Welcome to the website of Kadi4Mat, the generic and open source virtual research environment.
About
Kadi4Mat, or Kadi for short, is a generic and open source virtual research environment. Originally developed in the context of materials science, Kadi4Mat can be used for the management of any type of research data within different research disciplines and use cases. Its goal is to combine the ability to manage and exchange data, the repository component, with the possibility to analyse, visualise and transform said data, the electronic lab notebook (ELN) component. The focus on the repository component is on warm data, i.e. unpublished data that is yet to be analysed further, while in the ELN component it is on the automated and documented execution of heterogeneous workflows via an application programming interface (API). In this way, a customizable framework is created that facilitates good research data management (RDM) practices and collaboration between researchers.
Further information:
- Kadi4Mat at RDM@KIT
- Kadi4Mat in the Helmholtz Research Software Directory
- Kadi4Mat in the ELN Finder
- Use of Kadi4Mat at the Division MFM (TU Darmstadt)
The Cluster of Excellence for Post-Lithium Storage POLiS employs Kadi4Mat as integral RDM platform. In that context, guides were created that give an overview of RDM, describe the usage of Kadi4Mat for FAIR RDM, sketch the theory and methods of RDM, and answer frequently asked questions.
Finally, a series of tutorial videos was produced, which shows how to use the basic functionality of Kadi4Mat for building up a flexible and customisable research data management solution. Furthermore, it shows how to publish data via Zenodo, a public, open source research data repository.
This video was produced in the context of the FestBatt project and shows an overview of the cluster-wide research data management as well as corresponding data science applications.
The use of RDM infrastructure helps to structure and link data in research projects in consideration of the FAIR data principles. However, the development of artificial intelligence (AI) models as part of a project usually occurs independent of the used RDM software. Unless the AI model developer documents the used methods and uploads them with the results to the RDM database, the re-usability of the data by other researchers is hindered and the FAIR principles are harmed (see Figure 1a). This use-case presents the implementation of the FAIR data principles in the development process of a AI model using Kadi4Mat in combination with KadiAI. The aim was to record the connections between raw data, model architecture, hyperparameters, and results to enable future comparative and transfer studies. The suggested new AI project development process is shown in Figure 1b. For the automatic linking of components the KadiAI ontology shown in Figure 2 was developed and applied.
Figure 1
Figure 2
The use case involves the evaluation of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data from 75 subjects from the COSYCONET study, which contains around 600 patients with chronic obstructive pulmonary disease (COPD) of different disease severity. For the successful analysis of this data breathing motion as shown in the figure below has to be identified and removed. Since manual breathing detection is cumbersome, an AI model for automatic breathing detection was developed by training and testing three different AI hypermodels.
The automatic synchronization of the AI project with Kadi4Mat results in records with metadata and files for each component (see Figure 3 and Figure 4). Due to the usage of the KadiAI ontology, the links between the single records lead to the automatic generation of a knowledge graph for the whole AI project (see Figure 5). This use-case shows the successful implementation of a concept that automates RDM for AI applications, minimizing the overhead for the individual researcher and recording rich metadata and links between entities toward FAIR data.
Figure 3
Figure 4
Figure 5
Researchers use different ELNs and similar research data management systems. To ensure interoperability, a common file format is required, for which RO-Crates are suitable. RO-Crate is a container format for packaging research data and metadata. It consists of a ZIP archive, used to bundle all the data, and a JSON-LD (JSON for Linked Data) file to describe the archive's structure and contents. Kadi4Mat facilitates the export and import of RO-Crates, enabling interoperability between different systems.
The Resource Description Framework (RDF) is a standard for describing metadata in a graph-like format and serves as the foundation for many ontology modeling languages. It describes the connections between entities as machine-readable triples (subject, predicate and object) as shown in the figure below. Kadi4Mat facilitates the export of metadata in RDF, further improving metadata interoperability.
The Shapes Constraint Language (SHACL) is a W3C standard for describing and validating RDF graphs. Kadi4Mat templates support the export of SHACL, facilitating the creation of shareable and reusable metadata schemas.
The following image shows an ontology that was created using the Protégé software in the context of battery research. While Kadi4Mat does not yet support the use ontologies out of the box, its HTTP API faciliates use-cases such as instantiating ontologies. This can be achieved by running a script to process the corresponding, serialised ontology.
The following image shows such an instantiated ontology, showing the created records, as grouped within a single collection, and their links. These kinds of graphs can be generated and visualised automatically within the graphical user interface of Kadi4Mat.
Kadi4Mat is being developed at the Karlsruhe Institute of Technology (KIT) as part of several research projects, including:
- FestBatt, the Cluster of Competence for Solid-state Batteries, funded by the German Federal Ministry of Education and Research (BMBF) under grant number 03XP0435D.
- POLiS, the Post Lithium Storage Cluster of Excellence, funded by the German Research Foundation (DFG) under project number 390874152.
- AQua, the Battery Competence Cluster Analytics/Quality Assurance, funded by the German Federal Ministry of Education and Research (BMBF) under grant number 03XP0315B.
- MoMaF, the Science Data Center for Molecular Materials Research, funded by the Ministry of Science, Research and the Arts Baden-Württemberg (MWK-BW) under grant number 34-7547.222.
- NFDI4Ing, the National Research Data Infrastructure for Engineering Sciences, funded by the German Research Foundation (DFG) under project number 442146713.
Ecosystem
KadiAPY
Use Kadi4Mat's HTTP API more easily with the KadiAPY library. It offers high-level interaction with the API in Python as well as a Command Line Interface, as shown below. The library supports both Linux and Windows.
KadiAI
Integrate and implement your Artificial Intelligence (AI) and Machine Learning (ML) algorithms with KadiAI. Leverage interactive dashboards to design, train, and tune data-driven models or enhance your custom AI scripts with next-level research data management.
KadiStudio
Design and execute your scientific workflows with KadiStudio, a flexible workflow editor. Use a wide range of existing or customised tools to create reproducible research.
KadiFS
Access and edit your data directly with the filesystem integration KadiFS based on FUSE. Connect your computers and devices to directly interface with the Kadi4Mat ecosystem.
Citing
Main publication:
Brandt, N., Griem, L., Herrmann, C., Schoof, E., Tosato, G., Zhao, Y., Zschumme, P. and Selzer, M., 2021.
Kadi4Mat: A Research Data Infrastructure for Materials Science. Data Science Journal, 20(1), p.8. DOI:
http://doi.org/10.5334/dsj-2021-008
Managing FAIR Tribological Data Using Kadi4Mat (2022)
https://doi.org/10.3390/data7020015
Generating FAIR research data in experimental tribology (2022)
https://doi.org/10.1038/s41597-022-01429-9
Structured Data Storage for Data-Driven Process Optimisation in Bioprinting (2022)
https://doi.org/10.3390/app12157728
KadiStudio: FAIR Modelling of Scientific Research Processes (2022)
http://doi.org/10.5334/dsj-2022-016
KadiStudio use-case workflow: Automation of data-processing for in situ micropillar compression
tests (2023)
https://doi.org/10.5334/dsj-2023-021
Strukturiertes Management von Forschungsdaten in den Ingenieurwissenschaften (2024)
https://doi.org/10.5445/IR/1000168522
Instances
While everyone can deploy an instance of Kadi4Mat themselves, listed below are all instances of Kadi4Mat currently hosted at the KIT:
Contact
Institute of Nanotechnology (INT)
Michael Selzer
Kadi4Mat at the IAM/INT
Feedback may also be sent tofeedback-kadi4matlists.kit.edu