“Scatter plot” papers · AI for Research

Scatter Plot

University of Pittsburgh Press eBooks · 2020

Cited by 1

Scatter plot.

Ivan Bozin (771770), Yulin Ge (771771), Joseph Kuchling (771772) et al. · Figshare · 2015

A. The higher proportion of T2*w lesions visible on phase images in early compared to chronic disease stages is displayed. B. The scatter plot indicates an inverse correlation between phase visibility and disease duration.

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Scatter Plot

Cheng WU · Zenodo (CERN European Organization for Nuclear Research) · 2025

Version 2025-10-11 Now works on Igor 9. Tutorial: https://www.bilibili.com/video/BV1oEWtz7ECS/ https://youtu.be/sHM2i9ND3VU Scatter Plot is a useful toolkit based on Igor Pro, designed to enhance data visualization efficiency in atmospheric science. While many existing generalized data visualization software options have been widely used, they often fall short in meeting specific research needs in this field. This gap motivated the development of Scatter Plot. The program incorporates the Deming and York algorithms for linear regression, which account for uncertainties in both the X and Y variables, making it more suitable for atmospheric applications. Scatter Plot is equipped with various features for data analysis and graph plotting, including batch plotting, data masking through a user-friendly interface, color coding along the Z-axis, and the ability to filter and group data across different time scales—such as year, season, month, hour, and day of the week. For more details regarding the evaluation and application of Scatter Plot, please refer to For more details regarding the evaluation and application of Scatter Plot, please refer to Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi: https://doi.org/10.5194/amt-11-1233-2018, 2018. Please cite this paper if Scatter Plot is used in your publication. The latest version of the program can be found on my website: https://wucheng.weebly.com/ https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416 Scatter Plot toolkit adoption in research publications: Wu, Y., Liu, D., Xu, H., Shan, M., Li, S., Tian, P., Hu, K., and Wang, J.: Analysis of atmospheric pollutant characteristics and regional transport in coastal area along the East China Sea, J. Environ. Sci., 156, 225-238, doi: https://doi.org/10.1016/j.jes.2024.06.040, 2025. Li, W., Qi, Y., Liu, Y., Wu, G., Zhang, Y., Shi, J., Qu, W., Sheng, L., Wang, W., Zhang, D., and Zhou, Y.: Daytime and nighttime aerosol soluble iron formation in clean and slightly polluted moist air in a coastal city in eastern China, Atmos. Chem. Phys., 24, 6495-6508, doi: https://doi.org/10.5194/acp-24-6495-2024, 2024. Wu, B., Wu, C., Ye, Y., Pei, C., Deng, T., Li, Y. J., Lu, X., Wang, L., Hu, B., Li, M., and Wu, D.: Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China, Atmos. Environ., 321, 120347, doi: https://doi.org/10.1016/j.atmosenv.2024.120347, 2024. Hedges, M., Priestman, M., Chadeau-Hyam, M., Sinharay, R., Kelly, F. J., and Green, D. C.: Characterising a mobile reference station (MoRS) to quantify personal exposure to air quality, Atmos. Environ., 315, 120160, doi: https://doi.org/10.1016/j.atmosenv.2023.120160, 2023. Lin, W., Lai, Y., Zhuang, S., Wei, Q., Zhang, H., Hu, Q., Cheng, P., Zhang, M., Zhai, Y., Wang, Q., Han, Z., and Hou, H.: The effects of prenatal PM2.5 oxidative potential exposure on feto-placental vascular resistance and fetal weight: A repeated-measures study, Environ. Res., 234, 116543, doi: https://doi.org/10.1016/j.envres.2023.116543, 2023. Liu, H., Jia, M., Tao, J., Yao, D., Li, J., Zhang, R., Li, L., Xu, M., Fan, Y., Liu, Y., and Cheng, K.: Elucidating pollution characteristics, temporal variation and source origins of carbonaceous species in Xinxiang, a heavily polluted city in North China, Atmos. Environ., 298, 119626, doi: https://doi.org/10.1016/j.atmosenv.2023.119626, 2023. Liu, Y., Zhou, M., Zhao, M., Jing, S., Wang, H., Lu, K., and Shen, H.: Determination of Urban Formaldehyde Emission Ratios in the Shanghai Megacity, Environ. Sci. Technol., 57, 16489-16499, doi: https://doi.org/10.1021/acs.est.3c06428, 2023. Peng, Y., Wang, H., Wang, Q., Jing, S., An, J., Gao, Y., Huang, C., Yan, R., Dai, H., Cheng, T., Zhang, Q., Li, M., Hu, J., Shi, Z., Li, L., Lou, S., Tao, S., Hu, Q., Lu, J., and Chen, C.: Observation-based sources evolution of non-methane hydrocarbons (NMHCs) in a megacity of China, J. Environ. Sci., 124, 794-805, doi: https://doi.org/10.1016/j.jes.2022.01.040, 2023. Song, Z., Gao, W., Shen, H., Jin, Y., Zhang, C., Luo, H., Pan, L., Yao, B., Zhang, Y., Huo, J., Sun, Y., Yu, D., Chen, H., Chen, J., Duan, Y., Zhao, D., and Xu, J.: Roles of Regional Transport and Vertical Mixing in Aerosol Pollution in Shanghai Over the COVID-19 Lockdown Period Observed Above Urban Canopy, J. Geophys. Res., 128, e2023JD038540, doi: https://doi.org/10.1029/2023JD038540, 2023. Young, L.-H., Hsu, C.-S., Hsiao, T.-C., Lin, N.-H., Tsay, S.-C., Lin, T.-H., Lin, W.-Y., and Jung, C.-R.: Sources, transport, and visibility impact of ambient submicrometer particle number size distributions in an urban area of central Taiwan, Sci. Total. Environ., 856, 159070, doi: https://doi.org/10.1016/j.scitotenv.2022.159070, 2023. Dai, L., Zhang, L., Chen, D., and Zhao, Y.: Assessment of carbonaceous aerosols in suburban Nanjing under air pollution control measures: Insights from long-term measurements, Environ. Res., 212, 113302, doi: https://doi.org/10.1016/j.envres.2022.113302, 2022. Dao, X., Ji, D., Zhang, X., He, J., Sun, J., Hu, J., Liu, Y., Wang, L., Xu, X., Tang, G., and Wang, Y.: Significant reduction in atmospheric organic and elemental carbon in PM2.5 in 2+26 cities in northern China, Environ. Res., 211, 113055, doi: https://doi.org/10.1016/j.envres.2022.113055, 2022. Ding, J., Huang, W., Zhao, J., Li, L., Xiong, G., Jiang, C., Ye, D., Li, D., Wang, J., Yu, J., and Liu, R.: Characteristics and source origins of carbonaceous aerosol in fine particulate matter in a megacity, Sichuan Basin, southwestern China, Atmos. Pollut. Res., 13, 101266, doi: https://doi.org/10.1016/j.apr.2021.101266, 2022. Liu, X., Zheng, M., Liu, Y., Jin, Y., Liu, J., Zhang, B., Yang, X., Wu, Y., Zhang, T., Xiang, Y., Liu, B., and Yan, C.: Intercomparison of equivalent black carbon (eBC) and elemental carbon (EC) concentrations with three-year continuous measurement in Beijing, China, Environ. Res., 209, 112791, doi: https://doi.org/10.1016/j.envres.2022.112791, 2022. Liang, Y., Wu, C., Wu, D., Liu, B., Li, Y. J., Sun, J., Yang, H., Mao, X., Tan, J., Xia, R., Deng, T., Li, M., and Zhou, Z.: Vertical distributions of atmospheric black carbon in dry and wet seasons observed at a 356-m meteorological tower in Shenzhen, South China, Sci. Total. Environ., 853, 158657, doi: https://doi.org/10.1016/j.scitotenv.2022.158657, 2022. He, G., Deng, T., Wu, D., Wu, C., Huang, X., Li, Z., Yin, C., Zou, Y., Song, L., Ouyang, S., Tao, L., and Zhang, X.: Characteristics of boundary layer ozone and its effect on surface ozone concentration in Shenzhen, China: A case study, Sci. Total. Environ., 791, 148044, doi: https://doi.org/10.1016/j.scitotenv.2021.148044, 2021. Liang, Y., Wu, C., Jiang, S., Li, Y. J., Wu, D., Li, M., Cheng, P., Yang, W., Cheng, C., Li, L., Deng, T., Sun, J. Y., He, G., Liu, B., Yao, T., Wu, M., and Zhou, Z.: Field comparison of electrochemical gas sensor data correction algorithms for ambient air measurements, Sens. Actuators B Chem., 327, 128897, doi: https://doi.org/10.1016/j.snb.2020.128897, 2021. Wu, C., Liu, B., Wu, D., Yang, H., Mao, X., Tan, J., Liang, Y., Sun, J. Y., Xia, R., Sun, J., He, G., Li, M., Deng, T., Zhou, Z., and Li, Y. J.: Vertical profiling of black carbon and ozone using a multicopter unmanned aerial vehicle (UAV) in urban Shenzhen of South China, Sci. Total. Environ., 801, 149689, doi: https://doi.org/10.1016/j.scitotenv.2021.149689, 2021. Liu, B., Wu, C., Ma, N., Chen, Q., Li, Y., Ye, J., Martin, S. T., and Li, Y. J.: Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China, Sci. Total. Environ., 709, 136109, doi: https://doi.org/10.1016/j.scitotenv.2019.136109, 2020. Wong, Y. K., Huang, X. H. H., Louie, P. K. K., Yu, A. L. C., Chan, D. H. L., and Yu, J. Z.: Tracking separate contributions of diesel and gasoline vehicles to roadside PM2.5 through online monitoring of volatile organic compounds and PM2.5 organic and elemental carbon: a 6-year study in Hong Kong, Atmos. Chem. Phys., 20, 9871-9882, doi: https://doi.org/10.5194/acp-20-9871-2020, 2020. Sun, J. Y., Wu, C., Wu, D., Cheng, C., Li, M., Li, L., Deng, T., Yu, J. Z., Li, Y. J., Zhou, Q., Liang, Y., Sun, T., Song, L., Cheng, P., Yang, W., Pei, C., Chen, Y., Cen, Y., Nian, H., and Zhou, Z.: Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou, Atmos. Chem. Phys., 20, 2445-2470, doi: https://doi.org/10.5194/acp-20-2445-2020, 2020a. Sun, T., Wu, C., Wu, D., Liu, B., Sun, J. Y., Mao, X., Yang, H., Deng, T., Song, L., Li, M., Li, Y. J., and Zhou, Z.: Time-resolved black carbon aerosol vertical distribution measurements using a 356-m meteorological tower in Shenzhen, Theor. Appl. Climatol., 140, 1263–1276, doi: https://doi.org/10.1007/s00704-020-03168-6, 2020b. Ji, D., Gao, W., Maenhaut, W., He, J., Wang, Z., Li, J., Du, W., Wang, L., Sun, Y., Xin, J., Hu, B., and Wang, Y.: Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement, Atmos. Chem. Phys., 19, 8569-8590, doi: https://doi.org/10.5194/acp-19-8569-2019, 2019. Wu, C., Wu, D., and Yu, J. Z.: Estimation and Uncertainty Analysis of Secondary Organic Carbon Using One-Year of Hourly Organic and Elemental Carbon Data, J. Geophys. Res., 124, 2774-2795, doi: https://doi.org/10.1029/2018JD029290, 2019. Liu, B., He, M. M., Wu, C., Li, J., Li, Y., Lau, N. T., Yu, J. Z., Lau, A. K. H., Fung, J. C. H., Hoi, K. I., Mok, K. M., Chan, C. K., and Li, Y. J.: Potential exposure to fine particulate matter (PM2.5) and black carbon on jogging trails in Macau, Atmos. Environ., 198, 23-3

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Scatter plot.

Jiaqin Chen (17391339), Junning Zhang (8614701), Xiaolu Ma (3350690) et al. · Figshare · 2023

(A) Hp-CagA and GERD; (B) Hp-Catalase and GERD; (C) Hp-GroEL and GERD; (D) Hp-IgG and GERD; (E) Hp-OMP and GERD; (F) Hp-UREA and GERD; (G) Hp-VacA and GERD. (DOCX)

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Scatter Plot

WU, Cheng · Figshare · 2019

Version 20190501 Scatter Plot is a handy tool to maximize the efficiency of data visualization in atmospheric science. Many existing generalized data visualization software had been extensively used, but they remain unable to fulfill a number of specified research purposes in atmospheric science. That becomes the motivation of Scatter Plot development. The program includes Deming and York algorithm for linear regression, which considers uncertainties in both X and Y, and is more realistic for atmospheric applications. Scatter Plot is Igor based, and packed with a variety of useful features for data analysis and graph plotting, including batch plotting, data masking via GUI, color coding in Z-axis, data filtering and grouping on different time scales (year, season, month, hour, day of week, etc). For more details regarding the evaluation and application of Scatter Plot, please refer to Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:10.5194/amt-11-1233-2018, 2018. Please cite this paper if Scatter Plot is used in your publication. The latest version of the program can be found on my website: https://sites.google.com/site/wuchengust/ https://wucheng.weebly.com/ https://doi.org/10.5281/zenodo.832416 Adoption in research publications: Ji, D., Gao, W., Maenhaut, W., He, J., Wang, Z., Li, J., Du, W., Wang, L., Sun, Y., Xin, J., Hu, B., and Wang, Y.: Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement, Atmos. Chem. Phys., 19, 8569-8590, doi: 10.5194/acp-19-8569-2019, 2019. Wang, N. and Yu, J. Z.: Size distributions of hydrophilic and hydrophobic fractions of water-soluble organic carbon in an urban atmosphere in Hong Kong, Atmos. Environ., 166, 110-119, doi: 10.1016/j.atmosenv.2017.07.009, 2017. Wu, C., Huang, X. H. H., Ng, W. M., Griffith, S. M., and Yu, J. Z.: Inter-comparison of NIOSH and IMPROVE protocols for OC and EC determination: Implications for inter-protocol data conversion, Atmos. Meas. Tech. doi: 10.5194/amt-9-4547-2016, 2016. Zhou, Y., Huang, X. H. H., Griffith, S. M., Li, M., Li, L., Zhou, Z., Wu, C., Meng, J., Chan, C. K., Louie, P. K. K., and Yu, J. Z.: A field measurement based scaling approach for quantification of major ions, organic carbon, and elemental carbon using a single particle aerosol mass spectrometer, Atmos. Environ., 143, 300-312, 2016.http://dx.doi.org/10.1016/j.atmosenv.2016.08.054 Qiao, T., Zhao, M., Xiu, G., and Yu, J.: Seasonal variations of water soluble composition (WSOC, Hulis and WSIIs) in PM1 and its implications on haze pollution in urban Shanghai, China, Atmos. Environ., 123, Part B, 306-314, 2015. http://dx.doi.org/10.1016/j.atmosenv.2015.03.010 List of programs I developed: ScatterPlot Histogram and Boxplot MRS RT-ECOC raw data processor Benchtop Sunset ECOC analyzer data processor DRI 2001A data Sorter SMPS Toolkit Mie Scattering Aethalometer data correction ======================================================================================================= Scatter Plot是一个方便的工具,可以最大限度地提高大气科学中数据可视化的效率。虽然有许多现有的通用数据可视化软件,但不能满足许多大气科学特定的研究目的,所以我开发自己的程序。本程序包括WODR, Deming和York算法进行线性回归,这三种算法考虑了X和Y都包含不确定性(观测误差),对大气的应用而言更加客观地反映真实情况。它是基于Igor的,并且包含大量用于数据分析和图形绘图的有用功能,包括批量绘图,通过图形界面实现数据掩蔽,Z轴的颜色编码,根据数据或字符串进行过滤和分组。有关Scatter Plot的评估和应用的更多细节,请参阅(如果你在文章中用到了本软件,请引用以下文章) Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:10.5194/amt-11-1233-2018, 2018. 关于程序的最新信息可以在我的网站上找到: https://sites.google.com/site/wuchengust/ https://wucheng.weebly.com/ https://doi.org/10.5281/zenodo.832416

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Scatter Plot.

Juliana Casimiro de Almeida (804587), Randall Lou-Meda (804588), Marion Olbert (804589) et al. · Figshare · 2015

Correlation in HD patients between A. ferritin and Hep-25 (r = 0.04; P = 0.82); B. serum iron and Hep-25 (r = 0.05; P = 0.79); and C. CRP and Hep-25 (r = 0.31; P = 0.12).

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Scatter plot.

Wenqiang Fu (1599883), Bin Zhu (182882), Jun Chen (4238) et al. · Figshare · 2024

The slope of each line corresponds to the estimated Mendelian randomization effect in different models.

Cited by 0Open access

Scatter Plot.

Juliana Casimiro de Almeida (804587), Randall Lou-Meda (804588), Marion Olbert (804589) et al. · Figshare · 2015

Correlation in PD patients between A. ferritin and Hep-25 (r = 0.77; P < 0.01); B. serum iron and Hep-25 (r = 0.56; P < 0.01); and C. CRP and Hep-25 (r = -0.23; P = 0.256).

Cited by 0Open access

Scatter Plot

Lourenção, Marina, Lourenção, Marina · Zenodo (CERN European Organization for Nuclear Research) · 2025

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Scatter Plot

WU, Cheng · Zenodo (CERN European Organization for Nuclear Research) · 2020

Version 20200818 New features: 1) Batch plotting- overlaid plots In overlaid mode, all traces are combined in a single plot, as shown below. Each trace will be assigned to a specified color according to the rainbow color table. Batch plotting can be applied on following scales: Year; Year/Month; Month; Year/Season; Season; YYYY/MM/DD; Day of week; Hour of day; text data (User defined, Weekday/weekend, Daytime/nighttime). Regressed slopes, intercepts, R2 and other metrics can be found in the csv file if “Export txt for batch plot” was selected. Demo video of overlaid plots: https://youtu.be/O7oHxJegKvU 2) weekday/weekend and daytime/nighttime quick selection. 3) Daytime/nighttime and weekend/weekday options are provided in Text by list data filtering. 4) The mask wave can be exported to a csv file for further use. Click “Export mask as txt” to export. Demo video: https://youtu.be/aK5JtxDwOwE Scatter Plot is a handy tool to maximize the efficiency of data visualization in atmospheric science. Many existing generalized data visualization software had been extensively used, but they remain unable to fulfill a number of specify research purposes in atmospheric science. That becomes the motivation of Scatter Plot development. The program includes Deming and York algorithm for linear regression, which considers uncertainties in both X and Y, and is more realistic for atmospheric applications. Scatter Plot is Igor based, and packed with a variety of useful features for data analysis and graph plotting, including batch plotting, data masking via GUI, color coding in Z-axis, data filtering and grouping on different time scales (year, season, month, hour, day of week, etc). For more details regarding the evaluation and application of Scatter Plot, please refer to Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi: https://doi.org/10.5194/amt-11-1233-2018, 2018. Please cite this paper if Scatter Plot is used in your publication. The latest version of the program can be found on my website: https://wucheng.weebly.com/ https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416 Adoption in research publications: Sun, J. Y., Wu, C.*, Wu, D.*, Cheng, C., Li, M., Li, L., Deng, T., Yu, J. Z., Li, Y. J., Zhou, Q., Liang, Y., Sun, T., Song, L., Cheng, P., Yang, W., Pei, C., Chen, Y., Cen, Y., Nian, H., and Zhou, Z.*: Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou, Atmos. Chem. Phys., 20, 2445-2470, doi: https://doi.org/10.5194/acp-20-2445-2020, 2020. Sun, T., Wu, C.*, Wu, D.*, Liu, B., Sun, J. Y., Mao, X., Yang, H., Deng, T., Song, L., Li, M., Li, Y. J., and Zhou, Z.*: Time-resolved black carbon aerosol vertical distribution measurements using a 356-m meteorological tower in Shenzhen, Theor. Appl. Climatol., doi: https://doi.org/10.1007/s00704-020-03168-6 , 2020. Liu, B., Wu, C., Ma, N., Chen, Q., Li, Y., Ye, J., Martin, S. T., and Li, Y. J.*: Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China, Sci. Total. Environ., 136109, doi: https://doi.org/10.1016/j.scitotenv.2019.136109, 2020. Wu, C*., Wu, D., and Yu, J. Z*.: Estimation and Uncertainty Analysis of Secondary Organic Carbon Using One‐Year of Hourly Organic and Elemental Carbon Data. J. Geophys. Res.-Atmos, 124, 2774-2795 doi:https://doi.org/10.1029/2018JD029290, 2019 Ji, D., Gao, W., Maenhaut, W., He, J., Wang, Z., Li, J., Du, W., Wang, L., Sun, Y., Xin, J., Hu, B., and Wang, Y.: Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement, Atmos. Chem. Phys., 19, 8569-8590, doi: https://doi.org/10.5194/acp-19-8569-2019, 2019. Liu, B., He, M. M., Wu, C., Li, J., Li, Y., Lau, N. T., Yu, J. Z., Lau, A. K. H., Fung, J. C. H., Hoi, K. I., Mok, K. M., Chan, C. K., and Li, Y. J*.: Potential exposure to fine particulate matter (PM2.5) and black carbon on jogging trails in Macau, Atmos. Environ., 198, 23-33, doi:https://doi.org/10.1016/j.atmosenv.2018.10.024, 2019. Wang, N. and Yu, J. Z.: Size distributions of hydrophilic and hydrophobic fractions of water-soluble organic carbon in an urban atmosphere in Hong Kong, Atmos. Environ., 166, 110-119, doi: 10.1016/j.atmosenv.2017.07.009, 2017. Cheng, C., Li, M.*, Chan, C. K., Tong, H., Chen, C., Chen, D., Wu, D., Li, L., Wu, C., Cheng, P., Gao, W., Huang, Z., Li, X., Zhang, Z., Fu, Z., Bi, Y., and Zhou, Z.*: Mixing state of oxalic acid containing particles in the rural area of Pearl River Delta, China: implications for the formation mechanism of oxalic acid, Atmos. Chem. Phys., 17, 9519-9533, doi:https://doi.org/10.5194/acp-17-9519-2017, 2017. Wu, C., Huang, X. H. H., Ng, W. M., Griffith, S. M., and Yu, J. Z.: Inter-comparison of NIOSH and IMPROVE protocols for OC and EC determination: Implications for inter-protocol data conversion, Atmos. Meas. Tech. doi: https://doi.org/10.5194/amt-9-4547-2016, 2016. Zhou, Y., Huang, X. H. H., Griffith, S. M., Li, M., Li, L., Zhou, Z., Wu, C., Meng, J., Chan, C. K., Louie, P. K. K., and Yu, J. Z.: A field measurement based scaling approach for quantification of major ions, organic carbon, and elemental carbon using a single particle aerosol mass spectrometer, Atmos. Environ., 143, 300-312, http://dx.doi.org/10.1016/j.atmosenv.2016.08.054 2016. Adoption in research publications (without acknowledgment): Qiao, T., Zhao, M., Xiu, G., and Yu, J.: Seasonal variations of water soluble composition (WSOC, Hulis and WSIIs) in PM1 and its implications on haze pollution in urban Shanghai, China, Atmos. Environ., 123, Part B, 306-314, 2015. http://dx.doi.org/10.1016/j.atmosenv.2015.03.010 List of programs I developed: ScatterPlot Histogram and Boxplot MRS RT-ECOC raw data processor Benchtop Sunset ECOC analyzer data processor DRI 2001A data Sorter SMPS Toolkit Mie Scattering Aethalometer data correction ======================================================================================================= Scatter Plot是一个方便的工具,可以最大限度地提高大气科学中数据可视化的效率。虽然有许多现有的通用数据可视化软件,但不能满足许多大气科学特定的研究目的,所以我开发自己的程序。本程序包括WODR, Deming和York算法进行线性回归,这三种算法考虑了X和Y都包含不确定性(观测误差),对大气的应用而言更加客观地反映真实情况。它是基于Igor的,并且包含大量用于数据分析和图形绘图的有用功能,包括批量绘图,通过图形界面实现数据掩蔽,Z轴的颜色编码,根据数据或字符串进行过滤和分组。有关Scatter Plot的评估和应用的更多细节,请参阅(如果你在文章中用到了本软件,请引用以下文章) Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi: https://doi.org/10.5194/amt-11-1233-2018, 2018. 关于程序的最新信息可以在我的网站上找到: https://www.x-mol.com/groups/wucheng https://sites.google.com/site/wuchengust/ https://wucheng.weebly.com/ https://doi.org/10.5281/zenodo.832416

Cited by 0Open access

Scatter Plot

WU, Cheng · Figshare · 2018

Scatter Plot is a handy tool to maximize the efficiency of data visualization in atmospheric science. Many existing generalized data visualization software are great, but could not satisfy many specify research purposes in atmospheric science so I develop my own program. The program includes Deming and York algorithm for linear regression, which considers uncertainties in both X and Y, that is more realistic for atmospheric applications. It is Igor based, and packed with lots of useful features for data analysis and graph plotting, including batch plotting, data masking via GUI, color coding in Z axis, data filtering and grouping. For more details regarding the evaluation and application of Scatter Plot, please refer to Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:10.5194/amt-11-1233-2018, 2018. Please cite this paper if Scatter Plot is used in your publication. The latest version of the program can be found on my website: https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416 ======================================================================================================= Scatter Plot是一个方便的工具,可以最大限度地提高大气科学中数据可视化的效率。虽然有许多现有的通用数据可视化软件,但不能满足许多大气科学特定的研究目的,所以我开发自己的程序。本程序包括WODR, Deming和York算法进行线性回归,这三种算法考虑了X和Y都包含不确定性(观测误差),对大气的应用而言更加客观地反映真实情况。它是基于Igor的,并且包含大量用于数据分析和图形绘图的有用功能,包括批量绘图,通过图形界面实现数据掩蔽,Z轴的颜色编码,根据数据或字符串进行过滤和分组。有关Scatter Plot的评估和应用的更多细节,请参阅(如果你在文章中用到了本软件,请引用以下文章) Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:10.5194/amt-11-1233-2018, 2018. 关于程序的最新信息可以在我的网站上找到: https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416

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Scatter Plot

WU, Cheng · Figshare · 2019

Version 20190114 Scatter Plot is a handy tool to maximize the efficiency of data visualization in atmospheric science. Many existing generalized data visualization software is great, but they remain unable to fulfill a number of specified research purposes in atmospheric science. That becomes the motivation of Scatter Plot development. The program includes Deming and York algorithm for linear regression, which considers uncertainties in both X and Y, and is more realistic for atmospheric applications. Scatter Plot is Igor based, and packed with a variety of useful features for data analysis and graph plotting, including batch plotting, data masking via GUI, color coding in Z-axis, data filtering and grouping on different time scales (year, season, month, hour, day of week, etc). For more details regarding the evaluation and application of Scatter Plot, please refer to Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:10.5194/amt-11-1233-2018, 2018. Please cite this paper if Scatter Plot is used in your publication. The latest version of the program can be found on my website: https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416 Adoption in research publications: Ji, D., Gao, W., Maenhaut, W., He, J., Wang, Z., Li, J., Du, W., Wang, L., Sun, Y., Xin, J., Hu, B., and Wang, Y.: Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement, Atmos. Chem. Phys., 19, 8569-8590, doi: 10.5194/acp-19-8569-2019, 2019. Wang, N. and Yu, J. Z.: Size distributions of hydrophilic and hydrophobic fractions of water-soluble organic carbon in an urban atmosphere in Hong Kong, Atmos. Environ., 166, 110-119, doi: 10.1016/j.atmosenv.2017.07.009, 2017. Wu, C., Huang, X. H. H., Ng, W. M., Griffith, S. M., and Yu, J. Z.: Inter-comparison of NIOSH and IMPROVE protocols for OC and EC determination: Implications for inter-protocol data conversion, Atmos. Meas. Tech. doi: 10.5194/amt-9-4547-2016, 2016. Zhou, Y., Huang, X. H. H., Griffith, S. M., Li, M., Li, L., Zhou, Z., Wu, C., Meng, J., Chan, C. K., Louie, P. K. K., and Yu, J. Z.: A field measurement based scaling approach for quantification of major ions, organic carbon, and elemental carbon using a single particle aerosol mass spectrometer, Atmos. Environ., 143, 300-312, 2016.http://dx.doi.org/10.1016/j.atmosenv.2016.08.054 Qiao, T., Zhao, M., Xiu, G., and Yu, J.: Seasonal variations of water soluble composition (WSOC, Hulis and WSIIs) in PM1 and its implications on haze pollution in urban Shanghai, China, Atmos. Environ., 123, Part B, 306-314, 2015. http://dx.doi.org/10.1016/j.atmosenv.2015.03.010 ======================================================================================================= Scatter Plot是一个方便的工具,可以最大限度地提高大气科学中数据可视化的效率。虽然有许多现有的通用数据可视化软件,但不能满足许多大气科学特定的研究目的,所以我开发自己的程序。本程序包括WODR, Deming和York算法进行线性回归,这三种算法考虑了X和Y都包含不确定性(观测误差),对大气的应用而言更加客观地反映真实情况。它是基于Igor的,并且包含大量用于数据分析和图形绘图的有用功能,包括批量绘图,通过图形界面实现数据掩蔽,Z轴的颜色编码,根据数据或字符串进行过滤和分组。有关Scatter Plot的评估和应用的更多细节,请参阅(如果你在文章中用到了本软件,请引用以下文章) Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:10.5194/amt-11-1233-2018, 2018. 关于程序的最新信息可以在我的网站上找到: https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416

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Scatter plot.

Ankush Ashok Saddhe (4373584), Rahul Arvind Jamdade (4373590), Kundan Kumar (4373587) · Figshare · 2017

The scatter plot represents the number of individuals in each species against their maximum intra-specific distances, as a test for sampling bias. (a) ITS2 locus (b) atpF-atpH locus (c) psbK-psbI locus and (d) rpoC1 locus.

Cited by 0Open access

Scatter Plot

WU, Cheng · Zenodo (CERN European Organization for Nuclear Research) · 2018

Scatter Plot is a handy tool to maximize the efficiency of data visualization in atmospheric science. Many existing generalized data visualization software are great, but could not satisfy many specify research purposes in atmospheric science so I develop my own program. The program includes Deming and York algorithm for linear regression, which considers uncertainties in both X and Y, that is more realistic for atmospheric applications. It is Igor based, and packed with lots of useful features for data analysis and graph plotting, including batch plotting, data masking via GUI, color coding in Z axis, data filtering and grouping. For more details regarding the evaluation and application of Scatter Plot, please refer to Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:10.5194/amt-11-1233-2018, 2018. Please cite this paper if Scatter Plot is used in your publication. The latest version of the program can be found on my website: https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416 ======================================================================================================= Scatter Plot是一个方便的工具,可以最大限度地提高大气科学中数据可视化的效率。虽然有许多现有的通用数据可视化软件,但不能满足许多大气科学特定的研究目的,所以我开发自己的程序。本程序包括WODR, Deming和York算法进行线性回归,这三种算法考虑了X和Y都包含不确定性(观测误差),对大气的应用而言更加客观地反映真实情况。它是基于Igor的,并且包含大量用于数据分析和图形绘图的有用功能,包括批量绘图,通过图形界面实现数据掩蔽,Z轴的颜色编码,根据数据或字符串进行过滤和分组。有关Scatter Plot的评估和应用的更多细节,请参阅(如果你在文章中用到了本软件,请引用以下文章) Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:10.5194/amt-11-1233-2018, 2018. 关于程序的最新信息可以在我的网站上找到: https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416

Cited by 0Open access

Scatter Plot

Lourenção, Marina, Lourenção, Marina · Zenodo (CERN European Organization for Nuclear Research) · 2025

Cited by 0Open access

Scatter Plot

2005

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Scatter plot

The Dictionary of Genomics, Transcriptomics and Proteomics · 2015

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Scatter Plot

Cheng WU · Zenodo (CERN European Organization for Nuclear Research) · 2025

Version 2025-10-11 Now works on Igor 9. Tutorial: https://www.bilibili.com/video/BV1oEWtz7ECS/ https://youtu.be/sHM2i9ND3VU Scatter Plot is a useful toolkit based on Igor Pro, designed to enhance data visualization efficiency in atmospheric science. While many existing generalized data visualization software options have been widely used, they often fall short in meeting specific research needs in this field. This gap motivated the development of Scatter Plot. The program incorporates the Deming and York algorithms for linear regression, which account for uncertainties in both the X and Y variables, making it more suitable for atmospheric applications. Scatter Plot is equipped with various features for data analysis and graph plotting, including batch plotting, data masking through a user-friendly interface, color coding along the Z-axis, and the ability to filter and group data across different time scales—such as year, season, month, hour, and day of the week. For more details regarding the evaluation and application of Scatter Plot, please refer to For more details regarding the evaluation and application of Scatter Plot, please refer to Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi: https://doi.org/10.5194/amt-11-1233-2018, 2018. Please cite this paper if Scatter Plot is used in your publication. The latest version of the program can be found on my website: https://wucheng.weebly.com/ https://sites.google.com/site/wuchengust/ https://doi.org/10.5281/zenodo.832416 Scatter Plot toolkit adoption in research publications: Wu, Y., Liu, D., Xu, H., Shan, M., Li, S., Tian, P., Hu, K., and Wang, J.: Analysis of atmospheric pollutant characteristics and regional transport in coastal area along the East China Sea, J. Environ. Sci., 156, 225-238, doi: https://doi.org/10.1016/j.jes.2024.06.040, 2025. Li, W., Qi, Y., Liu, Y., Wu, G., Zhang, Y., Shi, J., Qu, W., Sheng, L., Wang, W., Zhang, D., and Zhou, Y.: Daytime and nighttime aerosol soluble iron formation in clean and slightly polluted moist air in a coastal city in eastern China, Atmos. Chem. Phys., 24, 6495-6508, doi: https://doi.org/10.5194/acp-24-6495-2024, 2024. Wu, B., Wu, C., Ye, Y., Pei, C., Deng, T., Li, Y. J., Lu, X., Wang, L., Hu, B., Li, M., and Wu, D.: Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China, Atmos. Environ., 321, 120347, doi: https://doi.org/10.1016/j.atmosenv.2024.120347, 2024. Hedges, M., Priestman, M., Chadeau-Hyam, M., Sinharay, R., Kelly, F. J., and Green, D. C.: Characterising a mobile reference station (MoRS) to quantify personal exposure to air quality, Atmos. Environ., 315, 120160, doi: https://doi.org/10.1016/j.atmosenv.2023.120160, 2023. Lin, W., Lai, Y., Zhuang, S., Wei, Q., Zhang, H., Hu, Q., Cheng, P., Zhang, M., Zhai, Y., Wang, Q., Han, Z., and Hou, H.: The effects of prenatal PM2.5 oxidative potential exposure on feto-placental vascular resistance and fetal weight: A repeated-measures study, Environ. Res., 234, 116543, doi: https://doi.org/10.1016/j.envres.2023.116543, 2023. Liu, H., Jia, M., Tao, J., Yao, D., Li, J., Zhang, R., Li, L., Xu, M., Fan, Y., Liu, Y., and Cheng, K.: Elucidating pollution characteristics, temporal variation and source origins of carbonaceous species in Xinxiang, a heavily polluted city in North China, Atmos. Environ., 298, 119626, doi: https://doi.org/10.1016/j.atmosenv.2023.119626, 2023. Liu, Y., Zhou, M., Zhao, M., Jing, S., Wang, H., Lu, K., and Shen, H.: Determination of Urban Formaldehyde Emission Ratios in the Shanghai Megacity, Environ. Sci. Technol., 57, 16489-16499, doi: https://doi.org/10.1021/acs.est.3c06428, 2023. Peng, Y., Wang, H., Wang, Q., Jing, S., An, J., Gao, Y., Huang, C., Yan, R., Dai, H., Cheng, T., Zhang, Q., Li, M., Hu, J., Shi, Z., Li, L., Lou, S., Tao, S., Hu, Q., Lu, J., and Chen, C.: Observation-based sources evolution of non-methane hydrocarbons (NMHCs) in a megacity of China, J. Environ. Sci., 124, 794-805, doi: https://doi.org/10.1016/j.jes.2022.01.040, 2023. Song, Z., Gao, W., Shen, H., Jin, Y., Zhang, C., Luo, H., Pan, L., Yao, B., Zhang, Y., Huo, J., Sun, Y., Yu, D., Chen, H., Chen, J., Duan, Y., Zhao, D., and Xu, J.: Roles of Regional Transport and Vertical Mixing in Aerosol Pollution in Shanghai Over the COVID-19 Lockdown Period Observed Above Urban Canopy, J. Geophys. Res., 128, e2023JD038540, doi: https://doi.org/10.1029/2023JD038540, 2023. Young, L.-H., Hsu, C.-S., Hsiao, T.-C., Lin, N.-H., Tsay, S.-C., Lin, T.-H., Lin, W.-Y., and Jung, C.-R.: Sources, transport, and visibility impact of ambient submicrometer particle number size distributions in an urban area of central Taiwan, Sci. Total. Environ., 856, 159070, doi: https://doi.org/10.1016/j.scitotenv.2022.159070, 2023. Dai, L., Zhang, L., Chen, D., and Zhao, Y.: Assessment of carbonaceous aerosols in suburban Nanjing under air pollution control measures: Insights from long-term measurements, Environ. Res., 212, 113302, doi: https://doi.org/10.1016/j.envres.2022.113302, 2022. Dao, X., Ji, D., Zhang, X., He, J., Sun, J., Hu, J., Liu, Y., Wang, L., Xu, X., Tang, G., and Wang, Y.: Significant reduction in atmospheric organic and elemental carbon in PM2.5 in 2+26 cities in northern China, Environ. Res., 211, 113055, doi: https://doi.org/10.1016/j.envres.2022.113055, 2022. Ding, J., Huang, W., Zhao, J., Li, L., Xiong, G., Jiang, C., Ye, D., Li, D., Wang, J., Yu, J., and Liu, R.: Characteristics and source origins of carbonaceous aerosol in fine particulate matter in a megacity, Sichuan Basin, southwestern China, Atmos. Pollut. Res., 13, 101266, doi: https://doi.org/10.1016/j.apr.2021.101266, 2022. Liu, X., Zheng, M., Liu, Y., Jin, Y., Liu, J., Zhang, B., Yang, X., Wu, Y., Zhang, T., Xiang, Y., Liu, B., and Yan, C.: Intercomparison of equivalent black carbon (eBC) and elemental carbon (EC) concentrations with three-year continuous measurement in Beijing, China, Environ. Res., 209, 112791, doi: https://doi.org/10.1016/j.envres.2022.112791, 2022. Liang, Y., Wu, C., Wu, D., Liu, B., Li, Y. J., Sun, J., Yang, H., Mao, X., Tan, J., Xia, R., Deng, T., Li, M., and Zhou, Z.: Vertical distributions of atmospheric black carbon in dry and wet seasons observed at a 356-m meteorological tower in Shenzhen, South China, Sci. Total. Environ., 853, 158657, doi: https://doi.org/10.1016/j.scitotenv.2022.158657, 2022. He, G., Deng, T., Wu, D., Wu, C., Huang, X., Li, Z., Yin, C., Zou, Y., Song, L., Ouyang, S., Tao, L., and Zhang, X.: Characteristics of boundary layer ozone and its effect on surface ozone concentration in Shenzhen, China: A case study, Sci. Total. Environ., 791, 148044, doi: https://doi.org/10.1016/j.scitotenv.2021.148044, 2021. Liang, Y., Wu, C., Jiang, S., Li, Y. J., Wu, D., Li, M., Cheng, P., Yang, W., Cheng, C., Li, L., Deng, T., Sun, J. Y., He, G., Liu, B., Yao, T., Wu, M., and Zhou, Z.: Field comparison of electrochemical gas sensor data correction algorithms for ambient air measurements, Sens. Actuators B Chem., 327, 128897, doi: https://doi.org/10.1016/j.snb.2020.128897, 2021. Wu, C., Liu, B., Wu, D., Yang, H., Mao, X., Tan, J., Liang, Y., Sun, J. Y., Xia, R., Sun, J., He, G., Li, M., Deng, T., Zhou, Z., and Li, Y. J.: Vertical profiling of black carbon and ozone using a multicopter unmanned aerial vehicle (UAV) in urban Shenzhen of South China, Sci. Total. Environ., 801, 149689, doi: https://doi.org/10.1016/j.scitotenv.2021.149689, 2021. Liu, B., Wu, C., Ma, N., Chen, Q., Li, Y., Ye, J., Martin, S. T., and Li, Y. J.: Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China, Sci. Total. Environ., 709, 136109, doi: https://doi.org/10.1016/j.scitotenv.2019.136109, 2020. Wong, Y. K., Huang, X. H. H., Louie, P. K. K., Yu, A. L. C., Chan, D. H. L., and Yu, J. Z.: Tracking separate contributions of diesel and gasoline vehicles to roadside PM2.5 through online monitoring of volatile organic compounds and PM2.5 organic and elemental carbon: a 6-year study in Hong Kong, Atmos. Chem. Phys., 20, 9871-9882, doi: https://doi.org/10.5194/acp-20-9871-2020, 2020. Sun, J. Y., Wu, C., Wu, D., Cheng, C., Li, M., Li, L., Deng, T., Yu, J. Z., Li, Y. J., Zhou, Q., Liang, Y., Sun, T., Song, L., Cheng, P., Yang, W., Pei, C., Chen, Y., Cen, Y., Nian, H., and Zhou, Z.: Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou, Atmos. Chem. Phys., 20, 2445-2470, doi: https://doi.org/10.5194/acp-20-2445-2020, 2020a. Sun, T., Wu, C., Wu, D., Liu, B., Sun, J. Y., Mao, X., Yang, H., Deng, T., Song, L., Li, M., Li, Y. J., and Zhou, Z.: Time-resolved black carbon aerosol vertical distribution measurements using a 356-m meteorological tower in Shenzhen, Theor. Appl. Climatol., 140, 1263–1276, doi: https://doi.org/10.1007/s00704-020-03168-6, 2020b. Ji, D., Gao, W., Maenhaut, W., He, J., Wang, Z., Li, J., Du, W., Wang, L., Sun, Y., Xin, J., Hu, B., and Wang, Y.: Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement, Atmos. Chem. 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Univariate Scatter Plot

Andy Kirk · 2016

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Triple Scatter Plot

Wiley StatsRef: Statistics Reference Online · 2014

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