DataONE Education Modules
DataONE Data Management Module 05: Data Quality Control and Assurance
Quality assurance and quality control are phrases used to describe activities that prevent errors from entering or staying in a data set. These activities ensure the quality of the data before it is collected, entered, or analyzed, as well as actively monitoring and maintaining the quality of data throughout the study. In this lesson, we define and provide examples of quality assurance, quality control, data contamination and types of errors that may be found in data sets. After completing this lesson, participants will be able to describe best practices in quality assurance and quality control and relate them to different phases of data collection and entry. This 30-40 minute lesson includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
DataONE Data Management Module 02: Data Sharing
When first sharing research data, researchers often raise questions about the value, benefits, and mechanisms for sharing. Many stakeholders and interested parties, such as funding agencies, communities, other researchers, or members of the public may be interested in research, results and related data. This 30-40 minute lesson addresses data sharing in the context of the data life cycle, the value of sharing data, concerns about sharing data, and methods and best practices for sharing data and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
DataONE Data Management Module 01: Why Data Management
As rapidly changing technology enables researchers to collect large, complex datasets with relative ease, the need to effectively manage these data increases in kind. This is the first lesson in a series of education modules intended to provide a broad overview of various topics related to research data management. This 30-40 minute module covers trends in data collection, storage and loss, the importance and benefits of data management, and an introduction to the data life cycle and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise and handout.
DataONE Data Management Module 04: Data Entry and Manipulation
When entering data, common goals include: creating data sets that are valid, have gone through an established process to ensure quality, are organized, and reusable. This lesson outlines best practices for creating data files. It will detail options for data entry and integration, and provide examples of processes used for data cleaning, organization and manipulation and includes a downloadable presentation (PPT or PDF) with supporting hands-on exercise, handout, and supporting data files.