HackMD
Data Conversations: Planning Document
Introductions
Leighann Kimble
Leighann Kimble is a PhD candidate in Open Innovation, Open Science, Open Data, Data Sharing & Reuse at the department of Knowledge, Information and Innovation (KIN) of the School of Business and Economics at the Vrije Universiteit Amsterdam.
THE INNOVATION PROCESS IN BIOMARKER DISCOVERY: EXPLORING THE COLLABORATIVE CONTEXT OF MIRIADE
Maxine Mackintosh
Dr Maxine Mackintosh is the Programme Lead on Diverse Data at Genomics England, where she works on reducing inequalities in access to genomics based personalised medicine. She is also the co-founder of the grassroots organisation One HealthTech. She is passionate about supporting innovations in the healthcare sector with a particular interest in promoting equality in access to these innovations.
Equitable Genomic Data in Practice
Schedule
Need to adjust timings to UK timezone
- 12:50 - 13:05: sign-in
- 13:05 - 13:15: introduction to VU Data Conversations & TTW
- 13:15 - 13:30: Presentation 1 (Intro: LK)
- 13:30 - 13:35: Questions for Presentation 1
- Sample questions:
- 13:35 - 13:50: Presentation 2 (Into: LK)
- Sample questions:
- 13:55 - 14:00: Questions for Presentation 2
- 14:00 - 14:30: Shared discussion (ALS)
Tasks
- Check-in time
- 1 hour vs 2 hour format
- Q&A time
- Prepare slides (ALS)
- Gosh Page:
Notes from Anne
- I think it might be worth taking 2-3 minutes each to talk about The Turing Way (for folks from VU who might not be aware of the resource) and VU Amsterdam Data Conversations/your team (for folks at Book Dash who might not be familiar with your work)
- We have an additional 30 minutes in Book Dash timings (even though data conversations are usually just one hour!) We can use that time to expand the conversation outwords to folks, or give people's time back
- Facilitating conversation - will it be between the two of them? Or Lena facilitating?
Lena Karvovskaya
- VU Data Converstaions
Leighann Kimble
https://research.vu.nl/en/persons/leighann-kimble
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dimentia research
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process innovation
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bio-marker disovery research
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papers for GDPR challenges
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data reuse
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Context of the data (!)
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Quality
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- Lab sciences accoutn for differences tha's \
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Metadata not the only thing you
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Social conversations
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Documenting the process & process innovation 7 the challengse
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In developing these guides
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How do you account for the differences in terms of fields and
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Generalisability at the start
Qualitative research
Process innovation
Biomarket original topic extended
Challenges to OS
GDPR
Reuse of data
Bookdash
Week-long hackathon
A sprint to contribute to the book
Social session - stepping back from work to take a broader perspective
Generability at the start
The turing way is designed as mixing spot where people talk interdisciplinary
This requires certain generalisation
Struggling with metadata development
Data reuse -> more metadata
Is metadata all you need?
Social conversations
Work in process innovation
Biomarker selection
Frictionleess data toolkit
There is actually an article on friction in science and data
Motivation for data sharing and reuse
Researchers were actually excited to share data with each other and get feedback and collaborate
Issues are practical
Where do I start
How do I find interested people
Real concerns about publishing early
Difference between fields
Process innovation favours opennens - that gives practical impact opportunity
Early detection kits are only possible because of opennes and exchange with firms
Opennes is work in progress
Innovation is where disciplines talk to each other
The turing way introduced - role of Open Sceince
the challenges we see for the implementation
Generalizable research vs specific context
Liiiighan introduces her field
Vision for open science - generalizable, reproducible, hight quality research
How do you bring this to specific practices
And bring these lessons back to the Turing way
How does it happen in real time
- https://www.frontiersin.org/articles/10.3389/fneur.2022.890638/pdf
- Science friction: Data, metadata, and collaboration: https://journals.sagepub.com/doi/pdf/10.1177/0306312711413314?casa_token=beM3NdmL6hUAAAAA:M7dRZMwJXPztSdG1itcvLsOT7IIjUWpLYJJI2waAWKSObDZBv7O_cGS2kK4lKZc-el7GnQ5PzUqxug
VU Data Conversations
- Short presentations (1:1)
- 1 hour presentation format
- Discussion
Audience
- VU
- Doubts about whether open science is a good thing… last time, asked a question about pre-prints (look @ risks) OR people worried about
- People in research infrastructure roles
- Turing Way
- Mixed between folks with experience and others without.
- Leighann Kimble
- Barriers for data sharing. Researchers in the consortium are excited.
- Issues about where you start & who is interested in what you have to say or what you're working on. If I publish early - I won't be able to make use of my data: difference between fields.
- Process Innovation: openness is better. You can get more to the end point of the practical impact of your research. Can create early detection kits for clinical practices. No interaction
- Work in progress: differs each time – accessibility each o
- Researchers facing pressures from data stewards and legal departments
- Clinical & material restrictions on samples… blood & cerebral spinal fluid, you could reverse engineer this to identify people. What is the midway between these two? How is this not an inhibitor
Related Turing Projects, bringing someone from the :
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EDoN Initiative: https://edon-initiative.org/organisation/
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Heath RSS Lab (Emma Karoune): https://www.turing.ac.uk/research/research-projects/turing-rss-health-data-lab
Next steps
- Invite Leighann to Slack
- Invite Sophia + Eirini to speak at the data conversation (LK)
- Slack channel (ALS)
- Invite S + E to channel
- Write small description to the data conversation (ALS)
