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1. Data from "Homogeneous ice nucleation in an ab initio machine learning model"

2. Data from "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water"

3. Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

4. Fluorescence Reconstruction Microscopy Data: Keratinocyte (10x) Phase to Nuclei (DAPI)

5. Fluorescence Reconstruction Microscopy Data: MDCK (20x) DIC to Nuclei (DAPI) and E-Cadherin (RFP)

6. Fluorescence Reconstruction Microscopy: Complete Testing Dataset

7. Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTX

8. Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models