LAS 6292: Data Collection & Management

OBJECTIVES:  This course is designed for graduate students from any discipline – social sciences, humanities, biophysical sciences – and at all stages of their graduate program. It is an introduction to methods for collecting, organizing, managing, and visualizing both qualitative and quantitative data. Students will gain hands-on experience with best practices and tools; at the conclusion of this course students will be able to:

  • Describe the different types of research data;
  • Explain the need for and benefits of data management and sharing;
  • Describe and implement best practices for the collection, storage, management, archiving, and sharing of research data;
  • Find, download, and analyze publicly available data from repositories;
  • Carry out simple and reproducible data corrections and dataset organization;
  • Describe public policies and agency requirements for data management and sharing;
  • Articulate the major legal and ethical considerations regarding the collection, use, and storage of research data (e.g., privacy/human subjects, intellectual property);
  • Create and Implement and a Data Management Plan;
  • Identify and properly use tools and techniques for more efficient and secure data collection in the field.

COURSE FORMAT: I believe there is no better way to learn than by doing, which is why this course is taught (mostly) in a ‘flipped-course’ format. Students are expected to complete each week’s assigned reading and watch the short video lectures prior class. The class session will typically include an opportunity for students to ask questions about the pre-class materials and for the instructor to briefly summarize material or demonstrate challenging concepts; occasionally there will be a class discussion about the assigned reading. However, most of the class session will be spent working in small groups on exercises that reinforce that week’s concepts and techniques. Throughout the session I will be circulating between groups to assist with the assignment, work though mistakes, and discuss how the techniques can be applied to each student’s research.

All students will clean and organize real data sets — ideally their own — and prepare a data collection and management plan for their research projects. 

 

Course Schedule

Week

Date

Topic

 

1

1/15

‘Data’ across disciplines and The Research Data Lifecycle

2

1/22

File Formats, naming conventions, data storage, & security

3

1/29

Structure & format of Data & Datasets

4

2/5

Reproducible data (re)organization

5

2/12

Data validation & correction 1

6

2/19

Data validation & correction 2

7

2/26

Documentation: Metadata, Codebooks

8

3/5

Data Management Plans

9

3/12

Efficient data collection

10

3/19

Transcription & Translation

11

3/26

‘Paperless’ data collection

12

4/2

Automated data extraction

13

4/9

Legal and Ethical Issues

14

4/16

Data Sharing, Reuse, & Archives

15

4/23

Reading Days – no class

Finals Week

4/30

Submission of Final Projects by 5 pm