Hello everyone, I want to create a script where a list of columns is selected in a column dialog and, later on, appears in the column switcher of graph builder. The column name is unspecific in order to use the script for different data tables. I started to write a small code, however, in this version the "column switcher columns" need to be selected by the user manually after closing the column dialog. I am quite new to JSL and would appreciate some help to solve this problem. Names Default to Here(1);
dt = Current Data Table() << Get Column Names;
cd = Column Dialog(
colx = ColList( "X", Max Col( 1 ), Modeling Type( "Continuous" ) ),
coly = ColList( "Y", Min Col( 1 ), Modeling Type( "Continuous" ) ),);
Size( 400, 300 ),
Show Control Panel( 0 ),
Variables( X( colx ), Y( coly ) ),
Elements( Line( X, Y, Legend( 3 ) ) ),
Column Switcher( coly ) );
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Dear James, I just planned to create a similar wish but then I stumbled across your post. You have my full support, a built-in function for the generation of multiple y-axis in graph builder would be extremely helpful. Extended formatting features, e.g. colored y-axis, could improve the power of graph builder even more:
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Andreas Trautmann, R&D Scientist, Lonza AG Claire Baril, Scientist USP Development, Lonza AG
Design of Experiments (DOE) is a frequently used and time-saving tool in industrial biotechnology for optimizing microbial cultivation process parameters. Developing standard approaches for such experiments facilitate the transferability to customers as well as the comparability between similar projects.
In this case study, we show a general approach for the optimization of process parameters in small-scale bioreactors including a subsequent DOE model validation in pre-pilot scale. The software tool Custom Design in JMP was applied to generate an I-optimal response surface design with three center points. In total four continuous factors were chosen based on empirical knowledge and preliminary data from disposition runs.
One major goal of DoE approaches in industrial biotechnology is to increase yield, e.g. concentration or titer, of the final product. By applying the Custom Design in JMP we were able to increase product titer significantly. The generated model was able to accurately predict the output variable within the characterized range, even though three out of the twenty-four experimental runs were not successful. In addition, the JMP Data Exploration tools enabled a fast evaluation of the data quality as well as time-dependent factor correlations, which contributed to enhancing the DOE model.
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