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From YouTube: OpenShift Covid19 Case Study: HowsMyFlattening.Ca - Farbod Abolhassani UofT Guillaume Moutier RedHat
Description
OpenShift Covid19 Case Study: HowsMyFlattening.Ca Ontario COVID-19 Data Analytics
Farbod Abolhassani (UofT) Guillaume Moutier (Red Hat)
June 18, 2020
OpenShift Commons Briefing
hosted by Diane Mueller (Red Hat)
https://HowsMyFlattening.Ca
HowsMyFlattening is an ambitious Community Driven centralized data analytics and visualization hub monitoring Ontario’s response to COVID-19 that provides real-time insights informing and monitoring Ontario’s response to COVID-19. Each day, the HowsMyFlattening team analyzes and publicly posts travel patterns, hospital capacity, the effects of public health restrictions and loosening decisions, new cases and many other data points. It’s all hosted and deployed on Red Hat’s OpenShift platform. In this briefing, we’ll hear from University of Toronto’s Farbod Abolhassani and Red Hat’s Guillaume Moutier and find out how the HowMyFlattening team is making a difference in the COVID-19 pandemic.
Link to slides:
Part 1: https://live-timely-objzoywz.time.ly/wp-content/uploads/2020/06/OCB-HMF-Farbod-Abolhassani-Building-a-real-time-COVID-data-analytics-hub-in-Ontario.pdf
Part 2: https://live-timely-objzoywz.time.ly/wp-content/uploads/2020/06/OSCG-HMF-Data-Science-Platform-Guillaume-Moutier.pdf
A
All
right,
everybody
welcome
back
to
another
openshift
Commons
briefing.
This
time
we
have
some
of
my
very
favorite
Canadians
on
on
the
line
to
talk
about
the
work,
that's
being
done
by
a
group
of
volunteer
data
scientists
in
Ontario,
around
kovat
and
driving
them
data
transparency
and
some
really
wonderful
data
analytics
to
help
drive
and
help
fight
the
battle
with
kovat
19
up
here
in
Canada,
so
I'm
gonna,
let
firebot
and
Guillaume
introduce
themselves
and
we'll
have
live
Q&A
at
the
end.
A
B
B
So
how
and
how
we're
gonna
do
this
is
I'll,
have
a
slideshow
to
tell
you
guys
about
the
work
we've
been
doing
and
I'm
hoping
that
GM
will
take
over
from
there
and
couple
more
slides
and
then
a
demo
of
everything
without
further
ado.
Let's
talk
about,
how
is
my
flattening
so
to
give
you
the
context?
I
mean
we're
here
in
Ontario
but
I'm
sure,
depending
on
where
everyone's
from
similar
kind
of
context,
this
all
straight
I,
can
march
with
the
whole
academic
situation
on
the
rise.
B
You
know
we're
looking
across
to
Europe
and,
seeing
you
know
all
the
issues
they
were
having
and
back
here.
Nothing
had
changed.
You
know
some
of
our
on
some
of
the
team
is
physicians
in
the
hospital.
We
got
kind
of
quite
a
big
ferry
team
that
I'll
talk
about,
but
you
know
things
were
operating
is
normal
and
there
was
this
big
concern
looking
into
how
things
are
going
like.
How
are
we
actually
going
to
get
action?
B
So
let's
go
to
the
problem
statement,
so
we
knew
Tobin
was
coming
right.
We
could
see
that
from
the
European
example
and
the
the
the
normal
state
of
things
here,
Ontario
on
a
may
be
the
case
and
I.
Imagine
many
kind
of
health
care
state
of
things.
Is
that
there's
lots
of
data
and
it's
not
in
one
place
right
so
Tom
comes
in
PDFs.
The
government
has
started
reporting
some
stats
on
the
websites
of
frozen.
You
know
you
get
out
your
Ontario
get
out
of
your
spreadsheets
start
writing
this
down
every
day.
B
If
you
want
to
get
a
sense
of
what's
happening,
analysis
that
happens
in
health
care
tends
to
be.
You
know,
one-off
analysis.
Speed
is
a
huge
issue
where
things
take
weeks,
if
not
months,
I.
Quite
honestly,
we've
given
what
was
happening
in
Europe.
We
were
scared
of
what
we're
gonna
happen
here.
If
we
didn't
do
something
about
it,
though
Maupin
does
we
put
together
a
team.
You
know
like
Diane
mentioned,
it's
largely
volunteer,
driven
and
kind
of
a
community
approach
of
okay.
B
Let's
see
what
we
can
do
on
emerging
technology
to
help
and
contribute
in
some
way.
So
what
we
did
is
we
actually
got
together,
we're
quite
a
diverse
set
of
people.
So
it's
a
group
of
right
now.
Over
a
hundred,
you
know
people
with
different
backgrounds,
engineers,
computer
scientists,
clinicians.
We
got
PD
me
ologists
and
you
know
we're
all
together
to
collect
data
analyze
it
and
get
available
to
both
the
public
and
to
do
analysis
to
inform
decision
making
to
be
able
to
better
guide
the
the
response
to
cope
in
nineteen.
B
So
some
of
the
problems
that
we're
solving
before
from
the
previous
slide
is
around
real-time
database.
So
you
know,
having
did
my
proper
data
apply,
is
not
someone
just
in
Excel
doing
proper
live
visualizations
that
are
connected
to
a
database
I'm
doing
this
all
in
a
kind
of,
and
that
doesn't
take
months.
B
We
didn't
have
months
back
in
back
in
March
and
then
doing
this
all
under
a
scientific
process,
or
you
know
we,
we
do
it
in
a
peer-reviewed
kind
of
way
where
there
is
experts
in
epidemiology
medicine
and
data
Spence,
who
sign
off
on
everything
we
do
so
we're,
not
just
some
guy
in
an
Internet
you
can.
You
can
trust
the
analysis
reproduce
what
we
actually
did
part
of
the
challenge,
as
I
mentioned,
is
the
deed
wasn't
in
one
place?
B
Well,
the
first
thing
we
did
is
we
put
it
all
in
one
place
right,
so
we're
gathering
data
from
lots
of
different
sources
to
both
looking
at
Ontario,
specific
data,
Canadian
data
and
then
also
international
edom,
and
then
that
data's
kind
of
in
different
category.
So
it
covers
everything
from
your
typical
case,
theta
to
interesting,
more
unique,
I
would
say
around
mobility,
some
economics
data
that
we're
adding.
So
the
goal
is
have
one
site
where
researchers
and
others
who
want
to
do
analysis
have
to
use
the
data
and
find
it.
B
So
you
don't
want
to
go
over
all
over
the
Internet.
Oh
man
I
thought
about
it.
So
it's
not.
We
trusted
the
data
when
we
put
there,
so
you
can
trust
the
process
there.
So
what
came
from
that
data
is,
of
course,
the
analysis
bit
so
one
way
we
present
this
analysis
is
we
have
37
regional
dashboards,
so
one
is
looking
at.
You
know
all
the
key
on
the
KPIs
for
Ontario
and
then
in
Ontario.
B
We
have
Regional
Health
Authority
size
for
called
public
health
units,
so
we
the
dashboard
for
each
of
the
public
health
units
right
before
each
public
health
Kenya
was
trying
to
make
their
own
dashboard.
They
all
look
different,
and
if
you
lived
in
on
sphere
you
you
had
to
figure
out
where
to
go.
This
is
one
research
you
come
and
you
find
data
that's
relevant
to
you.
What
we
do
on
top
of
that
outside
of
just
the
the
dashboard,
is
kind
of
more
deep
dive
analysis
as
well.
B
So
we
have
14
plus
analysis
on
the
website
I'm
going
back
to
unsolved
board.
These
are
not
static
images.
These
are
actual
live,
interactive,
visualizations
that
are
rewound
connected
city
debate.
I'm
all
talk
about
kind
of
how
it
worked
a
bit,
but
that's
kind
of
how
we're
Jenna
friend
is
not
doing.
This
is
not
an
excel
shop,
we're
doing
things
that
are
you
know
at
the
low
to
medium
technical
sophistication
a
few.
This
is
just
the
highlight
one
of
our
analysis.
B
You
know
this
is
kind
of
a
new
thing
that
we
did
that
we're
looking
at
the
point
is
to
be
able
to
monitor
different
things.
They're
happening
the
pandemic,
so
one
thing
that
we
recently
caught
is
that
there
has
been
a
shift
in
trend,
find
the
age
distribution
of
cases
right.
So
this
would
not
be
possible
if
someone
was
running
this
analysis
three
day,
because
we
have
15
16
different
things,
we're
doing
and
we're
not.
B
We
don't
have
the
resources
demand
that
so
it's
all
automated
to
the
part
that
I
can
be
automated,
that's
kind
of
an
honest
if
I
imagine
most
of
the
people
on
this
color
are
fairly
technical
people.
This
is
not
a
technical
problem
that
we're
solving
it's
just
unheard
of
in
the
space
that
we're
doing
it
for
some
reason,
so
it's
just
come
catching
up
with
with
the
industry.
If
you
will
so
I'll
give
it
about
two
how
we
used
to
different
kind
of
visualization
software
or
our
website.
B
The
analysis
page
is
mainly
in
tableau.
The
reason
for
that
was
quite
honestly:
you
had
people
who
were
have
low
people
like
database
folks,
and
we
didn't
want
that
burden
of
coding
to
be
on
the
engineering
team
of
people,
so
part
is
mainly
done
in
tableau
how
it
actually
works.
We
had
to
go
through
lots
of
Hoops
to
make
Lua
update
live.
Basically,
the
only
way
you
can
do
it
in
a
reasonable
manner
offer
it
all
to
be
public
on
is
to
use
Google
sheets.
So
right
now
what
happens
is
our
data
from
the
database?
B
The
backend
is
updating
Google
sheets
every
day
at
different
intervals,
and
then
tableau
is
connected
to
that.
So
tableau
does
a
refresh
from
Google
sheets,
there's
believe
it
or
not.
No
direct
way
of
collecting
tableau
to
a
database
of
any
sort
or
an
API
or
anything
so
Google
sheets
is
your
way
if
you
want
to
have
a
public
thing,
but
it
works
and
that's
kind
of
how
we're
operating
the
other
bit
is
partly.
B
The
reason
for
this
is
the
standard
language,
and
you
know
for
data
science,
folks,
it's
open
source,
so
there's
no
licensing
cost.
So
that's
kind
of
the
the
reasoning
for
it
and
it's
quite
simple,
and
everyone
knows
how
to
use
it,
so
our
dashboarding
is
mainly
in
plotly,
whereas
the
analysis
page
the
visiting
makes
in
tableau
and
of
course,
I'll
talk
about
the
open
ships
in
a
second.
So
our
architecture
is
now
thanks
to
the
team
over
at
open
Smith
has
been
containerized
and
moved
over.
B
So
you
know
we
have
your
typical
standard,
pods
running.
We
have
different
staging
and
production
and
development
environments,
and
then,
of
course,
you
have
cron
jobs
that
run
for
the
data
pipelines
that
go
to
all
the
different
data
sources
that
we
have
an
updated
database
which,
as
I
mentioned
then
updates
to
visualizations,
whether
it's
through
plotly,
which
directly
interacts
with
the
database
or
through
the
Google
sheets,
work
around
the
other.
How
that
I
wanted
to
mention
again.
This
is
thanks
to
the
amazing
engineers
from
met
hotshots,
Oulu
and
Steve.
Who
made
this
happen?
B
Is
we
have?
We
moved?
Actually
our
entire
code
base
starting
github
if
we
moved
it
to
get
lab,
and
now
we
have
get
lab
runners.
That
is
three
with
OpenShift,
so
you
know
when
there's
this
pushed,
we
have
a
fully
connected
to
the
ICD
pipeline
that
builds
the
image
or
openshift
applies
a
template
to
build
the
image
and
to
get
all
the
resources,
and
you
know,
do
all
the
fun
kubernetes
stuff
to
make
sure
it
gets
deployed.
So
it's
literally
you've
got
a
new
branch.
It
gets
deployed
to
a
development
environment.
B
You
can
play
around
with
it.
You
merge
it
into
staging
stage
and
gets
updated
all
the
good
stuff
there.
So
that's
that's
been
quite
amazing.
I
know
you
I'm
talking
more
about
the
open,
tato
stuff,
the
how
behind
that,
but
it's
been
amazing
to
move
to
open
shift
and
take
advantage
of
all
the
all
the
great
benefits
of
kubernetes
know
from
shipped
I'll
talk
a
bit
about
our
team
mentioned.
We
got
at
a
diverse
group.
B
Again,
it's
largely
volunteer
driven,
it's
I
would
say
this
is
kind
of
very
new
for
me
as
well,
in
the
sense
that
we're
trying
to
do
an
open
source
open
community
bit
a
lot
of
folks
that
are
not
software
folks.
So
we
have
a
lot
of
clinicians.
We
have
epi
kind
of
researchers,
a
lot
of
em
me
grad
students
and
we're
trying
to
kind
of
onboard
them
into
this
whole,
get
lab
open
source
approach
and
teach
them
about.
B
B
Ontario
has
quite
this
Gordon
data.
So
when
you
build
all
these
automated
pipelines
and
cron
jobs,
that
updates
the
database
things
break
quite
often
and
some
things
we
could
predict
and
some
things
we
we
can't,
so
we
have
to
build
up
on
top
of
it.
So
this
is
a
an
example.
You
know,
you're
looking
at
the
accurate
episode
each
column
in
a
the
government
releases
and
all
they
put
like
things
that
don't
make
sense
right.
B
So
there's
things
like
2002
like
obviously
no
one
got
Co
bit
in
2002
or
it's
highly
unlikely,
most
likely
2020
accurate
episode
date
of
Club
am
right
like
stuff
like
that,
with
their
really
basic
stuff.
That
can
that
we
don't
see
coming
it
breaks
things
and
then
our
visitor
not
updated.
You
know
all
the
applications
of
that,
so
we've
been
kind
of
iterating
and
building
in
like
basic
ditional
object,
kind
of
filter,
some
of
this
stuff
out
again,
not
a
total
challenge.
More
just
a
mich
30
challenge.
B
B
So
this
is
gonna,
be
our
new
homepage,
so
this
is
a
little
actively
working
on
and
then
another
bait
where
TM
is
gonna,
go
into
open
data
hub,
which
is
gonna,
be
amazing.
Amazing
amazing
I
know
the
whole
team
is
excited
about
it,
which
is
where
we're
going
soon,
as
we
get
more
secure
data
that
not
everyone
can
have
access
to
when
there's
contractual
obligations
with
it.
B
This
will
be
our
solution
where
we
can
figure
out
the
access,
management
and
happy
of
different
people
access
different
groups
of
files,
and
you
know,
building
in
all
our
pipelines,
it's
something
that
is
easier
to
use
because,
as
I
mentioned,
a
lot
of
our
users
are
not
extremely
technical
folks.
So
this
kind
of
removes
a
lot
of
the
complexity,
which
is
we're
really
release
they're,
better
and
I'm
excited
to
talk
more
about
it
as
well.
I'll
briefly
talk
to
the
response.
As
I
mentioned,
this
is
largely
volunteer.
B
Driven
we've
been
getting
picked
up
in
the
news
a
few
times.
It's
been,
you
know
some
validation
for
those
of
you
who
are
not
mentioned
or
not
familiar
with
any
of
these
articles.
The
star
is
kind
of
the
Toronto
Star
as
a
big
deal
here.
If
you
will
it's
the
article
and
of
the
New
York
Times,
so
we
got
picked
up
with
them
and
there's
been
some
universities,
publications.
B
But
honestly,
the
big
thing
for
us
has
been
this
slide.
I
think
this
is
what
gives
drive
the
whole
community
is
on
our
website.
We
have
a
chat
bot
and
through
the
chocolate
anyone
can
kind
of
tell
us
how
they
feel
about
about
the
work,
we're
doing,
there's
like
a
five-star
rating
review,
and
then
people
can
submit
specific
feedback
and
I'm
just
leaving
it
on
a
screen
here
for
a
couple
seconds,
while
I'm
talking
I
hope
you
guys
are
able
to
just
read
through
a
few
of
them.
Honestly,
this
has
been
what's
driving.
B
The
passion
of
the
volunteers
here
is
that
the
positive
feedback
that
we've
gone
from
the
public
right
in
terms
of
making
it
a
more
accessible
allowing
people
to
know
what's
going
on
people
feeling
like
they
need
just
transferring
data,
but
that
may
be
lacking
the
way,
that's
just
providing
them
with
a
resource
right,
that's
what
it's
all
about.
All
of
the
almost
at
the
end
of
the
presentation
here,
the
lowest
two
slides
here
are
around
called
pulse
action.
B
We
really
need
more
help,
so
if
you're
all
just
call
if
you're
watching
this
there's
today
or
some
point
in
the
future,
please
go
to
our
gate
lab
the
link
is
at
the
bottom
of
every
slides
here.
So
get
laptop
how's
my
flattening,
but
please
I'm,
sorry,
I'm
good
I
just
got
a
call,
so
please
do
go
to
our
gate
lab
and
get
involved.
I
can
just
find
it.
I
get
lab
comm
how's,
my
flattening.
We
can
use
any
help
that
we
can
get
and
everyone
can
contribute.
B
We
have
lots
of
different
things
you
can
get
compute
to
so
please
do
get
involved.
There
I
usually
add
slide
for
I
mention.
Most
of
our
group
is
non-technical,
so
this
is
what
I
usually
show
to
show
them.
What
I'll
get
lit
is
and
what
I'm
talking
about,
but
I
imagine
most
familiar.
But
therefore,
so
you
can
figure
out
what
working
group
you'd
like
to
get
involved
with.
That's
it
for
me
and
somebody
were
we're.
A
small
organization
largely
volunteer
driven.
B
C
Thanks
a
lot
for,
and,
as
you
all
have
seen,
the
effort
that
is
made
by
this
community
of
researchers
of
data
scientists
of
students
and
everyone
that
is
involving
the
community
is,
is
really
really
important,
and
that's
exactly
why
at
reddit
and
in
the
Canadian
reddit
community,
we
wanted
to
help.
We
want
you
to
do
or
share
of
helping
this
community
build
and
grow.
So
what
I
will
talk
to
you
about?
Now
is
the
next
step.
C
I
would
say
where,
where
we
can
help
this
community
with
the
data
science
platform
that
that
they
want
to
build,
because
as
you've
seen
as
the
number
of
people
who
are
helping
on
this
on
this
website,
what's
more
important
on
the
data
analyzes
and
and
all
the
stuff
going
around,
as
this
team
is
growing,
they
did.
They
have
more
needs
to
be
able
to
to
have
working
environments,
especially
especially
shared
environments,
where
it
will
be
easy
for
them
to
collaborate
to
exchange
ideas,
to
exchange
data
or
other
resources.
C
As
far
bond
mentioned.
They
also
have
this.
This
need
of
managing
different
access
levels
because
they
have
some
more
restricted
data
sets.
Of
course,
you
know
some
may
contain
personal
information
or
sensitive
information,
so
those
are
the
requirements
that
we
talked
about
with
the
team
and
we
we
discussed
how
we
could
help
them
get
to
these.
This
next
level
in
terms
of
in
terms
of
platform
so
or
answer
to
that
was
leveraging
open
data
hub
up
and
then
a
hat
for
those
who
don't
know
is
is
a
project.
C
That's
right
at
its
it's,
not
a
product,
it's
a
it's
a
project
that
circus
is
very
well
how
you
can
deploy
data
science
platform
on
top
of
openshift,
so
it's
a
kind
of
a
metal
narrator
that
allows
you
to
very
easily
deploy
some
of
the
commonly
used
data
science
tools
out
there.
So
you
know
Jupiter,
amended
books
or
or
cube
flow
pipelines
or
Seldon
for
serving
models.
So
this
is
all
the
the
stuff
that
we
can
deploy
right
out.
Will
a
Pantera
hub?
C
Of
course
it's
not
everything
that
was
that
is
that
is
deployed
right
now
for
how's,
my
flattening,
it's
mostly
at
the
moment,
Jupiter
notebooks
and
shared
data
data
workspaces,
but
it's
a
similar
it's
about
getting
getting
from
there.
You
know
each
data
scientist
I
think
its
own
install
in
Sun,
installation
of
Jupiter
and
the
libraries
that
are
needed,
and
the
data
sets
and
stuff
like
that.
C
So
getting
from
this
world
to
this
one
we're
on
top
of
up
and
shift,
you
are
able
to
run
on
Jupiter
hub,
which
will
give
you
access,
which
will
give
you
access
to
to
notebooks,
to
easy
to
use
environments
and
a
lot
of
that
shared
environments
with
a
very
efficient
use
of
resources
and
and
all
the
management
that
goes
with
it.
Okay,
what
I
used
also
ID
set
up
is
yourself,
it's
Rousseff
that
is
deploying
in
these
in
this
installation,
because
it
allows
us
to
that.
C
You
have
different
types
of
storage
that
we
will
be
able
to
use
in
our
environments.
We
need
it
block
storage
to
create
TVs
for
the
users,
so
that
means
each
user
is
able
to
have
its
own
environment
to
store
its
own,
its
own
data
or
files,
but
at
the
same
time
we
needed
files.
We
need
to
share
that
data
so
as
Rousseff
and
OCS
are
providing
a
shared
file.
C
Storage
with
Troost
ffs
do
does
allows
us
to
have
our
WX
Peavey's
that
can
be
mounted
inside
all
the
parts
and
will
will
will
show
you
in
a
few
minutes
how
which
works.
So
that's
the
environment
I
will
close
this
and
show
you
how
it
works
so
I
have
created
in
in
the
project.
I
have
created
different
persistent
volume
claims
for
shared
data
with
different
access
levels.
C
Private
public
secret
I
have
another
one
here
for
public
notebooks
and
all
those
PVCs
are
using
these
storage
glass,
a
rope,
ropes
ffs,
which
allows
us
to
have
these
three.
No,
it's
not
not
selected
the
right.
One.
Sorry,
for
example,
for
these
shared
data,
private
PVCs.
It
allows
us
to
to
use
this
read,
write
many
access
mode,
so
that
means
that
several
containers
of
all
paths
will
be
able
to
mount
these
City
share
simultaneously.
C
What
is
interesting
is
in
this
way,
it's
very
easy
to
deploy
overlays
and,
in
this
case,
I
have
created
this
overlay,
which
I
called
shared
PVC
and
another
lay
is
basically
saying
Oh
in
this
specific
configuration
of
the
deployment.
You
will
do
a
replace
with
this
value,
and
here
I
have
the
code
in
a
more
readable
highlighting
so
this
code,
basically
just
say:
whenever
you
want
to
spawn
a
new
pod,
you
will
look
into
the
user
lists
private
users
and
secret
users.
C
If
there
are
n
means
they
will
also
have
readwrite
access
for
on
the
shared
folders,
and
you
would
mount
your
boot
appen.
Some
configuration
where
you
are
able
to
mount
in
the
persistent
volume
claim.
For
example,
here
there
shared
data
public
directly
inside
the
pod.
Okay,
that's
the
technical
part.
I'll
do
a
special
session
on
this
at
some
point,
but
let
us
see
how
it
works.
C
So,
let's
say
I'm
a
data
scientist
and
part
of
that
has
my
flattening
initiative.
I
will
have
access
to
to
my
environment
who,
this
to
this
lounge
launch
board
and
I
will
just
launch
my
Jupiter
environment.
It
will
require
me
to
sing
in
here:
I
have
some
credentials
that
I
can
use,
and
in
this
example,
I
will
launch
a
notebook
which
comes
with
the
SyFy
libraries,
the
Python
sidebar
ivories
and
also
comes
with
RDR
engine,
because
that
was
one
of
the
requirement
from
the
from
the
team
to
be
able
also
able
to
use
our
notebooks.
C
So
again,
this
is
a
customized
version
that
I
that
that's
a
actuated,
because
that's
that's
quite
easy
to
do
from
open
data
hub.
You
can
create
your
own
images
and
here
I
can
select
my
Department
size.
So
that
means
that
the
size
of
the
path
I
will
will
create.
I
could
have
some
more
environment
variables
and
then
I
will
just
burn
it.
C
C
C
So
let's
say
here:
I
open
this
notebook,
so
that's
my
environment,
where
I
am
able
to
work
and
that's
a
simulation.
I
have
run
based
on
a
notebook
that
the
team
provided
to
me.
But
on
top
of
this
personal
environment,
I
would
say:
I
have
access
now
to
a
shared
data
folder.
So
this
shared
data
folder
is
the
same
one
that
everyone
who
is
using
the
environment
will
be
able
to
see
and
hear
as
I
have
those
special
access.
I
have
access
to
private
and
secret
folders.
C
But
normally
you
know,
standard
people,
part
of
the
initiative
will
only
see
the
public
folders
and
that's
where
the
public
folder
that's
where
they
will
find.
You
know
some
datasets
that
they
are
able
directly
to
you
to
use
with
their
notebook.
So
that's
a
very
neat
way
to
provide
people
with
shared
data
that
they
can
directly
use
inside
their
environments,
and
we
have
also
created
shared
notebooks
environment
and
for
but
yesterday
rated
this
welcome
that
book.
C
Okay,
just
to
explain
how
which
works
and
to
give
some
some
demonstration
on
those
shared
data
that
that
are
accessible
by
everyone.
Okay,
so
that's
about
it!
For
this
presentation.
It's
again
it's
a
standard,
open
data
deployment,
no
first
base
with
Jupiter
hub.
Maybe
we
will
deploy
other
stuff
in
the
coming
weeks,
but
that's
a
very
interesting
implementation,
especially
with
those
shared
day
and
shared
notebooks
that
are
very
easily
done
with
the
rwx
capacity
that
we
have
in
in
Ruby
for
up
and
shift
container
storage,
and
that's
all
for
me.
A
All
right
so
as
a
Canadian
and
I
think
both
iRobot
and
Guillaume
as
well.
We're
we're
all
very
excited
about
this
project
happening
and
and
how
its
unfolded
very
very
rapidly.
This
there's
one
question
and
it
gives
me
a
good
segue
to
how
to
get
involved
and
I'm
gonna
share
my
screen
for
a
second
just
so
that
I
can
put
up
the
gitlab
page
here
on.
How
is
my
flattening-
and
you
can
tell
me
if
you
can
see
if
you
can
see
my
screen?
A
Why
did
we
decide
to
use
git
lab
or
why
did
why
did
fara
bod?
Maybe
you
have
a
some
insights
into
this,
but
well,
while
we're
talking
about
it,
if
you
are
looking
here,
the
URL
is
here
for
free
how's,
my
flattening
on
how
to
get
involved
and
how
to
get
started
in
using
some
of
the
projects
here
and
Farabaugh.
Do
you
have
some
insights
into
and
I
have
tons,
but
well
well,
why
you
decided
to
use
git
lab
and
how.
B
Integration
I
know,
there's
different
ways
of
doing
it,
but
that
was
one
day
the
other
bit
of
it
was
that
the
nice
thing
about
Caleb.
Other
than
it
just
an
open-source
project-
and
you
can
deploy
it,
I
mean
that's
just
a
whole
other
thing,
but
it
does
everything
in
one
place
for
us,
which
is
nice
right.
So
we
have
our
I
know.
Some
of
those
features
are
also
available
in
github
and
we're
just
almost
at
that
point.
B
A
preference
thing
in
terms
of
we
got
the
wiki's
here
we
can
use
issues
Wars
to
keep
track
of
everything,
and
then
all
this
is
my
CD
parts
that
come
with
git
lab,
which
were
nice,
so
that
was
part
of
it.
It
was
honestly
just
a
it
wasn't
a
because
of
how
small
of
an
organization
we
are.
We
don't
do
proper
kind
of
went
picking.
The
library
like,
let's
do
an
assessment
of
what's
out
there
in
the
field.
B
A
I
think,
like
I've,
been
involved,
I
don't
know,
maybe
a
month,
I,
don't
even
know
this.
It's
been
60
days,
the
whole
project,
so
I,
don't
know
when
I
first
twig
to
what
you
guys
were
doing
and
when
somebody
reached
out
for
some
help
on
doing
this,
but
the
other.
The
other
piece
of
the
puzzle
is
that
this
group
of
folks
weren't
really
savvy
about
open
source
practices.
Or
you
know
you
had
you.
You
were
savvy
about
open
data
and
data
privacy
and
all
of
those
things
you
had
a
lot
of
experience.
A
Some
of
the
data
scientists
and
analysts
were
really
up
on
on
all
of
that
that
side
of
the
coin,
but
creating
a
community
around
this
and
like
the
different
approaches
to
project
management
and
how
to
make
things
open
and
one
of
like
using
git,
lab
or
github
or
anything
and
putting
all
of
this
out
in
the
open
was
part
of
the
the
learning
curve
to
I.
Think
and
Allah.
C
A
Us
a
very
nice
way
to
do
that
publicly
and
to
start
you
know,
writing
the
onboarding
documentation
as
a
group
and
collaborating
on
that
and
getting
that
up
and
running.
So
that
was
that
was
an
interesting
thing
to
watch
the
phenomena
of
everybody
piling
on
with
you
know,
thumbs
ups
and
here's
a
link
to
another
data
source
and
all
of
this,
like
the
coordination
that
had
to
get
assembled
and
take
place
and
use
in
figuring
out
a
way
to
to
make
that
all
public
and
and
secure
at
the
same
time.
A
So
some
of
the
pieces
of
the
project
like
tableau
are
not
open
source
software
and
needed
licenses
and
stuff.
So
there
were
a
lot
of
pieces
along
that
line,
logistics
wise
that
we
had
to
figure
out
as
well
as
what
Guillaume
has
done
with
some
of
the
Red
Hat
team,
getting
it
up
and
running
in
a
scalable
way,
so
yeah.
B
They
need
to
build
on
top
of
that.
That
was
actually
really
pointed,
but
I
actually
forgot
to
mention
the
presentation.
So
we
have
a
very
small
kind
of
technical
team
and,
quite
honestly,
none
of
us
had
any
experience
with
opens
like
none,
absolutely
none.
So
what
so?
That's
another
kind
of
I
mentioned
only
when
Steve
and
obviously
Kyo,
who
helped
us,
move
some
of
the
the
technical
infrastructure
and
set
it
out
on
cruise
ship.
B
B
I'm
honesty
right,
like
we
operated
on
slack
at
first,
and
we
ran
into
this
issue
as
more
people
came
on
board
that
the
communication
was
getting
very
siloed
right,
but
a
lot
of
conversations
were
happening
in
private
pass
between
folks
and
to
catch
someone
up,
but
like
literally,
we
would
screenshot
conversations
with
other
people
just
so
you
don't
have
to
rephrase
that
it's
just
like
just
read
the
conversation.
I
just
had
with
other
person,
we
had
a
whole
challenge.
B
B
B
So
it's
kind
of
becoming
more
more
of
a
community
thing,
but,
like
anyone
can
go
right
now
in
our
on
our
collapse,
y'all
did
you
see
all
the
discussions
now
is
not
on
private
slack
channels,
all
the
meetings
you
know
when
it
happens,
that's
all
documents,
if
there's
wiki's
for
things
now,
you
know
like
happily,
how
does
that
analysis
go
up
on
the
websites
before
you
to
be
I
talked
to
three
other
people
and
you
knew
how
it
happened.
But
if
someone
just
is
joining
the
community,
they
have
no
idea,
but
now
we
publicize
that.
A
The
other
interesting
thing
was
once
we
were
digging
into
you
know
was
how
experienced
some
of
the
clinicians
and
basically
their
clinician
data
scientists
and
even
your
role.
Parabot,
is
you
know
you
you,
you
you
and,
like
a
doctor,
been
fine
and
a
whole
bunch
of
other
folks.
Have
this
deep,
deep
knowledge
of
the
data
and
the
data
sources
and
how
to
do
the
analysis?
And
then
there
was
you
know.
So
there
are
sort
of
these
data,
scientist,
doctors
or
engineers
that
were
volunteering
their
time
and
how
to
take
advantage
of
that.
A
And
then
there
are
all
these
other
people
who
I
kind
of
coined.
Word
data
scouts,
people
who
were
finding
data
sources,
and
then
you
know
just
posting
them
in
slack,
which
you
know
has
all
kinds
of
issues
and
stuff.
So
if
you
could
talk
a
little
bit
about,
oh,
like
you,
have
all
these
disparate
data
sources,
how
you're
aggregating
them
and
bringing
them
together
a
little
bit
and
make
that
talk
a
little
bit
about
where
how
you're,
combining
all
those
data
sources
into
decent
analysis,
sure.
B
So
how
the
website
is
structured,
you'll,
see
on
the
right-hand
side
of
that
gate
lab
there
for
working
groups.
If
you
will
on
there
is
the
website
development
group,
so
that's
their
kind
of
more
technical
folks.
There
is
the
data
collection
group
and
that's
the
data
scouting
group,
that's
what
some
of
whom
Jaina's
refers.
You
and
that
group
is
honestly,
have
been
amazing.
So
we've
done
a
couple
of
different
projects
and
I'll
talk
about
them.
B
One
is
you
know:
there's
60
some
medical
students
we're
just
monitoring
their
time
and
their
grad
students,
and
quite
literally
just
volunteers
from
the
community
who
are
collecting
a
brand
new
dataset.
That
does
not
exist
around
non-pharmaceutical
interventions,
but
what
I
mean
by
that
is.
You
know
the
government
announces
different
policies
in
response
to
kovat,
so
order
was
shut
down.
Schools
were
closed.
There's
no
data
stores
like
if
you
want
to
find
out
when
these
things
happened.
B
You
would
struggled
like
you
have
to
place
like
google
it
and
read
news
articles
and
put
date
time
right.
So
what
they're
doing
is
they're
actively
monitoring
across
the
country
when
all
these
regional
down
to
a
regional
level
right
little
city,
a
versus
City
B,
these
announcements
are
being
made
and
there's
a
whole
and
a
review
process
around
it.
I
mean
they've
built
this
whole
group
right
just
conduct
collecting
these
on
this
data
set
around
non-pharmaceutical.
So
that's
been
amazing.
B
Maybe
I'll
speak
a
bit
broadly
outside
of
what
we
do
just
outside
of
the
NPI,
which
is
you
know,
wonderful
projects
like
Diana
alluded
to.
There
is
lots
of
data
sources
that
there
are
non
traditional
untraditional
them,
so
we
have
to
come
up
with
when
I
was
a
creative
way
of
dealing
with
this,
though
some
is
know
what
I
mentioned
the
beginning:
the
governments,
for
example,
posts
they
have
one
page
where
in
every
day
there
is
an
HTML
people
that
they
update
with
new
cases.
B
B
So
again,
another
convert
this
PDF
into
actually
a
usable
format
that
people
can
just
get
at
times
and
like
a
time
trend
and
a
proper
kind
of
actionable
like
at
almost
an
Excel
CSV
right
like
a
CSV
file
and
keeping
historical
terms
of
that,
so
there's
been
little
mini
pipelines
built
for
each
of
these.
Where
it's
a
this
is
our
report
PDF
to
database
pipeline.
This
is
our
convert.
The
government
website
to
database
pipe
block.
B
There's
basically
lots
of
data
pipeline
sitting
and
running
throughout
the
day
now
on
a
corn
chef
that
just
go
scour
the
internet,
if
you
will
from
all
these
different
sources
and
update
the
data
piece
which
in
turn
updates
that
poop
sheets
work
around
that
I
told
everyone
note
that
then
updates
the
visualizations
right.
So
if
you
go
on
our
website,
you'll
see,
there's
visualizations
on
capacity
is
depends
in
cases.
There
is
RT
right,
so
those
are
all
coming
from.
B
Those
pipelines
are
populating
the
database
and
then
again
daily
con
jobs
run
that
updated
in
the
caboose,
which
is
our
medium
or
if
it's
partly
it's
in
fact,
you
direct
kind
of
a
from
a
database
that
friends
and
analysis
and
updates
it.
So
that's
high
level
how
everything
works,
how
it's
connected
to
the
data
I.
A
C
I'm
wondering
if
I
may
be
I
guess
you
know
what
far
but
expand
is
if
the
perfect
demonstration
of
the
struggling
that
data
scientists
or
researchers
are
facing
with
the
data.
You
know
and
that's
why
I
like
this
project,
because
what
we
help
do
is
exactly
what
technology
and
open
source
can
bring
to
to
the
research
world,
because
you
know
we
are
for
us,
it's
pretty
standard.
You
know
to
have
cron
jobs,
running
into
containers
and
connecting
to
the
data
moving
here
and
there
that's
the
kind
of
tools.
C
That's
research,
that
researchers
don't
even
know
that.
Okay,
so
here
bringing
this
technology
skill
set
that
we
were
able
to
do
directly
to
to
do
this
research
is
again
the
perfect
demonstration
on
how
how
I
would
say,
IT
tools-
you
know
the
standard,
DevOps
and
practices
and
the
technology
that
we've
been
using
for
the
past
few
years
have
definitely
they
of
their
place
in
modern
in
modern
research.
A
So
there's
really
a
you
know
if
any
good
outcome
comes
from
kovat
and
all
of
the
things
here
is
the
our
ability
to
respond
quickly
using
DevOps
and
using
open
source
and
using
these
models
and
the
pre-existing
stuff
to
help
with
pandemics
and
other
emergencies
like
this.
But
it's
also
what's
been
really
interesting
to
me.
Is
there
had
been
prior
to
all
the
covert
stuff,
a
lot
of
conversations
that
I've
had
in
the
community
with
data
scientists
about
how
much
and
data
scientists
they
love
their
Jupiter
notebooks?
A
They
love
that
they
don't
love
dealing
with
the
infrastructure
and
so
the
more
we
can
just
enable
them
to
have
like
Jupiter
hub
their
notebooks
and
their
pipelines.
All
hooked
up
the
better
and
the
last
thing
we
want
them
doing
is
and
ones
like
wasting,
but
you
know
wasting
their
time.
You
know
setting
up
CI
CD
pipelines,
you
know
deploying
kubernetes
clusters
and
all
of
that,
so
the
more
we
can
automate
that
and
make
that
I
hate.
A
C
Want
to-
and
in
fact
you
know
it's
a
little
bit
worse
than
that,
because
you
know
without
things
like
open
data
hub,
they
won't,
even
you
know,
do
them
serve
the
CI
CDs
and
the
pipeline's.
They
will
do
everything
manually.
You
know
as
far
about
described-
and
you
know
at
the
beginning,
just
grabbing
the
Google
sheets
and
then
transforming
them
manually
and
stuff
like
that.
So
it's
not
even
going
to
a
very
integrated
step,
it's
just
even
the
first
step
in
using
modern
tools.
That
is,
that
is
a
challenge
for
for
most
people.
C
A
I
think
we
saw
that
the
same
thing.
There
was
another
kovat
project
in
finland,
called
fever
map
fever
map
net,
and
there
was
the
good
thing
about
it
was
it
was
came
out
of
a
hackathon
and
it
was
all
most
of
it
was
developers
and
they
were
applying
it
and
how
quickly
they
were
able
to
leverage
the
technology
to
move
forward
with
their
fever
mapping
project
so
I
think
that's
really.
A
One
of
the
keys
here
is
where
we
that
technology
folks,
who
all
want
to
contribute
and
move
this
forward
and
fight
the
battle
against
kovat,
can
actually
contribute
to
this
project.
So
if
you're
out
there
and
listening
and
want
to
get
involved
again,
come
back
to
the
house,
my
flattening
c
a.get
lab
page
or
their
home
page
where
there
are
links
to
that
and
and
jump
in
I
wanted
to
ask
fara
Bob
as
well
and
iam
as
to
where
do
you
see
this
going
after
we?
A
You
know
the
life
for
this
project,
my
flattening
and
the
learnings
going
after
this
project
once
we're
beyond
this.
So
you
know
for
the
wider
impact
at
the
province
in
the
province
of
Ontario
and
I'm
I'm
over
and
just
put
my
I'm
in
British
Columbia
over
in
BC,
so
I
shout
out
to
dr.
Bonnie,
Henry
and
keeping
us
safe
out
here
and
BC.
So
where
do
you
think
this
is
all
going
to
and
this
learning
you
done
fair
about?
Oh.
B
B
We
want
to
be
able
to
show
the
value
of
transparency
community
driven
analytics
right,
be
able
to
you
know
what
we're
really
pushing
for
is
to
change
the
the
norm
of
how
things
are
done
so
going
back
how
things
are
right
now
in
healthcare
analytics
it's
very
siloed,
so
there's
different
institutions
that
hold
different
it
appeases
and
it's
extremely
difficult
to
share
data,
because
every
institution
is
responsible
for
their
data
and
if
something
goes
wrong
in
terms
of
sharing
the
data,
then
it's
from
done
right.
So
that's
the
issue.
B
So,
with
data
sharing
is
quite
hard
in
Ontario
and
the
people
who
have
access
to
the
data
you
have
to
be,
you
jump
through
a
ridiculous
number
of
Hoops
to
get
access
to
very,
very
basic
data,
so
it's
not
very
open
and
that's
honestly,
what
we're
trying
to
change.
That's
what
that's
part
of
our
mission
here
so
you'll
see.
That's
that's
part
of
what
open
data
hub
is
I'm.
Hoping
will
help
us
all
four
I.
Let
me
talk
about
it.
B
A
bit
so,
like
you
mentioned,
there
is
three
buckets
of
access
right
now
there
is
the
super-secret
buckets
the
sensitive
bucket
and
then
to
the
public
bucket:
hey,
don't
default,
I
wanted
access
to
the
public
data
right,
so
I
think
we
we
should
be
able
to
put
any
data-
that's
not
at
all
compromising
patient
information
on
that
bucket,
but
then
there
is
a
sensitive
bit.
So,
for
example,
there
may
be
contractual
obligations
or
terms
of
use
on
that.
B
It's
important
at
only
certain
people
have
access
to,
but
that
doesn't
mean
that
there's
there
should
be
a
process
set
up
for
anyone
to
get
access
to
it
as
long
as
they
follow
the
Terms
of
Use
right.
So
that's
I,
guess
that
that's
a
transparency
that
were
pushing
for
that
get
access
to
health
care
data.
To
not
be
there
should
not
be
a
prerequisite
to
be
a
member
of
a
private
club
to
the
a
member
of
this
to
get
access
to
health
care
data
right.
There's
I!
B
Think
that
part
of
what
we've
been
trying
to
show
is
a
lot
of
our
analyst.
A
lot
of
our
and
asked
on
our
spit
is
done
by
what
the
term
we
like
to
call
citizen
scientist
right
so
normal
typical
folks.
Would
you
probably
honestly
not
have
access
to
the
data,
otherwise
I
think
that's
a
big
thing:
we're
trying
to
push
for
outside
of
code
outside
of
the
pandemic
outside
of
the
pandemic
bubble?
B
If
you
will,
outside
of
the
lifespan
of
this
project,
is
gene
data
more
accessible
to
folks,
making
it
transparent
and
making
you
know
making
it
more
open
community
driven.
So
people
get
access
to
these
things
and
you
don't
have
to
be
a
member
of
a
specific
club
or
a
specific
group
to
get
access
to
it.
How
open
or
how
opened
it
up
is
enabling
that
is
because
now
we
have
this
kind
of
secured
access
pit
that
we
can
use,
then
you
can
now
pipelines
throughout
the
different
buckets
into
each
other
right.
B
So
there
may
be
a
data
source
that
only
a
few
people
can
then
get
access
to,
but
that
doesn't
mean
aggregate
data
can't
be
accessed
by
more
people
right.
So
now
that
we
have
this
infrastructure,
we
can
use
open
data
hub.
Give
people
access
to
aggregated
data
that
belonga
has
that
those
issues
or
found
privacy
around
it,
but
it
still
makes
the
data
transparent.
B
It
still
makes
a
useful
good,
honestly,
I
think
the
thing
that
we're
all
trying
to
get
to
here
is
to
get
access
to
aggregated
a
kind
of
going
back
to
the
a
20-bit.
The
thing
that
honestly
makes
a
difference
is
usually
aggregated
data.
We
don't
need
patient
goal
information
for
99%
of
the
things
right,
so
that
aggregated
data
is
the
challenge
right
and
that's
what
we're
hoping
to
solve
for
open
data.
Yeah.
A
C
A
Was
one
of
the
ethnicity
and
was
like?
No?
No?
No.
No.
We
need
to
break
it
down
a
little
bit
better,
so
I
think
there's
a
lot
of
room
for
improvement
in
the
the
also
the
cross
mapping
of
different
data
sources.
So
when
we
have
stats
stats,
Canada
data
population
on
that,
and
then
we
have
the
health
and
there's
a
whole
lot
of
work.
A
That
still
needs
to
be
done
in
that
space
as
well,
so
I
think
there's
like
we're
gonna
have
we
have
a
lot
of
work
ahead
of
us,
but
the
I
think
the
thing
that
you
guys
have
shown
the
light
on
is
how
these
citizen
scientists
can
help
bring
that
to
the
front.
What
it's
very
encouraging
has
been
to
see
the
Ontario
province
give
thumbs
ups,
and
you
know
thank
yous
and
respond
positively,
as
opposed
to
like
trying
to
shut
you
down
or
anything
like
that.
A
Do
the
work
you're
doing-
and
you
know
a
lot
of
that
I
think
I
showed
out
to
dr.
ben
fine
in
his
fine
diplomacy
skills
and
his
influence
with
the
different
groups.
He's
done.
Some
amazing
work
getting
getting
us
a
little
bit
further
in
so
I'm
I'm
gonna.
It's
gonna
be
interesting
to
see
how
all
of
these
learnings
and
the
collaboration
between
all
of
these
different
players,
because
their
immune
is
U
of
T
people,
there's
folks
from
at
least
a
dozen
other
companies
and
partnerships
that
you
guys
have.
A
You
know
well
beyond
the
tableau
and
plotly
and
get
lab
stuff,
there's
like
at
least
10
or
20
different
companies
that
are
putting
their
resources
and
their
time
and
effort
into
this
project
beyond
the
ton.
The
army
of
students
who
have
been
coming
in
I
have
seen
so
many
U
of
T
University
of
Toronto.
If
you're
not
Canadian,
you
have
T
folks
on
this
that
it's
just
amazing
to
see,
and
you
know.
Hopefully
this
inspires
a
whole
nother
generation
of
people
to
collaborate
in
the
open
and
also
to
help
in
efforts
and
initiatives
like
this.
A
It
was
last
edited
by
Farah,
but
if
you
a
couple
of
hours
or
20
hours
ago,
and
that
where
there's
tons
of
folks
Spencer
has
been
doing
the
community
project
management
for
this
Marlies
there's,
you
know
there's
a
lot
of
people
who
are
helping
to
make
this
thing
happen.
So
it's
it's,
certainly
not
a
party
of
one
and
yeah
honestly,
I
think
the
the
next
step
is
to
onboard
the
Ontario
government
and
collaborating
on
this
to
I.
Think
that'll
be
that'll,
be
the
fundraiser.
C
A
They're
coming
they're
coming
to
the
party-
and
you
know
this
is
this-
is
another
another
adventure
in
open
data
too
so
I
think
there's
as
we
go
through
this,
and
we
see
that
you
know
the
see
the
screen
scraping
from
government
sites
is
one
thing,
but
to
actually
have
those
data
sources
directly
in
the
pipeline,
so
you're
not
screen
scraping,
would
be
awesome
too.
Yeah.
B
I
think
that's
kind
of
goes
to
what
you're
saying
around
how
technology
that's
almost
more
than
standard
right,
we're
kind
of
introducing
into
this
field
that
does
not
use
it
right.
So
I
think
that
will
hopefully
also
enable
parties
that
are
sharing
data
with
us.
I
feel
more
comfortable.
Okay,
I
briefly
talked
to
the
you
know,
different
to
your
doctors
and
using
aggregation
pipelines
to
make
the
more
public
right.
There
should
be
no
reason
why
some
form
of
almost
all
D
can
be
public.
It
doesn't
have
to
be.
B
A
So
I
think
that's
the
one
of
the
lessons
learned
here
is
figuring
out
how
to
get
the
trust
in
the
citizen
scientist
so
that
we
enable
the
government
to
trust
that
you
know.
We've
we've
got
the
right
processes
in
place.
We
have
the
right
security
and
privacy
and
we're
being
compliant
we're
not
sharing
data
that
we
shouldn't
we're,
not
accenting
accessing
data
that
we
shouldn't
we're.
Ensuring
everybody's
privacy
is
respected,
but
also
the
power
of
the
community
to
help
find
the
anomalies
to
help
find
the
trends
to
do
the
modeling.
A
This
is
affecting
not
that
that
space
stuff
isn't
either,
but
this
is
really
stuff
that
is
life-changing
and
can
really
help
figure
out
where
we
need
to
get
more
ICU
seats
and
your
beds
rather
new
ventilators,
where
the
curve
is
not
flattening
where
you
know
it's
reoccurring,
so
that
the
pieces
here
really
are
life,
saving
efforts
and
to
be
able
to
bring
to
bear
all
of
these
resources.
Is
it
pretty
amazing
and
to
do
it
in
a
way
that
everyone
trusts
the
outcome
and
I?
A
A
You
have
to
make
sure
you're
compliant
you're,
respecting
privacy
and
all
of
that,
and
that
that's
really,
you
guys,
have
done
a
very
amazing
job,
working
through
all
that
and
I
think
that
in
part
is
because
so
many
of
the
clinicians
who
are
on
here
and
I'm
completely
impressed
by
the
roster.
If
someone
goes
to
the
the
website
and
sees
who's
weighing
in
on
this,
it's
it's
not
a.
A
There
are
a
lot
of
students,
but
it's
not
a
student
driven
effort,
so
there's
some
pretty
senior
people
in
the
Ontario
Health
System
working
on
this
and
doctors
and
clinicians,
who
have
who
really
have
been
helpful
and
E
to
making
sure
that
we
do
everything
up
on
the
up-and-up
and
not
break
any
rules
or
regulations.
So.
B
Maybe
I'll
briefly
comment
on
that
last
bit.
I
think!
That's!
That's
a
credibility
challenge
that
I
hope
we're
also
solving
for
is
because
everything
now
we've
put
on
the
site,
whether
it's
a
new
data
source
or
a
new
analysis,
I
signed
off
on
by
experts
in
the
field
like
it's,
not
some
random
person
who
is
looking
at
this,
it's
all
in
the
open
and
you
can
see
all
the
comments
I
get
generated,
but
you
know
our
leadership
you
mentioned
dr.
Ben
Fong
was
a
radiologist,
was
a
clinician,
also
engineering,
background,
amazing
guy
and
then
dr.
B
Laurie,
Rosella
and
Valley
Betazoid,
who
have
different
backgrounds,
who
kind
of
bring
poll
package?
If
you
will
so
Ben.
Had
that
clinical
background
Ally
had
the
data
science
analytics
background
and
Laura
has
the
epidemiology
and
scientific
expertise
and
that
kind
of
that
that
trio
spins
off
on
everything,
so
we
I
kind
of
what
we're
trying
to
do
is
my
not
be
part
of
the
hype.
If
you
will
so
we
want
our
analysis
to
be
actually
scientifically
sound.
So
that's
why
there
is
on
top
of
the
open
community
bits.
B
A
Perfect
so
Guillaume
we're
we're
almost
at
the
end
of
the
hour,
we're
close
to
the
end
of
the
hour
or
we're
at
the
end
of
the
hour,
one
of
those
things
any
last
words
about
where
you're
thinking
of
what
you're
thinking
of
doing
next.
What's
on
the
roadmap
next
for
open
data
hub
and
how
is
my
flattening
project?
What's
what's
up
next
I.
C
Guess
make
what
what
we
will
have
a
look
into
is
maybe
look
at
the
agro
pipelines
because
yeah
you
know,
farbod
is
doing
a
farm
identity,
team,
yeah,
they're,
doing
a
lot
of
things
that
are
related
to
to
the
stream
of
workflows
and
automated
workflows
and
treatment
of
data
and
stuff
like
that.
So
I
guess
that
the
next
thing
we
will
we
look
into
just
to
see
if
it
fits
their
their
their
needs.
There's
also
the
cube
flow
pipelines
that
we
want
to
look
into
trying
to
again
automate
a
little
bit
more.
C
A
With
that
I'm
just
gonna,
thank
both
of
you
very
much
for
all
of
your
efforts
in
this
and
the
entire
house,
my
flattening
team,
which
is
getting
bigger
every
day
and
hopefully
we'll
get
a
little
more
awareness
out
of
doing
this
one
and
get
a
few
more
resources
as
well
and
more
eyeballs
on
the
project
and
continue
to
help
the
province
of
Ontario,
make
their
decisions
based
on
good
actual
data
and
in
real-time
as
close
as
possible.
So
thank
you
again
for
all
of
your
efforts,
fair
Abad
and
guillaume.