Software Open Access

Scatter Plot

Cheng WU


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nmm##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Deming Regression</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">York Regression</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Weighted orthogonal distance regression</subfield>
  </datafield>
  <controlfield tag="005">20190929190604.0</controlfield>
  <controlfield tag="001">3464233</controlfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">10922161</subfield>
    <subfield code="z">md5:34622092ddadba0df327f24a1ea7f8b7</subfield>
    <subfield code="u">https://zenodo.org/record/3464233/files/ScatterPlot20190501.zip</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2019-05-01</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">software</subfield>
    <subfield code="p">user-zenodo</subfield>
    <subfield code="o">oai:zenodo.org:3464233</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Jinan University</subfield>
    <subfield code="0">(orcid)0000-0003-1288-968X</subfield>
    <subfield code="a">Cheng WU</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Scatter Plot</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-zenodo</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">http://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Version 20190501&lt;/p&gt;

&lt;p&gt;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&amp;nbsp;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.&amp;nbsp; 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). &amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;For more details regarding the evaluation and application of Scatter Plot, please refer to&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Wu, C.&lt;/strong&gt;&amp;nbsp;and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting,&amp;nbsp;&lt;strong&gt;Atmos. Meas. Tech.&lt;/strong&gt;, 11, 1233-1250,&amp;nbsp;&lt;a href="https://doi.org/10.5194/amt-11-1233-2018"&gt;doi:10.5194/amt-11-1233-2018&lt;/a&gt;, 2018.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Please cite this paper if Scatter Plot is used in your publication.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The latest version of the program can be found on my website:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://sites.google.com/site/wuchengust/"&gt;https://sites.google.com/site/wuchengust/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://doi.org/10.5281/zenodo.832416"&gt;https://doi.org/10.5281/zenodo.832416&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Adoption in research publications:&lt;br&gt;
&lt;br&gt;
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.&lt;br&gt;
&lt;br&gt;
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.&lt;br&gt;
&lt;br&gt;
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.&lt;br&gt;
&lt;br&gt;
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.&lt;a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.atmosenv.2016.08.054"&gt;http://dx.doi.org/10.1016/j.atmosenv.2016.08.054&lt;/a&gt;&lt;br&gt;
&lt;br&gt;
&lt;br&gt;
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.&amp;nbsp;&lt;a href="https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.atmosenv.2015.03.010"&gt;http://dx.doi.org/10.1016/j.atmosenv.2015.03.010&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;=======================================================================================================&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;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。&lt;/p&gt;

&lt;p&gt;有关Scatter Plot的评估和应用的更多细节,请参阅(&lt;strong&gt;如果你在文章中用到了本软件,请引用以下文章&lt;/strong&gt;)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Wu, C.&lt;/strong&gt;&amp;nbsp;and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting,&amp;nbsp;&lt;strong&gt;Atmos. Meas. Tech.&lt;/strong&gt;, 11, 1233-1250,&amp;nbsp;&lt;a href="https://doi.org/10.5194/amt-11-1233-2018"&gt;doi:10.5194/amt-11-1233-2018&lt;/a&gt;, 2018.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;关于程序的最新信息可以在我的网站上找到:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://sites.google.com/site/wuchengust/"&gt;https://sites.google.com/site/wuchengust/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://doi.org/10.5281/zenodo.832416"&gt;https://doi.org/10.5281/zenodo.832416&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.832416</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.3464233</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">software</subfield>
  </datafield>
</record>
2,619
184
views
downloads
All versions This version
Views 2,619866
Downloads 18443
Data volume 3.1 GB469.7 MB
Unique views 1,872657
Unique downloads 15243

Share

Cite as