Classification accuracies (%) by comparing models using five data enhancement methods.

This systematic review of the literatu was conducted with the PRISMA method, to explore the contexts in which the use of open government data germinates, identifying barriers to its use and identifying, the role of data literacy among those barriers to use; and the role of open data in promoting informal learning that supports the development of critical data literacy. This file includes a codebook of the main characteristics that were studied in a systematic literature review, where data from 66 articles related to Open Data Usage were identified and coded. Also, the file includes an analysis of Cohen's Kappa, a concordance statistic used to measure the level of agreement among researchers in classifying articles on the characteristics defined in the Codebook. Finally, it includes main tables of the results' analysis.

In this paper, we propose a novel approach to reduce the noise in Synthetic Aperture Radar (SAR) images using particle filters. Interpretation of SAR images is a difficult problem, since they are contaminated with a multiplicative noise, which is known as the “Speckle Noise”. In literature, the general approach for removing the speckle is to use the local statistics, which are computed in a square window. Here, we propose to use particle filters, which is a sequential Bayesian technique. The proposed method also uses the local statistics to denoise the images. Since this is a Bayesian approach, the computed statistics of the window can be exploited as a priori information. Moreover, particle filters are sequential methods, which are more appropriate to handle the heterogeneous structure of the image. Computer simulations show that the proposed method provides better edge-preserving results with satisfactory speckle removal, when compared to the results obtained by Gamma Maximum a posteriori (MAP) filter.

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The LOL, LOLv2-Real, LSRW, DICM, LIME, MEF and NPE datasets can be acquired from the following links

A low-light image enhancement dataset using wavelet-based diffusion models.

The goal of this study was to conduct a national empirical assessment of post-release employment and recidivism effects based on legislative intent for inmates participating in Prison Industries Enhancement Certification Program (PIECP) as compared to participants in traditional industries (TI) and those involved in other than work (OTW) activities. The research design for this study was a quasi-experimental design using matched samples. The inmates were matched using six criteria. Exact matches were made on race, gender, crime type, and category matches on age, time served, and number of disciplinary reports. A cluster sampling strategy was used for site selection. This strategy resulted in a selection of five states which were not identified in the study. The researchers then collected data on 6,464 individuals by completing record reviews of outcomes for the 3 matched samples, each of approximately 2,200 inmates released from 46 prisons across 5 PIECP states between January 1, 1996, and June 30, 2001. Variables include demographic information, time incarcerated, number of disciplinary reports, crime type, number of major disciplinary reports reviewed, group type, number of quarters from release to employment, censored variables, number of quarters from employed to job loss, time from release variables, number of possible follow-up quarters, proportion of follow-up time worked, wage variables, number of quarters worked variables, no work ever, and cluster number of case.

Nanoparticles exhibit broad applications in materials mechanics, medicine, energy and other fields. The ordered arrangement of nanoparticles is very important to fully understand their properties and functionalities. However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. Our findings reveal that the combination of SD generated by rendering software with merely 15% Authentic Data (AD) shows better performance in training deep learning model. The trained U-Net model shows that Miou of 0.8476, accuracy of 0.9970, Kappa of 0.8207, and Dice of 0.9103, respectively. Compared with data enhancement alone, our approach yields a 1% improvement in the Miou metric. These results show that our proposed strategy can achieve better prediction performance without increasing the cost of data acquisition.

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Nanoparticles exhibit broad applications in materials mechanics, medicine, energy and other fields. The ordered arrangement of nanoparticles is very important to fully understand their properties and functionalities. However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. Our findings reveal that the combination of SD generated by rendering software with merely 15% Authentic Data (AD) shows better performance in training deep learning model. The trained U-Net model shows that Miou of 0.8476, accuracy of 0.9970, Kappa of 0.8207, and Dice of 0.9103, respectively. Compared with data enhancement alone, our approach yields a 1% improvement in the Miou metric. These results show that our proposed strategy can achieve better prediction performance without increasing the cost of data acquisition.

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The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station. EMIT uses imaging spectroscopy to take measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize methane (CH4) and carbon dioxide (CO2) point-source emissions by measuring gas absorption features in the shortwave infrared bands. The EMIT Level 2B Carbon Dioxide Enhancement Data (EMITL2BCO2ENH) Version 2 data product is a total vertical column enhancement estimate of carbon dioxide in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCO2ENH provides per-pixel carbon dioxide enhancement data used to identify carbon dioxide plume complexes, per-pixel carbon dioxide uncertainty due to sensor noise, and per-pixel carbon dioxide sensitivity that can be used to remove bias from the enhancement data. The EMITL2BCO2ENH Version 2 data product includes methane enhancement granules for all captured scenes, regardless of carbon dioxide plume complex identification. Each granule contains three Cloud Optimized GeoTIFF (COG) files at a spatial resolution of 60 meters (m): Carbon Dioxide Enhancement (EMIT_L2B_CO2ENH), Carbon Dioxide Uncertainty (EMIT_L2B_CO2UNCERT), and Carbon Dioxide Sensitivity (EMIT_L2B_CO2SENS). The EMITL2BCO2ENH COG files contain carbon dioxide enhancement data based primarily on EMITL1BRAD radiance values.Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules near the end of an orbit segment reaching 150 km in length.Known Issues Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.Improvements/Changes from Previous Versions Carbon dioxide uncertainty and sensitivity variables have been added. For more details on the uncertainty variable, see Section 6 of the Algorithm Theoretical Basis Document (ATBD) and Section 4.2.2 for details on the sensitivity variable. Enhancement, uncertainty, and sensitivity data are now included for all granules, including those without plume complexes. Version 1 of this product only included enhancement data for granules where plumes were present. The matched filter used to produce carbon dioxide enhancement data has been improved by adjusting the channels used to those that fall within 500-1340 nanometer (nm), 1500-1790 nm, or 1950-2450 nm. More details can be found in Section 4.2.3 of the ATBD.

Biological data analysis is the key to new discoveries in disease biology and drug discovery. The rapid proliferation of high-throughput ‘omics’ data has necessitated a need for tools and platforms that allow the researchers to combine and analyse different types of biological data and obtain biologically relevant knowledge. We had previously developed TargetMine, an integrative data analysis platform for target prioritisation and broad-based biological knowledge discovery. Here, we describe the newly modelled biological data types and the enhanced visual and analytical features of TargetMine. These enhancements have included: an enhanced coverage of gene–gene relations, small molecule metabolite to pathway mappings, an improved literature survey feature, and in silico prediction of gene functional associations such as protein–protein interactions and global gene co-expression. We have also described two usage examples on trans-omics data analysis and extraction of gene-disease associations using MeSH term descriptors. These examples have demonstrated how the newer enhancements in TargetMine have contributed to a more expansive coverage of the biological data space and can help interpret genotype–phenotype relations. TargetMine with its auxiliary toolkit is available at https://targetmine.mizuguchilab.org. The TargetMine source code is available at https://github.com/chenyian-nibio/targetmine-gradle.

The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station. EMIT uses imaging spectroscopy to take measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) EMIT Open Data Portal.In addition to its primary objective described above, EMIT has demonstrated the capacity to characterize methane (CH4) and carbon dioxide (CO2) point-source emissions by measuring gas absorption features in the shortwave infrared bands. The EMIT Level 2B Carbon Dioxide Enhancement Data (EMITL2BCO2ENH) Version 2 data product is a total vertical column enhancement estimate of carbon dioxide in parts per million meter (ppm m) based on an adaptive matched filter approach. EMITL2BCO2ENH provides per-pixel carbon dioxide enhancement data used to identify carbon dioxide plume complexes, per-pixel carbon dioxide uncertainty due to sensor noise, and per-pixel carbon dioxide sensitivity that can be used to remove bias from the enhancement data. The EMITL2BCO2ENH Version 2 data product includes methane enhancement granules for all captured scenes, regardless of carbon dioxide plume complex identification. Each granule contains three Cloud Optimized GeoTIFF (COG) files at a spatial resolution of 60 meters (m): Carbon Dioxide Enhancement (EMIT_L2B_CO2ENH), Carbon Dioxide Uncertainty (EMIT_L2B_CO2UNCERT), and Carbon Dioxide Sensitivity (EMIT_L2B_CO2SENS). The EMITL2BCO2ENH COG files contain carbon dioxide enhancement data based primarily on EMITL1BRAD radiance values.Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules near the end of an orbit segment reaching 150 km in length.Known Issues Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.Improvements/Changes from Previous Versions Carbon dioxide uncertainty and sensitivity variables have been added. For more details on the uncertainty variable, see Section 6 of the Algorithm Theoretical Basis Document (ATBD) and Section 4.2.2 for details on the sensitivity variable. Enhancement, uncertainty, and sensitivity data are now included for all granules, including those without plume complexes. Version 1 of this product only included enhancement data for granules where plumes were present. The matched filter used to produce carbon dioxide enhancement data has been improved by adjusting the channels used to those that fall within 500-1340 nanometer (nm), 1500-1790 nm, or 1950-2450 nm. More details can be found in Section 4.2.3 of the ATBD.

Austin Transportation’s Transit Enhancement program is developing focused, localized infrastructure solutions to improve the speed and reliability of CapMetro’s existing bus service, while making that service easier and safer to access.

Data on the names and locations of the hiking trails enhanced under the project

This data contains active Appearance Enhancement and Barber Business and Area Renter Licenses. Each record will be a business license which holds business address and/or license number information.

This repository contains the code and data underlying the publication "Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source" in Biomedical Optics Express 14, 3532-3554 (2023) (doi.org/10.1364/BOE.487345).

The reader is free to use the scripts and data in this depository, as long as the manuscript is correctly cited in their work. For further questions, please contact the corresponding author.

Description of the code and datasets

Table 1 describes all the Matlab and Python scripts in this depository. Table 2 describes the datasets. The input datasets are the phase corrected datasets, as the raw data is large in size and phase correction using a coverslip as reference is rather straightforward. Processed datasets are also added to the repository to allow for running only a limited number of scripts, or to obtain for example the aberration corrected data without the need to use python. Note that the simulation input data (input_simulations_pointscatters_SLDshape_98zf_noise75.mat) is generated with random noise, so if this is overwritten de results may slightly vary. Also the aberration correction is done with random apertures, so the processed aberration corrected data (exp_pointscat_image_MIAA_ISAM_CAO.mat and exp_leaf_image_MIAA_ISAM_CAO.mat) will also slightly change if the aberration correction script is run anew. The current processed datasets are used as basis for the figures in the publication. For details on the implementation we refer to the publication.

Table 1: The Matlab and Python scripts with their description


    Script name
    Description


    MIAA_ISAM_processing.m
    This scripts performs the DFT, RFIAA and MIAA processing of the phase-corrected data that can be loaded from the datasets. Afterwards it also applies ISAM on the DFT and MIAA data and plots the results in a figure (via the scripts plot_figure3, plot_figure5 and plot_simulationdatafigure).


    resolution_analysis_figure4.m
    This figure loads the data from the point scatterers (absolute amplitude data), seeks the point scatterrers and fits them to obtain the resolution data. Finally it plots figure 4 of the publication.


    fiaa_oct_c1.m, oct_iaa_c1.m, rec_fiaa_oct_c1.m, rfiaa_oct_c1.m 
    These four functions are used to apply fast IAA and MIAA. See script MIAA_ISAM_processing.m for their usage.


    viridis.m, morgenstemning.m
    These scripts define the colormaps for the figures.


    plot_figure3.m, plot_figure5.m, plot_simulationdatafigure.m
    These scripts are used to plot the figures 3 and 5 and a figure with simulation data. These scripts are executed at the end of script MIAA_ISAM_processing.m.


    Python script: computational_adaptive_optics_script.py
    Python script that applied computational adaptive optics to obtain the data for figure 6 of the manuscript.


    Python script: zernike_functions2.py
    Python script that gives the values and carthesian derrivatives of the Zernike polynomials.


    figure6_ComputationalAdaptiveOptics.m
    Script that loads the CAO data that was saved in Python, analyzes the resolution, and plots figure 6.


    Python script: OCTsimulations_3D_script2.py
    Python script simulates OCT data, adds noise and saves it as .mat file for use in the matlab script above.


    Python script: OCTsimulations2.py
    Module that contains a python class that can be used to simulate 3D OCT datasets based on a Gaussian beam.


    Matlab toolbox DIPimage 2.9.zip
    Dipimage is used in the scripts. The toolbox can be downloaded online or this zip can be used.






The datasets in this Zenodo repository


    Name
    Description


    input_leafdisc_phasecorrected.mat
    Phase corrected input image of the leaf disc (used in figure 5).


    input_TiO2gelatin_004_phasecorrected.mat
    Phase corrected input image of the TiO2 in gelatin sample.


    input_simulations_pointscatters_SLDshape_98zf_noise75
    Input simulation data that, once processed, is used in figure 4.

exp_pointscat_image_DFT.mat

exp_pointscat_image_DFT_ISAM.mat

exp_pointscat_image_RFIAA.mat

exp_pointscat_image_MIAA_ISAM.mat

exp_pointscat_image_MIAA_ISAM_CAO.mat

    Processed experimental amplitude data for the TiO2 point scattering sample with respectively DFT, DFT+ISAM, RFIAA, MIAA+ISAM and MIAA+ISAM+CAO. These datasets are used for fitting in figure 4 (except for CAO), and MIAA_ISAM and MIAA_ISAM_CAO are used for figure 6.

simu_pointscat_image_DFT.mat

simu_pointscat_image_RFIAA.mat

simu_pointscat_image_DFT_ISAM.mat

simu_pointscat_image_MIAA_ISAM.mat

    Processed amplitude data from the simulation dataset, which is used in the script for figure 4 for the resolution analysis.

exp_leaf_image_MIAA_ISAM.mat

exp_leaf_image_MIAA_ISAM_CAO.mat

    Processed amplitude data from the leaf sample, with and without aberration correction which is used to produce figure 6.

exp_leaf_zernike_coefficients_CAO_normal_wmaf.mat

exp_pointscat_zernike_coefficients_CAO_normal_wmaf.mat

    Estimated Zernike coefficients and the weighted moving average of them that is used for the computational aberration correction. Some of this data is plotted in Figure 6 of the manuscript.


    input_zernike_modes.mat
    The reference Zernike modes corresponding to the data that is loaded to give the modes the proper name.

exp_pointscat_MIAA_ISAM_complex.mat

exp_leaf_MIAA_ISAM_complex

    Complex MIAA+ISAM processed data that is used as input for the computational aberration correction.

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