Readme

Where do I start?

You can load this project in RStudio by opening the file called 'veni_forest.Rproj'.

Project structure

File	Description	Usage
README.md	Description of project	Human editable
veni_forest.Rproj	Project file	Loads project
LICENSE	User permissions	Read only
.worcs	WORCS metadata YAML	Read only
preregistration.rmd	Preregistered hypotheses	Human editable
prepare_data.R	Script to process raw data	Human editable
manuscript/manuscript.rmd	Source code for paper	Human editable
manuscript/references.bib	BibTex references for manuscript	Human editable
renv.lock	Reproducible R environment	Read only

Reproducibility

This project uses the Workflow for Open Reproducible Code in Science (WORCS) to ensure transparency and reproducibility. The workflow is designed to meet the principles of Open Science throughout a research project.

To learn how WORCS helps researchers meet the TOP-guidelines and FAIR principles, read the preprint at https://osf.io/zcvbs/
To get started with worcs, see the setup vignette
For detailed information about the steps of the WORCS workflow, see the workflow vignette
For a brief overview of the steps of the WORCS workflow, see below.

WORCS: Steps to follow for a project

Phase 1: Study design

Create a (Public or Private) remote repository on a 'Git' hosting service
When using R, initialize a new RStudio project using the WORCS template. Otherwise, clone the remote repository to your local project folder.
Add a README.md file, explaining how users should interact with the project, and a LICENSE to explain users' rights and limit your liability. The worcs project template does this automatically.
Optional: Preregister your analysis by committing a plain-text preregistration and tagging the commit as "preregistration".
Optional: Upload the preregistration to a dedicated preregistration server
Optional: Add study Materials to the repository

Phase 2: Writing and analysis

Create an executable script documenting the code required to load the raw data into a tabular format, and de-identify human subjects if applicable
Save the data into a plain-text tabular format like .csv. When using open data, commit this file to 'Git'. When using closed data, commit a checksum of the file, and a synthetic copy of the data.
Write the manuscript using a dynamic document generation format, with code chunks to perform the analyses.
Commit every small change to the 'Git' repository
Cite essential references with @, and non-essential references with @@

Phase 3: Submission and publication

Use dependency management to make the computational environment fully reproducible
Optional: Add a WORCS-badge to your project's README file
Make a Private 'Git' remote repository Public
Optional: Create a project page on the Open Science Framework
Connect your 'OSF' project page to the 'Git' remote repository
Add an open science statement to the Abstract or Author notes, which links to the 'Git' remote repository or 'OSF' page
Render the dynamic document to PDF
Optional: Publish the PDF as a preprint, and add it to the OSF project
Submit the paper, and tag the release of the submitted paper, as in Step 3.

Notes for cautious researchers

Some researchers might want to share their work only once the paper is accepted for publication. In this case, we recommend creating a "Private" repository in Step 1, and completing Steps 13-18 upon acceptance.

Access to data

Some of the data used in this project are not publically available. Synthetic data with similar characteristics to the original data have been provided. Using the function load_data() will load these synthetic data when the original data are unavailable. Note that these synthetic data cannot be used to reproduce the original results. However, it does allow users to run the code and, optionally, generate valid code that can be evaluated using the original data by the project authors. Synthetic data with similar characteristics to the original data have been provided. Using the function load_data() will load these synthetic data when the original data are unavailable. Note that these synthetic data cannot be used to reproduce the original results. However, it does allow users to run the code and, optionally, generate valid code that can be evaluated using the original data by the project authors. Synthetic data with similar characteristics to the original data have been provided. Using the function load_data() will load these synthetic data when the original data are unavailable. Note that these synthetic data cannot be used to reproduce the original results. However, it does allow users to run the code and, optionally, generate valid code that can be evaluated using the original data by the project authors. To request access to the original data, open a GitHub issue.