Employ quality assurance and quality control procedures that enhance the quality of data (e.g., training participants, routine instrument calibration) and identify potential errors (null values, outliers, etc.) and techniques to address them.
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Information about quality control and quality assurance are important components of the metadata:
To assure that metadata correctly describes what is actually in a data file, visual inspection or analysis should be done by someone not otherwise familiar with the data and its format. This will assure that the metadata is sufficient to describe the data. For example, statistical software can be used to summarize data contents to make sure that data types, ranges and, for categorical data, values found, are as described in the documentation/metadata.
The integration of multiple data sets from different sources requires that they be compatible. Methods used to create the data should be considered early in the process, to avoid problems later during attempts to integrate data sets. Note that just because data can be integrated does not necessarily mean that they should be, or that the final product can meet the needs of the study. Where possible, clearly state situations or conditions where it is and is not appropriate to use your data, and provide information (such as software used and good metadata) to make integration easier.
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.
The plan should be reviewed by others to make sure the plan is comprehensive.
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.
Quality control practices are specific to the type of data being collected, but some generalities exist:
Analytical results:
Observations (such as bird counts or plant cover):
Codes should be used to indicate quality of data:
Dates and times:
Data Types:
Geographic coordinates:
When searching for data, whether locally on one’s machine or in external repositories, one may use a variety of search terms. In addition, data are often housed in databases or clearinghouses where a query is required in order access data. In order to reproduce the search results and obtain similar, if not the same results, it is necessary to document which terms and queries were used.
Missing values should be handled carefully to avoid their affecting analyses. The content and structure of data tables are best maintained when consistent codes are used to indicate that a value is missing in a data field. Commonly used approaches for coding missing values include:
Whatever missing value is chosen, it should be used consistently throughout all data associated files and identified in the metadata and/or data description files.
Outliers may not be the result of actual observations, but rather the result of errors in data collection, data recording, or other parts of the data life cycle. The following can be used to identify outliers for closer examination:
Statistical determination:
Visual determination:
Comparison to related observations:
No outliers should be removed without careful consideration and verification that they are not representing true phenomena.
Data tables should ideally include values that were acquired in a consistent fashion. However, sometimes instruments fail and gaps appear in the records. For example, a data table representing a series of temperature measurements collected over time from a single sensor may include gaps due to power loss, sensor drift, or other factors. In such cases, it is important to document that a particular record was missing and replaced with an estimated or gap-filled value.
Specifically, whenever an original value is not available or is incorrect and is substituted with an estimated value, the method for arriving at the estimate needs to be documented at the record level. This is best done in a qualifier flag field. An example data table including a header row follows:
Day, Avg Temperature, Flag 1, 31.2, actual 2, 32.3, actual 3, 33.4, estimated 4, 35.8, actual
As part of any review or quality assurance of data, potential problems can be categorized systematically. For example data can be labeled as 0 for unexamined, -1 for potential problems and 1 for “good data.” Some research communities have developed standard protocols; check with others in your discipline to determine if standards for data flagging already exist.
The marine community has many examples of quality control flags that can be found on the web. There does not yet seem to be standards across the marine or terrestrial communities.
Provide versions of data products with defined identifiers to enable discovery and use.
Items to consider when versioning data products:
