Research Guides: GRAD 589: Master of Science in Artificial Intelligence (MSAI) Capstone: Data Management

Why Manage Data?

Record amounts of data are being generated on a daily basis. With constant improvements in technology, higher precision and coverage in data creation, collection, and acquisition and a higher capacity for storage, there is an increased importance in managing, integrating, and re-using data. As more and more publishers and funding agencies require researchers to share their data, developing good data management practices early in you career will make it easier to keep your data organized, meet funder requirements, and prepare data for sharing with others.

Data management addresses the lifecycle of your research output including its creation, organization, accessibility, archiving and distribution. "Data" types can include:

Observational: data that is captured real-time such as sensor data and survey data
Experimental: data collected from lab equipment such as gene sequences and magnetic field readings
Simulation: data generated from test models such as climate models and economic models
Derived or Compiled: data that is aggregated or analyzed such as data mining and compiled databases
Reference: data that is collected, reviewed, and published such as databanks and data portals

Resources

Data Management Glossary
Cornell University's Research Data Management Service has complied a basic glossary of data management terms.
Data Sharing & Management Snafu in 3 Short Acts
A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci. This is what shouldn't happen when a researcher makes a data sharing request! Topics include storage, documentation, and file formats.
DataONE Best Practices
The DataONE Best Practices database provides individuals with recommendations on how to effectively work with their data through all stages of the data lifecycle.

Training

Data Science and Machine Learning Training
Training modules developed by the Purdue Rosen Center for Advanced Computing (RCAC).
LinkedIn Learning Video Courses
Unlimited access to over 21,000 expert-led courses including dozens related to data scholarship, for example: Excel: Managing and Analyzing Data, SQL for Data Analysis, Python Data Analysis, and Everybody's Introduction to Tableau.

Sage Campus
Supports the teaching and learning of skills and research methods through hundreds of hours of structured online learning. Online courses are self-paced and instructor-led and included a mix of interactive content, video, and formative assessments. The topics cover critical skills and research methods for across all stages of academic study, including navigating information, data literacy, research skills, data science skills, and getting published.