Stanford Neurosciences Professional Development Seminar April 2013

Comprehensive data management and collaboration
in life sciences

Barry Wark, Ph.D.
Founder and President, Physion

barry@physion.us
Twitter @barryjwark

Wednesday, April 10, 13

Barry Wark


The nature of scientiﬁc research has changed, challenging
the fundamentals of the scientiﬁc method

There are technological solutions that can help you
overcome these challenges

Think globally, act locally


The nature of scientiﬁc research has changed
fundamentally

Biology is a context dependent system. Studying
context dependence requires lots of data.
‣Data volume ‣ Analytical tools
• High-content screening: desktop confocal • Central computing resources, elastic
can image 25,000 samples per day provisioning

• Human genome $5000, and falling fast • Open source software democratizes
contribution and distribution
• IonWorks Barracuda® can perform 6,000
whole-cell patch clamp experiments per hour ‣Teams
‣Data variety • Experimental and analytical specialization

• “Coherent” data sets (e.g. Sage, Personal • Research cores and constortia
Genome Project)
• Distributed across organizations and
• Behavior, anatomy, physiology, genomics institutions
experiments on the same subject


What is scientiﬁc data?

Goal: synthesize understanding
of the world
•Subject history •Derived values
•Subject preparation •Analysis
•Procedure •Intuition
•Measurements •Conclusions
•Simulation •Intellectual trajectory


Data management is a growing challenge

http://stats.stackexchange.com/questions/16889/ideas-for-lab-notebook-software


Data management landscape

Enterprise SDMS
Complexity/cost

Analytical tools

ELN

Paper notebook

Knowledge management



Enterprise SDMS
Complexity/cost

Analytical tools

ELN
OSF
Paper notebook Figshare

Acquisition Analysis

Pipeline stage


Enterprise SDMS
Complexity/cost

Analytical tools

Ovation

ELN
OSF
Paper notebook Figshare

Acquisition Analysis

Pipeline stage

Ovation’s data model describes science

Ovation is built to represent the language of science. Scientiﬁc data, regardless of
discipline, ﬁts this model.

Analogous example shows that representing music in the appropriate language of the domain
provides an appropriate data model

Music, in the language of the domain expert. Computer representation in the language of
May include margin notes, etc. the domain expert (including “margin notes”
from composer, conductor, etc.). Any genre
of music is representable.

Lab notebook representation Ovation representation 13


Ubiquitous data model is the correct granularity for
knowledge transfer

Ovation’s data model is more granular than an ELN. Instead of loosing information
during conversion to (and from) a report format such as a Word document or PDF,
Ovation allows data to be transferred in the natural language and granularity of
science.

Information lost in transfer

Analogous example shows that transferring data via a “report” (a sound recording) produces an information bottleneck

Data transferred directly

Seamless collaboration and data transfer removes information bottlenecks 14


Common data model enables collaboration

Interoperability across institutional boundaries is easier with Ovation than other
solutions. Unlike ad-hoc or customized data management systems, every Ovation
customer uses the same data model.

Individual Global
Collaborators
researcher community

Data transfer via Ovation data model

15


Ovation Scientiﬁc Data Management System®
• Comprehensive data management

• Multi-modality

• Multi-user annotation

• Analysis provenance

• Seamless user experience

• Double-click installation

• Integration with existing tools: Matlab, Python,
R, Java

• Guide to success

• Eﬀective collaboration

• Distributed and co-located experts

• Data ownership maintained

• Cloud-based replication and archiving


What is the exact record of modern research?
Source

ID: xyz123
Birthday: Dec-1-2010
Number of offspring: 2
Source
Mother:
Father: Source

Greg Schwartz Noldus


Integrated analysis workﬂow

Analysis pipelines that begin with a search, facilitate
automatic incorporation of new results

Acquire Organize Search Analyze

%% Run a simple query
iterator = context.query('Epoch', ' ...criteria... ');

while(iterator.hasNext())
currEpoch = itrator.next();
...analyze currEpoch...
end


Integrated analysis workﬂow

Acquire Organize

Search Analyze

Acquire Organize

Replication technology allows Ovation to replicate a subset of the database for data locality within a computational cluster.

Execute workﬂows on a local or cloud cluster

Share data in context

DerivedResponse
Trial
name: spikes
parameters: {…}
code: spikes.m

Stimulus Response

ovation:///f694d05a-131b-4644-aa7c-f6e8934e60c0/

DerivedResponse
Trial
name: spikes
parameters: {…}
code: spikes.m

Stimulus Response


Share data in context

Project Source

Experiment Experiment
Device

Trial Group

DerivedResponse
Trial Trial Trial
name: spikes
parameters: {…}
code: spikes.m

Stimulus Response
Stimulus Response


Ovation enables researchers to extract more
knowledge from existing data
• Lab’s lifetime work was enough data to answer fundamental questions about signal
and noise in the early visual system
• Data was locked in individual’s ad-hoc data management
• Ovation enabled meta-analysis of this existing data
• New graduate students start with the old data, not new experiments et al. • Arrestin Competition
(38):11867–11879 Doan

psin is pro-
d for each
e transduc-
convert the
nge in cur-
mptions, we
␣ and ␥0/␴
the single-
GRK1ϩ/Ϫ, “Ovation has changed the way we do science…”
—Fred Rieke
able 2). Be-

Whose data?
Open vs. Proprietary science

•Funding agency •Personal options
mandates
•Creative Commons
•NIH and NSF require
•Portable Legal Consent
data management plans
(human subjects)
for new applications
•Blogs, Twitter
•New repositories
•Open Science
Framework
•Figshare


Our vision: living data sets

Data

Data

Data


ovation.io

• Store and archive all your data • Make your data available wherever you
need it

• Safe, secure, highly reliable cloud
storage • Replicate and synchronize data to
multiple devices

• “Oﬄine” archiving
• Beneﬁt from our scalable cloud-based
architecture
• Collaborate locally and globally

• Pay for what you use
• Share selected data with designated
users or the public
• Simple monthly fee


Data replication with ovation.io


Neuron
Inference in Visual Adaptation

Collaboration with ovation.io

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an Increase in Temporal Contrast Depends on the Period between Contrast Switches
RGC (holding potential 10 mV) in response to a single switch in stimulus contrast (6%–36%,
n (A) and 32 s in (B).
als as in (A) and (B). Exponential fits to the response following an increase in contrast are shown in red.

Figure 1. The Time Course of Adaptation following an Increase in Temporal Contrast Depends on the Period between Contrast Switches
nt (mean ± SEM) of the exponential fit to the response following an increase in contrast (6%–36%) for
OFF) as a function of stimulus switching period.

(A and B) Inhibitory synaptic current to an OFF-transient RGC (holding potential 10 mV) in response to a single switch in stimulus contrast (6%–36%,
Meister, 2002; nonrectified, the r.m.s. current was fit with the same function.
mean $400 R*/rod/s; red). The switching period was 16 s in (A) and 32 s in (B).
ynamics of the The exponential amplitude A and baseline c did not change
(C and D) significantly as a function of the switching period approximately 100 trials as in (A) and (B). Exponential fits to the response following an increase in contrast are shown in red.
Mean synaptic currents from (not shown).
Figure 1E shows the population average time constant as
(E) Population-averaged (n z 10 for each period) time constant (mean ± SEM) of the exponential fit to the response following an increase in contrast (6%–36%) for
a function of period. The average effective time constant of
adaptation scales approximately linearly across a broad range
stall RGC types (ON, OFF-sustained, OFF-transient, and ON-OFF) as a function of stimulus switching period.
of switching periods ($8–32 s). The observed scaling fails for
ion depend on short periods but extends to the longest period (T = 32 s) that
eriodic switch we could measure reliably. A similar relationship was observed
scribed below, when comparing the time constant of an exponential fit to only
se in contrast the first 8 s of 8, 16, and 32 s periods (not shown). Thus the effect
et al., 2001; Smirnakis et al., 1997; Baccus and Meister, 2002;
is not simply the result of fitting an exponential to a nonexponen-
ptic currents in tial response over varying time windows. These results indicate
nonrectified, the r.m.s. current was fit with the same function.
Kim and Rieke, 2001). Here we focus on the dynamics of the
a stimulus that that a fixed first-order process does not govern the dynamics
period of 16 s of contrast adaptation in mouse retina. Instead, the adapting
The exponential amplitude A and baseline c did not change
slow component of adaptation.
d across trials machinery has access to multiple timescales.
trast stimulus, Dynamics of Adaptation to Luminance
significantly as a function of the switching period (not shown).
synaptic input To test the generality of multiple-timescale dynamics of adapta-
urse of several tion, we measured responses to periodic changes in mean light
Figure 1E shows the population average time constant as
Contrast and Luminance Adaptation
slow relaxation intensity (luminance). As for contrast adaptation, the dynamics of
ase in contrast adaptation following an increase in luminance depended on the
a function of period. The average effective time constant of
Exhibit Multiple Timescales
nputs are con- stimulus switching period.
adaptation scales approximately linearly across a broad range

Early access for Stanford Neurosciences Program

In conjunction with this seminar, we are providing early-
access accounts on ovation.io for
Stanford Neuroscience Program students

•Collaboration events
•Survey
•Adoption
•Feedback!
•How much data?

Prize for most collaborative student


Getting started with Ovation

✓Signup
✓Download
✓Get started

http://ovation.io info@ovation.io @ovation_io

Stanford Neurosciences Professional Development Seminar April 2013

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