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Part 1: Document Description
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Citation |
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Title: |
Training data for reconstructing current density from magnetic field (50±50 μm standoff, 256x256 resolution) |
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Identification Number: |
doi:10.7910/DVN/OPEX5N |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2025-01-14 |
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Version: |
1 |
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Bibliographic Citation: |
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 |
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Citation |
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Title: |
Training data for reconstructing current density from magnetic field (50±50 μm standoff, 256x256 resolution) |
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Identification Number: |
doi:10.7910/DVN/OPEX5N |
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Authoring Entity: |
Reed, Niko (University of Maryland) |
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Turner, Matthew (University of Maryland) |
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Daly, Declan (University of Maryland) |
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Tang, Jiashen (University of Maryland) |
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Walsworth, Ronald (University of Maryland) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Walsworth, Ron |
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Depositor: |
Reed, Niko |
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Date of Deposit: |
2025-01-11 |
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Holdings Information: |
https://doi.org/10.7910/DVN/OPEX5N |
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Study Scope |
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Keywords: |
Computer and Information Science, Physics, Inverse Problems, Quantum Sensing, Machine Learning, Convolutional Neural Networks, Optically Detected Magnetic Resonance, Nitrogen vacancy centers in diamond |
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Abstract: |
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 8 images. This dataset contains data with a 256x256 resolution, and standoff distances sampled from a normal distribution with μ=50 μm, σ=10 μm. Trained models, model training scripts (for MAGIC-UNet architecture), related experimental data, and validation data used in publication can be found at https://doi.org/10.7910/DVN/SD6PVP |
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