Reproducible science is our top priority. Therefore, it is imperative to establish an efficient workflow that allows for collaboration in a convenient manner. To achieve those above, please stick to the guidelines and suggestions below as much as possible.

Onboarding

Every new member that joins the research team should:

• Apply for vpn through the university helpdesk service.
• Create a GitHub account.
• Verify that owncloud account is active and working.
• Teams is used for communication and project management. Make sure to have been invited to both of them.
• Download and start to maintain a reference manager. We recommend Zotero, as the freeware is compatible with both Microsoft Word and Google Docs.
• Read the standard scripting practices described here.

Data management

• In general, we use a folder called "data" within each repository.
• If data comes from another data source, we include the data source in a README.
• Raw data is available in the corresponding GitHub repository unless file-size is too large (> 100MB).
• When data file-size is too large, we store it in owncloud and include the link to the README file for reproducibility.
• When storing raw data, always include metadata describing what is in the file and what the columns mean. Remember, we consider this data as read-only.
• If we clean the data, we use a folder called "raw" or similar to differentiate original data from manipulated data easily.
• Some useful suggestions and ideas can be found in the Cambridge Data Management Website.

Analysis

• We do our data analysis in GitHub repositories to facilitate collaboration and sharing.
• We use scripts to process data, make models, do analyses, and many others. In addition, we avoid spreadsheets, gis applications, or other software.
• R is used by most team members and the data.table, ggplot2 packages in specific. Of course, other software/packages are welcome.
• We try to comment a lot in our code.

Results-Presentation

• We aim to fully reproducible papers, like this example.
• We publish git repositories through Zenodo upon publication of a manuscript.
• Collaborative manuscripts are written either in Overleaf or Google Drive.

Guides and tutorials

• R for data science
• R/R studio for pure beginners
• Rstudio with GitHub
• R with git
• Zotero
• data.table vignette
• writing good software

Acknowledgements

Much of the inspiration for preserving an efficient, reproducible workflow came from openscapes, while some of the material used here was adapted from Pinsky lab.