Learning Objectives

This week, students will be able to:

• list the elements of the data life cycle
• articulate the relevance of good data management for scientific research
• identify the differences between good and bad data entry and management
• recognize bad data organization and why it is problematic for research
• implement quality assurance and control measures for data entry in spreadsheets using excel
• list current measures used by the scientific community in ecology and evolution to preserve data long term

Welcome!

• Introductions
• Why are you taking this class?
• Introduce yourself to one of your neighbors you don’t know

Syllabus

• Go through the syllabus
• Do you have any questions about it?
• Choose grading scheme
• Choose office hours

Schedule

The schedule shows a list of topics that will be covered each week of the course.

The schedule reflects the flipped course structure, and organizes homework and in-class activities by topic:

• Homework
  • Prepare: Readings or activities to do before relevant class
  • Strengthen: Exercises to strengthen concepts discussed during class
• Lectures/live-coding
  • Lecture notes used in class
    • Not expected to be read in advance; may be useful for review
    • May not match lecture precisely
• In-class activities
  • Individually or jointly
  • A challenge that will support the construction of a mental model of your own
    • May require additional work time after class

Lecture: Best practices for data entry and quality assurance using excel

In-class discussion of reading for Day 1

• showcase data repository websites (GenBank, GBIF, Dryad, Zenodo)

In-class discussion of readings for Day 2

• In-class jamboard group activity.

Live coding: Lesson Quality Assurance and Control

• What is the difference between quality assurance and quality control.
• Quality assurance
  • Demo data validation in excel using this data file figshare.com/files/2252083. Students download it to their computer, too, and follow along.
• Quality control
  • Saving a copy of the original raw data is key.
  • Start a README file describing the documents you create in your project.
    • Use a template following readme best practices from https://data.research.cornell.edu/content/readme
  • Sorting to check for invalid data: Exercise 1
    • Ensure that data sorting is expanded to the whole data table, so data is not corrupted.
  • Conditional formatting to scan data for outliers: Exercise 2
    • Use this cautiously, it might corrput the data.

In-class exercises

• Instructions and group activities.

A minute feedback

• Provide some quick feedback for this session here.