Select a Best Practice below to learn more about the “Collect” stage in the Data Life Cycle.
What is a Lesson?
Lessons are education materials publically available to support skillbuiliding of best data management practices. Lessons can include pdf and powerpoint slide decks, handouts, and supporting exercises and data that you can download and incorporate into your teaching materials. Materials are licensed as CC0 and you may enhance and reuse for your own purposes. In many cases slides can be previewed in the embedded slideshare viewer. We also provide one-page synopses (with space for contact information) that can be used to promote Data Management training events at your institution.
Please consider sharing your own data management teaching materials. For more information see our Frequently Asked Questions and Contributor Guidelines.
What is the “Collect” stage?
Observations are made either by hand or with sensors or other instruments and the data are placed a into digital form. You can structure the process of collecting data up front to better implement data management.
More information can be found in the Best Practices Primer.
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 ... (click for more)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 b... (click for more)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 ... (click for more)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 ... (click for more)