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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 and Software team of TU Delft Library with TU Delft Data Stewards, the Education Support team at TU Delft Library, the TU Delft New Media Centre, the TU Delft Digital Competence Center (DCC) and TU Delft researchers. 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 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 At TU Delft this course is offered as a blended course during three weeks. The learners explore the content of the modules at their own pace following a determined shedule and meet the instructors for an interactive class session once a week. This is the publication of the RDM 101 v2.0, which has been under implementation since November 2023. All the changes are described in detailed in the README_v2.0 file in this publication. Some of the major changes are: The class sessions have been adapted to face-to-face format Some additional and optional content on Research Software has been added The Data Flow Map assignments have been updated and new templates are provided. A separate publication of the assignment is found at https://doi.org/10.5281/zenodo.10731934 The assignment of this course is based on the ‘Data Flow Kit’ - https://dataflowtoolkit.dk/; https://doi.org/10.5278/16k4-4n24 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
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 |
| views | 168 | |
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