A collection of python scripts used to generate CML from csv (CSV_to_CML.py), apply machine learning (Gradient Boosting using decision trees, prediction_viscosity.py), model based on eq 6 in the associated publication, and to plot the generated data (plot*.py).
Contains CML dictionaries created to store deep eutectic solvent data with CML.
Contains CML created for experimental, simulation and predicted data. Predictions were made based on experimental data using a gradient boosting decision tree.
Contains exp.csv, a collection of experimental data of CholineChloride:Glycerol:Water mixtures.
Contains sim.csv, a collection of molecular dynamics simulation data of CholineChloride:Glycerol:Water mixtures.
Contains Modelling_exp_Figure3.csv, a collection of modelled Eeta (energy activation of viscous flow), lnEta0 (viscosity at infinite temperature) values of CholineChloride:Glycerol:Water mixtures, based on experimental data, see the associated publication for details.
CML dictionary for compchem conventions allowing representation of thermophysical properties of deep eutectic solvents (DES) with CML. Examples of thermophysical properties are: density, viscosity, conductivity and water activity
The file contains FeaturePlots of 4 different cardiomyocyte markers (Actn2, Tnnc1, Actc1 and Ryr2) to demonstrate how annotation of clusters needs to be based on several markers and approaches, which in the aggregate allow for more reliable conclusions/results.
Data for global pH monitoring experiments for precipitation in publication: Microbial-induced calcium carbonate precipitation: An experimental toolbox for in situ and real-time investigation of micro-scale pH evolution.
Included data: calibration data and data for pH evolution of precipitation process (including spectrum before measurement, timeevolution of absorption intensity and spectrum after measurement).