|Title||Body||Technical Expertise Required||Cost||Additional Information|
|Develop a quality assurance and quality control plan|
Just as data checking and review are important components of data management, so is the step of documenting how these tasks were accomplished. Creating a plan for how to review the data before it is collected or compiled allows a researcher to think systematically about the kinds of errors, conflicts, and other data problems they are likely to encounter in a given data set. When associated with the resulting data and metadata, these documented quality control procedures help provide a complete picture of the content of the dataset. A helpful approach to documenting data checking and review (often called Quality Assurance, Quality Control, or QA/QC) is to list the actions taken to evaluate the data, how decisions were made regarding problem resolution, and what actions were taken to resolve the problems at each step in the data life cycle. Quality control and assurance should include:
For instance, a researcher may graph a list of particular observations and look for outliers, return to the original data source to confirm suspicions about certain values, and then make a change to the live dataset. In another dataset, researchers may wish to compare data streams from remote sensors, finding discrepant data and choosing or dropping data sources accordingly. Recording how these steps were done can be invaluable for later understanding of the dataset, even by the original investigator.
Datasets that contain similar and consistent data can be used as baselines against each other for comparison.
One efficient way to document data QA/QC as it is being performed is to use automation such as a script, macro, or stand alone program. In addition to providing a built-in documentation, automation creates error-checking and review that can be highly repeatable, which is helpful for researchers collecting similar data through time.
|Double-check the data you enter|
Ensuring accuracy of your data is critical to any analysis that follows.
When transcribing data from paper records to digital representation, have at least two, but preferably more people transcribe the same data, and compare resulting digital files. At a minimum someone other than the person who originally entered the data should compare the paper records to the digital file. Disagreements can then be flagged and resolved.
In addition to transcription accuracy, data compiled from multiple sources may need review or evaluation. For instance, citizen science records such as bird photographs may have taxonomic identification that an expert may need to review and potentially revise.