A Look at the Lessons from a Research Data Management (RDM) Workshop in Halle, September 2024
University Education Utilizing Real-World Data Management Practices
According to our second survey of the chemical community, a whopping 86% of participants believe that future students and working groups at their institute would benefit from having RDM and handling research data incorporated into their official curriculum. Yet, strangely, there are very few institutions offering such training.
With this issue on the minds of many, a workshop was held as part of a consortium meeting to explore the predicament. Four professors shared their experiences and current strategies for handling RDM at their respective institutions.
Professor from RWTH Aachen University led the way. The university has now made handling research data and RDM a mandatory part of a lab course for fifth-semester bachelor students. These students must now carry out the planning, documentation, and analysis for a given synthesis through the Chemotion ELN. The lab course also includes a learning unit on the basics of RDM, the FAIR principles, data management plans, metadata, and InChI & SMILES. Students must pass a final test to pass the lab course.
Introducing RDM and ELN into the teaching process at a lab stage has several advantages. First, it means no additional curricular changes and deploying such changes is often challenging. Second, it seems students have embraced the digital change; a higher proportion of students now enjoy working with ELNs, with fewer expressing a preference for traditional handwritten documentation.
The workshop then moved on to Nicolas Tielker and Stefan Kast from TU Dortmund. They plan to include aspects of RDM in a mandatory module for statistical methods, applicable to both the Bachelor of Chemistry and the Bachelor of Chemical Biology. The RDM aspects, including ELN, repositories, data/metadata, and ethical and legal aspects of RDM, will be addressed when the responsibility is transferred to a newly appointed professorship on "AI in Chemistry and Chemical Biology."
The use of various ELNs and LIMS in parallel remains a hurdle, as different working groups have different access, leading to daily discussions. Kast also pointed out that interoperability and a central, functional infrastructure are crucial for widespread RDM acceptance and utilization.
Georg Manolikakes from RPTU Kaiserslautern-Landau reported on the introduction of the Chemotion ELN in a mandatory lab course for students in their fifth semester. Initially successful, the ELN will be used for more experiments in the next round. RPTU offers a central ELN hosted by the local computing center, allowing students to use their ELN accounts throughout their further lab courses, bachelor and master thesis, and even their PhD. RDM basics are taught in an accompanying seminar, while digital competencies, including RDM aspects, will be included in a mandatory module "Basic Skills of Scientific Working" in the Bachelor of Chemistry starting in Fall 2024.
Ricardo Mata from the University of Göttingen reported on the introduction of programming and consolidated data analysis practices early in the academic journey. Jupyter notebooks are used, with students provided access through a Jupyter cloud service, and they are encouraged to study with them throughout their time as students. The university has also committed to ELN use, with a focus on eLabFTW.
From Ulm University, Benedikt Wiedemann discussed RDM integration in engineering and natural sciences. Goals include determining and comparing the suitability of various ELNs for different requirements, suggesting other courses, acquiring case studies for the development of Chemotion/LabIMotion and eLabFTW for teaching purposes, and teaching both students, teachers, and supervisors to improve the usage of RDM tools in the institutes. The university implemented RDM and various ELNs in six practical courses, with an exercise on metadata schema when cooking pasta particularly successful as it uses humor to increase awareness of the topic.
The Importance of Integrating RDM in Teaching
- RDM is becoming increasingly important, making it an essential skill for future students.
- ELNs save time once you know how to use them, and early learning can prove beneficial for final theses.
- Teaching practical skills early on helps to reduce the amount of time required during bachelor/master/PhD theses.
- Adjusting coursework to meet the skills of new students is essential, as practical computation courses are now mandatory from the seventh grade in school in BW.
How to Integrate RDM in Teaching
- To foster students' digital skills, a broader approach is necessary. Begin with strategic department planning.
- Look for fellow advocates. The more lecturers and teaching assistants support the decision, the higher the acceptance.
- Are you in the process of revising your curriculum? Now's the perfect opportunity.
- Integrating RDM into practical courses is easy to implement and offers quick benefits.
- The more practical RDM is taught, the higher the level of acceptance.
Supporting Your Digital Transformation Efforts
- Centralized installations are easier to manage than local ones.
- Reach out to your local computing center early.
- Establish login procedures at an early stage for optimal and permanently usable roles and rights.
- Not every ELN is ideal for all applications. Consider the specific requirements and choose the right tool.
- Plan a training concept. Increasing the familiarity of teachers and course leaders with ELN technology can help boost student acceptance.
Theo Bender
### Enrichment Data:Overall:Despite the widely recognized importance of Research Data Management (RDM) in the chemical and broader scientific communities, its formal teaching in academic institutions remains limited due to various challenges such as resource constraints, cultural resistance, knowledge gaps, and a lack of institutional support.
Key Challenges in Teaching RDM
Resource Constraints and Infrastructure- Limited Availability of Appropriate Solutions: Many institutions struggle to find or develop software and platforms that cater to the unique needs of scientific researchers, hindering the effective teaching of RDM.- Training and Staffing Issues: Comprehensive RDM training requires resources, skilled personnel, and dedicated staff; however, many institutions lack these necessities, or they fail to prioritize RDM development in their teaching strategies.
Cultural and Institutional Barriers- Resistance to Change: Established research cultures may resist adopting new data management practices due to perceived barriers or additional burdens.- Inconsistent Academic Incentives: The academic reward system often prioritizes publications and grant acquisitions over data management skills or contributions, deterring both teaching and learning RDM.
Knowledge and Awareness Gaps- Perceived Complexity: RDM spans a broad range of activities, from data storage and organization to sharing and archiving, making it hard for educators and students to understand and integrate it appropriately.- Lack of Standardized Guidance: There is currently no universally adopted standard for RDM instruction, leading to inconsistent implementation across institutions.
Implementation and Support- Limited Curricular Integration: RDM is often treated as a supplementary topic, resulting in superficial coverage or omission.- Insufficient Institutional Support: Many institutions fail to provide the necessary resources for robust RDM education, such as data repositories or software platforms tailored for academic needs.
Initiatives and Progress
Despite these challenges, various efforts aim to increase openness and RDM adoption, such as those led by organizations like NFDI4Chem. These initiatives offer resources, training, and practical guidance to both researchers and institutions, working to bridge the aforementioned gaps by addressing underlying issues.
Summary Table
| Challenge | Description ||---------------------------------|-------------------------------|| Resource Constraints | Limited availability of tailored solutions and resources || Cultural/Institutional Barriers | Resistance to change, inconsistent academic incentives || Knowledge/Awareness Gaps | Perceived complexity, lack of standardized guidance || Implementation/Support Issues | Limited curricular integration, insufficient institutional support |
- The integration of RDM in education-and-self-development is key for enhancing future students' mental-health, health-and-wellness, and scientific achievements.
- Learning about RDM and handling research data at an early stage, such as through lab courses, can lead to increased efficiency and better learning experiences in health-and-wellness, science, and research data management professionals.
- Supporting the digital transformation in education-and-self-development requires a keen focus on centralizing installations, Reach-ing out to computing centers for guidance, and creating comprehensive training concepts to ensure a smooth transition and optimal user experience.
- Quality RDM education contributes significantly to the advancement of science, education-and-self-development, and mental-health by fostering students' skills in handling research data and adhering to the FAIR principles, thus promoting better learning, collaboration, and self-development.
