Prediction

QM9 provides quantum chemical properties (at DFT level) for a relevant, consistent, and comprehensive chemical space of small organic molecules. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships. Source: QM9 Dataset Image Source: https://pubs.acs.org/doi/pdf/10.1021/ci300415d

The unique Spatial Dynamic Wind Power Forecasting dataset: SDWPF, which includes the spatial distribution of wind turbines, as well as the dynamic context factors. Whereas, most of the existing datasets have only a small number of wind turbines without knowing the locations and context information of wind turbines at a fine-grained time scale. By contrast, SDWPF provides the wind power data of 134 wind turbines from a wind farm over half a year with their relative positions and internal statuses.

This dataset comprises extensive multi-modal data related to the experimental study of ultrasonically excited pulsating fluid jets used for bone cement removal. Conducted at the Institute of Geonics, Ostrava, Czech Republic, the study explores the effect of varying standoff distances on erosion profiles, under controlled parameters including a fixed nozzle diameter, sonotrode frequency, supply pressure, and robot arm velocity. The dataset includes numerical data representing ablation profiles, captured as a large CSV file, and audio recordings captured using a high-resolution microphone. Ablation profiles are measured at standoff distances that span several discrete lengths, ensuring diverse sample conditions. The audio data derives from high-fidelity recordings at a 38.4 kHz sampling rate in .wav format, capturing the fluid jet's impact sounds as it interacts initially with a metal plate and subsequently with the bone cement. This audio information was processed into Mel Spectrograms, which efficiently capture the frequency distribution crucial for predictive analysis. The dataset is organized into training and testing subsets, with the initial portions used to train predictive models leveraging Mel Spectrogram inputs to predict erosion profiles. This dataset offers insights into the dynamic interactions between a pulsating fluid jet and target materials, providing a foundation for predictive modeling in non-invasive surgical procedures.