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  1. 7. Troubleshooting and FAQ
  2. 7.1   Recommended Software Versions
  • Pipeline Documentation
  • 1. Motivation: The Importance of Flux Data Standardization and Reproducibility
    • 1.1   Note: High Frequency Data Processing
  • 2. Software Installation
    • 2.1   Install Software: Git (optional)
    • 2.2   Download Biomet.net Library
    • 2.3   Install Software: Matlab
    • 2.4   Configure Matlab for Biomet.net
    • 2.5   Install Software: R/RStudio
    • 2.6   Install Software: Python (optional)
  • 3. Data Cleaning Principles
  • 4. Quick Start Tutorial - Recommended for First-Time Users
    • 4.1   Quick Start Tutorial: Project Directory Structure and Matlab Configuration
    • 4.2   Quick Start Tutorial: Create Database from Raw Data and Visualize Contents
    • 4.3   Quick Start Tutorial: Create your First Stage INI File for Data Cleaning
    • 4.4   Quick Start Tutorial: Create Your Second Stage INI File for Data Cleaning
    • 4.5   Quick Start Tutorial: Third Stage Cleaning and Converting to Ameriflux Output
  • 5. Full Documentation: Features, Details, and Other Useful Information for Advanced Users
    • 5.1   Full Documentation: Project Directory Structure and Matlab Configuration
    • 5.2   Full Documentation: Create Database from Raw Data and Inspect Contents
    • 5.3   Full Documentation: First Stage INI files
    • 5.4   Full Documentation: Second Stage INI Files
    • 5.5   Full Documentation: Third Stage Cleaning and Converting to Ameriflux Output
  • 6. Data Visualization
    • 6.1   Matlab plotApp
    • 6.2   R-Shiny App
    • 6.3   Other Biomet.net Plotting Tools
  • 7. Troubleshooting and FAQ
    • 7.1   Recommended Software Versions
    • 7.2   Recently Added Features

On this page

  • 7.1.   Software: Current Recommended Versions
    • Software Overview
    • Recommended Software Versions:

7.1.   Software: Current Recommended Versions

Software Overview

  • All three stages of data cleaning (plus conversion to Ameriflux output if needed) can be run from Matlab. Our plotApp for data visualization and analysis during data cleaning is run from Matlab too.

  • Note that the third stage Matlab script invokes an R-script, which you will also need installed on your local computer.

  • Additionally, our R-Shiny data visualization tool uses R/R-Studio, and we recommend this app for viewing your data in real-time.

  • The scripts and tools that run the data cleaning are kept in a Git repository (called Biomet.net). These can be downloaded without having Git installed.

  • Optionally, you can install Git and create your own GitHub account, in case you wish to contribute code to the Biomet.net library.

  • Some users may also need Python installed, for example, if you are processing high-frequency data.

Recommended Software Versions:

Updated on: 5 November 2024

Software Recommended version as of above date
Matlab 2024a (at least 2023b)
R v4.3.3
RStudio 2024.09.0-375
Python 3.9.6 for Mac
Git Latest available

Your computer/system administrators should be able to help you with all these installations if necessary.