Executive Summary
To move forward and improve how data stewardship is implemented, the impacts of current and past training need to be well understood. Qualitative interviews were carried out with a data officer and data stewards in order to get a better understanding of training and the impact of data stewardship. The results of this work can also be used as an informative reference point for the current state of data stewardship and in particular the current role of training in this.
The questions asked mainly surrounded three areas, how training is run, feedback collected and resource limitations. Regarding training and feedback, it was clear that high demand and positive feedback was demonstrating an immediate need for RDM courses. As for resource limitations, there are already planned improvements with new courses and additional hires to address current training needs.
Introduction
An important part of data stewardship is the role of researchers themselves adhering to good research data management (RDM). Data stewards can advise, consult, and train in small groups but for data stewardship to be improve, essential knowledge and core skills are required in researchers. Training courses run by the doctoral education programme in collaboration with data steward teaching are therefore a crucial aspect of achieving the goals of policy and FAIR principles.
As part of the policy framework, the library is responsible for:
‘Working with faculties to provide advice and training in good data management in a disciplinary manner (with Graduate School and Education and Student Affairs)’
Developing training courses can cultivate both skills and awareness of good RDM and data stewardship and therefore are a sensible part of any approach to reaching the policy goals and pushing research to be FAIR.
To understand the impact of data stewardship, the Data Stewardship Coordinator set out to qualitatively analyse the policy implementation, training and consultation across faculties and generate an informative report to be used as reference moving forward. As part of this, understanding how training is implemented and the feedback from various parties to the courses can help provide insights into the impact of data stewardship may be having on research through training. Here we discuss interviews with a data officer and data stewards, below is a report of the findings and some reflections.
Methodology
Semi-structured qualitative interviews were carried out with a data officer and data stewards to ask questions about RDM training at TUDelft, the demand for training, difficulties and feedback collected. The interviewees were selected based on their role in data stewardship, particularly through involvement in training. The interviews lasted about an hour and were not audio/video recorded but responses and key notes were taken during the interviews.
How the training is being run
The most popular training course is the research data management 101 course (RDM101) which provides students with essential knowledge and basic skills for managing research data. The training ensures that researchers will be capable of integrating good data management practices into their work as well as pushing them to reflect on how to work efficiently and reproducibly with their data.
The core objectives of the RDM101 course are realising the importance of good research data management, identifying the different types of research data and the regulations, policies and legal requirements needed for handling them, understand the main components of FAIR data principles and design research data management strategies that can cultivate best research data management practice.
The course runs 4 sessions a year with 25 participants in each round, the course is typically oversubscribed double the number of available places. Each session runs over three weeks of both self-study and virtual classes covering the essentials of RDM, FAIR principles and research data management planning. Teaching currently involves assignments carried out by students being reviewed by teaching staff, ensuring knowledge can be applied as well as simply understood.
Feedback
When asked about feedback, it was reported that students typically find this approach to teaching optimal as the spread-out nature of the course ensures that information is never overwhelming and the engagement with assessments is seen as a useful approach for applying and reinforcing the taught topics.
As well as positive general feedback on the RDM101 course itself, the requests from students are being addressed. The main requests revolve around further support on personal/confidential data handling. As per this request a personal data-oriented course has been set up for the department that deals with this kind of data the most. Ensuring code is reproducible was also brought up and has a workshop specifically being set up that will cover the issue over 6 half days of teaching.
Resources
As the feedback suggests, these short, guided, and self-taught sessions are seen as the ideal way to run the course. However, the requirement for assessments and engagement means that there’s a high resource demand in terms of staff and cost. There is currently double the applicants per place for the RDM101 course and so the allocation of 3 more trainers is in progress to meet the demand.
Discussion
When trying to understand the impacts of data stewardship through training schemes like this, the greatest qualitative indicators we can gauge are through demand for the course as well as the feedback and opinions of other stakeholders. When discussing the popularity of the course it was made clear that the course is oversubscribed by almost double, suggesting that there is a substantial utility in the course and a need by PhDs to learn these skills. The feedback from data stewards regarding the topic further highlights this need as during interviews with data stewards, there was a strong opinion that students who’d engaged on the RDM101 course had much greater understanding and awareness of good RDM practice when it came to data planning.
Training is also having downstream impacts across areas of data stewardship as consultations have been made easier by supplementing the abilities of PhD student in creating data management plans and good RDM. As for feedback, there are many plans in place for new courses and hires that will help fulfil current demands for training that are likely to only further progress towards meeting policy goals.