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START readme
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This file describes the files included in this dataset.

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## General
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+ Author(s): Viola Kurm; Johnny Visser; Mirjam Schilder; Els Nijhuis; Joeke Postma; Gerard Korthals
+ Project: 3750433360 Beter Bodembeheer
+ Contact: viola.kurm@wur.nl, http://orcid.org/0000-0003-1261-9778


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## Title
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Data from: Soil suppressiveness against Pythium ultimum and Rhizoctonia solani in two land management systems and eleven soil health treatments


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## Methods
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# Introduction

This project aimed to assess the effect of 11 soil health treatments and 2 landmanagement systems of a long-term field
experiment called "Soil Health Experiment" on soil disease suppressiveness against Pythium ultimum and Rhizoctonia solani. 
Disease suppressiveness was assessed in 2018 and 2019 in bioassays using field soil in which garden cress and sugar beet 
seedlings were grown and inoculated with P. ultimum and R. solani respectively.
In addition, soil fungi and bacteria were quantified ausing qPCR. Fungal and bacterial community composition were assessed
by Illumina amplicons sequencing in 2019.
HWC was measured in 2019 as an indicator of soil carbon in the microbial biomass.


# Measurements

+ Pythium ultimum bioassay
Visual assessment of the percentage of diseased plants were made with and without inoculation with P. ultimum after 2 weeks. 
In addition, fresh weight of the above ground plant parts was determined. This experiment was conducted in 4 technical replicates.

+ Rhizoctonia solani bioassay
Visual assessment of the percentage of disease before inoculation and percentage of disease spread 21 days after inoculation
with R. solani.

+ Bacteria and fungi quantification
DNA was extracted from the soil samples according to procedure described in Andreo-Jimenez, Schilder, Nijhuis, Beest, Bloem, 
Visser, Os, Brolsma, Boer, Postma and Stabb (2021) Chitin- and keratin-rich soil amendments suppress Rhizoctonia solani disease 
via changes to the soil microbial community. Appl Environ Microbiol. using the MagAttract PowerSoil DNA isolation Kit.
For bacteria the primers Eub338: 5’ -ACTCCTACGGGAGGCAGCAG-3’ [21] and Eub518: 5’- ATTACCGCGGCTGCTGG-3’ [22] were used 
and for fungi the primers ITS1: 5’- TCCGTAGGTGAACCTGCGG-3’ [23] and 5.8S: 5’- CGCTGCGTTCTTCATCG-3’. 

+ Soil microbial community composition
Primers E341F: 5’-CCTACGGGNGGCWGCAG-3’ and 783Rabc: mixture in 1:1:1 ratio of 5’-CTACCAGGGTATCTAATCCTG-3’; 5’-CTACCGGGGTATCTAATCCCG-3’; 
5’-CTACCCGGGTATCTAATCCGG-3’ were used to target the bacterial V3-V4 16S rRNA gene region, and primers 5.8SFun: 
5’-AACTTTYRRCAAYGGATCWCT-3’ and ITS4fun: 5’-AGCCTCCGCTTATTGATATGCTTAART-3’ were used for the fungal ITS2 region. 
Amplicons were purified and libraries were prepared according to Illumina guidelines (Illumina, San Diego, CA, USA) 
and paired-end sequenced with 2x250 cycles for 16S V3-V4 libraries and 2x300 cycles for ITS2 libraries on an Illumina MiSeq platform, 
and all reads were demultiplexed. The 16S rDNA sequences were generated in two sequencing runs to generate more data per sample. 
The 16S and ITS sequence reads are available at the short read archive (SRA) at NCBI under the accession number PRJNA870764. 
Sequence preprocessing was done using Qiime2 version 2018.4 on the Galaxy server of the University of Graz in June 2019. 
The demultiplexed sequence data were denoised using DADA2, which included run specific quality control and filtering, primer removal, 
merging of paired end reads and chimera filtering and generating a feature table with amplicon sequence variants (ASVs) hereafter called OTU,
per sample and a set of unique representative sequences per run as a result. Subsequently, the data of the repeated 16S runs were merged into one data set. 
Classification was done with a Naïve Bayes classifier using the silva 132 release database  for 16S data. The classifier was pretrained on the 
extracted 16S V3-V4 region of the SILVA database. The ITS sequences were processed as described above. Classification was done with pretrained Naïve Bayes 
classifier using the Unite developer Qiime release for all Eukaryotes 2019 database for full length ITS sequences.  


+ HWC
HWC It was assessed as the increase in dissolved carbon after a 16 hour extraction from 4 g soil in 30 ml water at 80°C 
as described by Ghani, Dexter and Perrott (2003) Hot-water extractable carbon in soils: a sensitive measurement for determining impacts of 
fertilisation, grazing and cultivation. Soil Biol Biochem. 



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## Files
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- Codebook.csv
- HWC, bacterial abundance, Rhizoctonia.csv
- Pythium.csv
- Metadata_sequencing.csv
- OTU_table_16SrRNA.csv
- OTU_table_ITS.csv
- Taxa_table_16SrRNA.csv
- Taxa_table_ITS.csv



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## FolderContents
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--- HWC, bacterial abundance, Rhizoctonia.csv
Contains the HWC measurements, qPCR results and disease incidence data with Rhizoctonia solani for all landmanagement systems, treatments and 
blocks.

--- Pythium.csv
Contains the disease incidence with Pythium ultimum for all landmanagement systems, treatments, blocks, and technical replicates.

--- Metadata_sequencing.csv
Contains sample IDs for sequencing of fungi and bacteria, associated field number landmanagement system, treatment and block.

--- OTU_table_16SrRNA.csv
Contains the OTU table of relative abundances of bacteria genes with OTUs as rows and samples as columns.

--- OTU_table_ITS.csv
Contains the OTU table of relative abundances of fungi with OTUs as rows and samples as columns. 

--- Taxa_table_16SrRNA.csv
Contains the taxonomy of bacterial OTUs.
 
--- Taxa_table_ITS.csv
Contains the taxonomy of fungal OTUs.



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## FileFormats
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+ .csv
+ .txt



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## CodeBook
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Please view the related article https://doi.org/10.1007/s00248-023-02215-9 for information, meaning, and abbreviations.

Please view Codebook.csv (where this readme file is also found) for documentation of abbreviations, 
column names, datapoints, etc. The file Codebook.csv uses the columns:

+ index = a number used to distinguish the different entries.
+ code = the abbreviation, variable / data / column name used.
+ used = location where the code is used. (filename, foldername, columnname, datapoint, protocol, etc.).
+ meaning = the literal meaning of the code. (e.g., fully written out abbreviation)
+ Unit = Unit of the measurement

Note that this file is ';' delimited.




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