Main Data History
Show Index Toggle 1 comments
  •  Quick Edit
  • BETYdb Data Entry Workflow

    David LeBauer, Moein Azimi, David Bettinardi, Rachel Bonet, Emily Cheng, Michael Dietze, Patrick Mulrooney, Scott Rohde, Andy Tu


    This is the user guide for entering data into the BETYdb database. The goal of this guide is to provide a consistent method of data entry that is transparent, reproducible, and well documented. The steps here generally accomplish one of two goals. The first goal is to provide data that is associated with the experimental methods, species, site, and other factors associated with the original study. The second goal is to provide a record of all the transformations, assumptions, and data extraction steps used to migrate data from the primary literature to the standardized framework of the database.

    Table Of Contents

    • Getting Started \ref{sec:getting_started}
    • Preparing Publications \ref{sec:preparing_publications}
    • Adding Data
      • Citations \ref{sec:citation}
      • Site \ref{sec:site}
      • Treatments \ref{sec:treatments}
      • Managements \ref{sec:managements}
      • Traits \ref{sec:traits}
      • Yields \ref{sec:yields}
    • Bulk uploads \ref{sec:bulk_upload}
    • Extracting Data From Figures \ref{sec:extracting-data}
    • QA/QC \ref{sec:qaqc}

    Getting Started \label{sec:getting_started}

    You will need to create the following accounts:

    • BETYdb (To use the database; request "creator" access during signup to enter data; request "manager" to perform QA/QC
    • Mendeley is used to track and annotate citaitons
    • Google Docs is used to prepare and transform data prior to entry.
    • Github and Redmine are used to track data that need to be checked and/or corrected.

    Finding data

    BETYdb is designed for both previously published data and 'primary' data. Most of this documentation assumes that you have already identified a data set that you want to upload, or have a set of papers from which you would like to extract data and summary statistics.


    If you are planning to do a meta-analysis, even if this is not your first time, please read 'Uses and Misuses of Meta-analysis in Ecology" (Koricheva 2014). Many texts are available, but the recent "Handbook of Meta-analysis in Ecology and Evolution" is probably the most comprehensive and specific for plant sciences.

    For a meta-analysis, the first step is to find papers that contain the target data.

    The easiest approach to use a search engine such as Web of Science, Google Scholar, or Microsoft Academic Search. Starting with queries such as "scientific name + trait", and allowing these results to guide further queries. Often, the references (particularly of meta-analyses and reviews) and forward citations will point to other studies.

    Another starting point for the programmatically inclined - which aids in documenting searches - is to submit queries programmatically. Carl Davidson wrote a python script to search for citations based on species and trait name. In addition, the rOpenSci project has a suite of R packages for searching publications.