DOI: 10.48436/jsk20-t1389
title: Raw data, R scripts and R datasets for statistical analyses from the research article 'Advancing Glycyrrhiza glabra L. cultivation and hairy root transformation and elicitation for future metabolite overexpression'

description: Dataset description

This dataset was created during the research carrried out for the PhD of Negin Afsharzadeh and the research article arising from this research:

Afsharzadeh, N. et al. Advancing Glycyrrhiza glabra L. cultivation and hairy root transformation and elicitation for future metabolite overexpression. Horticulturae 11, 1–17 (2025). doi: 10.3390/horticulturae11010062

The main purpose of this dataset is to create a record of the raw data that was used in the analyses in the manuscript.

This dataset includes:



raw data generated from experiments stored in an Excel spreadsheet with each sheet corresponding to a specific experiment or part of an experiment (Afsharzadeh_et_al_2024.xlsx) 

R script used to analyse the raw data in the software, R (Afsharzadeh_et_al.R)

datasets that were used to analyse the data in the statistical software, R (germindata.txt, light.txt)


Context and methodology

Brief description of experiments: 

In this study, we aimed to optimize approaches to improve the biotechnological production of important metabolites in G. glabra. The study is made up of four experiments that correspond to particular figures/tables in the manuscript and data, as described below.

Experiment 1: 

We tested approaches for the cultivation of G. glabra, specifically the breaking of seed dormancy, to ensure timely and efficient seed germination. To do this, we tested the effect of different pretreatments, sterilization treatments and growth media on the germination success of G. glabra.

This experiment corresponds to: 



Manuscript: Table 1 and Figure 1

Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Table_1'); Afsharzadeh_et_al.R; germindata.txt


Experiment 2 (Table 2):

We aimed to optimize the induction of hairy roots in G. glabra. Four strains of R. rhizogenes were tested to identify the most effective strain for inducing hairy root formation and we tested different tissue explants (cotyledons/hypocotyls) and methods of R. rhizogenes infection (injection or soaking for different durations) in these tissues.

This experiment corresponds to: 



Manuscript: Table 2

Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Table_2')


Experiment 3 (Figure 2):

Eight distinct hairy root lines were established and the growth rate of these lines was measured over 40 days. 

This experiment corresponds to: 



Manuscript: Figure 2, Table S2

Data: Afsharzadeh_et_al_2024.xlsx (Sheet 'Figure_2')


Experiment 4 (Figure 3): 

We aimed to test different qualities of light on hairy root cultures in order to induce higher growth and possible enhanced metabolite production. A line with a high growth rate from experiment 3, line S, was selected for growth under different light treatments: red light, blue light, and a combination of blue and red light. To assess the overall impact of these treatments, the growth of line S, as well as the increase in antioxidant capacity and total phenolic content, were tracked over this induction period.

This experiment corresponds to: 



Manuscript: Figure 3, Figure S4

Data: Afsharzadeh_et_al_2024.xlsx (Sheets 'Figure_3_FW', 'Figure_3_FRAP', 'Figure_3_Phenol'); Afsharzadeh_et_al.R; light.txt


Technical details

To work with the .R file and the R datasets, it is necessary to use R: A Language and Environment for Statistical Computing and a package within R, aDHARMA. The versions used for the analyses are R version 4.4.1 and aDHARMA version 0.4.6.

The references for these are: 

R Core Team, R: A Language and Environment for Statistical Computing 2024. https://www.R-project.org/

Hartig F, DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models 2022. https://CRAN.R-project.org/package=DHARMa
description: Licenses: The CC-BY license applies to all the data. All distributed code is under the specified GNU GPLv3+ license.