Scatter Plot

Resource information

Format

zip

Original repository

19159791

DOI

10.5281/zenodo.19159791

Credit basis

Creators in Zenodo record metadata

Access

Open files available from the original Zenodo deposit

Last checked

Nov 5, 2026

License

cc-by-4.0

Last record update

Nov 5, 2026

About this resource

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