Plan and follow your data
Create machine actionable DMPs.
Configure to best fit your discipline.
Link to EOSC components out of the box.
Share easily in your repository.
Bring your Data Management Plans closer to where data are generated, analysed and stored.
This module introduces publishers to the Editorial Reference Handbook, a practical resource collaboratively developed by academics and publishers. The handbook assists in-house editorial staff to operationalise a set of checks fostering good practices for sharing datasets, software, materials and other digital objects.
By the end of this module, publishers will:
- Know what checks to perform and how to implement them in practice
- Learn how to improve clarity of data policies and guidance to authors (especially in terms of which standards and repositories to use)
- Gain practical guidance on making Availability Statements clearer and more rigorous
This module examines reproducibility challenges in qualitative research, covering methodological, epistemological, and practical considerations.
The module consists of four parts and ends with an assessment quiz:
- Part 1: Reproducibility and Qualitative Research
- Part 2: Open Science and Qualitative Research
- Part 3: How to do Open Qualitative Research
- Part 4: How to Support Open Qualitative Research
This module is based on the review paper Cole, N. L., Ulpts, S., Bochynska, A., Kormann, E., Good, M., Leitner, B., & Ross-Hellauer, T. (2024, December 23). Reproducibility and replicability of qualitative research: an integrative review of concepts, barriers and enablers. https://doi.org/10.31222/osf.io/n5zkw_v1
By the end of this module, learners will be able to:
- Understand the relationship between reproducibility and qualitative research
- Understand the relationship between qualitative research and open science practices
- Know which open science practices are possible for qualitative research
- Know how established qualitative research practices support transparency
This learning module explains the critical relationship between reproducibility and the trustworthiness of artificial intelligence (AI). It emphasizes the understanding of how reproducibility impacts credibility and explains the different levels of AI reproducibility that researchers may strive for. The module also elaborates on barriers, such as inconsistent data collection and lack of transparency, as well as drivers, like standardized practices and tools that can support reproducibility. Finally, it illustrates how the presented barriers and drivers interact and aims to foster an understanding of this interaction in order to enhance the reproducibility of AI systems, thereby leading to a more reliable and valuable research practice in the field of AI.
By the end of this module, learners will be able to:
- Understand the relationship between reproducibility and the trustworthiness of AI
- Know the different levels of AI reproducibility
- Be aware of Barriers and Drivers of reproducibility
- Understand the relation of barriers, drivers, and degree of AI reproducibility
