OverviewTeaching: A FAIR bit of time. min Exercises: Specific exercises will be available via specific lessons. minQuestions
Why is FAIR important?
Who is this modulde for?
How can I get some help if I get stuck on solving for an exercise or a question?
When and where are the future ReproNim training workshops?Objectives
This module should provide you with the ability to work with your data in a FAIR manner
Provide researchers with the proper information on FAIR data so that they can be submitted to the specified workflows and executions environments in a reproducible fashion
A key foundation needed to support reproducible research is the proper handling of research outputs. There are a number of best practices and tools available to researchers to ensure that their data is sufficiently FAIR (Finable, Accessible, Interoperable, and Reusable).
A ReproNim module is a set of steps or “lessons”, in which we have gathered material on the web to guide you through the acquisition of a concept or tool or technique. You will take this journey and for each lesson, we try to then ask you a few questions or do some exercise so that you will have an idea of how much you feel comfortable with the material to be acquired.
The module is for you, if you are a biomedical researcher or an informatics researcher or student, is working with neuroimaging (or not) and you want to know about reproducibility and data. To ensure data supports reproducible research, the FAIR principles were issued through FORCE11: the Future of Research Communications and e-Scholarship. The FAIR principles put forth characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to ensure understandability, assist discovery and reuse by third-parties. Wilkinson et al.,2016. FAIR stands for: Findable, Accessible, Interoperable and Re-usable.
You will learn how to properly work with data to ensure that they are FAIR. This module will also provide information on technologies an platforms that can be utilized within your research.
That really depends on your familiarity with concepts covered in the episodes and your capacity to write some code. If you have no familiarity at all, this may take you a longer time, for instance 2 full weeks. If you have good familiarity, some of the information will be already partially known and it may take you a few days to go in detail through this material.
You can learn a lot without coding, however, some of the lessons and exercises will require some coding. So, yes, you should code. We have (mostly) adopted python for the language, it may not be your first choice but we think some knowledge of python coding will help you anyways. We will try to help as much as possible by providing tutorials, examples, and links to installation instructions.
The ReproNim training events can only accommodate a limited number of participants. Nevertheless, we are committed to openness and we are committed to providing our materials in an open format, with liberal licenses, and through a publicly accessible website. You can also contribute to this training module - just fork us on Gihub!
There are a number of practical guidelines and best practices for ensuring data supports reproducible research
This module is in line with our overall goal of making science (including scientific training) more open by ensuring that data is made FAIR (Findabile, Accessible, Interoperable, and Reusable).