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| Formato: | Recurso digital |
| Idioma: | inglês |
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Zenodo
2025
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| Acesso em linha: | https://doi.org/10.5281/zenodo.15148021 |
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| _version_ | 1866901748544700416 |
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| author | Ming, Liyan |
| author_facet | Ming, Liyan |
| contents | <p>This dataset supports the study titled "Luminescence-enabled three-dimensional temperature bioimaging", in which we developed a machine learning-assisted 3D thermometry approach based on the near-infrared (NIR) emission of Ag₂S nanothermometers (NTs). The dataset includes:</p> <p>Calibration (training) data: Hyperspectral emission spectra of Ag₂S NTs collected under varying tissue types, tissue thicknesses, and temperature conditions (360 combinations total). Each condition includes 500 extracted spectra for robust model training.</p> <p>Trained neural network model: The CNN+DNN model used to extract temperature and depth information from hyperspectral images.</p> <p>Validation data: 20% calibration data, and independent datasets used to validate the performance of the model, including 3D thermometry experiments in tissue phantoms, ex vivo tissues, and in vivo mouse models.</p> <p>Temperature simulation data: Results from a thermal transfer simulation conducted independently to support the validation experiments.</p> <p>The data are organized into folders corresponding to training, validation, and simulation. Spectral data are stored in .csv format, while model files are in .h5. Code can be accessed using Python. </p> <p>These data support key findings in the paper. The dataset enables verification of 3D temperature mapping performance in various conditions.</p> <p>File: all_data_phantom_and_real_tissue.dat</p> <p>Description: all calibration data (emission spectra) for training model</p> <p>File: validation-d.csv</p> <p>Description: violin plotting of d predictions in figure 2 e</p> <p> </p> <p>File: New_3D_all-loss.txt</p> <p>Description: training loss</p> <p> </p> <p>File: 3capillaries_silmulation_0.13mm.csv</p> <p>Description: the temperature transfer simulation of validation experiments: cube phantom tissue (Fig.3e)</p> <p> </p> <p>File: validation-T.csv</p> <p>Description: violin plotting of T predictions in figure 2 e</p> <p> </p> <p>File: labels_phantom_and_real_tissue.dat</p> <p>Description: the labels of the calibration emission spectra</p> <p> </p> <p>File: 3cappilaries-binary.csv</p> <p>Description: prediction of phantom tissue validation (Fig.3d)</p> <p> </p> <p>File: 3cappilaries-binary.gif</p> <p>Description: gif of Fig.3d</p> <p> </p> <p>File: 3cappilaries_spectra_with_empty_data.csv</p> <p>Description: spectra pass to the algoritm(neural networks) for predictions</p> <p> </p> <p>File: 2capillaries_silmulation_0.09mm.csv</p> <p>Description: the temperature transfer simulation of validation experiments: ex vivo tissue (Fig.2J)</p> <p> </p> <p>File: 2_capillaries-emission_with_empty_data.csv</p> <p>Description: spectra pass to the algoritm(neural networks) for predictions</p> <p> </p> <p>File: 2cappilaries-binary.csv</p> <p>Description: predict results (Fig.3i)</p> <p> </p> <p>File: 2cappilaries-binary.gif</p> <p>Description: gif file of Fig.3i</p> <p> </p> <p>File: cone_prediction_center.csv</p> <p>Description: the center capillary in cone. predictions of d and T (Supplementary Figure 19e)</p> <p> </p> <p>File: ReconstructionCone_Coords_X_Y_Z.csv</p> <p>Description: Reconstruction of Cone Supplementary Figure 20</p> <p> </p> <p>File: cone_animation.gif</p> <p>Description: gif of Supplementary Figure 19e</p> <p> </p> <p>File: enbemded_capillaries_animation_10min.gif</p> <p>Description: gif of Supplementary Figure 21d</p> <p> </p> <p>File: cone_simulation-9mm.csv</p> <p>Description: heat transfer simulation of cone, Supplementary Figure 19f</p> <p> </p> <p>File: enbemded_capillaries_fitting_3D_simulation_T.csv</p> <p>Description: simulations of enbemded capillaries (Supplementary Figure 21e)</p> <p> </p> <p>File: enbemded_capillaries_T_d_pred_down.csv</p> <p>Description: predictions of enbemded_capillaries, Supplementary Figure 22 A</p> <p> </p> <p>File: enbemded_capillaries_T_d_pred_up.csv</p> <p>Description: predictions of enbemded_capillaries, Supplementary Figure 22 B</p> <p> </p> <p>File: raton2_vessels_3D_plot.csv</p> <p>Description: prediction of in vivo experiments Fig 4c</p> <p> </p> <p>File: raton2_vessels_spectra_correct.csv</p> <p>Description: Corrected spectra from blood vessels for predictions</p> <p> </p> <p>File: raton2_vessels_3D_plot_projection.gif</p> <p>Description: gif of fig</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15148021 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Luminescence-enabled three-dimensional temperature bioimaging Ming, Liyan luminescence imaging luminescence thermometry 3D thermometry <p>This dataset supports the study titled "Luminescence-enabled three-dimensional temperature bioimaging", in which we developed a machine learning-assisted 3D thermometry approach based on the near-infrared (NIR) emission of Ag₂S nanothermometers (NTs). The dataset includes:</p> <p>Calibration (training) data: Hyperspectral emission spectra of Ag₂S NTs collected under varying tissue types, tissue thicknesses, and temperature conditions (360 combinations total). Each condition includes 500 extracted spectra for robust model training.</p> <p>Trained neural network model: The CNN+DNN model used to extract temperature and depth information from hyperspectral images.</p> <p>Validation data: 20% calibration data, and independent datasets used to validate the performance of the model, including 3D thermometry experiments in tissue phantoms, ex vivo tissues, and in vivo mouse models.</p> <p>Temperature simulation data: Results from a thermal transfer simulation conducted independently to support the validation experiments.</p> <p>The data are organized into folders corresponding to training, validation, and simulation. Spectral data are stored in .csv format, while model files are in .h5. Code can be accessed using Python. </p> <p>These data support key findings in the paper. The dataset enables verification of 3D temperature mapping performance in various conditions.</p> <p>File: all_data_phantom_and_real_tissue.dat</p> <p>Description: all calibration data (emission spectra) for training model</p> <p>File: validation-d.csv</p> <p>Description: violin plotting of d predictions in figure 2 e</p> <p> </p> <p>File: New_3D_all-loss.txt</p> <p>Description: training loss</p> <p> </p> <p>File: 3capillaries_silmulation_0.13mm.csv</p> <p>Description: the temperature transfer simulation of validation experiments: cube phantom tissue (Fig.3e)</p> <p> </p> <p>File: validation-T.csv</p> <p>Description: violin plotting of T predictions in figure 2 e</p> <p> </p> <p>File: labels_phantom_and_real_tissue.dat</p> <p>Description: the labels of the calibration emission spectra</p> <p> </p> <p>File: 3cappilaries-binary.csv</p> <p>Description: prediction of phantom tissue validation (Fig.3d)</p> <p> </p> <p>File: 3cappilaries-binary.gif</p> <p>Description: gif of Fig.3d</p> <p> </p> <p>File: 3cappilaries_spectra_with_empty_data.csv</p> <p>Description: spectra pass to the algoritm(neural networks) for predictions</p> <p> </p> <p>File: 2capillaries_silmulation_0.09mm.csv</p> <p>Description: the temperature transfer simulation of validation experiments: ex vivo tissue (Fig.2J)</p> <p> </p> <p>File: 2_capillaries-emission_with_empty_data.csv</p> <p>Description: spectra pass to the algoritm(neural networks) for predictions</p> <p> </p> <p>File: 2cappilaries-binary.csv</p> <p>Description: predict results (Fig.3i)</p> <p> </p> <p>File: 2cappilaries-binary.gif</p> <p>Description: gif file of Fig.3i</p> <p> </p> <p>File: cone_prediction_center.csv</p> <p>Description: the center capillary in cone. predictions of d and T (Supplementary Figure 19e)</p> <p> </p> <p>File: ReconstructionCone_Coords_X_Y_Z.csv</p> <p>Description: Reconstruction of Cone Supplementary Figure 20</p> <p> </p> <p>File: cone_animation.gif</p> <p>Description: gif of Supplementary Figure 19e</p> <p> </p> <p>File: enbemded_capillaries_animation_10min.gif</p> <p>Description: gif of Supplementary Figure 21d</p> <p> </p> <p>File: cone_simulation-9mm.csv</p> <p>Description: heat transfer simulation of cone, Supplementary Figure 19f</p> <p> </p> <p>File: enbemded_capillaries_fitting_3D_simulation_T.csv</p> <p>Description: simulations of enbemded capillaries (Supplementary Figure 21e)</p> <p> </p> <p>File: enbemded_capillaries_T_d_pred_down.csv</p> <p>Description: predictions of enbemded_capillaries, Supplementary Figure 22 A</p> <p> </p> <p>File: enbemded_capillaries_T_d_pred_up.csv</p> <p>Description: predictions of enbemded_capillaries, Supplementary Figure 22 B</p> <p> </p> <p>File: raton2_vessels_3D_plot.csv</p> <p>Description: prediction of in vivo experiments Fig 4c</p> <p> </p> <p>File: raton2_vessels_spectra_correct.csv</p> <p>Description: Corrected spectra from blood vessels for predictions</p> <p> </p> <p>File: raton2_vessels_3D_plot_projection.gif</p> <p>Description: gif of fig</p> |
| title | Luminescence-enabled three-dimensional temperature bioimaging |
| topic | luminescence imaging luminescence thermometry 3D thermometry |
| url | https://doi.org/10.5281/zenodo.15148021 |