More than 30 research institutions in FAIRagro represent the relevant disciplines in plant, soil, and environmental sciences and develop sustainable solutions for working with research data. Our goal is to provide researchers and other users with tools and services that facilitate the implementation of their projects and support them in terms of FAIR research data management—from creating data management plans to searching for and analyzing data, all the way to archiving their own datasets. In this way, our services help lay the groundwork for sustainable plant production. Here we show which services these are and at which stage of the data lifecycle they are used. The list is updated regularly.
Knowledge Base Dive deep into selected RDM topics.
Search Hub Find repositories and datasets.
SciWIn Make your scientific workflow visible.
Data Quality Services Improve the quality of your data.
DMP Service Create your own DMP.
Data Life Cycle Animation is adapted from ELIXIR RDM toolkit, used under CC BY 4.0. This Data Life Cycle Animation is licensed under CC0.
Search Hub – Search Service on Infrastructures, Repositories and the available Datasets
The FAIRagro Search Hub provides a searchable overview of available infrastructures and repositories, including performance metrics, and—based on comprehensive metadata descriptions of agricultural system data—offers user-friendly access to all resources available in the FAIRagro repositories. In an advanced version, the Search Hub will include ranking functions as well as qualitative, quantitative, and FAIR criteria, and will enable data curation.
Knowledge Base
Our central resource for questions, guides, tools, and legal issues in research data management (RDM) for the agrosystem research community.
SciWIn -The Scientific Workflow
SciWIn is a central infrastructure service that makes it much easier to describe complex and multi-stage procedures for the automated processing of data. SciWIn enables researchers to easily store, organize, share and reproduce the workflows for processing their data – promoting collaboration and transparency in line with the FAIR principles.
Data quality – data quality and plausibility service
Based on standardized data quality metrics (Measures 3.1 and 3.3), we will develop a data quality and plausibility service to facilitate and improve data assessment and reusability for model applications (Measures 1.4, 3.3 and 3.4).
RDMO – The research data management organizer
FAIRagro will operate one instance of the research data management organizer (RDMO) tool as service for the community (Measures 2.1 and 4.1). The development of subject-specific templates for the creation of DMPs customized to the needs as described in the use cases and further communities are addressed in the work program (Measure 3.2).