►
Description
Sept. 10th, 2021
Moderator: Niall Gaffney
A
Over
the
coming
year,
we
had
a
nice
little
experiment
last
spring,
where
we
started
to
have
more
sort
of
topical
panels
coming
and
and
start
to
talk
with
folks
to
look
not
just
at
sort
of
presenting.
You
know
current
existing
infrastructures,
but
to
talk
about
the
needs
and
and
the
utility
of
different
things
put
together
in
in
ways
to
try
and
sort
of
build
out
some
community
around
these
first
one
that
we
did
involved
jay
and
a
few
other
folks
around
sort
of
the
cyber
security
topic.
A
A
I
think
I
know
most
everybody,
but
it's
probably
a
good
idea
for
us
to
just
do
sort
of
a
round
of
introductions
for
those
of
you
who
don't
know
me,
I'm
niall
gaffney,
I'm
the
director
for
data
intensive
computing.
My
background
is
astronomy
and
astrophysics,
but
largely
around
archival
science
and
and
data
workflows
and
data
analysis.
A
So
I
previously,
prior
to
coming
to
tac
eight
years
ago,
I
was
working
for
the
folks
who
run
the
operations
for
the
hubble
space
telescope
and
a
few
other
facilities
that
are
orbiting
the
earth
about
300
miles
up
and
have
a
lot
of
background
in
just
you
know
how
to
make
data
workable
and
so
that's
sort
of
where
I
come
from,
but
you
know
enjoying
working
in
a
lot
of
other
fields.
Now
taking
the
experiences
I
have
and
learning
new
things.
B
B
Sure
I'm
john
goody,
I'm
the
executive
director
of
the
mgh
pcc,
I'm
the
steering
committee
for
this
merry
band
now
I'll,
just
a
little
connection
niall
my
father
when
he
worked
for
the
navy
was
responsible
for
moving
the
hubble
space
telescope
from
california
to
to
florida
before
it
was
launched
really
a
little
bit
of
history.
Yeah.
A
Well,
they're
doing
that
with
the
web
telescope
by
by
boat
as
well,
going
through
panama,
so
yeah,
and
there
there
may
or
may
not
be
some
extra
security
on
that
one
right
people
hear
the
words
gold-plated
mirror.
Anyway,
that's
cool
all
right
and
of
course,
everything
shifted
here,
but
I'll
go
with
the
next
person
anton
yeah.
B
Hello,
everyone,
my
name
is
anthony
and
I'm
a
senior
hpc
engineer
at
the
innovation
university,
supporting
the
research
computing
infrastructure,
large
cluster,
so
my
background
is
mostly
the
large
data
and
data
transfers,
plus
I'm
leading
the
cyber
security
efforts
to
improve
around
our
instrument.
Thank
you.
C
Hi
christine
kirkpatrick,
I'm
at
the
san
diego
super
computer
center,
I
mean
usually
I'm
at
my
house.
Obviously
my
spare
bedroom,
I'm
an
infrastructure
and
a
data
person
and
I'm
a
pi
at
the
the
west
hub,
among
other
things,
my
personal
research.
These
days
is
in
how
to
make
data
ai
ready
in
an
efficient
way,
maybe
leave
it.
There.
D
Thank
you
now
hi,
I'm
jay,
I'm
professor
yen
in
computer
engineering
at
rit,
rochester
institute
technology,
I'm
also
the
director
of
global
outreach
for
global
sciences
institute
at
rit.
So
thank
you
now
for
talking
about
the
earlier
workshop
panel
thing
that
we
did
back
in
spring.
So
since
then
there
were
a
few
different
things
going
on
around
the
world.
They
include
a
workshop
that
sagar
now
we
organized
in
kde,
and
there
was
another
one
that
was
organized
by
university
of
virginia
indian
university,
including
vaughan
and
george
washington
university.
D
That's
talking
about
data
curation
for
cyber
security,
research
and
education.
There
was
a
few
others,
ai
ml
for
cyber
security
workshops
happening
over
the
summer
time,
and
I
think
that
we'll
continue
to
see
more
and
more
of
that
happening
and
then
going
on.
So
thank
you
for
inviting
me
back
to
this
working
group
and
look
forward
to
continue
contributing
to
to
this.
The
mission.
E
F
G
Hi
everybody
I'm
melissa,
craigan.
I
work
at
sdsc
with
christine,
formerly
head
of
the
midwest
big
data
hub,
I'm
part
of
a
few
other
projects,
I'm
also
working
on
ai
readiness,
but
from
an
organizational
perspective,
and
and
also
looking
at
federal
data
decision
making
in
ai
readiness
as
well.
I
think
that's
all
I'll
say
for
the
moment
I
I
will
later
on.
A
Excellent
looking
forward
to
it
all
right
well
welcome
everybody.
I
know
you
know
it's
a
little
earlier
on
a
friday,
but
it's
still
friday
after
a
week,
a
compact
week
of
zoom
meetings
for
all.
So
thanks
for
joining
today,
just
a
couple
of
things
before
we
start
talking
about
the
the
new
direction
on
all
of
this.
So
we
we
do
want
to
get
as
much
input
from
folks
about
topics
and
and
other
pieces
that
that
would
be
useful
to
for
us
to
understand.
A
You
know
what
what
potential
topics,
what
potential
things
we
could
do,
there's
a
google
form
that
has
been
circulating
for
a
little
while
folks
haven't
seen
it.
Maybe
we
can
send
out
another
reminder:
email
with
that
to
the
list,
but
jeremy
just
went
ahead
and
posted
the
posted.
The
shortened
url
there
in
the
chat.
A
The
very
end
of
zoom
fatigue
on
fridays,
which
I
know
is
real
trust
me,
but
we
want
to
you,
know,
make
it
more
convenient
for
people
and,
as
I
mentioned
to
christine,
make
it
so
that
I'm
the
one
who's
missing
lunch
when
I
come
as
opposed
to
others.
So
you
can't
pick
a
perfect
time
so,
but
the
topic
really
for
today
is
that
we
want
to
hear
from
folks
about
suggestions
or
recommendations
or
just
talk
about
what
sort
of
topics
we
want
to
cover
in
the
coming
year.
A
So
over
the
summer
there
was
sort
of
some
back
and
forth
in
emails,
and
so
we
wanted
to
have.
You
know
something
at
least
set
up
for
october
on
this,
and
I
know
there's
a
lot
of
good
stuff
going
on
around
data
archive,
and
this
has
sort
of
been
the
true
cold
storage
and
real
archiving,
not
not
just
sitting
on
disks
somewhere
but
a
real
preservation.
A
Talk
about
how
how
what's
working,
what's
not
working
and
and
what
we
need
to
do
to
be
able
to
do
a
better
job
of
of
something
that
I
think
people
don't
appreciate
how
complex
it
is
outside
of
the
people
who
actually
wind
up
doing
this
archiving.
It's
not
just
sticking
something
in
a
closet,
so
be
nice
to
hear
from
folks
we're
going
to
do
sort
of
a
follow
up
sort
of
cyber
security
panel.
A
Florence
is
going
to
help
talk
with
with
vaughn
and
some
other
folks
to
sort
of
get
that
going
and
maybe
hear
about
the
pieces
that
jay
was
talking
about
as
well,
so
we
can
touch
base
back
in
that
in
november
and
then
looking
at
december,
they've
got
right
now
sort
of
a
microscopy
panel.
I
know
we've
got
some
interesting
things
going
on
in
many
different
areas
around
around
different
forms
of
microscopy
and
imaging,
which
would
be
a
nice
panel
to
have.
But
I
don't
know
we
don't
haven't.
A
You
know
in
the
this
this
time
scale,
but
but
you
know
moving
out
into
other
sort
of
breakout
areas
and
then
hopefully
reporting
back
to
us
about
what's
going
on
and
keeping
us
informed
on
things.
So
so
with
that,
I
will
stop
droning
on
about
all
this
and
open
up
the
floor
and
since
melissa
already
said
she
had
some
pitches.
Why
don't
I
just
go
ahead
and
toss
it
over
to
you,
melissa.
G
G
Prichard
at
brookhaven
she's
been
working
on
computational
reproducibility
for
a
couple
of
years
now,
and
I
know
that
we've
got
some
other
folks
in
in
our
sort
of
colleague,
colleague
group
here
who
are
working
on
on
reproducibility
as
well,
and
I
was
thinking
a
a
nice
webinar
with
a
couple
of
people.
Doing
computational,
particularly
for
machine
learning.
Ai
would
be
would
be
great
because
it
directly
affects
you,
know:
management
of
software
and
workflows
and
and
even
sometimes
hardware
setup.
A
I
think
both
very
good
topics-
reproducibility,
is
just
such
a
huge
topic,
it's
hard
to
cover
in
an
hour.
It's
just
you
know
just
just
saying
software
reproducibility
is
hard
enough,
but
I
think
that
that
would
be
a
fantastic
one
coming
up
and-
and
I
think
yeah
it
would
be
good
for
us
to
hear
more
about
the
public
access,
repo
and
and
and
how
how
we
can
make
this
most
useful
for
folks.
So
I
think
those
both
be
very
good
topics.
B
B
To
bring
up
in
a
different
domain
I'll
end
up
putting
jim
on
the
spot
jim
in
week,
had
an
email
exchange
about
the
work
you're
doing
with
cask
on
research
priorities,
and
I
think
the
two
of
us
we're
thinking
that
that
might
be
the
risk
for
for
a
panel
session
at
some
point
in
the
first
half
of
next
year.
Is
that
still
striking
was
a
good
idea,
and
if
so,
we
can
elaborate
on
it.
F
F
You
know
how
how
to
do
that
in
a
way
that
perhaps
does
a
good
job,
at
least
at
capturing
the
diversity
of
entities
out
there
who
are
working
on
some
of
these
top
priorities
might
might
be
approaching,
and
this
is
where
it
would
be
great
to
get
the
sort
of
thoughts
from
this
group
as
to
you
know
how
to
how
to
bring
focus
to
something
and,
and
so
just
to
put
a
little
more
toward
this.
So
you
could
maybe
comment
effectively
on
it
is,
is
what
cask
is
doing?
F
Answers
to
this
poll,
and
and
that's
that's
useful
for
us
as
an
organization
representing
these
groups,
because
we're
going
to
put
in
our
fall
meeting
a
little
bit
more
meet
on
these
four
things
by
having
some
working
groups
within
that
meeting,
to
help
us
make
sure
that
we're
we're
we're
all
on
the
same
page
about
what
it
means.
What
these
top
priorities
mean
to
our
member
organizations.
B
F
F
Yeah
it
does,
I
can.
I
can
give
you
the
the
at
least
running
titles,
of
all,
four
compliance
and
cyber
security.
No
surprise
there
tops
the
list,
support
for
team
science,
slash,
end-to-end
workflows,
so
that's
one
one
topic,
and
so
the
challenges
associated
with
supporting
team
science
or
end-to-end
workflows
is
number
two
keeping
staff
skill
sets
sharp
addressing
diversity
needs
among
our
staff.
Retaining
staff
recruiting
staff
is
number
three
number
four
is
trying
to
improve.
F
So
those
are
the
top
five
priorities
again,
like
I
said
those
really
stood
out.
There
are
there's
a
bucket
that
we
have
full
of
other
things
that
are
really
important
priorities
as
well,
but
there
just
wasn't
as
much
agreement
around
those
and
and
we're
going
to
be
treating
those
in
terms
of
our
position.
Paper
is
more
of
these
are
emerging
needs.
These
are
also
important
things,
but
you
know
there
wasn't
as
much
consensus
as
far
as
our
membership
was
concerned
as
there
as
there
was
in
the
the
top
four
that
I
just
mentioned.
A
But
then
maybe
the
microscopy
panel,
we
can
talk,
you
know
I'm
just
thinking.
Maybe
we
could
do
then
have
somebody
or
some
focus
on
you
know.
If
there
are
particular
topics
I
don't
know
if
microscopy
is
probably
not
right,
but
there
are
others
where
team
science
would
come
in
to
play
and
and
talk
about.
You
know
the
shortcomings
and
and
and
what
we
may
want
to
start
to
look
for
proposals
around.
F
B
But
that
does
yeah
jim,
I
think,
take
us
full
circle
to
your
original
suggestion,
which
was
to
have
someone
representing
the
perspective
of
the
large
cast
community
in
certain
areas
where
the
first
one
might
have
been.
The
long-term
storage,
but
by
I
agree
microscopy
is
one
that
fits
squarely
in
the
team
science
domain.
A
Well
and
hearing
you
sort
of
frame
it
that
way
as
well.
I
mean
the
I
you
know.
We
just
finished
up
here
this
four-year
darpa
project
around
synthetic
biology,
which
essentially
you
know
in
the
end.
What
we
wound
up
having
to
do
was
to
make
sure
that
we
captured
the
entire.
You
know
you
called
it
the
onion.
You
know
I
I
think
about
it
as
as
the
circle
of
experimental
science.
A
You
know
there's
intent
and
then
there's
what
happens
and
then
there's
aligning
all
of
that
information
because
nobody
can
ever
agree
on
on.
You
know
how
to
describe
even
something
as
simple
as
water
in
chemistry
experiment,
but
you
know
data
normalization
and
then,
and
then
you
know
actually
then
having
common
products
that
are
useful,
especially
in
an
ai
world,
which
was
what
you
know.
That
was
a
lot
of
regression
going
on.
In
the
end,
those
sort
of
things
would
be
useful
to
sort
of
expose.
A
B
They
didn't
they
ended
up
founding
a
company,
but
but
that
that
notion
and
that
notion
of
this
branch
of
biology
moving
from
research
into
engineering
where,
instead
of
discovery,
you're
actually
trying
to
manipulate
the
world
as
a
both
a
cool
theme
and
and
one
that
fits
totally
in
our
wheelhouse.
I
think.
A
Yeah,
I'd
have
to
make
sure
that
I
mean
it's
darpa
and
it's
not
that
anything
secret,
but
you
know
I.
I
would
have
to
make
sure
that
that
we
can.
You
know
we
can
talk
about
the
details,
but
the
actual
flow,
and
how
we
did
things
are,
is
definitely
something
I
can
talk
to.
You
don't
want
an
astronomer
talking
about
the
biology
trust
me,
but
I
wasn't
suggesting
that
we
do
that,
but.
A
D
A
D
Interrupt
because
the
reproducibilities
of
the
concern,
not
all
the
concerns
on
the
workflow,
I
think
this
is
one
thing
that
I
haven't
heard
a
lot
of
conversation
in
this
group.
So
if
this
is
not
of
interest,
then
don't
worry
about
it.
It's
more
about
you
know
from
the
cyber
security
or
even
espionage
perspective,
that
they
are
more
intellectual,
property,
thefts
or
agents
or
spies.
Really
that
doing
a
lot
of
things
through
this
data.
D
So
there
has
been
conversation
in
open
source
community
and
cyber
security
open
source
community,
particularly
thinking
about
that,
what
cut?
What's
the
trade-off
between
making
everything
open
source
reproducible,
which
is
great,
I'm
academia?
I
like
that.
At
the
same
time,
fbi
agents
is
saying
that
be
careful
and
other
people
from
certain
countries
when
you're
coming
here
to
steal
your
stuff
and
not
necessarily
coming
physically
coming
to
your
domain.
So
so
I
I'm
curious
whether
this
this
group
has
talked
about
whether
the
workflow
right,
the
reproducibility
and
how's.
D
That
trading
off
with
concerns
from
let's
say
fbi
is
that
of
interest
for
this
group
or
not,
and
as
a
researcher
I
I
am
curious
about
it
right.
So
I
won't
put
my
stuff
on
open
source
domain.
I
want
to
put
the
data
there.
I
want
to
put
the
metadata
there
and
I
won't
have
a
workflow
for
my
student.
That
works,
but
am
I
concerned
about
someone's
going
to
knock
on
my
door
saying
that
hey
your
process
is
not
appropriate.
D
E
So,
let's
not
let's
not
confuse
open
source
and
reproducibility
right,
so
reproducibility
is
that
you
can
give
someone
else.
Your
results
or
the
way
that
you
did
it
and
they
can
do
the
same
thing
right,
open
source
means
that
I
let
the
whole
world
see
that.
So
I
think
reproducibility,
even
if
you're
not
going
to
open
source,
is
still
important
because
whoever
you're
collaborating
with
you
know
whether
it's
the
business
or
whether
you're
other
collaborating
with
other
researchers.
E
You
still
need
to
be
able
to
have
that
element
of
reproducibility
and
understanding
of
how
you
got
the
results
that
you
got
so
that
someone
else
could
possibly
do
it.
So
I
I
do.
I
do
see
that
concern
though
jay
throughout
throughout
the
industry.
Like
nobody,
none
of
my
customers
want
me
to
open
source
their
their
ai
that
we're
doing
for
cyber
security.
It's
pretty
hush.
A
F
And
it's
it's
not
always
in
a
way
right
that
we
expect.
For
example,
we've
worked
with
very
large
companies
developing
code
that
they
were
explicit
about
making
public
as
rapidly
as
possible,
which
was
interesting
right.
Why?
Why
would
you
do
that?
And-
and
the
answer
is
you
know
getting-
that
into
the
public
domain-
serves
their
purpose
of,
not
being
you
know,
in
a
position
of
paying
royalties
on
something
that
someone
later
actually
captured
and
made
into
a
proprietary
package,
and
so
we've
had.
F
You
know
interesting
engagements
with
private
sector
that
have
both
involved.
You
know
really
keeping
things
secure
to
having
a
very
accelerated
path
to
making
them
public
more
so
than
actually
some
of
our
academic
colleagues,
who
kind
of
want
to
make
sure
that
the
publication
gets
out
there.
First
they're,
often
big
into
the
pre-publication,
to
make
sure
that
they
don't
again
pay
royalties
to
someone
who
makes
it
proprietary
later.
D
Jim,
I
think
that's
a
very
good
point,
so
I
I
thank
you
for
all
of
you
sharing
that
and
jim
sharing
that
the
story,
I
think
a
lot
of
this
is-
will
be
a
good
panel
conversation
in
my
opinion
that
helps
researchers,
practitioners
to
think
about
what's
their
role.
I
think
this
go
back
to
the
workflow
workflow,
not
just
about
data,
but
also
about
source
code.
D
What
what
a
good
practice
is
to
avoid,
in
my
personal
opinion,
not
lawyer
or
fbi
agent
right,
but
in
my
personal
opinion,
that
sharing
it
out
quickly
is
a
good
point,
because
I
think
overprotective
of
a
lot
of
these
things,
I'm
not
talking
about
private
personal
information.
Talk
about
protected
of
of
some
of
these
algorithms
or
innovation.
Advances
is
actually
making
it
harder
for
the
good
guys
to
make
it
easier
for
the
bad
guys
following
the
law
and
not
to
do
anything
about
it.
F
F
There's
that,
beginning
stage
of
you
know
getting
the
data
from
the
instrument
you
know
to
to
either
researchers
I
mean
to
researchers
in
some
form
that
they
can
begin
to
do.
Analyses
is
a
whole
subject
that
we
spend
a
lot
of
time
on
I'm
sure
everyone
here,
looking
at
various
aspects
of
technology
and
and
compliance
around,
and
so
then
there
are
other
issues
in
terms
of
data
sharing
and
so
forth
that
get
into
some
of
these
compliance
and
security
issues.
I
wonder
what
I'm
getting
at
is.
B
A
A
A
A
You
know
workflow
and
metadata
normalization,
which
I
think
is
you
know,
that's
that's
a
big
deal
here
for
for
data
sharing
and
you
know,
we've
had
many
people
talk
about
individual
systems
to
do
that,
but
to
have
best
practices,
exposed
or
or
or
ways
to
try,
and
do
you
know
a
better
job
of
not
having
to
reinvent
the
wheel?
Every
time
would
be
good.
A
F
That
that's
a
really
good
way
to
look
at
it.
Denial,
I
think,
like
maybe
just
one
thing
would
be
to
try
to
identify
the
challenges
associated
around
around
data
management
with
these
end-to-end
workflows
or
the
team
science
that
we're
addressing,
because
that's
gonna,
that's
gonna
lead
to
things
like
reproducibility
and
maybe,
as
as
a
as
a
topic,
it's
gonna
yeah.
A
No,
no,
no,
I
agree
yeah
and
then
I
I
the
the
other
ones
that
sort
of
resonated
with
sort
of
this.
This
openness
versus
privacy
versus
intellectual
property.
Nobody
said
that,
but
you
know
ip
is
important
and
that's
exactly
what
what
carolyn's
talking
about
with
those
machine
learning
models.
Nobody
wants
to
give
up
the
ip
of
what
they
are,
but
you
know
I
think
those
are
important,
the
the
tug
and
pull
of
that
and
how
you
can
do
that
you
know
have
those
work
together
in
an
environment.
A
A
I
don't
know
that
that
might
be
an
interesting
one
to
do
on
its
own,
but
then
getting
into
you
know
if
we
go
through
workflows
and
all
these
things
and
then
finally
get
into
what
we're
really
talking
maybe
talking
about,
is
you
know
how
do
we
get
this
whole
system
working
in
a
distributed
fashion?
How
do
you
do
sort
of
this
big
data
sharing
and
have
then
things
like
ai
and
machine
learning
come
into
play?
B
B
But
we
have,
I
think,
we've
established
that
we
have
a
target
rich
environment
which
is
great
yeah.
I
think
we've
named
more
than
five
great
panels
and
I
don't
know
if
we
have
time,
but
if
we
were
able
to
do
some
winnowing,
which
I
think
was
where
you
were
going
now,
also
yeah
to
topics
that
we
could
then
go
recruit
speakers
on.
A
C
A
If
you
could
just
jeremy,
can
you
link
in
here
in
a
you
know,
just
let's
get
a
google
doc
so
that
we
can
all
throw
in
pieces
of
our
notes
and
maybe
start
to
asynchronously
come
up
with
some
sort
of
I'll
come
up
with
some
sort
of
schedule.
I
will
I
will
go
ahead
and-
and
you
know
put
one
in
as
being
I
guess
I
could
do
it.
A
A
I
will
also
say
we
are
still
looking
for
panelists
for
the
november
and
december
meetings.
So
if
people
I
I
will
yeah
we,
I
can
put
those
in
there
as
well.
If
people
have
suggestions
for
other
folks
on
that,
but
we
can
we
can
continue
to
work
on
those
a
little
bit
as
we
go
forward,
but
you
know
go
see
if
we
can
get
a
spring
of
of
meetings
going
through,
and
I
would
propose
that.
F
A
F
There
is
a
timing
thing
here
where,
for
example,
cast
will
be,
you
know,
cask
is
developing
this
position
paper
or,
and
one
of
the
items
is
specifically
around
what
we're
talking
about.
We
we
could
put
together.
I
could
commit
to
one
of
those
two
meetings,
probably
the
december
meeting,
to
sort
of
bring
the
bring
a
panel
together
to
at
least
frame
the
data
challenges
related
to
end-to-end,
workflows,
team
science
and
then,
and
then
that
way
we
would.
You
know
that
would
be
a
larger
framing
issue.
F
A
Yeah,
no,
so
what
I
was
going
to
do
is
I
was
going
to
go
ahead.
Let's
just
make
the
dock
be
the
schedule-
and
here
you
know,
we've
got
november
december
october
november
december,
already
sort
of
burned
out
and
I'll
take
a
crack
at
flushing
out
the
spring,
and
then
you
all
can
throw
darts
at
it
and
tell
me
where
I'm
wrong
and
start
suggesting
folks.
That
would
be
good
to
talk
for
talk
to
these
panels
and
go
from
there.
A
Sure,
no,
I
was
going
to
do
that.
The
top
of
it
will
be
sort
of
the
bullpen.
You
know
we'll
do
the
scrum
style,
perfect,
wonderful,
so
yeah
we'll
have
we'll
have
the
bolt
band
ready
to
go
and
then
we
can.
You
know
if
other
people
think
that
I've
gone
wrong
or
that
there
are
other
topics
to
add.
We
can
just
do
it
that
way:
yeah
and
yeah.
In
fact.
Maybe
this
is
something
that
we
should
have
as
just.
A
C
I
would
just
have
a
meta
recommendation
that
we
make
sure
that
our
different
ideas
are
communicated
in
a
way
that
can
bring
in
people
who
aren't
aware
they
might
be
interested
like
the
microscopy
is
a
good
example
that
would
be
really
easy
to
advertise.
So
it's
just
the
same
old
people
who
already
know
about
it
versus
saying
you
know
this
exposes
issues
of
large
scale
data
and
you
know
high
data
volumes
or
for
whatever
we
want
to
say.
A
No,
I
completely
agree,
and
so
maybe
that's
something
that
we
ought
to
capture
in
here
as
well
is
is
marketing.
You
know,
where
else
should
we?
You
know
here's
a
topic
for
february.
You
know
what
other
places
should
we
just
send
out
feelers
so
that
people
might
be
interested
in
coming
to
the
meeting
and
then.
B
C
The
second
thing,
though,
is,
I
think
we
also
need
to
hammer
home
the
the
times
when
we
represent
something
that's
not
being
discussed
and
widely
in
other
venues
right.
The
classic
example
is
cyber
security
as
a
big
data
challenge
right
and
so
maybe
also
draw
that
out
as
well
to
help
people
look
more
towards
this
series.