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‘Data’ across disciplines & the Research Data Life Cycle Objectives Meet the instructor and other students Gain an appreciation for why I (EB) think this subject matter is important Understand how different disciplines define and use ‘data’ Learn the ‘Research Data Life-Cycle’ Overview of the Course Structure and review of the syllabus Please read / watch these before class: Readings: None Videos: None

In-Class Exercise Please complete the ‘Student Research and Data Management Interests’ survey: link to survey. After class: Be sure you complete and submit the assignment by 9 am Friday Prepare for next session (assigned reading, videos, etc).

File Formats, Naming Conventions, Data Storage & Security Objectives and Competencies for this session: Describe and implement conventions for proper naming of files Explain the difference between proprietary and open formats Learn how to efficiently organize their research data files Learn the preferred format for storing and archiving different types of data files Become familiar with different options for cloud data storage and backup Develop and implement a plan for short- and long-term data storage, back-up, and archiving Learn rules and policies for data security Become familiar with tools for such tasks as batch renaming of files, cloud data storage, and automated data backup.

BREAKOUT EXCERCISE: FIELD DATA STORAGE AND BACKUP Robin is a graduate student studying Malaria in Tanzania. The research project requires visiting communities, collecting mosquitoes from different sources of standing water (for later identification at the FLMNH and statistical analyses of mosquito diversity and abundance) and conducting semi–structured interviews in the community, where people are asked such things as their age, family income, their access to health care, if they have ever had malaria, their family income, use of mosquito nets.

Objectives and Competencies for this session: Be able to identify different categories of data Learn best practices for data entry Recognize and avoid common problems with data entry and formatting in spreadsheets Learn and be able to implement ‘Tidy’ format for data tables in spreadsheets Identify problems with and approaches for proper handling of dates in spreadsheets Learn how to export data from spreadsheets in open format Pre-Class Preparation: Readings Tesi, W.

Breakout 1: Group Discussion Much of your future as a researcher will be spent cleaning and correcting data, but you can reduce the time spent on this task (and the associated stress) considerably by implementing some good practices from the start.

Week 4: Reproducible data (re)organization Objectives and Competencies for this session: Understand and be able to explain the importance of a well-documented, reproducible workflow Be able to outline a logical workflow for data importing and correction Be able to load a file as a dataframe in RStudio, edit the data frame, and save the corrected version as a csv file Pre-Class Preparation: Readings:

Topic 1: Intro to Reproducibility: (10 Min) Why should we practice ‘Reproducible research’?: Breakout: (15 min) Program my lunch Returning results: (30 min) Break (10 min) Coding Corrections: Intro (20 min) The code below is for .

Week 5: QA/QC 1: Data entry and validation Objectives and Competencies for this session: Be able to define quality control and quality assurance as it relates to data collection, entry, and management.

QA/QC 1: Data Entry MS Excel ‘Speak Cells’ Tutorial Video. This will allow you to select rows or columns of Excel and have them read back to you. *Alternative video: it’s not as thorough, but it is a bit easier to see the menu on this video speak cells on enter Excel https://youtu.