TrojAI cyber-apk-nov2023 Test Dataset

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of small feed forward multi-layer perceptron type neural network models classifying APK feature vectors as malware or clean. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

TrojAI cyber-network-c2-feb2024-test Dataset

TrojAI cyber-network-c2-feb2024-holdout Dataset

Round rl-lavaworld-jul2023-holdout Dataset

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of Reinforcement Learning agents trained to navigate the Lavaworld Minigrid environment. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

TrojAI cyber-apk-nov2023 Holdout Dataset

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of small feed forward multi-layer perceptron type neural network models classifying APK feature vectors as malware or clean. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Data corresponding to Figures for Adkins et al J. Quant Spectrosc. Radiat. Transf. (2023). https://doi.org/10.1016/j.jqsrt.2023.108732

The development of large residual elastic strains and stresses during laser powder-bed fusion (LPBF) additive manufacturing is one of the most significant barriers to widespread adoption. Accurate modeling of these strains and stresses is broadly recognized as an effective tool for mitigating these challenges, but rigorous validation data are needed. This data publication includes measurement data from diffraction-based characterizations of residual elastic strains in as-built (not heat treated) artifacts manufactured as part the the 2018 Additive Manufacturing Benchmark Series (AM Bench).

Included here are figures and relevant data for the work "Estimating Uncertainty in Robot Kinematics and Pose Measurements with Expectation-Maximization". We present a method to validate the measurement uncertainty of a metrology instrument without a priori estimates in the context of a kinematic calibration using Expectation-Maximization methods and extend our results to characterize post-calibration pose uncertainty for the manipulator throughout a workspace.

A violent tornado outbreak occurred on December 10-11, 2021 in the Midwest US. One of the tornadoes, known as the Quad-State tornado, tracked across four states and devastated the downtown area of Mayfield, KY, producing high-end EF-4 damage. The data here provides a series of wind speed and direction time histories of the Quad-State tornado for 44 damaged residential houses in Mayfield, KY, which can be useful for detailed forensic analysis of the residential building damage.

After a five-year renovation of the National Institute of Standards and Technology (NIST) Boulder, CO, antenna measurement facility, the Antenna On-Axis Gain and Polarization Measurements Service SKU63100S was reinstated with the Bureau International des Poids et Mesures (BIPM). In addition to an overhaul of the antenna facility, the process of reinstatement involved a comprehensive measurement campaign of multiple international check-standard antennas over multiple frequency bands spanning 8 GHz to 110 GHz.