principal designer and engineer of the SDG-Warehouse pipeline. He authored the procedural scene-composition logic, the multi-camera rig parameterization, the four scenario specifications and their event randomization, the end-to-end Isaac Sim generation pipeline that produced every clip in this release, and the WebDataset packaging and Hugging Face publication tooling for both the RGB and artifacts tiers. He is the corresponding author for any external communication regarding the dataset.
+Questions, bug reports, and collaboration enquiries: Nalin Dadhich, NVIDIA Corporation — [ndadhich@nvidia.com](mailto:ndadhich@nvidia.com).
 
 ## Ethical considerations