# Training
**Source**: https://www.brookes.ac.uk/sites/research-support/data-management/training
**Parent**: https://www.brookes.ac.uk/sites/research-support
Many training courses are available, both internal and external, on various aspects of research data management for Oxford Brookes research staff and students.
### Oxford Brookes University: Research Data Management Training
As part of the [EXPLORE@Brookes](https://www.brookes.ac.uk/staff/working-at-brookes/learning-and-career-development/academic-enhancement-and-development/explore-brookes/) programme for staff researchers, there are regularly scheduled sessions in research data management. For research students, the Graduate College has [scheduled workshops](https://www.brookes.ac.uk/students/research-degrees-team/current-students/graduate-college/events-and-networking/graduate-college-research-student-training/graduate-college-training-programme). Alternatively, there are some external options suggested below.
### MANTRA
[Mantra](https://mantra.ed.ac.uk/) teaches researchers to manage digital data as part of the research process. Mantra provides a solid, comprehensive overview with excellent practical modules.
### Software Carpentry
[Data Carpentry](https://software-carpentry.org/)is an open education resource. It is recommended for staff currently teaching and needing to convey data management principles.
### DataTree
[DataTree](https://datatree.org.uk/) training is focused on environmental science and sponsored by the [Natural Environment Research Council (NERC)](http://www.nerc.ac.uk/). Researchers applying to NERC are advised to do this training. However, the training also includes valuable data visualisation and policy modules with a broader focus.
### Foster Open Science EU
[Foster training](https://www.fosteropenscience.eu/about) is helpful for researchers applying for European Union funding. Its focus is responsible research and innovation. [The Responsible Data Sharer](https://www.fosteropenscience.eu/node/2223) is recommended for those who want to do open science but have intellectual property or ethics constraints.
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