Competent Handling of Data
Data Literacy in Studies and Research
Data play an important role in many scientific works. They can either be deliberately collected through experiments, surveys, or observations or derived from existing sources such as books and websites. At the same time, data are often generated unconsciously, for example, through the use of digital and especially online-based applications.
There are different types of data, including statistics and tables, texts, images and videos, as well as personal data—i.e., information about people such as names or addresses.
When working with data, there are some important rules to follow:
- Copyright: Not all data may be freely used or shared. Check whether a source allows its data to be used and cite it correctly.
- Protection of Personal Data: Information about individuals is particularly sensitive. If you collect such data, you need the consent of the individuals and must ensure that they remain protected, for example, through anonymization.
- Documentation and Organization: Keep track of where your data comes from, how you collected it, and how it is structured. This makes future work easier and ensures transparency.
Data literacy means handling data responsibly and carefully. This ensures that your work meets scientific standards and that you avoid legal or ethical issues.
Research Data Management
When working with data in a research project (including student projects with a research focus), it is important to document the process and results in a transparent manner. Therefore, a Data Management Plan (DMP) is created at the beginning of the project. This plan can evolve dynamically throughout the project and is only fully completed at the end.
There is no fixed format for a DMP; it may vary depending on the field of study and type of project. However, a DMP generally includes the following phases of the data lifecycle:
Planning / Collecting: In the planning phase, it is determined which data are needed and to what extent they should be collected, taking ethical aspects such as data protection and copyright into account. During the collection phase, the data are gathered and documented according to these guidelines. Both phases ensure a systematic, transparent, and responsible use of data.
Processing / Analyzing: In this phase, data are imported, cleaned, and analyzed. Before processing, a backup of the raw data should always be created to prevent data loss. It is also crucial to document every step from processing to analysis in detail. This ensures that the data can be correctly interpreted and that the entire process remains reproducible.
Preserving / Sharing: For long-term storage, the data, along with documentation on data processing and analysis, should be archived in suitable repositories. Making data available and offering open-access options enables broad usage and promotes scientific exchange. This increases research transparency and contributes to quality assurance as well as data reuse.
Reusing: The reuse of data plays a central role in science, as it allows researchers to conduct independent analyses, validate published results, and gain new insights from the same data. This enhances scientific quality, improves reproducibility, and saves resources by avoiding duplicate data collection.
A DMP ensures that research data remain sustainable, accessible, and reproducible.
There is a wealth of information available on various websites, including checklists, templates, and examples for creating data management plans, such as on the ZHB website or forschungsdaten.info