
Regardless of the research field, there are various ways of collecting and documenting data. Poor data management can result in losing time trying to locate data files, loss of data on a larger and smaller scale, failure to keep track of new versions and updates, failure to provide accessibility of data to other researchers for reproducibility and replicability, etc. All this can lead to more effort and frustration for researchers. Therefore, it is essential for all researchers to start thinking about managing research data correctly at the outset of any research project. This six-module course called Research data Management 101 (RDM 101), is aimed at PhD candidates (especially in their first year) who require a hands-on introduction to Research Data Management (RDM) and Data Management Plans (DMPs). The course was created in a collaborative effort of the Research Data Services team of TU Delft Library with TU Delft Data Stewards, the Education Support team at TU Delft Library, the TU Delft New Media Centre and TU Delft researchers. This is a three weeks blended course, which has been offered since October 2020 by the Research Data Services of TU Delft Library in an online mode (due to COVID measures). The course has been delivered using the Learning Management System (LMS) Brightspace. However, the aim of publishing all the structure, content and materials of the course is to facilitate the adoption and adaptation to other learning platforms. The RDM 101 course is structured in six successive modules that can be taken at the own pace of the course participant within 3 weeks. i.e. two modules per week. Each week also includes an assignment (data flow map: https://doi.org/10.5281/zenodo.6325938) and a virtual class to engage the participants in practice and to discuss the highlights of each module. The Modules of the RDM 101 course are: Module 1. The importance of RDM Module 2. The Essentials of research data Module 3. FAIR principles Module 4. Realizing FAIR data Module 5. How to plan for RDM (using a DMP tool) Module 6. Reflection on RDM strategy Parts of the content of this course are based on: Holmstrand, K.F., den Boer, S.P.A., Vlachos, E., Martínez-Lavanchy, P.M., Hansen, K.K. (Eds.) (2019). Research Data Management (eLearning course). doi: 10.11581/dtu:00000047 The assignment of this course is based on the ‘Data Flow Kit’ - https://dataflowtoolkit.dk/. A separate publication of the assignment is found at https://doi.org/10.5281/zenodo.6325938
{"references": ["Holmstrand, K.F., den Boer, S.P.A., Vlachos, E., Mart\u00ednez-Lavanchy, P.M., Hansen, K.K. (Eds.) (2019). Research Data Management (eLearning course). doi: 10.11581/dtu:00000047", "Research Cycle - Edinburgh Napier University. https://staff.napier.ac.uk/services/information-services/research-cycle/pages/home.aspx", "ReadMe file template - Research Data Management Service group - Cornell University - https://data.research.cornell.edu/content/readme", "Data Flow Kit - https://dataflowtoolkit.dk/"]}
FAIR data, Course Material, Research Data Management, Training, Data Management Plan
FAIR data, Course Material, Research Data Management, Training, Data Management Plan
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
