Data Management Training Leiden Law School
This course aims to familiarize PhD candidates with the basic concepts of research data management (RDM). It will also focus on the most important aspects of RDM specifically related to qualitative and quantitative data, and on dealing with sensitive data. It will help participants to draft the first version of their data management plan (DMP).
- Target group
- PhD candidate
- Michelle van den Berk (Digital Scholarship Librarian) Mareike Boom (Data Steward) Esther van Ginneken (Assistant professor) Eric van Hoof (IT Consultant, Privacy Officer) Joanne Yeomans (Digital Scholarship Librarian)
This course is organized for all new PhD candidates of Leiden Law School. The training course will be offered in collaboration with the Centre for Digital Scholarship (CDS) at Leiden University Libraries. The course touches upon all aspects of research data management, with examples drawn from the research practice at Leiden Law School. Participants select up to three additional modules depending on the type of research that they plan to undertake. During the course, participants will write their own data management plan, present it during the last session, and receive feedback from their peers and the trainers.
Mode of instruction
This course has two different routes for participants depending on the type of research that they are conducting, a standard route for researchers conducting empirical research and a shorter route for participants who do not work with data.
All participants get a short preparatory assignment and follow the introductory workshop (2 hours) in the first week which introduces the principles and best practices of responsible data management. The session touches especially upon the question of research data in law and on working with data provided by third parties (such as commercial partners or governmental organisations). It will help to answer the question “Do I collect (personal) data in my research project?”. For those participants not working with data, all additional workshops after the introduction are optional.
Depending on the type of research being carried out, participants will follow up to three extra workshops. Afterwards, participants will have several weeks to work on the first version of their data management plan (DMP). The group of participants will be divided into smaller groups to present their DMP in the final workshop.
|Introduction to data management||Thursday 3 February 2022||10.00-12.00||All participants|
Best practices for protecting and sharing research data
|Thursday 10 February 2022||10.00-11.00||All participants working with empirical data|
Data management in qualitative research
|Thursday 10 February 2022||11.30-12.30||All participants using qualitative methods|
Data management in quantitative research
|Wednesday 11 February 2022||10.00-11.00||All participants using quantitative methods|
Data Management Plan (DMP) presentation
|Thursday 24 March 2022||Follows, timeslots per group of 4 participants||All participants writing a full DMP|
Interested PhD candidates are welcome to join all the additional modules.
All participants will receive a short introductory assignment.
Participants conducting empirical research might get short assignments for the additional modules and will draft the first version of their DMP to present in the final workshop. The DMP should be ready before the start of the data collection and is also needed for the PhD evaluation meeting held at the end of the first year.
Please register via e-mail and let us know what kind of research you will carry out:
- I do not think I manage any data
- I believe I will use quantitative methods in my research
- I believe I will use qualitative methods in my research
- I believe I will use both quantitative and qualitative methods in my research
The participation in this course is obligatory for PhD candidates, PhD fellows and contract PhDs. Though not obligatory for (non-funded) external PhDs, they are also very welcome to join this course.
Please contact the PhD Dean if you cannot complete the course before the evaluation meeting and/or start of your data collection.