RESEARCH Data Management

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Research data come in many shapes and forms—physical or digital, big or small, uniform or varied. Whatever their form, data are the evidence for your research findings and proper data management is critical for good research practice and compliance. Research data management is “The organization, documentation, storage, and preservation of the data resulting from the research process” (NNLM). In order to manage data properly, researchers will need to be aware of key considerations in each of the above areas, organization, documentation, storage, and preservation. This course will cover the research data lifecycle along with key practices and decisions in each stage. Additionally, the course will include how data management can be leveraged to make the research process smoother using tools and resources available on campus.

Learning Outcomes: At the conclusion of this course participants will be able to:

• Describe the research data lifecycle.
• Define data management and its importance.
• List at least three resources to facilitate data management.

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Researchers world-wide have been moving towards open research, meaning all the results of research in any discipline should be shared without restrictions. Granting agencies in the U.S. have mandated the sharing of research outputs since 2013. For research to be shared it must be managed appropriately. Changing technology is impacting the way research is being conducted as is interdisciplinary and collaborative work. Managing the research appropriately starts with the writing of the grant proposal, continues throughout the research process and even after the closeout of the project with the outputs being shared. This class will cover federal and University policies surrounding data ownership, and stewardship. Understanding data management plans, secure campus storage options and how to share your research outputs will be discussed.

The class will cover the basics, such documenting the research, developing file naming systems, metadata and why it is important, and campus resources to assist you in managing your research. We will also show you where you can find data already in repositories for you to use in your research.

Learning Outcomes: At the conclusion of this unit, you should be able to:

• Define data management.
• Summarize the importance of good data management.
• Describe the research data lifecycle. 

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This course introduces data cleaning, wrangling, and exploration using OpenRefine, a powerful open-source tool for working with messy data. Participants will learn essential techniques for transforming, correcting, and organizing datasets to ensure data quality and reliability by exploring the interface and key features of OpenRefine, including General Refine Expression Language (GREL) transformations, facets, filters, and clustering techniques. Through hands-on exercises, attendees will gain practical experience in handling common data issues and preparing data for analysis. We’ll also cover how OpenRefine can be used to gain key insights and perform basic qualitative coding. This course is open to all; no experience with coding languages, data cleaning, or data analytics required. 

Learning Outcomes: By the end of the class, participants will be able to:

• Discuss the fundamentals of data cleaning and wrangling and the importance of data quality
• Navigate OpenRefine’s interface and perform basic GREL functions for transforming data in OpenRefine
• Apply various transformation techniques to standardize and normalize data values effectively and efficiently
• Detect and correct common data errors including inconsistencies, duplicates, and missing values
• Prepare datasets for further analysis and visualization in other tools and software

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Working with Edward Tufte’s theory of data graphics, this class will cover best practices in graphical communication for creating effective data visualizations. Topics to be addressed include the data-ink ratio, accessibility, pre-attentive processing, cognitive load, visual emphasis, and misleading visualizations.

Class Objectives:

• Identify graphic design principles related to data visualization practices;
• Discuss how human perception affects data visualization design;
• Critique and propose solutions to misleading visualizations;
• Demonstrate a foundational knowledge of trustworthy, accessible, and elegant data visualization design.

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Data collection and management in clinical research studies sounds simple, but pitfalls are common and usually lessons are learned the hard way. This course will discuss key concepts in thinking about accurate and adequate data collection in clinical research studies. We will focus heavily on how to assure data are collected sensibly, practically, and with purpose.  We will discuss in detail how to assure that appropriate data elements are collected to support the study outcomes. We will also discuss pitfalls and mishaps in data collection and identify methods to avoid problems and erroneous or missing data. We will also review regulatory aspects of data collection, often underappreciated by researchers, and other administrative issues that can impact your data. Finally, we will discuss the function and purpose of a Data Coordinating Center and approaches for multi center data collection.

Class Objectives: At the conclusion of this class, you should be able to:

• Discuss key concepts in thinking about accurate and adequate data collection in clinical research studies.
• Review methods to assure data are collected sensibly, practically, and with purpose.  We will discuss in detail how to assure that appropriate data elements are collected to support the study outcomes.
• Identify pitfalls and mishaps in data collection and describe practical methods to avoid erroneous or missing data.  
• Review regulatory aspects of data collection, data use agreements, and other administrative issues that can impact data.
• Understand the function and purpose of a Data Coordinating Center.