Datasets:

neurips26-PSML
/

SIIB-Time

@@ -162,6 +162,8 @@ Since annotation is fully automated, inter-annotator disagreement does not apply
 # 4- Data Quality
 ## 4.1- Suitability
 This dataset was designed specifically to support research on operator learning as a surrogate modeling approach for power systems resolution-invariant time-domain simulation. Its suitability for this purpose rests on three structural properties. First, the paired EMT/RMS design provides matched trajectory pairs for the same 3,000 scenarios at two fundamentally different simulation resolutions, EMT at 50 µs and RMS at 1 ms. No existing public dataset provides this pairing for inverter-based systems. Second, both GFM and GFL control modes are included, enabling benchmarking of surrogate models across qualitatively different inverter dynamics. Third, the 3,000 scenarios span two inverter control modes and a wide range of disturbance types, load conditions, grid impedance values, and power references, providing the diversity needed to train and evaluate generalizable operator learning models. Beyond operator learning, the dataset is also suitable for several adjacent research tasks: physics-informed machine learning for inverter dynamics, stability classification benchmarking, engineering education, and general ML-based surrogate modeling for power systems time-domain simulation. The dataset is particularly well-suited for machine learning due to:
@@ -249,7 +251,7 @@ Contact information for the dataset maintainers will be provided upon publicatio
 [20] Natural Resources Canada. 2019. Personal vehicles. Transportation energy efficiency. Retrieved April 29, 2026 from https://natural-resources.canada.ca/energy-efficiency/transportation-energy-efficiency/personal-vehicles

 # 4- Data Quality
+Along with the details provided below, a datasheet is provided in the [Datasheet.md](https://huggingface.co/datasets/neurips26-PSML/SIIB-Time/blob/main/Datasheet.md) file following [21].
 ## 4.1- Suitability
 This dataset was designed specifically to support research on operator learning as a surrogate modeling approach for power systems resolution-invariant time-domain simulation. Its suitability for this purpose rests on three structural properties. First, the paired EMT/RMS design provides matched trajectory pairs for the same 3,000 scenarios at two fundamentally different simulation resolutions, EMT at 50 µs and RMS at 1 ms. No existing public dataset provides this pairing for inverter-based systems. Second, both GFM and GFL control modes are included, enabling benchmarking of surrogate models across qualitatively different inverter dynamics. Third, the 3,000 scenarios span two inverter control modes and a wide range of disturbance types, load conditions, grid impedance values, and power references, providing the diversity needed to train and evaluate generalizable operator learning models. Beyond operator learning, the dataset is also suitable for several adjacent research tasks: physics-informed machine learning for inverter dynamics, stability classification benchmarking, engineering education, and general ML-based surrogate modeling for power systems time-domain simulation. The dataset is particularly well-suited for machine learning due to:
 [20] Natural Resources Canada. 2019. Personal vehicles. Transportation energy efficiency. Retrieved April 29, 2026 from https://natural-resources.canada.ca/energy-efficiency/transportation-energy-efficiency/personal-vehicles
+[21] Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2021. Datasheets for datasets. Commun. ACM 64, 12 (December 2021), 86–92. https://doi.org/10.1145/3458723