Lesson plan 4: Data creation
FAIR elements:
Findable
The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, making this is an essential component of the FAIRification process.
F1. (Meta)data are assigned a globally unique and persistent identifier
F2. Data are described with rich metadata (defined by R1 below)
F3. Metadata clearly and explicitly include the identifier of the data they describe
F4. (Meta)data are registered or indexed in a searchable resource
Accessible
Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.
A1. (Meta)data are retrievable by their identifier using a standardised communications protocol
A1.1. The protocol is open, free, and universally implementable
A1.2. The protocol allows for an authentication and authorisation procedure, where necessary
A2. Metadata are accessible, even when the data are no longer available
Interoperable
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
I2. (Meta)data use vocabularies that follow FAIR principles
I3. (Meta)data include qualified references to other (meta)data
Reusable
The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.
R1. (Meta)data are richly described with a plurality of accurate and relevant attributes
R1.1. (Meta)data are released with a clear and accessible data usage license
R1.2. (Meta)data are associated with detailed provenance
R1.3. (Meta)data meet domain-relevant community standards
Primary audience(s): Bachelor's, master's, PhD degree students
Learning outcomes:
Can define research data
Can explain the steps of the research data lifecycle
Can practically apply theoretical knowledge about proper RDM measures to be taken at the stage of data creation
Summary of tasks/actions:
Introduce the definition of research data and research data lifecycle
Learners create the research data lifecycle: Learners receive cards with key terms of the lifecycle. In groups, they should arrange the cards into a research data lifecycle, discussing what the terms might mean. At the end of the session, they should present their results to the other groups [Biernacka et al. 2020].
How can data be created?
New data collection
Reuse of existing data (see also lesson plan 9)
Learners go to a repository (at best, a discipline-specific one suitable for their research field) and find data that they could use for their research.
First steps while creating data
Selection of research design
Quantitative
Qualitative
Research instruments
Questionnaires/surveys
Interviews
Field observations
Other
Data planning (see also lesson plan 2)
Learners write a short data management plan based on a template. It does not have to be very detailed. It is important for participants to think about the data and write down their initial thoughts in bullet points.
Locate existing research data (see also lesson plan 10)
See task Reuse of existing data (2,b,i)
Collect new research data
Capture and create metadata (see also lesson plan 6)
Create a board, e.g. Padlet, Miro or a flipchart, and let the learners write down which metadata they think would be useful for their data/in their discipline. Discuss.
Materials/Equipment
Computer
Internet
For 1a: cards with key terms or virtual tool, e.g. Padlet
For 3f: a virtual board or flipchart
References
Biernacka, K., Bierwirth, M., Dolzycka, D., Helbig, K., Neumann, J., Odebrecht, C., Wilkes, C., Wuttke, U. (2020). Train-the-Trainer Concept on Research Data Management (Version 3.0): Zenodo. http://doi.org/10.5281/zenodo.4071471
Last updated