Research Data Management
Data management in the information age is an absolutely required skill, and a base to cope with the wealth of data and to avoid an information overload. Therefore Research Data Management is a Structural Priority Area of the Cluster. It will follow the paradigm of data scarcity and will contribute toward the goal of developing predictive digital representations of entire sourcing-processing-operating process chains. As a common standard in the discipline of data science, we use the FAIR standard (Findable, Accessible, Interoperable and Reusable).
Measures to achieve this will include:
- Ensuring data FAIRness via the use of centrally installed and maintained electronic laboratory notebooks (ELN) and workflow management systems (WMS);
- Establishing a seamless flow of data along the process chains through the development of semantically correlated, interoperable ontologies;
- Using the semantically correlated datasets to develop digital shadows of the elementary process units and the production facility;
- Cultivating open-science while respecting ethical and legal considerations;
- Offering continuous training to all scientists in data management, data literacy and data science;
- Providing secure access to shared analytical and computational infrastructure for materials characterization and modeling across the Participating Institutions.
Research Data Management Guidelines
This document holds for all employees of the Martian Mind Excellence Cluster and serves as a reference for how to save data. It delivers reasons for the question of why one should think about data management and gives instructions and examples. It helps organizing everyone’s own data, but it is also mandatory for data sharing so that others can understand the data structure and are able to interpret the information derived from the data.
For the sake of transparency and to serve as inspiration to others, we would like to share this document publicly. Please note that this is a working document that might be subject to change.
Download Research Data Management Guidelines
Contact
Norbert Riefler
Data Steward
Data are a scientific asset on its own and a source for further scientific investigations. The daily incoming scientific data require clear and concise metadata and further description. Therefore, a data environment is established with an Electronic Laboratory Notebook (ELN) as a central data hub. It contains anotated scientific results together with additional information about semantic relations (ontology), physical process data (energy, mass), and decision relevante parameters (uncertainty).
Jannik Wildner
Data Management
I develop the data and workflow management system of the Cluster, from the electronic lab notebook to the automated and data driven methods that complement it. My challenge is to keep documenting easy for the individual researcher while making the findings clear and usable for the many disciplines working alongside them.
wildnerprotect me ?!uni-bremenprotect me ?!.de
+49 421 218 51216
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University of Bremen, FZB Rm 1490, Badgasteiner Str. 3, 28359 Bremen
