The data quality implementations rely on the metadata (as defined in the tutorial). Updates and extensions of the metadata concept are a work in progress.

Here, we list all existing implementations of the project, with links to their respective documentation. Additional examples, alternative implementations, and contributing code guidelines are available as (tutorials2.html).

Metadata requirements

Any extensive data quality report requires not only study data, meaning for example clinical measurements, but also metadata. Metadata refers to attributes that describe for one part expectations about the study data. Such expectations can be quite diverse, ranging from the number of expected observations in a data set to properties of single variables such as data type or inadmissible values. The check of observed data properties against formalized expectations is the basis of most data quality indicators.

To be easily usable, such information must be organized in a structured form. For dataquieR, a spreadsheet type structure with several tables (as briefly described below) is necessary. In addition to expectations, these tables also contain descriptions about the objects of interest such as variable names, variable and value labels, or information to control the generation of output in the reports, such as the role or order of variables in a report.

Below find a list of potential metadata tables of relevance. Among these, the item-level metadata table is the most essential.

Item-level metadata

Item-level metadata refer to descriptions and expectations about single data elements (variables/items), e.g., columns in the study data table.

The setup of item-level metadata is described in the Tutorial section

Cross-item level metadata

Cross-item level metadata contains descriptions and expectations about the joint use of two or more data elements for the purpose of data quality assessments. A distinct table is necessary as there is a 1:n relationship of potential assessments to any single data element.

The setup of cross-item level metadata will shortly be available in the Tutorial section.

Dataframe-level metadata

Dataframe-level metadata refers to descriptions and expectations about the provided data-frames.

The setup of dataframe-level metadata will shortly be available in the Tutorial section.

Segment-level metadata

Segment-level metadata refers to descriptions and expectations about the provided segments (e.g., different examinations of a study).

The setup of segment-level metadata will shortly be available in the Tutorial section.


Indicator functions

List of all Functions

Below find a list of all dataquieR functions that can be used to trigger single aspects of a data quality assessments. Their use is recommended for rather specific applications. For standard reports it may be more feasible to use the dq_report function.

Mapping the Concept to Functions

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

Support functions

The indicator functions are supported by 187 support functions. The main task of these function is to ensure a stable operation of dataquieR in the light of potentially deficient data.This 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

Square\(^2\) is a web-application having all study data and metadata are stored in databases. The application targets a different user type with low technical requirements on the user side. Square manages user rights and roles to enable assessments without direct access to the underlying study data. Square² may prohibit direct study data access. Reporting is only possible for assigned subsets of the study data. From a data protection perspective, this is a huge advantage for complex studies with many collaborators. All routines developed in this project are integrated and Square\(2\) 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.