Update README.md

README.md

@@ -12,5 +12,122 @@ tags:
 - memory
 - network
 - storage
+- telemetry
+- anomaly-detection
+- performance
 pretty_name: Reveal
----
+---
+
+# 🛰️ Dataset Card for **Reveal: Hardware Telemetry Dataset for Machine Learning Infrastructure Profiling and Anomaly Detection**
+
+## Dataset Details
+
+### Dataset Description
+
+**Reveal** is a large-scale, curated dataset of **hardware telemetry** collected from high-performance computing (HPC) while running diverse machine learning (ML) workloads.  
+It enables reproducible research on **system-level profiling**, **unsupervised anomaly detection**, and **ML infrastructure optimization**.  
+
+The dataset accompanies the paper  
+📄 *“Detecting Anomalies in Systems for AI Using Hardware Telemetry”* (Chen *et al.*, University of Oxford, 2025).  
+Reveal captures low-level hardware and operating system metrics—fully accessible to operators—allowing anomaly detection **without requiring workload knowledge or instrumentation**.
+
+- **Curated by:** Ziji Chen, Steven W. D. Chien, Peng Qian, Noa Zilberman (University of Oxford, Department of Engineering Science)  
+- **Shared by:** Ziji Chen (contact: ziji.chen@eng.ox.ac.uk)  
+- **Language(s):** English (metadata and documentation)  
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+
+---
+
+### Dataset Sources
+
+- **Paper:** [Detecting Anomalies in Systems for AI Using Hardware Telemetry](https://arxiv.org/abs/submit/6934461)  
+- **DOI:** [10.5281/zenodo.17470313](https://doi.org/10.5281/zenodo.17470313)  
+
+---
+
+## Uses
+
+### Direct Use
+
+Reveal can be used for:
+- Research on **unsupervised anomaly detection** in system telemetry  
+- Modeling **multivariate time-series** from hardware metrics  
+- Studying **cross-subsystem interactions** (CPU, GPU, memory, network, storage)  
+- Developing **performance-aware ML infrastructure tools**  
+- Training or benchmarking anomaly detection models for **AIOps** and **ML system health monitoring**
+
+### Out-of-Scope Use
+
+The dataset **should not** be used for:
+- Inferring or reconstructing user workloads or model behavior  
+- Benchmarking end-user application performance  
+- Any use involving personal, confidential, or proprietary data reconstruction  
+
+---
+
+## Dataset Structure
+
+Reveal consists of time-series telemetry, derived features, and automatically labeled anomaly segments.
+
+
+
+**Core fields include:**
+- `timestamp`: UTC time of sample  
+- `host_id`: host or node identifier  
+- `metric_name`: name of the measured counter  
+- `value`: recorded numeric value  
+- `subsystem`: {CPU, GPU, Memory, Network, Storage}  
+
+---
+
+## Dataset Creation
+
+### Curation Rationale
+
+Modern ML workloads are complex and opaque to operators due to virtualization and containerization. Reveal was created to **enable infrastructure-level observability** and anomaly detection purely from hardware telemetry, without access to user workloads.
+
+### Source Data
+
+#### Data Collection and Processing
+
+- Collected using: `perf`, `procfs`, `nvidia-smi`, and standard Linux utilities  
+- Sampling interval: 100 ms  
+- ~150 raw metric types per host, expanded to ~700 time-series channels   
+
+#### Workloads and Systems
+
+- **Workloads:** >30 ML applications (BERT, BART, ResNet, ViT, VGG, DeepSeek, LLaMA, Mistral)  
+- **Datasets:** GLUE/SST2, WikiSQL, PASCAL VOC, CIFAR, MNIST  
+- **Systems:**  
+  - Dual-node GPU HPC cluster (NVIDIA V100 & H100, Intel Xeon CPUs, InfiniBand HDR100)  
+
+#### Who are the data producers?
+
+All data was generated by the authors in controlled environments using synthetic workloads.  
+No user or private information is included.
+
+### Annotations
+
+#### Personal and Sensitive Information
+No personal, identifiable, or proprietary data.  
+All records are machine telemetry and anonymized.
+
+---
+
+## Bias, Risks, and Limitations
+
+- Collected on specific hardware (NVIDIA/AMD CPUs, NVIDIA GPUs); behavior may differ on other architectures.  
+- Reflects **controlled test conditions**, not production cloud variability.  
+
+---
+
+## Citation
+
+**BibTeX:**
+```bibtex
+@article{chen2025reveal,
+  title={Detecting Anomalies in Systems for AI Using Hardware Telemetry},
+  author={Chen, Ziji and Chien, Steven W. D. and Qian, Peng and Zilberman, Noa},
+  journal={arXiv preprint arXiv:submit/6934461},
+  year={2025}
+}