Slides from my short talk at INCF 2013 (neuroinformatics annual meeting) in Stockholm. I talk about realities of data sharing and a proposal to make it easier through use and adoption of electronic lab notebooks. Project a collaboration between carnegie mellon university and elsevier research data services.

1. Towards Reusable Experiments:
Making Metadata While You
Measure
Shreejoy Tripathy
PhD student, Carnegie Mellon
Email: stripat3@gmail.com
Twitter: @neuronJoy

2. Lots of great tools for data sharing…

3. Barriers to data sharing
• Social
– “What’s in it for me? How will I get credit?”
– “It’s my data, not yours”
– “The benefit to me isn’t worth the time I put into it”
– “What if I get scooped?”
• Methodological
– “How do I share data? What do I share?”
– “Going back and annotating my files to share is super-
time consuming”
– Specifying file formats, data standards
– Building FTP servers and nice user interfaces

4. Project idea
• How can we make a standard neuroscience
wet lab more data-sharing savvy?
• Incorporate structured workflows into the
daily practice of a typical electrophysiology lab
(the Urban Lab at CMU)
– What does it take?
– Where are points of conflict?

5. Key insights/motivations
1. Effective data
sharing includes raw
data files +
experimental
metadata (typically
stored in a lab
notebook)
SDB_MC_12_voltages.mat

6. Key insights/motivations
1. Share raw data files
+ experimental
metadata
2. You know the most
about an
experiment when
you’re performing it

7. Key insights/motivations
1. Share raw data files +
experimental
metadata
2. You know the most
about an experiment
when you’re
performing it
3. Improved data
practices should
make labs more
productive

8. Project schematic

9. Project schematic

10. Metadata data app
• Electronic lab
notebook models
sequential slice-
electrophysiology
workflow
– Replaces pen-and-
paper lab notebook

11. Metadata data entry
• Electronic lab
notebook allows
structured data entry
Animal Strain

12. Metadata data entry
• Electronic lab
notebook allows
structured data entry
(i.e., dropdown
menus)
– Allows incorporation
of semantic ontologies
• Important to strike a
balance between
structure and
flexibility
MGI:3719486

13. Metadata data entry
MGI:3719486
• Electronic lab
notebook facilitates
entry of new content,
like registration of
recorded neurons to
brain atlas

14. Data integration
• Syncing of metadata
app and
electrophysiology data
acquisition via server
– Each trace of
experimental data
annotated with
metadata
• IGOR-Pro specific,
support pClamp, other
acquisition packages as
needed later

15. Data dashboard (web-based)

16. Data dashboard (future-steps)
• Use collected
metadata to sort
experiments
– Like mouse strain,
neuron type, animal
age
• Enable in-browser
analyses
– Track provenance
of analyzed data
back to raw data

17. Next steps
• Use built tools
– Populate data server with many experiments
• Is use of e-notebook too prohibitive?
– If yes, continue to iterate
– What can we ask now that we couldn’t before?
• It is much easier to ask exploratory questions, like
– How is the cell type that Shawn records different from the one that Matt
records?
• Exposing data to neuroscience databases
– NIF, INCF Dataspace, neuroelectro.org
• How adaptable are these solutions for use in other
labs?
• Who is going to pay for this?

18. Acknowledgements
• Carnegie Mellon
– Shreejoy Tripathy
– Nathan Urban
– Shawn Burton
– Rick Gerkin
– Santosh
Chandrasekaran
– Matthew Geramita
• Elsevier Research
Data Services
– Anita de Waard
– Mark Harviston
– Jez Alder
– Sarah Tyrchniewicz
– David Marques
– (funding!)

19. Next steps
• Roll out updated app to experimentalists
• Populate database with the contents of many
experiments
• Flesh out Data dashboard functionality
• Investigate the new things that we can achieve
given these tools

20. Effective data sharing is…
• Not just experimental data file
– But also the experimental metadata: what was
done? What does this variable mean? This is
usually stored in PHYSICAL lab notebooks,
understandable by only the experimenter
• Effective data sharing – someone who is not
the person who collected the data can
understand the experiment and data

21. App user testing
• “I don’t like the way the app forces me
through a specific workflow, I want to enter
experimental data when I see fit”
• “I’m not opposed to the idea of dropdowns,
but I want more flexibility, more text fields”
• “When I use a lab notebook, I only write down
the absolute minimum. Can the app’s fields
be prepolated with the results of an old
experiment?”

22. What is effective data sharing?
• Effective data sharing – someone who is not
the person who collected the data can
understand the experiment and data
– i.e., datasets should be more or less self-
describing
– >90% of data sharing use cases are an
experimentalist sharing data with a future version
of herself or with a labmate

23. Neuroinformatics successes don’t
come from large-scale multi-lab data
sharing
• NeuroSynth
• NeuroElectro?