MATH+ provides various services to support research data management (RDM) for its members. These include information on Open Access and related areas, assistance in all areas of research data management as well as the provision of infrastructure, such as compute or storage capacity. For all RDM related questions please contact rdm@mathplus.de. This picture shows the conceptual overview of the Data Lake Environment.
Research Data Management
Effective research data management (RDM) has several advantages for science. First, it ensures the integrity and accuracy of research data by establishing protocols for data collection, storage, and analysis. This can increase the reproducibility and transparency of research findings, and facilitate collaboration and data sharing across research teams and disciplines. Second, RDM can improve the efficiency of research by reducing the time and resources required to manage and analyze data. Third, RDM can enhance the discoverability and reuse of research data, making it more accessible to other researchers and enabling new discoveries and insights.
Open Access
Open access to scientific research offers several advantages. First and foremost, it allows for the widespread dissemination of knowledge and enables researchers and the public to access and use research findings without paywalls or other barriers. This can accelerate the pace of scientific discovery and innovation, foster collaboration and interdisciplinary research, and increase the visibility and impact of scientific research. Additionally, open access can help to address issues of equity and social justice, as it enables researchers and communities with limited resources to access scientific knowledge and participate in research. To support this attitude, MATH+ operates a Zenodo Community (https://zenodo.org/communities/mathplus/) where research data sets, presentations, posters and various other research results are published open access.
IT Infrastructure
Effective research data management (RDM) relies on a robust IT infrastructure. Some key components of such an infrastructure include:
- Data storage and backup solutions, which ensure the safe and secure storage of research data and provide redundancy in case of system failure.
- Data management software, which allows researchers to organize, describe, and analyze research data efficiently.
- Data sharing and collaboration tools, which enable researchers to share data and collaborate with colleagues and collaborators securely.
- Data security measures, including encryption, access controls, and authentication protocols, which protect research data from unauthorized access, theft, or misuse.
- Data preservation and archiving solutions, which ensure that research data remains accessible and usable over the long term, even as technology and data formats evolve