Research Data Management (RDM) is one of the most important prerequisites of scientific activity and is a requirement of many funding agencies and both part of good scientific integrity and embedded in many institutional RDM/Open Science policies. RDM affects all phases of the research process with the goal of securing and storing data in a long-term, person-independent manner that is discoverable, accessible, verifiable, interoperable, and reusable according to FAIR principles.
In the implementation of the RDM, the orientation towards the research data life cycle helps to show which measures are useful in each case and which services & tools are offered at KIT for this purpose. The RDMToolKIT offers you a presentation "on demand" for the phases of the research data life cycle, which you can compile according to your wishes. Please fill out this contact form.
For a personal arrangement, please feel free to contact∂rdm.kit.edu.
Research Data and Research Data Management (RDM)
Research data is understood to mean all (digital) data that arises in the course of a scientific research process. The types of research data with which the respective researchers work are as diverse as the specialist disciplines: the spectrum ranges from measurement data, laboratory values, samples, audiovisual information, texts, survey data, software code to objects from collections and much more. This module teaches you the basics of research data management - from beginners to professionals.
- Definition, variety and forms of research data
- Why research data management?
- Research Data Life Cycle
- RDM and Good Scientific Practice
- Specifications from funders and policies on the RDM
Legal Aspects of the RDM
Directive (EU) 2019/1024 of the European Parliament and of the Council defines "research data" as documents in digital form, other than scientific publications, collected or generated in the course of scientific research activities and as evidence in the research process used or that are generally considered necessary in the research community for the validation of research findings and results.
Within the framework of this research data process, the law provides framework conditions. The most common legal issues are dealt with in the following areas of law and offered in combinable modules.
- Copyright protection of research data
- Ownership of data
- Right of use
- Reusability of research data
- Publish and share research data
- License Licensing issues when publishing research data
- CC Licenses
- Digital Peer Publishing Licenses
- ODC - Licensing
- Data License Germany
- Good scientific practice and authorship
- Guidelines to ensure good scientific practice (DFG code of 2019)
- State higher education laws: LHG-BW
- Statute for ensuring good scientific practice at KIT
- data protection law
- Legal framework and material scope
- Principles for the processing of personal data
- Lawfulness of data processing
- Affected Rights
- iVA: the interactive Virtual Assistant
Data Literacy / Data Management
Effective data management can help optimize your research results, increase the impact and reach of your research, and support open scientific research. The data literacy / data management module provides you with basic knowledge of data management and explains best practice examples for the documentation, organization, sharing, archiving and publication of your research data in accordance with the FAIR principles. In this module you will learn how to organize, archive and (re)use your research data in a structured and efficient manner.
- Data organization and tools
- Data description with metadata and metadata standards
- Data storage and data formats
- Share, reuse and quote data
- Services & Tools for collaborative work at KIT (international)
Data Management Plans (DMPs)
Data Management Plans (DMP) are not only a requirement of research funders, but also a fundamental part of good data management that enables discoverability, accessibility, interoperability and reusability of your data. With the help of software tools such as ARGOS or the Research Data Management Organizer (RDMO), you can not only create a DMP but also digitally manage your entire research project. In this module, data management plans and their importance in the research context are briefly explained. Based on a research project, a DMP is created "live" in the RDMO and the use of the tool is explained. Meanwhile, you can try out the software yourself and create your own DMP.
- Definition, components and DMP tools
- Specifications from sponsors regarding DMPs
- RDMO presentation in detail
- Hands on RDMO
Electronic Lab Notebooks (ELNs)
Laboratory notebooks play an important role in the planning, implementation and evaluation of scientific experiments. Paper-based laboratory notebooks are increasingly being replaced by Electronic Laboratory Notebooks (ELNs). These have the advantage that they can be connected directly to measuring devices, analysis programs or storage systems, which means that results are easier to understand and research becomes more efficient. In this module you will learn the basics of ELNs, we will support you in selecting the right ELN for your research work. In addition, you will be introduced to three ELNs (Chemotion, Kadi and eLabFTW) that you can test yourself using exercise data.
- Basics of Electronic Laboratory Notebooks (ELNs)
- Benefits of ELNs
- Assistance in selecting suitable ELNs
- Practical examples: Chemotion, Kadi und eLabFTW
Your research data can be stored and published in repositories. Ideally, there are specialist repositories (e.g. Chemotion) or institutional repositories (e.g. RADAR4KIT) or generic online storage services (e.g. Zenodo) that you can choose for storing your research data. International repositories of all scientific disciplines are documented via the re3data.org service. In addition to the appropriate specialist community, important aspects for a suitable repository are the possibility of long-term archiving, e.g. via tape storage and the assignment of Persistent Identifiers (PID), e.g. Digital Object Identifier (DOI) to make the generated data citable. In this module you will be introduced to the basics of repositories - search, selection and other services.
- Definition and types
- Search for repositories in general
- Search services and registers for repositories
- Examples of subject-specific repositories (customizable)
- Repositories at KIT, overview
- RADAR4KIT and KITopen
- re3data in detail
- Hands on re3data
Storage and computing infrastructures in BW
Various services are available to researchers at universities in Baden-Württemberg for calculating, sharing and storing or archiving research data. These differ according to the needs of the researchers, because it is either about data in ongoing research projects that are still being worked on, or about completed data sets that are to be archived. In this unit you will get an overview of the available storage and computing infrastructures, their access conditions and the respective advantages.
- bwHPC - calculation in BW
- Save and share in BW: bwSync&Share, bwCloudScope
- Storing Hot Data: SDS∂HD
- Long-term storage: bwDataArchive