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This research compendium describes how to analyze oviposition preference data from an experiment conducted in D. suzukii in 2018, in South of France (CBGP, Montpellier, France). here.
The analyses of this research compendium are published in JOURNAL NAME in DATE.
✍️ Citation - Link to come.
Corresponding Investigator and first author:
Name: Laure Olazcuaga
Institution: CBGP, INRAE, France and Colorado State University, USA
Email: olaz.laure@gmail.com
ORCID: 0000-0001-9100-1305
Co-investigator and last author:
Name: Nicolas Rode
Institution: CBGP, INRAE, France
Email: nicolas.rode@inrae.fr
ORCID: 0000-0002-1121-4202
For the complete list of authors of the manuscript, see the manuscript.
📅 2018.
📍 South of France.
We are grateful to Graeme D. Batten and Lindsay C. Campbell for insightful discussions on the chemical composition of fruits. L.O. acknowledges support from the European Union program FEFER FSE IEJ 2014-2020 (project CPADROL) and the INRAE scientific department SPE (AAP-SPE 2016). N.O.R. acknowledges support from the CeMEB LabEx/University of Montpellier (ANR-10-LABX-04-01).
✍️ Olazcuaga et al. (XXX), Data from: XXXXX
The 🔨 dev_history.R file allow to rebuild our research compendium from scratch for maximum reproducibility.
The 📂 figures directory contains the figures generated during the analyses.
The 📂 R directory contains the functions used in the analyses.
The 📂 man directory contains the documentation for the functions.
The 📂 data directory contains the data sets used in the analyses.
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📊 DATA_PHOSPHORUS : Dataset containing oviposition preference data..
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📊 DATA_FRUITS : Dataset containing offsrping performance and oviposition stimulation data.
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📊 Data_Compo_Olazcuaga2019 : Dataset of fruit composition from Olazcuaga et al. 2019
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📊 Data_Preference_Olazcuaga2019 : Dataset of oviposition preference from Olazcuaga et al. 2019 used for power analysis
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📋 Readme_data : A detailed description of all the dataset.
The 📂 reports directory contains the .Rmd
files used to build each part of the analyses and produce the final figures. They also have rendered versions and .html
suitable for reading in a web browser.
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📋 RMD_maintext : All the analyses that can be found in the main text.
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📋 RMD_poweranalysis : Power analysis to determine the number of replicates for control arenas
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📋 RMD_maintext : All the analyses that can be found in the main text.
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📋 RMD_poweranalysis : Power analysis to determine the number of replicates for control arenas
This research compendium has been developed using the statistical programming language R. To work with the compendium, you will need installed on your computer the R software itself and optionally RStudio Desktop.
You can download the compendium by cloning this repository:
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open the
.Rproj
file in RStudio -
open scripts
.Rmd
in reports folder and run it to produce all the analyses and associated reports. -
launch the
README.html
to be able to explore the contents on your web browser