We introduce ECT-Mat, the first large-scale, experimentally collected dataset for planar Electrical Capacitance Tomography (ECT) under both contact and non-contact conditions. The dataset contains 18,432 high-resolution capacitance matrices acquired with a custom-designed 3×3 electrode sensor array, covering three representative materials (glass, resin, wood) and three geometric shapes (circle, triangle, square). Each sample includes raw mutual capacitance data, ground-truth object labels, and baseline Linear Back Projection (LBP) reconstructions. Unlike existing simulation-based benchmarks, ECT-Mat captures real-world measurement characteristics, including dielectric variability, positional uncertainty, and environmental noise, thereby bridging the gap between numerical modeling and practical sensing. To ensure reproducibility and reuse, all sensor fabrication files, acquisition codes, and calibration protocols are openly released. ECT-Mat provides a standardized benchmark for inverse problem research, supports machine learning–based image reconstruction, and enables applications in touchless material sensing and human–computer interaction. By combining scale, diversity, and openness, ECT-Mat establishes a foundation for advancing both physics-informed and data-driven approaches to capacitance tomography.

“Research on external risk prediction of Belt and Road major projects based on machine learning” research used dataset with dataset and associated code

“Research on external risk prediction of Belt and Road major projects based on machine learning” research used dataset with dataset and associated code

“Research on external risk prediction of Belt and Road major projects based on machine learning” research used dataset with dataset and associated code

“Research on external risk prediction of Belt and Road major projects based on machine learning” research used dataset with dataset and associated code

PQA算法功能的实现代码，代码包含8，10，12，14顶点的ER图（prob = 0.5）以及三正则图，每个规模下，每个图类型分别有20个。我们在这些随机生成的图上测试了PQA以及其他算法的性能。

CoRR (Consortium for Reliability and Reproducibility) is committed to building an open platform for sharing resources of brain imaging science, aiming to provide standardized benchmarking datasets for test-retest reliability and reproducibility assessment in functional and structural connectomics research. To achieve this goal, CoRR integrated multimodal magnetic resonance imaging (MRI) datasets from multiple laboratories worldwide in 2014, based on which:1. Establish test-retest reliability and reproducibility standards for commonly used connectivity measurments using MRI methods;2. Clarify the reliability fluctuation range of the above measurements in different imaging stations and retesting schemes;3. Build a standardized retest MRI dataset to serve the validation of new connectivity measurements. Since 2024, an enhanced version of CoRR has been updated to enhance the neuropsychometrics on brain imaging assessments. Through standardized image preprocessing processes and the psychometric design expanded from a single retest to to 9 retests, and the raw MRI datasets have been aligned to a standard brain spaces. In addition, all preprocessed brain imaging data is open to the public through the Science Data Bank, providing standardized and unified data support and technical references. We believe that this open science practice will largely foster the transdisciplinary research of human neuroscience. This sample includes 74 typically developing children. Each participant was scanned twice within a session. Three modalities (T1/T2/EPI) of brain images were acquired for all subjects. Subjects were presented with a fixation cross and were instructed to keep their eyes open, fixate on the cross displayed on the screen, relax, and move as little as possible

Section 3  Dataset Description for CDA and QDA Experiments File/Folder Details:All data is available in the main text and supplementary information or at XX. Folder Name: data1Description: This folder contains two data files in .dat format: “init_state_Psi.dat” and “init_state_Vor.dat”.1.      File Name: init_state_Psi.datData Structure: 32 rows × 16 columns, two-dimensional arrayDescription: This file stores the initial stream function field data generated based on a specific equation, with parameter values as described in the main text. The data corresponds to the stream function field distribution shown in Figure 1(A) of the manuscript.2.      File Name: init_state_Vor.datData Structure: 32 rows × 16 columns, two-dimensional arrayDescription: This file stores the initial vorticity field data obtained via inversion from the initial stream function field (from “init_state_Psi.dat”) using the quasi-geostrophic (QG) model equation (Eq. (1)). This data corresponds to the initial vorticity distribution shown in Figure 1(B) of the manuscript. Folder Name: data2Description: This folder contains four .dat files: “x_control_Psi.dat”, “xa_cda_Psi_512.dat”, “xa_qda_Psi_512.dat”, and “xtr_Psi.dat”.1.      File Name: x_control_Psi.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains control experiment results of 720 QG model simulations using the initial stream function background field as input. It serves as the baseline without data assimilation. Each column represents the stream function field at a time step (512 grid points), which can be reshaped into a 32 × 16 spatial field using the np.reshape() function. The data corresponds to the stream function control time series shown in Figure 2.2.      File Name: xtr_Psi.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains the temporal evolution of the true stream function field (from “init_state_Psi.dat”) over 720 QG model integration steps. It is used to evaluate the performance of Classical Data Assimilation (CDA) and Quantum Data Assimilation (QDA). The data format is consistent with “x_control_Psi.dat” and corresponds to the true field evolution shown in Figure 2.3.      File Name: xa_cda_Psi_512.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains all stream function fields generated during 120 CDA assimilation cycles (720 time steps). Every six columns constitute a full assimilation cycle: the 1st column is the analysis field, and the 2nd to 6th columns are five forecast steps using the QG model. The data corresponds to the CDA results shown in Figure 2.4.      File Name: xa_qda_Psi_512.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription:This file contains the stream function field sequence generated during 120 QDA assimilation cycles (720 time steps). The structure is identical to that of “xa_cda_Psi_512.dat”. In each group of six columns, the 1st column is the quantum-assimilated analysis field, followed by five QG model forecast steps. The data corresponds to the QDA results shown in Figure 2. Folder Name: data3Description: This folder contains four .dat files: “x_control_Vor.dat”, “xa_cda_Vor_512.dat”, “xa_qda_Vor_512.dat”, and “xtr_Vor.dat”.1.      File Name: x_control_Vor.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file stores 720 QG model simulations of the initial vorticity background field as control experiments without assimilation. The format is consistent with “x_control_Psi.dat” in data2. The data corresponds to the vorticity control field evolution shown in Figure 3.2.      File Name: xtr_Vor.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains the temporal evolution of the true vorticity field (from init_state_Vor.dat) over 720 QG model steps, used to evaluate CDA and QDA. The data corresponds to the true vorticity evolution shown in Figure 3.3.      File Name: xa_cda_Vor_512.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains the vorticity field data generated during 120 CDA assimilation cycles (720 time steps). The data supports the CDA vorticity assimilation results presented in Figure 3.4.      File Name: xa_qda_Vor_512.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains the vorticity field sequences produced during 120 QDA assimilation cycles, corresponding to the QDA assimilation performance shown in Figure 3. Folder Name: data4Description: This folder contains one .xlsx file: “rmse_xa_qda_cda_Psi.xlsx”File Name: rmse_xa_qda_cda_Psi.xlsxData Structure: 720 rows × 8 columns, two-dimensional arrayDescription: This file records the Root Mean Square Error (RMSE) of the stream function analysis fields obtained by CDA and QDA under different observational configurations (8, 32, 128, and 512 observation points). Each column represents the RMSE time series (720 steps) for a specific method-observation pairing, providing a quantitative assessment of assimilation performance. The data supports the RMSE comparison of stream function fields shown in Figure 4. Folder Name: data5Description: This folder contains one .xlsx file: rmse_xa_qda_cda_Vor.xlsx1.      File Name: rmse_xa_qda_cda_Vor.xlsxData Structure: 720 rows × 8 columns, two-dimensional arrayDescription: This file records the RMSE of the vorticity analysis fields from CDA and QDA under different observational configurations (8, 32, 128, and 512 observation points). Each column corresponds to a time series of RMSE (720 steps) for a specific method and observation point configuration. The data supports the vorticity RMSE comparison shown in Figure 5. Folder Name: data6Description:This folder contains six .xlsx files: “sub-region_1.xlsx”, “sub-region_2.xlsx”, “sub-region_3.xlsx”, “sub-region_4.xlsx”, “sub-region_5.xlsx”, and “sub-region_6.xlsx”.1.      File Name: sub-region_1.xlsxData Structure: 2000 rows × 3 columns, Excel spreadsheetDescription: This file records quantum optimization data obtained from the optical CIM device  during the first data assimilation of the stream function field in the first spatial sub-region. It includes the Hamiltonian values and corresponding evolution times from 2000 consecutive measurement steps recorded over a single continuous operation cycle of the CIM. The data supports the Hamiltonian evolution analysis shown in Figure 7 for sub-region 1. The remaining files (“sub-region_2.xlsx” to “sub-region_6.xlsx”) share the same structure and correspond to quantum optimization results in the other spatial sub-regions.

Section 3  Dataset Description for CDA and QDA Experiments File/Folder Details:All data is available in the main text and supplementary information or at XX. Folder Name: data1Description: This folder contains two data files in .dat format: “init_state_Psi.dat” and “init_state_Vor.dat”.1.      File Name: init_state_Psi.datData Structure: 32 rows × 16 columns, two-dimensional arrayDescription: This file stores the initial stream function field data generated based on a specific equation, with parameter values as described in the main text. The data corresponds to the stream function field distribution shown in Figure 1(A) of the manuscript.2.      File Name: init_state_Vor.datData Structure: 32 rows × 16 columns, two-dimensional arrayDescription: This file stores the initial vorticity field data obtained via inversion from the initial stream function field (from “init_state_Psi.dat”) using the quasi-geostrophic (QG) model equation (Eq. (1)). This data corresponds to the initial vorticity distribution shown in Figure 1(B) of the manuscript. Folder Name: data2Description: This folder contains four .dat files: “x_control_Psi.dat”, “xa_cda_Psi_512.dat”, “xa_qda_Psi_512.dat”, and “xtr_Psi.dat”.1.      File Name: x_control_Psi.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains control experiment results of 720 QG model simulations using the initial stream function background field as input. It serves as the baseline without data assimilation. Each column represents the stream function field at a time step (512 grid points), which can be reshaped into a 32 × 16 spatial field using the np.reshape() function. The data corresponds to the stream function control time series shown in Figure 2.2.      File Name: xtr_Psi.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains the temporal evolution of the true stream function field (from “init_state_Psi.dat”) over 720 QG model integration steps. It is used to evaluate the performance of Classical Data Assimilation (CDA) and Quantum Data Assimilation (QDA). The data format is consistent with “x_control_Psi.dat” and corresponds to the true field evolution shown in Figure 2.3.      File Name: xa_cda_Psi_512.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains all stream function fields generated during 120 CDA assimilation cycles (720 time steps). Every six columns constitute a full assimilation cycle: the 1st column is the analysis field, and the 2nd to 6th columns are five forecast steps using the QG model. The data corresponds to the CDA results shown in Figure 2.4.      File Name: xa_qda_Psi_512.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription:This file contains the stream function field sequence generated during 120 QDA assimilation cycles (720 time steps). The structure is identical to that of “xa_cda_Psi_512.dat”. In each group of six columns, the 1st column is the quantum-assimilated analysis field, followed by five QG model forecast steps. The data corresponds to the QDA results shown in Figure 2. Folder Name: data3Description: This folder contains four .dat files: “x_control_Vor.dat”, “xa_cda_Vor_512.dat”, “xa_qda_Vor_512.dat”, and “xtr_Vor.dat”.1.      File Name: x_control_Vor.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file stores 720 QG model simulations of the initial vorticity background field as control experiments without assimilation. The format is consistent with “x_control_Psi.dat” in data2. The data corresponds to the vorticity control field evolution shown in Figure 3.2.      File Name: xtr_Vor.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains the temporal evolution of the true vorticity field (from init_state_Vor.dat) over 720 QG model steps, used to evaluate CDA and QDA. The data corresponds to the true vorticity evolution shown in Figure 3.3.      File Name: xa_cda_Vor_512.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains the vorticity field data generated during 120 CDA assimilation cycles (720 time steps). The data supports the CDA vorticity assimilation results presented in Figure 3.4.      File Name: xa_qda_Vor_512.datData Structure: 512 rows × 720 columns, two-dimensional arrayDescription: This file contains the vorticity field sequences produced during 120 QDA assimilation cycles, corresponding to the QDA assimilation performance shown in Figure 3. Folder Name: data4Description: This folder contains one .xlsx file: “rmse_xa_qda_cda_Psi.xlsx”File Name: rmse_xa_qda_cda_Psi.xlsxData Structure: 720 rows × 8 columns, two-dimensional arrayDescription: This file records the Root Mean Square Error (RMSE) of the stream function analysis fields obtained by CDA and QDA under different observational configurations (8, 32, 128, and 512 observation points). Each column represents the RMSE time series (720 steps) for a specific method-observation pairing, providing a quantitative assessment of assimilation performance. The data supports the RMSE comparison of stream function fields shown in Figure 4. Folder Name: data5Description: This folder contains one .xlsx file: rmse_xa_qda_cda_Vor.xlsx1.      File Name: rmse_xa_qda_cda_Vor.xlsxData Structure: 720 rows × 8 columns, two-dimensional arrayDescription: This file records the RMSE of the vorticity analysis fields from CDA and QDA under different observational configurations (8, 32, 128, and 512 observation points). Each column corresponds to a time series of RMSE (720 steps) for a specific method and observation point configuration. The data supports the vorticity RMSE comparison shown in Figure 5. Folder Name: data6Description:This folder contains six .xlsx files: “sub-region_1.xlsx”, “sub-region_2.xlsx”, “sub-region_3.xlsx”, “sub-region_4.xlsx”, “sub-region_5.xlsx”, and “sub-region_6.xlsx”.1.      File Name: sub-region_1.xlsxData Structure: 2000 rows × 3 columns, Excel spreadsheetDescription: This file records quantum optimization data obtained from the optical CIM device  during the first data assimilation of the stream function field in the first spatial sub-region. It includes the Hamiltonian values and corresponding evolution times from 2000 consecutive measurement steps recorded over a single continuous operation cycle of the CIM. The data supports the Hamiltonian evolution analysis shown in Figure 7 for sub-region 1. The remaining files (“sub-region_2.xlsx” to “sub-region_6.xlsx”) share the same structure and correspond to quantum optimization results in the other spatial sub-regions.

The original data titled 'Cryptocurrency Data Analysis Based on Complex Networks'