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Assuring the quality of your data: A natural history collection community perspective
Drawing from examples within the taxonomic and natural history collection communities, this webinar will detail the data types and challenges of biological specimen data. We’ll explore the value and import of data quality open resources, the current status of data assurance practices as well as some more practical 'how-to’ examples of implementing data quality assurance (QA) methods. For example, QA of publicly shared data is critical for effective use and reuse. As part of the data life cycle, QA often occurs following the collection of data. However, planning for quality assurance in advance of data collection is time efficient and much cheaper than trying to clean up and standardize later. QA planning can help avoid commonly occurring data collection / entry errors.