We'll go through this outline in this order, not expecting to cover them all.

Setting the stage

Imagine that you are a new professor, just starting getting a group and research programme going... or solo researcher or a small research group starts a study to have data for their PhD students, but have limited funds and technical expertise.

• Or, you are a small startup company trying get investment and build income quickly... in the research realm so need to follow best practices/requirements for data management... relies on data collection for business. Needs to get operational quickly, but doesn't yet have funds to hire technical personnel.

• Or, you are a large, multi-national/center consortium that wants to keep better track of who's working on what, and how to discover and share data added to the project... or has an aim of widely disseminating their data for maximal, and cost-effective, use by their collaborators and others.

All of these could use the framework to abide by the best practices in FAIR data management.

We're still working out a better name, but for now we're calling it DIF

These aims may seem vague, but bare with me.

Just for some clarification, infrastructure here meaning the computational structure of the data and all its support structures, for instance, how the files and folders are structured, where the data files are saved and what file format, how to connect to data. In many ways like the roads and buildings of a city, where data is the people moving about.

"Framework" on the other hand is the bundle or package that contains the instructions to create an infrastructure, that someone can take and use to create the infrastructure somewhere else. You can think of this as the blueprint for building a city.

Again, these might not be really tangible to grasp what this actually means.

Increasing need in science for...

• Computational tools and technologies
• Secure and reliable IT infrastructure
• Greater openness and transparency
• More reproducibility of studies
• Highly technical skills and knowledge ... especially in relation to data management.

Questions like:

• How do store your data? In what file format?
• Where do you store your data and how do you name the files?
• How do you keep track of changes to the data?
• (For multi-center studies) Who has which datasets and how do you combine them together?
• How do you or your collaborators find out what variables there are in the data, what do they mean?
• When there are errors or problems in your data, and you've already published with or analyzed on it, how can you easily determine which publications used the in correct data and how can you easily update the publications with the correct data?
• How can you easily share your data with colleagues or reviewers to check your findings?

• Funding agencies don't fully recognize these challenges, so don't provide funding
• Researchers aren't aware of or understand the issues, or don't have skills to tackle them
• People with needed technical skills leave for industry

Development of new ... methods and technologies within data science, ..., data engineering, ...

FAIR = Findable Accessible Interoperable Reusable

• Non-replication is a known major problem, but extent of non-reproducible results is unknown. Barriers to addressing the problem include:
  • Lack of incentives to be reproducible
  • Emphasis on novelty and original work

• Estimating the reproducibility of scientific studies is currently very difficult because of:
  • Nearly non-existent publishing of code/data
  • General lack of awareness of and training in it

While I've been teaching these general topics since my Masters, this course specifically I started during my postdoc because one, there was a need for more computational skills in my field and two, because the awareness around reproducibility and open science was very lacking.

The course is teaching reproducible research in R to PhD students and postdocs who do biomedical research, largely diabetes research. Participants are working/full-time researchers (including PhD students), not necessarily in an undergraduate context and related to learning data analysis or more practical type skills.

This course is 3 full days, composing of 5 code along sessions where the instructor types and the learners follow along, a few lectures, and a final group project. For more info on the course, check out the links below.

Briefly discuss before showing website.

And to end, please, if you try out the material, lets us know! We'd love more feedback on it! Thanks for listening!