Supported metadata

Any extensive data quality report requires study data (for example, clinical measurements) and metadata. dataquieR supports a spreadsheet-type structure with several tables, which is described in more detail in metadata annotation tutorial.


Below, we list all the existing implementations in dataquieR (see Download for installation instructions) with links to their respective documentation. Additional examples, alternative implementations, and contributing code guidelines are available as tutorials.

Indicator functions

List of all Functions

These are functions from dataquieR that can be used to trigger single data quality checks. Their use is recommended for rather specific applications. It may be easier to use the dq_report2 function for standard reports.

Mapping the Concept to Functions

All dataquieR’s functions are linked to the underlying data quality concept as described in the table below.

Support functions

The indicator functions are aided by 258 support functions. The main task of these functions is to ensure a stable operation of dataquieR in light of potentially deficient data, which requires extensive data preprocessing steps.


In Stata, the package dqrep can be used for data quality analyses. It can be installed using the following command syntax:

net from
net install dqrep, replace

Note: In case of issues when installing dqrep with the net command, please download this package and extract the files locally. Afterwards, they can be installed with the net command using the local folder name.


dqrep stands for “Data Quality REPorter”. This wrapper command triggers an analysis pipeline to generate data quality assessments. Assessments range from simple descriptive variable overviews to full scale data quality reports that cover missing data, extreme values, value distributions, observer and device effects or the time course of measurements. Reports are provided as .pdf or .docx files which are accompanied by a data set on assessment results. Reports are highly customizable and visualize the severity and number of data quality issues. In addition, there are options for benchmarking results between examinations and studies.

There are two essentially different approaches to run dqrep:

First, dqrep can be used to assess variables of the active dataset. While most functionalities are available, checks that depend on varying information at the variable level (e.g. range violations) cannot be performed. Any variable used in a certain role (e.g. observervars, keyvars) must be called for in varlist.

Second, dqrep can be used to perform checks of variables across a number of datasets that are specified in the targetfiles option. In addition, a metadatafile can be specified that holds information on variables and checks using the metadatafile option. This allows for a more flexible application on variables in distinct data sets, making use of all implemented dqrep functionalities.

For more details on the conduct of dqrep see this help file.


A Web Application for Data Monitoring in Epidemiological and Clinical Studies

Square² is a JAVA web application that stores study data and metadata in databases, and offers a graphical user interface (GUI) to target all steps in the data quality assessment workflow. The application differentiates between user types, enabling user rights and roles to allow assessments without direct access to the study data or only. For example, reporting may only be possible for assigned study data subsets. From a data protection perspective, this is a huge advantage for complex studies with many collaborators. All routines developed in this project are integrated into Square², which can easily be extended by similar packages that follow dataquieR’s code and metadata format conventions.

Square\(^2\) will be made available under the AGPL-3.0. The current version comes as a docker-stack (docker-compose.yml and images on request), which will be available from and Docker Hub.