The FAIRagro Data Quality Tools are a suite of standalone tools, each of which addresses a specific data quality need at a particular stage of the data lifecycle. The tools are selectively linked and share information with one another where this adds value for the researcher—but they do not all need to be connected. The set of tools is developed with a workflow-oriented approach, so that users do not work in isolated tools but rather within workflows—and the results are accordingly interoperable and structured.
FAIRagro Data Quality Services: a portfolio of independent tools featuring interoperable workflows and lifecycle-oriented quality support—with the FAIRagro portal serving as a common entry point for navigation and orientation.
BonaRes DQ-Kit
The BonaRes DQ-Kit has been developed specifically for users (data producers and consumers, data managers, and interface providers) with limited programming skills. It focuses on both intrinsic (descriptive statistics, completeness, consistency) and contextual (timeliness) data quality. It allows users to upload tabular data in .csv format and generates a data quality (DQ) report.
FAIR Assessment Toolkit
This toolkit is designed for researchers in the field of agricultural sciences and data managers focused on data quality (DQ) to generate a single machine-readable result by combining existing automated FAIR assessment tools. In particular, this ensures the FAIR compliance of publications. The browser-based tool accepts DOIs as input and outputs the results in machine-readable formats (json, ttl, etc.).
Metadata Set
The Publication Metadata Set serves as a guide for the optimal use of schema.org as a metadata standard. It enables RDI operators and researchers focused on data quality (DQ) to optimize metadata quality and ensure harmonization across individual RDIs—thereby improving search functionality within the FAIRagro Search Hub.
Data Fitness Explorer
The Data Fitness Explorer automates the extraction of “fitness for purpose” rules for data quality (DQ) from scientific articles based on ISO 19157, making these requirements explicit and machine-readable. It is designed for data stewards, subject matter experts, infrastructure providers—and anyone involved in FAIR data practices. The interactive web application is AI-powered and accepts scientific articles in PDF, TXT, and MD formats as input; it outputs structured JSON as well as Excel and HTML tables.
Standards Inventory
This FAIRagro service helps researchers in the field of agricultural sciences and data stewards identify and apply appropriate standards for their data. This collection of data standards significantly reduces the heterogeneity of metadata in datasets by utilizing shared vocabularies and ontologies. It is a browser-based, searchable directory.
DQ4Agri
DQ4Agri assists with data quality (DQ) analysis right on-site during data collection (and can also be used for existing datasets). In particular, field researchers in agricultural systems science can check the intrinsic data quality of their collected data—such as descriptive statistics or “outliers”—right in the field. It is a browser-based software into which measurement data, such as time series, is entered, and from which statistics, summaries, and key metrics are output as visual representations and as XML. Want to learn more? Please have a look at our interview with Sven Gedicke (TA 3).
AgriMatch
Deep learning and the harmonization of administrative data are enabling an increasing number of more comprehensive datasets containing polygons of agricultural land—with varying levels of quality and detail. AgriMatch provides a matching algorithm to create synergies between the different sources by merging this data and to evaluate the quality of the model outputs. To the article: 10.1080/17538947.2026.2632420
TrialHarvest-AI
TrialHarvest-AI supports data stewards, as well as developers and providers of research data infrastructures, in the automated (AI-powered) extraction of high-quality, standardized metadata from the scientific literature on agricultural research, particularly in the field of long-term experiments (LTEs). This significantly reduces the workload compared to manual metadata collection—while simultaneously improving its quality and FAIR compliance: paving the way for climate-friendly agriculture, reliable yield modeling, and informed policy-making.
