Opening webinar for the Open Access week 2016

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OPEN DATA
sharing the main actor of a scientific story
pcmasuzzo
24 October 2016 paola masuzzo

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What is exactly open data?
Why should you make your data open?
How can you make your data open?
My open data story

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Open data implies freedom to access, use
and re-use for any purpose
http://opendefinition.org/od/

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Open data implies freedom to access, use
and re-use for any purpose
http://opendefinition.org/od/; http://opendefinition.org/licenses/
There are many open knowledge definition conformant licenses
CC0 waiver
https://creativecommons.org/publicdomain/zero/1.0/
CC BY (Attribution only)
https://creativecommons.org/licenses/by/4.0/
CC BY-SA (Attribution ShareAlike)
https://creativecommons.org/licenses/by-sa/4.0/

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Research data need to be treated as
first-class citizens in science
Vines et al., Current Biology, 2014; image courtesy Auke Herrema

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Research data need to be treated as
first-class citizens in science
Vines et al., Current Biology, 2014; image courtesy Auke Herrema
Data should
themselves be
considered the
primary output
of research

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One could just argue that data produced
with public funds belong to the public
Image courtesy Auke Herrema

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But there are so many more great reasons
for data to be open
develop new
analysis methods
improve research
practices
guarantee data
preservation
reduce cost of
science
engage
with citizens
increase visibility and
collaborations
science-driven motivations
society-driven motivations
data users
benefits
data producers
benefits
enhance reproducibility
ask new questions
advance science

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Open data means more hands at work, more
brain power and faster innovations
Gina Kolata, The New York Times, 2010; SCIENCEMAG 2016 - Williamson et al., 2016

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Open data creates a culture of transparency
and potentially discourages fraud
Wicherts et al., PloS one, 2011
“Willingness to share research data is related to the strength of
the evidence and the quality of reporting of statistical results”

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Open data means more reproducibility and
better research practices
Monya Baker, Nature, 2016; image courtesy Auke Herrema

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Open data means also visibility and a higher
chance to get cited
Piwowar et al., PeerJ, 2013
citation
advantage

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The Panton Principles are a pretty good
starting point
1. When publishing data, make an explicit and robust statement of your
wishes.
2. Use a recognized copyright waiver or license that is appropriate for data.
3. If you want your data to be effectively used and added to by others, it
should be open as defined by the Open Knowledge/Data Definition—in
particular, non-commercial and other restrictive clauses should not be used.
4. Explicit dedication of data underlying published science into the public
domain via PDDL (http://opendatacommons.org/licenses/pddl/1-0/) or
CCZero (http://creativecommons.org/publicdomain/zero/1.0/) is strongly
recommended and ensures compliance with both the Science Commons
Protocol for Implementing Open Access Data and the Open Knowledge/Data
Definition.
http://pantonprinciples.org

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A lot of repositories are available to upload
research materials and data

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You certainly don’t need to know more than
1,500 repositories by heart
https://biosharing.org/databases/

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There also exist a number of data journals

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Making data available is only one half of the
open data equation
intelligent access to the data and interoperability are crucial
Wilkinson et al., 2016, Scientific Data; https://www.force11.org/group/fairgroup

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Cell migration experiments are complex and
produce diverse and rich data sets
sample
preparation
image
acquisition
image
processing
data
analysis
Servier Medical Art, CC-BY 3.0; Cell Image Library, CC-BY 3.0

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Cell migration experiments are complex and
produce diverse and rich data sets
Servier Medical Art, CC-BY 3.0; Cell Image Library, CC-BY 3.0
• paper laboratory
notebooks
• electronic
laboratory
notebooks
• spreadsheets
• text files
• protocols
• papers...
• raw files
• XML files
• proprietary
microscope or
acquisition software
files  ND2 for
Nikon, LIF for Leica,
OIB or OIF for
Olympus, LSM or ZVI
for Zeiss
• OME-TIFF
• image files with
pixel values and
metadata
• png, jpeg, tiff, avi
• text files describing
processing
algorithms
extracted features
• graphs, plots
• analysis pipelines
computational
algorithms...
sample
preparation
image
acquisition
image
processing
data
analysis

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CellMissy is our open-source tool for cell
migration data management and analysis
0 3h 6h
wound
cells
Experiment
Data Analyzer
Data Loader
Collective cell migration Single-cell migration
Experiment Manager
Masuzzo et al., Bioinformatics, 2013; https://github.com/compomics/cellmissy

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CellMissy enables data and metadata
exchange
lab A

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exchange
lab A lab B

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exchange
lab B
This is one file in CellMissy! (≈10 MB)
lab A

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But we can easily extend this concept
to a bigger scale
Data
Repository
Local Software

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And so we did it!
Cell migration workshop, Ghent, March 2014; Masuzzo et al., Trends in Cell Biology, 2015

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An open data exchange ecosystem for cell
migration research is now on its way
Masuzzo et al., Trends in Cell Biology, 2015

CC BY-SA 4.0Image courtesy Auke Herrema

Opening webinar for the Open Access week 2016

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