You are speaking about the improvement of known models or developing new ones that all using already published experimental work of other researchers.

That is basically what mathematical and computational modeling is doing and very good at. You collect data, made an input of them and insert them into a model. Model is evaluated and results compared to known results from experimental data.

The model can be solved mathematically, i.e. using calculus, differential equations, numerically using a discretized variant of the mathematical equations, or by computational models based on algorithms (water-based modeling, cellular automata, lattice-Boltzmann method, etc.).

So basically the model must describe the relationship between input data and the overall behavior/response of the observed system as precisely as possible.

You just cite or directly include data in your paper. Smaller data can go in a form of table or list but later data must be provided as a link to their source either a closed one or an open one.

Newly included data should somehow improve existing models or lead to a brand new model that are better capturing the overall behavior of the model.

Thank you for your explanation kroc

It really depends on the type of modelling you are doing. If you are you are doing the biochemical modelling of photosynthesis, you can try different Rubisco kinetics (published data for different species) and compare your modelled response with the measured.

Hello Aysar,

Personally, I adapt the data from the original article, which I quote. Sometimes it is necessary to seek permission from the author or publisher, which is generally granted in my personal experience.

Thank you Jacques, that is certainly very helpful

I. About simulation and experimental data ― Numerical models can be used to generate data ― numerical simulation ― which can be just as experimental as if generated by physical models. What seems of utmost importance is to precisely characterize the source of data; such as industrial data, pilot plant data, or laboratory data. What classifies data as experimental is not its source, but the compliance with the so-called experimental (scientific) method.

II. Application example ― The recursive least squares algorithm (RLS) allows for (real-time) dynamical application of least squares regression to time series. Years ago, while investigating adaptive control and energetic optimization of aerobic fermenters, I have applied the RLS algorithm with forgetting factor (RLS-FF) to estimate the parameters from the KLa correlation, used to predict the O2 gas-liquid mass-transfer, while giving increased weight to most recent data. Estimates were improved by imposing sinusoidal disturbance to air flow and agitation speed (manipulated variables). The power dissipated by agitation was accessed by a torque meter (pilot plant). The proposed (adaptive) control algorithm compared favourably with PID. Simulations assessed the effect of numerically generated white Gaussian noise (2-sigma truncated) and of first order delay. This investigation was reported at (MSc Thesis):

Thesis Controlo do Oxigénio Dissolvido em Fermentadores para Minimi...