►
From YouTube: Education & Workforce WG: Developing the Workforce for Data Science at the National Library of Med.
Description
July 2022
Presenter: Lisa Federer
Institution: National Library of Medicine
A
There
is
a
lot
of
content
to
cover
here,
so
I'm
going
to
go
relatively
quickly
through
the
slides,
but
I'm
happy
to
get
into
any
more
specifics
about
any
of
the
aspects
of
our
program
that
you'd
like
me
to
delve
a
little
bit
more
into
there.
We
go
so
I
just
want
to
situate
this
a
little
bit
within
sort
of
our
organization's
goals.
A
One
aspect
of
what
we're
working
under
is
the
nih
strategic
plan
for
data
science,
which
has
five
overarching
goals
for
how
nih
will
sort
of
modernize
our
data
ecosystem
and
enhance
capacity
for
doing
data.
Science
work
and
one
of
those
overarching
goals
is
enhancing
the
workforce
for
biomedical
data
science.
A
They
did
a
lot
of
work
in
the
initial
planning
phase
to
sort
of
get
a
sense
of
what
we
needed
as
an
institution.
They
did
interviews
with
a
number
of
our
staff.
They
developed
a
transformation
roadmap
and
they
developed
some
personas
and
use
cases
that
I'll
talk
a
little
bit
more
about
to
help
people
identify
training
that
was
appropriate
for
them.
A
A
And
then
we
culminated
the
year
with
a
data
science
open
house
that
I'll
also
talk
a
little
bit
more
about
a
couple
of
other
sort
of
activities
that
were
included
in
this
year-long
data.
Science.
Training
program
was
the
development
of
course,
catalog
a
data
science,
readiness
survey
and
then
the
development
of
data
science
training
plans
that
I'll
also
dive
a
little
bit
more
into,
which
were
again
designed
to
help
people
get
connected
with
training
that
was
most
suitable
for
their
sort
of
level
of
expertise
and
their
particular
role.
A
So
I
mentioned
these
personas
that
the
contractors
developed
these
were
sort
of
profiles
of
various
different
ways
that
people
might
engage
with
data
or
do
data
science
in
their
work.
Recognizing
that
you
know
we
are
a
large
institution,
we
have
1700
staff
and
contractors,
and
people
have
different
levels
of
expertise
in
data
science,
and
they
also
have
different
sort
of
needs
for
what
they
might
do
with
data
science
in
their
work.
So
it's
really
not
a
one-size-fits-all
approach
to
data
science,
training
and
workforce
development.
A
It's
this
recognition
that
people
are
starting
from
sort
of
a
different
starting
line
and
they
have
a
different
finish
line.
So
these
eight
different
skill,
development
profiles
or
personas
were
designed
to
be
something
these
aspirational
profiles
that
at
least
one
of
these
would
sort
of
resonate
with
everyone,
and
they
would
choose
that
skill
development
profile
as
a
way
to
sort
of
guide
their
training
and
their
development.
A
The
data
science
readiness
survey
was
sort
of
how
we
paired
people
with
the
training
that
would
help
them
get
to
that
aspirational
state
in
their
skill
development
profile.
So
people
completed
they
selected
the
skill
development
profile
that
resonated
with
them.
They
completed
a
self-evaluation
of
their
current
data,
science,
skill,
proficiencies
and
then,
depending
on
which
profile
they
selected.
They
may
do
one
or
both
of
these
practical
assessments
that
were
you,
know,
sort
of
like
actual
tests
of
your
data
science,
knowledge
this
pairing
of
the
skill
development
profile.
A
With
this
data
science,
readiness
survey
essentially
allowed
us
to
do
sort
of
a
gap
analysis
for
each
individual.
So
what
you
see
here
on
the
screen
is
this
sort
of
visualization
of
the
difference
between
a
hypothetical
person's
current
skill
developer
skill
profile,
what
they
know
already
coming
into
this
and
what
they
would
need
to
know
to
get
to.
You
know
full
performance
for
the
skill
development
profile
they
had
selected.
So
in
this
example,
this
person
maybe
has
selected
a
profile
where
they
need
to
know.
A
You
know
quite
a
lot
about
data
mining
and
integration,
but
they're
only
at
a
basic
level
as
they
are
coming
into
this
program.
So
the
the
sort
of
gap
here
between
this
basic
and
the
full
performance
level
for
data
mining
and
integration,
they
would
bet
then
be
given
a
set
of
courses
from
the
course
catalog
that
would
get
them
from
that
point.
A
A
to
point
b,
so
each
person,
each
member
of
the
staff
who
completed
one
of
these
skill
development
or
I'm
sorry,
data
science,
readiness,
surveys
and
selected
a
profile
would
then
get
this
sort
of
customized
set
of
courses
that
they
could
take
to
get
them
from
their
current
level
of
expertise
to
what
was
sort
of
outlined
as
the
expertise
for
the
skill
development
profile.
They
selected
this
data
science.
Readiness
survey
also
gave
us
an
opportunity
to
understand
sort
of
institution-wide
where
we
were
with
our
proficiencies.
A
These
10
different
areas
are
were
identified
by
the
contractor
as
the
sort
of
set
of
proficiencies
for
data
science
and
again,
this
sort
of
gave
us
a
view
of
where
we
had
a
lot
of
expertise
and
where
we
maybe
didn't
have
as
much
expertise
and
where
we
might
want
to
think
of
providing
opportunities
for
the
institution
I
mentioned
as
well.
The
data
science
fundamentals
program
there
were
some
staff
that
you
know
did
want
to
dive
more
deeply
into
skills
and
have
an
opportunity
to
apply
those
to
their
work.
A
So
this
was
an
intensive
10-week
program
in
a
collaboration
with
general
assembly
that
gave
25
federal
staff
the
opportunity
to
develop
those
skills
more
deeply.
So
this
was
something
a
lot
of
people
were
interested
in,
but
we
have
limited
spaces,
so
we
had
a
number
of
different
ways
that
we
sort
of
selected
people
for
participation
in
that
program.
A
This,
like
I
said,
was
a
very
intensive,
deep
dive
into
data
science
and
was
rounded
out
with
a
capstone
project
that
allowed
people
to
actually
put
some
of
this
work
into
practice,
and
it
was
very
exciting
to
see
the
different.
You
know
projects
that
people
put
together
many
related
to
their
work
at
nlm,
so
there
were
some
really
practical
outcomes
of
that
training.
A
We
ended
the
first
year
with
a
sort
of
celebration
of
all
that
we'd
done
and
all
that
we'd
accomplished.
This
was
back
in
2019
when
we
could
still
get
together
in
big
groups
and
celebrate.
So
it
was
a
sort
of
a
half
day
celebration
of
you
know
the
data
science
training
program
featuring
things
that
people
had
accomplished
during
that
year.
We
had
poster
sessions.
We
had
you
know
food
to
encourage
people
to
get
together,
and
you
know
talk
to
their
colleagues
and
learn
more
about
what
all
everyone
is
up
to.
A
So
I'm
not
going
to
go
through
all
of
this,
but
we
had
a
very
impactful
first
year
of
the
program
reaching
a
lot
of
different
staff.
We
had
people
completing
lots
of
different
training
opportunities,
so
it
was
very
exciting
and,
as
we
sort
of
rounded
out
that
first
year,
the
question
was
what
next
people
were
interested
in
continuing
and
particularly
interested
in
exploring
opportunities
to
apply
some
of
what
they
learned
to
their
work.
A
We
offered
a
second
round
of
the
data
science
fundamentals
course,
and
we
also
began
a
data
science
mentoring
program
that
again
was
designed
to
help
give
people
opportunities
to
put
actually
into
practice
the
things
that
they'd
learned.
We
also
were
sort
of
realizing
that
people
needed.
You
know
different
sort
of
levels
of
engagement.
Sometimes
people
had
you
know
a
lot
of
time
that
they
could
put
in
to
something,
like
you
know,
60
hours
over
10
weeks,
for
the
data
science
fundamentals,
but
not
everyone
has
that
time
to
dedicate.
A
A
The
mentorship
program
that
we
did
ended
up
being
a
little
bit
different
than
what
we
had
expected.
Our
applications
were
open,
starting
in
february
end
of
february
2020
and
wrapping
up
on
march
17th
2020,
and
so
you
may
recall
that
date
as
being
right
around
the
time
that
many
of
us
moved
to
a
full,
fully
remote
telework
sort
of
situation
at
the
start
of
covid.
A
So
we
did
not
get
the
full
round
of
mentors
and
mentees
that
we
were
hoping
for,
because
I
think
a
lot
of
people
were
understandably,
you
know
having
other
things
on
their
mind.
At
the
time-
and
we
did
have
to
do
a
lot
of
sort
of
moving
and
switching
up
of
things
to
accommodate
this
as
a
fully
remote
and
virtual
program,
as
opposed
to,
inter
in
person
mentoring.
A
But
I
think
we
did
a
really
good
job
of
of
doing
that,
and
so
we
ended
up
with
four
mentor
mentee
pairs
that
worked
together
on
a
again
sort
of
a
capstone
project,
and
this
just
sort
of
gives
a
overview
of
sort
of
the
time,
commitment
and
expectations.
So
we
did
have.
It
was
very
much
a
structured
mentoring
program
where
we
had
activities
that
the
mentors
and
mentees
came
to
together,
all
virtual.
A
A
We
featured
what
we
called
a
spring
fling
where
we
showcased
some
of
the
work
that
people
had
done.
The
mentoring
participants
got
to
share
their
work,
people
that
had
been
in
that
second
round
of
the
fundamentals
talked
about
some
of
their
capstone
projects.
So
an
opportunity
to
you
know
hear
a
little
bit
about
what
we
were.
What
people
were
doing.
A
We
also
continued
this
in
the
winter
period,
with
a
winter
webinar
series
that
focused
on
some
different
aspects
of
data
science
with
presentations
from
our
staff,
so
that
rounded
up
our
our
second
year,
and
I
will
say
that
you
know
we
sort
of
put
a
pause
on
things
as
we
were
doing.
Some
evaluation
of
what
we
had
done
and
where
we
go
with
this
in
the
future
is
kind
of
an
open
question
at
this
point.
A
But
I
am
happy
to
share
any
more
about
any
of
the
particulars
that
we've
done
with
any
questions
that
you
have,
and
I
just
want
to
recognize
the
rest
of
the
team
who
worked
on
this
project.
We
had
representation
from
each
of
the
divisions
at
nlm
to
make
sure
that
we
were
really
you
know
getting
opportunities
that
spoke
to
everyone,
so
I
will
stop
there
and
I
think
if
there's
any
questions
happy
to
take
those.
B
B
A
Yeah,
it's
not
something
that
we
have
publicly
posted,
but
I'd
be
happy
to
share
it
with
the
caveat
that
they
did
put
this
together
a
couple
years
ago.
So
it's
not
something
that
we've
been
sort
of
like
actively
maintaining,
so
it's
very
possible
that
it's
not
fully
up
to
date
anymore.
Some
of
the
the
opportunities
listed
in
the
catalog
are
nih
specific,
but
a
lot
of
them
are
things
that
would
be.
B
A
B
That'll
work,
that'll
work,
any
other
questions
or
comments
for
lisa.
In
her
presentation,
I
had
a
question
about
what
level
of
time
commitment
on
the
team's
part
your
part
and
the
other
parts
of
the
nlm
team
did
it
take
to
you
know,
work.
How
much
was
it
on
your
side
and
how
much
was
on
the
contractor's
side
to
be
able
to
pull
out
this
across
the
whole
institute.
A
Yeah,
I
would
say
the
contractors
definitely
did
a
lot
of
the
heavy
lifting
we
had
a
large.
I
would
say,
probably
like
five
to
ten
people
at
various
different
times
of
the
contractors
working
on
this.
So
that
was
a
significant
time
commitment
on
their
part,
but
it
was
also
a
significant
time
commitment
on
the
part
of
the
nlm
team
and
one
of
the
things
that
we
had
actually
talked
about
as
a
team
is
sort
of
at
the
end
of
that
second
year.
A
If
we
were
going
to
be
continuing
this,
it
was
not
really
feasible,
for
you
know
all
of
us
to
sort
of
continue
doing
this.
On
top
of
our
you
know,
day
jobs.
If
you
will
so
it's
something
that
if
we
had
or
if
we
do
when
we
do
continue
moving
forward,
it's
really
something
that
we
felt
as
a
team
that
it
needs
a
you
know
an
fte
dedicated
to
doing
this
work.
A
B
B
A
Others
were
people
that
sort
of
came
in
with
a
more
advanced
data
science,
knowledge
because
that's
like
you
know,
part
of
what
they
do
for
their
work.
So
we
had
a.
It
was
a
pretty
diverse
group
of
of
mentors
with
different
sort
of
levels
of
expertise
and
areas
of
expertise,
and
then
we
paired
those
with
mentees
that
you
know
we
hoped
would
it
would
be
sort
of
a
good
fit
in
terms
of
what
the
mentee
wanted
to
learn
and
what
the
mentor
had
to
offer.