A framework for an open and scalable infrastructure for health data exemplified by the DD2 initiative

# A framework for an open and scalable infrastructure for health data exemplified by the DD2 initiative

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.footer-right[
Project description: [steno-aarhus.github.io/dif-project](https://steno-aarhus.github.io/dif-project)  
Slides: [slides.lwjohnst.com/steno/2022-01-13](https://slides.lwjohnst.com/steno/2022-01-13/)
]

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## Purpose of talk and "take home messages"

**Will cover**:

- Introduce the project

- Bring awareness to issue and its importance

- How project will help as a (potential) solution

**Won't cover**:

- Any technical details of the project (it's more of a software/data engineering project)

???

Not cover...

Another caveat, since this is a newly started project, that is way more of the
computational and technical side maybe many of you are interested in hearing about.

I could talk easily talk about all the technical details of this project for
many hours, but I will spare you that.

Because of this the talk will be on the shorter side, unless people
have questions that get more into the details...

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## Background: 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?

---

## Current barriers to addressing these needs

- 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

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## Recent new funding: NNF Data Science Research Infrastructure 5 year grant

### ... which lead to this current project

???

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

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## Brief backstory:

### Application process started by the DD2 study to update their own infrastructure and SDCA was asked to join

???

Not going to go into DD2.

Myself, Alisa, and Annelli were asked to join in the application process
and we ended up taking the lead on it and expanding it into a project that could
be used by more than just the DD2 study.

---

## Aims of the Framework

???

These aims may seem vague, but bare with me.

1. **Primary aim**: Create and implement an efficient, scalable, and open source
data infrastructure framework that connects data collectors, researchers,
clinicians, and other stakeholders, with the data, documentation, and findings
(starting within the DD2 study)

2. **Secondary aim**: Create this framework so that *other research groups and
companies*, who are unable or can't build something similar, can relatively
easily implement it and modify as needed for their own purposes.

> In short: Make a software product that makes it easier to find, store, and
use data for research projects, and make it so that it is easy and free to use 
for others.

???

Again, these might not be really tangible to grasp what this actually means.
That's where visuals are really powerful.

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.center[
![Detailed schematic of the framework for building a data infrastructure for scientific studies.](images/detailed-schematic.svg)
]

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## A few example uses of this Framework

1. Solo researcher or a small research group starts a study to have data for
their PhD students, but have limited funds and technical expertise.

2. A small startup company that relies on data collection for business needs to get
operational quickly, but doesn't yet have funds to hire technical personnel.

3. A large multi-center, multi-country study 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.

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## Guiding principles of Framework

1. Follow and enable FAIR principles

2. Openly licensed and re-usable (e.g. CC-BY)

3. State-of-the-art principles and tools in software design

4. Friendly to beginner and non-technical users

---

## Short-term plan

> [Full 5 year timeline found on website.](https://steno-aarhus.github.io/dif-project/#deliverables-and-milestones)

- Hire software/data engineers as soon as possible

- Developing "Minimum Viable Product" of first component within ~2 years

- Emphasize making training and documentation targeted to non-technical users throughout project