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Replication Data and Code Repository for: Prospects for Ultralow-Mass Nuclear Magnetic Resonance using Spin Defects in Hexagonal Boron Nitride
Apr 30, 2025
Daly, Declan; Reed, Niko; DeVience, Stephen; Yin, Zechuan; Cremer, Johannes; Beling, Andrew; Blanchard, John; Walsworth, Ron, 2025, "Replication Data and Code Repository for: Prospects for Ultralow-Mass Nuclear Magnetic Resonance using Spin Defects in Hexagonal Boron Nitride", https://doi.org/10.7910/DVN/ABGNZB, Harvard Dataverse, V1
This folder contains all the necessary simulation and plotting code for the paper. It also includes files related to generating the figures and their corresponding outputs. Please read below for a description of the contents and instructions.
Training data for reconstructing current density from magnetic field (50±50 μm standoff, 256x256 resolution)
Jan 14, 2025 - Magnetic Inverse Problem
Reed, Niko; Turner, Matthew; Daly, Declan; Tang, Jiashen; Walsworth, Ronald, 2025, "Training data for reconstructing current density from magnetic field (50±50 μm standoff, 256x256 resolution)", https://doi.org/10.7910/DVN/OPEX5N, Harvard Dataverse, V1
This dataset contains 157,696 simulated magnetic field/current density/standoff distance sets, created to teach neural networks to solve the magnetic inverse problem. The data is divided into 154 sets of files (B, J, and standoff distance), with 1024 different configurations per file. Configurations alternate between straight and curved wires every...
Training data for reconstructing current density from magnetic field (500±50 μm standoff, 64x64 resolution)
Jan 14, 2025 - Magnetic Inverse Problem
Reed, Niko; Turner, Matthew; Daly, Declan; Tang, Jiashen; Walsworth, Ronald, 2025, "Training data for reconstructing current density from magnetic field (500±50 μm standoff, 64x64 resolution)", https://doi.org/10.7910/DVN/SJDS2O, Harvard Dataverse, V1
This dataset contains 157,696 simulated magnetic field/current density/standoff distance sets, created to teach neural networks to solve the magnetic inverse problem. The data is divided into 154 sets of files (B, J, and standoff distance), with 1024 different configurations per file. Configurations alternate between straight and curved wires every...
Replication Data for: Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images
Jan 13, 2025 - Magnetic Inverse Problem
Reed, Niko; Bhutto, Danyal; Turner, Matthew; Daly, Declan; Oliver, Sean; Tang, Jiashen; Olsson, Kevin; Ku, Mark; Rosen, Matthew; Walsworth, Ronald, 2025, "Replication Data for: Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images", https://doi.org/10.7910/DVN/SD6PVP, Harvard Dataverse, V2
This repository contains the dataset and associated resources used in the scientific paper titled Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images. The dataset includes: trained models ready for inference, validation data (including data shown in paper), experimental data (including data shown in paper), i...
Training data for reconstructing current density from magnetic field (50 ± 10 μm standoff, 64x64 resolution)
Jan 13, 2025 - Magnetic Inverse Problem
Reed, Niko; Turner, Matthew; Daly, Declan; Tang, Jiashen; Walsworth, Ronald, 2025, "Training data for reconstructing current density from magnetic field (50 ± 10 μm standoff, 64x64 resolution)", https://doi.org/10.7910/DVN/QPCS0I, Harvard Dataverse, V1
This dataset contains 157,696 simulated magnetic field/current density/standoff distance sets, created to teach neural networks to solve the magnetic inverse problem. The data is divided into 154 sets of files (B, J, and standoff distance), with 1024 different configurations per file. Configurations alternate between straight and curved wires every...
Magnetic Inverse Problem(University of Maryland)
Jan 13, 2025
Datasets relating to magnetic inverse problems
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