Seitenhistorie

Contact your research data management team already at this phase! Solid planing is the basis for all further steps. Inform yourself about the prerequisites for research data management in the tender text of third-party funders and consult with your research data management team regarding data management plans.

This step in the data life cycle is often the most tedious one, since here the actual data are being generated. No matter if observations, (image)data or the output from analytical devices - a detailed documentation of the data acquisition process is more than essential. It allwos for traceability of the whole process and helps to identifiy anf fix mistakes early on. It is important to also aquire standardized metadata, which also simplify later traceability. Documentation of data, their respective metadata and further information on the project should be done in form of a (electronic) lab journal. On case existing data or probes are going to be used, legal rights have to be clarified beforehand. Mind the compliance with FAIR criteria for your data already at this phase.

In order to interprete data and deduce results your entire knowhow and knowledge is needed. It is important that you apply common standards of your research area and document them as well. For yourself and the collaboration with your project partners it is significant that a shared system for data collection and data organisation is used. Processing and analysis of the data can comprise various steps such as digitalising, transcribing, examining, validating, cleaning, anynomising, statistical analysis and interpreting.

The structure of the collected data again has effects on the data storage solution needed. Importantly, you should already think about long-term archiving and if access to stored data should also be granted to external project partners and collaborators. It is advisable that you inform yourself about availale data storage solutions offered by your local research data management. Take care to backup all your data and metadata to avoid data loss.

Most raw data need to be stored for at least 10 years, depending on the type of data. Towords that end it is necessary that data migrate to long-living formats and that they and their backup are stored on long-living media. Additionally, data should follow open access rules and be made available via repositories to ensure long-term examination of scientific findings. Also here, your research data management team can be of help.

For this step of sharing/publishing defining access conditions is fundamental. These access rights and usage rights also contain the possible award of patents or licenses. In addition, the use of persisitent identifiers (PIDs) is advisable to help with unambigous identification and referensing.

All steps so far are not only meant to optimize data for own usage. Another aim should be to enable permanent usability for publication purposes and for the scientific community. Only then can a steady exchange between researchers and moving forward of science itself be ensured.