4.2 Data research 4.3 Data collection
Overall, the data collection should cover the following aspects:
- Standardised sampling protocols
- Carrying out the experiments, observations, measurements, simulations, etc.
- at the same time: metadata collection and creation
- at the same time: documentation of the data collection
- Generation of digital raw data (e.g. by digitising or transcribing)
- Storage of the data in a uniform format
- Systematic and consistent folder structure
- Backup and management of data
It is important to create an experimental design in advance with a collection strategy on which data will be collected (What? Where? When? Who? How?) including calibration, controls, repetitions, randomisation, etc. You need to determine suitable metadata standards and methodologies, and to consider how and where to store the data and how to capture provenance (e.g. of samples and instruments). Be always aware of the data quality. If you work with sensitive, confidential, or human-related data, consider aspects such as permissions or consent, data protection, and data security.
In addition, errors occurring during data collection affect the downstream research process and in the worst-case lead to incorrect results without notice. This makes it all the more important to be careful during the data collection phase.