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From YouTube: Visual graph tools for weaving together data to coordinate decentralized research - Martin Karlsson
Description
Lateral (Discourse Graphs w/ Ceramic) presented by Martin Karlsen at IPFS Camp 2022 in Lisbon, Portugal - https://2022.ipfs.camp/
A
A
And
if
you
consider
what
you
can
do
with
tools
being
built
on
ipfs
and
things
like
that,
how
do
you
increase
awareness
of
those
capabilities
and
really
share
the
learnings
and
the
the
different
information
that's
generated
there
so
before
I
start
that
I
want
to
do
a
quick,
just
show
of
hands
to
get
a
sense
of
the
audience
so
and
also
to
get
everyone
back
in
the
mood
after
the
break.
So
how
many
of
you
here
are
developers
just
a
show
of
hands?
A
Okay,
and
how
many
of
you
here
consider
yourself
to
have
a
research
or
science
background
all
right
and
then
the
final
question
and
most
important
one?
How
many
of
you
have
ever
done?
A
literature
review?
Okay,
great!
So
the
reason
I
ask
that
is
because
it's
Central
to
our
approach
to
solving
this
question.
So
what
I'm
going
to
be
talking
about
today
is
this
practical
path
to
have
a
visual
web3
native
graph
to
weave
together
historical
in
progress
and
future
data,
and
we've
built
this
on
top
of
Ceramics.
A
So
I
guess,
you
could
say
we're
somewhat
abstracted
up
from
ipfest,
but
for
any
developers
out
there
we'll
also
share
some
of
our
experience,
doing
that
so
before
I
dive
into
that
I'll
first
start
a
little
bit
of.
Why
are
we
here
at
all,
so
this
DSi?
Why
I'm
so
excited
about?
It
is
because
it's
helping
us
really
get
a
new
momentum
to
really
solve
this
long-standing
problem,
because
the
world
today
for
researchers
is
quite
a
harsh
one.
A
There's
in
Academia
postgraduates
have
a
high
rate
of
depression
much
higher
than
the
norm,
for
example
within
organizations
you
have
the
traditional
challenges
of
keeping
track
of
work,
training,
people
and
ensuring
learnings
and
findings
are
shared
and
I.
Think
fundamentally
for
society.
It
feels
like
there's
a
disconnect
between
societal
needs
and
what
the,
what
what
really
we
would
be
able
to
do
as
researchers
if
we
had
a
better
Loop
between
society
and
those
researchers,
and
so
that's
why
we
are
very
passionate
about
creating
these
new
coordination
mechanisms.
A
Now
zooming
in
a
little
bit
to
the
topic
of
of
today.
One
of
the
key
aspects
that
we're
helping
with
is
removing
these
blockers
to
the
efficient
use
of
knowledge
and
data,
because
within
science
and
within
other
areas,
there
are
a
few
of
these
tendencies
that
I
think
have
been
mentioned
earlier
as
well,
where,
for
example,
you
have
people
working
in
silos,
and
you
know
the
typical
thing
being
you're
at
some
conference
a
year
later
and
you
meet
someone
and
you're
like
really
you're.
A
Also
doing
this,
it
would
have
been
great
to
know
that
12
months
ago,
you
also
have
the
issue
of
the
willingness
to
share,
because
in
science,
especially
people
might
be
working
on
something
and
they
feel
very
protective
over
what
they're
creating.
They
also
can
feel
insecure
about
how
well-baked
it
is,
and
so
this
is
another
thing
that
can
hinder
actually
sharing
data.
So
you
need
to
create
a
space
that
resolves
that
and
then
there's
also
just
the
awareness
of
what
tools
and
data
is
available.
A
It
basically
translates
literature
reviews
into
a
global
decentralized
coordination
graph,
and
when
you
combine
that
with
decentralized
storage
mechanisms,
you
get
an
extremely
exciting
mechanism
for
how
you
can
then
collect
the
questions
that
we
need
solved.
The
different
claims
that
our
people
are
coming
with
for
how
to
solve
them
the
evidence
for
that,
so
that
people
can
learn
from
others
more
efficiently
and
accelerate
our
progress.
A
It
also
importantly
means
that
in
today's
world,
you've
probably
seen
policy
makers
not
always
so
up
to
date
on
everything
and
I
think
this
is
another
thing
that
we
hope
to
solve,
because
by
putting
this
information
in
these
graph
representations,
you
can
create,
like
a
a
trail
like
a
breadcrumb
trail
from
the
high
level
question.
That
is
the
policy
makers
thinking
through
to
the
Deep
science,
and
this
means
that
you
can
have
really
cool
mechanisms
that
help
inform
policy
makers
and
help
also
citizens
hold
them
accountable.
A
And
so
this
is
the
graph
element
that
I'll
be
talking
about
today,
and
this
is
what
we're
building
on
ceramic,
and
so
one
key
element
to
discuss
for
before
I
now
jump
into
the
demo
is
discourse
graphs.
So
discourse
graphs
is
a
data
schema
that
was
developed
by
Joelle
Chan,
funded
by
protocol
Labs
shout
out
to
Sylvia
and
Kerala
from
the
network
research
team.
A
The
nodes
in
that
data
schema
are
the
question
that
you're
looking
to
answer
the
claims
that
are
being
made
to
inform
that
question
the
evidence
that
supports
or
opposes
those
claims
and
then
the
source
of
that
evidence.
So
it's
a
super
simple
data
schema
to
follow,
but
it's
very
flexible
to
a
lot
of
different
scenarios.
That's
why
we're
so
excited
about
it.
A
But
it
also
helps
you
really
quickly
find
evidence
because
it
chops
up
all
your
papers
into
paragraphs
and
subsections
and
lets
you
search
by
keyword
and
when
you
find
a
result,
you
like
it
helps
you
find
similar
results
across
the
other
documents.
So
you
can
really
quickly
gather
evidence,
but
importantly,
what
it
also
lets
you
do
is
then
export
it
as
a
discourse
graph.
A
So
you
get
this
very
exciting
New
World
for
bringing
together
researchers
using
this,
and
the
other
thing
to
mention
is
that
this
is
now
a
representation
of
things
as
they
are
today.
So
the
evidence
is
from
literature,
but
the
next
step
that
we'll
be
working
on
is
essentially
that
you
can
then
bring
in
evidence
nodes
that
are
living
evidence
nodes.
So,
for
example,
if
I
were
to
send
a
giant
space
mayor
up
to
Mars
and
start
actually
doing,
the
terraforming
I
could
have
a
live
feed
of.
A
Is
my
data
actually
matching
up
to
what
I
assumed
in
my
theory-
and
these
are
the
types
of
things
that
you
then
have
this
living
map
of
the
decisions
that
you're
making?
How
well
that's
working
and
so
on,
and
also
it
means
that
it
opens
up
for
if,
if
I
need,
new
information,
I
can
actually
request
that
directly
from
the
graph.
A
And
so
this
is,
for
example,
why
we're
very
excited
in
working
with
lab
Dow
and
what
they're
building,
because
you
could
actually
say:
here's
a
research
Gap
and
make
a
call
directly
to
the
lab
exchange
and
get
data
back
from
them
instantly,
and
so
these
are
all
the
pieces
that
are
coming
together
to
have
a
massive
acceleration
effect
for
the
researcher,
workflow
and
so
yeah.
That's
that's
I.
Think
the
the
key
point
of
this
demo
I
think
also
feel
free
if
you're
a
developer
and
interested
in
ceramic.
A
Please
come
and
talk
to
us,
because
they've
just
released,
compose,
DB
and
I.
Think
an
important
theme
in
this
conference
as
well
is
like
the
ease
at
which
you
can
now
build
in
this
environment
is
wild.
The
speed
at
which
you
can
do
things
is
incredible
because
of
all
the
layers
that
many
of
the
people
at
this
conference
have
helped
build,
and
so
thank
you
for
that,
as
well
from
our
perspective
and
yeah.
So
this
is
just
to
reinforce
that
here,
for
example,
with
labdo.
A
This
ability
to
call
for
an
experiment
could
then
be
as
simple
as
an
additional
node,
and
this
is
very
much
what
what
we're
now
aiming
to
facilitate
is
that
we
see
a
lot
of
different
tools
that
you
can
layer
on
top
of
this
graph.
So
with
radical,
for
example,
they
have
drips
which
lets
you
split
funding
and
have
programmatic
ways
of
doing
that.
A
We're
setting
up
a
project
where
that'll
be
about
coordinating
Labs
that
are
looking
at
Consciousness
from
different
perspectives,
because
it's
a
very
interesting
but
very
complex,
multi-disciplinary
space.
So
these
tools
can
let
us
get
those
multiple
perspectives
to
actually
move
forward
on
a
deeper
understanding
there
and
another
one
that
we're
looking
at
is
essentially
energy,
because
this
is
a
very
top
of
Mind
issue,
of
course,
for
the
world.
A
So
anyone
here
who's
interested
in
this
space
or
have
tools
that
they're
building
that
they'd
like
to
make
interoperable
a
key
theme
all
that
data
that
lives
on
ceramic,
that
is
then
public
and
not
encrypted,
is
available
to
play
with
as
it
gets
generated.
So
you
know
you
could
think
of
a
dedicated
interface,
just
the
questions,
a
dedicated
interface,
just
a
sourcing
particular
types
of
evidence,
all
speaking
to
the
same
data
model,
with
all
participants
feeling
comfortable
with
a
decentralized
network
that
we're
all
writing
to.
So
that
is
my
talk.