hi thanks very much for coming I’m gonna tell you a little bit about one thing
tell you a little bit about one thing
tell you a little bit about one thing that’s next for science and I’m gonna
that’s next for science and I’m gonna
that’s next for science and I’m gonna use an example from my own research but
use an example from my own research but
use an example from my own research but what I’m talking about is broadly
what I’m talking about is broadly
what I’m talking about is broadly applicable to science writ large I’m
applicable to science writ large I’m
applicable to science writ large I’m gonna talk to you about very big data
gonna talk to you about very big data
gonna talk to you about very big data that’s been set free data for my whole
that’s been set free data for my whole
that’s been set free data for my whole career has been big I’ve always had full
career has been big I’ve always had full
career has been big I’ve always had full diskettes now discs now raids when I
diskettes now discs now raids when I
diskettes now discs now raids when I started they were 92 kilobytes and I
started they were 92 kilobytes and I
started they were 92 kilobytes and I filled them then a hundred megabytes
filled them then a hundred megabytes
filled them then a hundred megabytes then 10 gigabytes and now huge amounts
then 10 gigabytes and now huge amounts
then 10 gigabytes and now huge amounts so as physiologists we looked at data in
so as physiologists we looked at data in
so as physiologists we looked at data in the kilobytes to may be megabytes worth
the kilobytes to may be megabytes worth
the kilobytes to may be megabytes worth of data genomics which has done a
of data genomics which has done a
of data genomics which has done a fabulous job both of being big and of
fabulous job both of being big and of
fabulous job both of being big and of sharing and being open works in the
sharing and being open works in the
sharing and being open works in the megabytes thinking a bytes range I’m
megabytes thinking a bytes range I’m
megabytes thinking a bytes range I’m interested in shape I’m interested in
interested in shape I’m interested in
interested in shape I’m interested in the shape of skeletons and the soft
the shape of skeletons and the soft
the shape of skeletons and the soft tissue that surrounds them and those
tissue that surrounds them and those
tissue that surrounds them and those data are huge so they run from the
data are huge so they run from the
data are huge so they run from the gigabytes to terabytes
gigabytes to terabytes
gigabytes to terabytes for single datasets and date has always
for single datasets and date has always
for single datasets and date has always been big relative to our capabilities
been big relative to our capabilities
been big relative to our capabilities but until very very recently data was
but until very very recently data was
but until very very recently data was not free data was locked into little
not free data was locked into little
not free data was locked into little silos in scientists labs
silos in scientists labs
silos in scientists labs where you couldn’t get at them where I
where you couldn’t get at them where I
where you couldn’t get at them where I couldn’t get at them where their
couldn’t get at them where their
couldn’t get at them where their colleagues couldn’t get at them where
colleagues couldn’t get at them where
colleagues couldn’t get at them where their spouses and children couldn’t get
their spouses and children couldn’t get
their spouses and children couldn’t get at them nobody could get at these data
at them nobody could get at these data
at them nobody could get at these data sometimes ten years later they couldn’t
sometimes ten years later they couldn’t
sometimes ten years later they couldn’t get at so I’m gonna talk to you about
get at so I’m gonna talk to you about
get at so I’m gonna talk to you about big morphological data sets the data set
big morphological data sets the data set
big morphological data sets the data set I’m gonna talk about is computed
I’m gonna talk about is computed
I’m gonna talk about is computed tomography scans CT scans first thing I
tomography scans CT scans first thing I
tomography scans CT scans first thing I tell you what a CT scan is which
tell you what a CT scan is which
tell you what a CT scan is which requires some fabulous finger puppetry
behold a photorealistic bird look only
behold a photorealistic bird look only at the screen and you see the bird and
at the screen and you see the bird and
at the screen and you see the bird and it is mysterious to you because you
it is mysterious to you because you
it is mysterious to you because you didn’t see me come in with a bird but
didn’t see me come in with a bird but
didn’t see me come in with a bird but now there’s one but if I freeze my hand
now there’s one but if I freeze my hand
now there’s one but if I freeze my hand in place now look only at the screen and
in place now look only at the screen and
in place now look only at the screen and rotate it through a hundred and eighty
rotate it through a hundred and eighty
rotate it through a hundred and eighty degrees watching just those shadows in
degrees watching just those shadows in
degrees watching just those shadows in your brain you can now not only see that
your brain you can now not only see that
your brain you can now not only see that that wasn’t a bird you can put your
that wasn’t a bird you can put your
that wasn’t a bird you can put your fingers in the same position as my
fingers in the same position as my
fingers in the same position as my fingers were in you can deconvolve those
fingers were in you can deconvolve those
fingers were in you can deconvolve those shadows into the 3d shape of my hand CT
shadows into the 3d shape of my hand CT
shadows into the 3d shape of my hand CT scanning is exactly that except instead
scanning is exactly that except instead
scanning is exactly that except instead of light it uses x-rays so instead of a
of light it uses x-rays so instead of a
of light it uses x-rays so instead of a black and white shadow there’s a
black and white shadow there’s a
black and white shadow there’s a grayscale shadow with the darkest bits
grayscale shadow with the darkest bits
grayscale shadow with the darkest bits being the most radio-opaque and the
being the most radio-opaque and the
being the most radio-opaque and the lightest bits being the least
lightest bits being the least
lightest bits being the least radio-opaque
radio-opaque
radio-opaque so the CT scan generates a 3d volume of
so the CT scan generates a 3d volume of
so the CT scan generates a 3d volume of density
density
density the end result looks like that that is a
the end result looks like that that is a
the end result looks like that that is a piranha a beautiful fish that I scanned
piranha a beautiful fish that I scanned
piranha a beautiful fish that I scanned because I wanted to see its teeth which
because I wanted to see its teeth which
because I wanted to see its teeth which are the business end of the thing right
are the business end of the thing right
are the business end of the thing right that’s what everyone wants to see
that’s what everyone wants to see
that’s what everyone wants to see but the moment I looked at that scan I
but the moment I looked at that scan I
but the moment I looked at that scan I realized I knew something about how it
realized I knew something about how it
realized I knew something about how it lives way over on the side there you can
lives way over on the side there you can
lives way over on the side there you can see two ribs that have been bisected
see two ribs that have been bisected
see two ribs that have been bisected because this is a schooling fish that’s
because this is a schooling fish that’s
because this is a schooling fish that’s really well armed and when they have
really well armed and when they have
really well armed and when they have their little discussions amongst each
their little discussions amongst each
their little discussions amongst each other they lead to serious wounds so
other they lead to serious wounds so
other they lead to serious wounds so these models of skeletons can tell us
these models of skeletons can tell us
these models of skeletons can tell us how fish lives and I’m interested in CT
how fish lives and I’m interested in CT
how fish lives and I’m interested in CT scanning all of the fishes not every
scanning all of the fishes not every
scanning all of the fishes not every individual but every species its 33,000
individual but every species its 33,000
individual but every species its 33,000 species of fish and at conventional CT
species of fish and at conventional CT
species of fish and at conventional CT scanning rates we’re talking about a 45
scanning rates we’re talking about a 45
scanning rates we’re talking about a 45 year project so just about when I hit
year project so just about when I hit
year project so just about when I hit middle age I should be done I plan to
middle age I should be done I plan to
middle age I should be done I plan to take up exercise between now and then
take up exercise between now and then
take up exercise between now and then the killer is this effort should cost
the killer is this effort should cost
the killer is this effort should cost between 3 and 300 million dollars and as
between 3 and 300 million dollars and as
between 3 and 300 million dollars and as much as I deeply love fish and I really
much as I deeply love fish and I really
much as I deeply love fish and I really do I mean I’m deeply fascinated with
do I mean I’m deeply fascinated with
do I mean I’m deeply fascinated with them I couldn’t justify spending those
them I couldn’t justify spending those
them I couldn’t justify spending those kinds of funds on scanning fish
kinds of funds on scanning fish
kinds of funds on scanning fish skeletons so we got a choice we can
skeletons so we got a choice we can
skeletons so we got a choice we can either scan fewer species or we can scan
either scan fewer species or we can scan
either scan fewer species or we can scan faster and I am fundamentally not a
faster and I am fundamentally not a
faster and I am fundamentally not a fewer kind of person
so we raised some philanthropic funds
so we raised some philanthropic funds actually a very large set of donations
actually a very large set of donations
actually a very large set of donations allowed us to buy a micro CT scanner
allowed us to buy a micro CT scanner
allowed us to buy a micro CT scanner that lives just down the hall in my
that lives just down the hall in my
that lives just down the hall in my office and from this scanner I
office and from this scanner I
office and from this scanner I discovered two truths to what should
discovered two truths to what should
discovered two truths to what should have been obvious truths but which to me
have been obvious truths but which to me
have been obvious truths but which to me at the time
at the time
at the time we’re not obvious at all the first of
we’re not obvious at all the first of
we’re not obvious at all the first of these is that all CT scanning
these is that all CT scanning
these is that all CT scanning interrogate s– a cylindrical volume how
interrogate s– a cylindrical volume how
interrogate s– a cylindrical volume how many people here have actually been in a
many people here have actually been in a
many people here have actually been in a CT scanner just give a raise van oh wow
CT scanner just give a raise van oh wow
CT scanner just give a raise van oh wow huge numbers that’s awesome so you’ve
huge numbers that’s awesome so you’ve
huge numbers that’s awesome so you’ve laid there you lay there in a bed and
laid there you lay there in a bed and
laid there you lay there in a bed and either the bed goes into a doughnut or
either the bed goes into a doughnut or
either the bed goes into a doughnut or in some models the donut runs down the
in some models the donut runs down the
in some models the donut runs down the bed that produces a beautiful
bed that produces a beautiful
bed that produces a beautiful three-dimensional image of the offending
three-dimensional image of the offending
three-dimensional image of the offending part of your body what you may not know
part of your body what you may not know
part of your body what you may not know is that everything inside the donut
is that everything inside the donut
is that everything inside the donut everything was scanned you and your
everything was scanned you and your
everything was scanned you and your offending part the bed the air around
offending part the bed the air around
offending part the bed the air around all of it was scanned but we know what
all of it was scanned but we know what
all of it was scanned but we know what air looks like and we know what beds
air looks like and we know what beds
air looks like and we know what beds look like so this was terribly
look like so this was terribly
look like so this was terribly inefficient we really should have piled
inefficient we really should have piled
inefficient we really should have piled at least half a dozen people on there
at least half a dozen people on there
at least half a dozen people on there with you run them all through it once
because everything was getting scanned
because everything was getting scanned so that was the first realization the
so that was the first realization the
so that was the first realization the second realization was more subtle and
second realization was more subtle and
second realization was more subtle and it it turns out to be true that you
it it turns out to be true that you
it it turns out to be true that you don’t want all of the resolution anytime
don’t want all of the resolution anytime
don’t want all of the resolution anytime you take a picture there’s a certain
you take a picture there’s a certain
you take a picture there’s a certain size for those pixels in CT the an
size for those pixels in CT the an
size for those pixels in CT the an analogy is voxels that is volume
analogy is voxels that is volume
analogy is voxels that is volume elements and all of you know when you
elements and all of you know when you
elements and all of you know when you get a new camera what do people ask you
get a new camera what do people ask you
get a new camera what do people ask you oh how many megapixels is that all right
oh how many megapixels is that all right
oh how many megapixels is that all right that’s what they ask the first question
that’s what they ask the first question
that’s what they ask the first question they don’t ask like how much was it do
they don’t ask like how much was it do
they don’t ask like how much was it do you like it that you take an icepick
you like it that you take an icepick
you like it that you take an icepick you know how many megapixels there are
you know how many megapixels there are
you know how many megapixels there are another resolution if you own a CT
another resolution if you own a CT
another resolution if you own a CT scanner that’s the first thing there so
scanner that’s the first thing there so
scanner that’s the first thing there so what’s the resolution second thing they
what’s the resolution second thing they
what’s the resolution second thing they ask is how big a thing can I put in
ask is how big a thing can I put in
ask is how big a thing can I put in there as though there were some universe
there as though there were some universe
there as though there were some universe where they would want to get five
where they would want to get five
where they would want to get five thousand slices per millimeter of an
thousand slices per millimeter of an
thousand slices per millimeter of an entire dead hippopotamus which would be
entire dead hippopotamus which would be
entire dead hippopotamus which would be more data than fit on all the hard disks
more data than fit on all the hard disks
more data than fit on all the hard disks in the United States right now you don’t
in the United States right now you don’t
in the United States right now you don’t want all the resolution so how much do
want all the resolution so how much do
want all the resolution so how much do you want I mean that’s a good question
you want I mean that’s a good question
you want I mean that’s a good question one that we’ve said about to answer we
one that we’ve said about to answer we
one that we’ve said about to answer we looked at a lot of studies and we found
looked at a lot of studies and we found
looked at a lot of studies and we found that it depends totally on what you’re
that it depends totally on what you’re
that it depends totally on what you’re doing with it and so what we had to do
doing with it and so what we had to do
doing with it and so what we had to do was figure out what the worst case was
was figure out what the worst case was
was figure out what the worst case was what what’s the most resolution you
what what’s the most resolution you
what what’s the most resolution you could want and it turns out that the
could want and it turns out that the
could want and it turns out that the most resolution you want for practical
most resolution you want for practical
most resolution you want for practical purposes is about two hundred and fifty
purposes is about two hundred and fifty
purposes is about two hundred and fifty slices across the head so give me a fish
slices across the head so give me a fish
slices across the head so give me a fish I can measure the width of its head and
I can measure the width of its head and
I can measure the width of its head and I can tell you what resolution you want
I can tell you what resolution you want
I can tell you what resolution you want to scan it that means that I can pre
to scan it that means that I can pre
to scan it that means that I can pre print cylinders that I can pack with
print cylinders that I can pack with
print cylinders that I can pack with fish the cylinder on your right is a 30
fish the cylinder on your right is a 30
fish the cylinder on your right is a 30 micron cylinder 33 slices per millimeter
micron cylinder 33 slices per millimeter
micron cylinder 33 slices per millimeter on your left a cylinder that’s larger
on your left a cylinder that’s larger
on your left a cylinder that’s larger it’s a 50 micron cylinder it gives you
it’s a 50 micron cylinder it gives you
it’s a 50 micron cylinder it gives you 20 slices per millimeter and I can just
20 slices per millimeter and I can just
20 slices per millimeter and I can just pack the fish in there this doesn’t work
pack the fish in there this doesn’t work
pack the fish in there this doesn’t work with humans in healthcare it’s an
with humans in healthcare it’s an
with humans in healthcare it’s an innovation I’ve proposed but it turns
innovation I’ve proposed but it turns
innovation I’ve proposed but it turns out that people object dead fishes don’t
out that people object dead fishes don’t
out that people object dead fishes don’t it’s one of the major reasons I work
it’s one of the major reasons I work
it’s one of the major reasons I work with them
the resolution you aim for it depends on
the resolution you aim for it depends on the fish size which means when you bring
the fish size which means when you bring
the fish size which means when you bring hundreds of fish to my lab to scan them
hundreds of fish to my lab to scan them
hundreds of fish to my lab to scan them the first thing I have you do is arrange
the first thing I have you do is arrange
the first thing I have you do is arrange them in size order we then pick the
them in size order we then pick the
them in size order we then pick the specimens that are going to be scanned
specimens that are going to be scanned
specimens that are going to be scanned at a particular resolution and we start
at a particular resolution and we start
at a particular resolution and we start loading up the cylinder we can fit 10 to
loading up the cylinder we can fit 10 to
loading up the cylinder we can fit 10 to 20 specimens in that cylinder and when
20 specimens in that cylinder and when
20 specimens in that cylinder and when we scan it we get this
we scan it we get this
we scan it we get this we have reimagined that staple of the
we have reimagined that staple of the
we have reimagined that staple of the mid week the fish burrito that is
mid week the fish burrito that is
mid week the fish burrito that is somewhere around 20 individual species
somewhere around 20 individual species
somewhere around 20 individual species in a group of sculpins scanned by a
in a group of sculpins scanned by a
in a group of sculpins scanned by a graduate student named Thaddeus boo sir
graduate student named Thaddeus boo sir
graduate student named Thaddeus boo sir Thaddeus came to the lab and and and we
Thaddeus came to the lab and and and we
Thaddeus came to the lab and and and we developed this technique this is the
developed this technique this is the
developed this technique this is the very first fish burrito that was ever
very first fish burrito that was ever
very first fish burrito that was ever scanned I know this because it is
scanned I know this because it is
scanned I know this because it is missing a key innovation that came about
missing a key innovation that came about
missing a key innovation that came about with the second fish burrito that was
with the second fish burrito that was
with the second fish burrito that was ever scanned little radio-opaque labels
ever scanned little radio-opaque labels
ever scanned little radio-opaque labels so that we could tell one fish from
so that we could tell one fish from
so that we could tell one fish from another because it turns out that only
another because it turns out that only
another because it turns out that only fattiest could do this next trick using
fattiest could do this next trick using
fattiest could do this next trick using free software you can then dissect one
free software you can then dissect one
free software you can then dissect one fish out of the burrito reposition it in
fish out of the burrito reposition it in
fish out of the burrito reposition it in some anatomically useful way do this
some anatomically useful way do this
some anatomically useful way do this over and over again and for 1/2 hour CT
over and over again and for 1/2 hour CT
over and over again and for 1/2 hour CT scan what was once an entire PhD
scan what was once an entire PhD
scan what was once an entire PhD dissertation is now the throat clearing
dissertation is now the throat clearing
dissertation is now the throat clearing for chapter 1 we can scan over 700
for chapter 1 we can scan over 700
for chapter 1 we can scan over 700 species per month with a single scanner
species per month with a single scanner
species per month with a single scanner not only that the scanners here it’s
not only that the scanners here it’s
not only that the scanners here it’s really heavy and I’m not letting anybody
really heavy and I’m not letting anybody
really heavy and I’m not letting anybody take it anywhere but the data can be
take it anywhere but the data can be
take it anywhere but the data can be processed off-site
processed off-site
processed off-site best of all the data can be processed
best of all the data can be processed
best of all the data can be processed with free open-source software that’s
with free open-source software that’s
with free open-source software that’s available on every platform Linux Mac PC
available on every platform Linux Mac PC
available on every platform Linux Mac PC whatever you want so we’re talking about
whatever you want so we’re talking about
whatever you want so we’re talking about very very big data over 2,000 species
very very big data over 2,000 species
very very big data over 2,000 species over 4000 specimens scan in the last
over 4000 specimens scan in the last
over 4000 specimens scan in the last year because sometimes you want to know
year because sometimes you want to know
year because sometimes you want to know what happens to the skeleton as
what happens to the skeleton as
what happens to the skeleton as something grows so you scan 20 of the
something grows so you scan 20 of the
something grows so you scan 20 of the same species we’ve scanned over 300
same species we’ve scanned over 300
same species we’ve scanned over 300 families of fishes and as of yesterday
families of fishes and as of yesterday
families of fishes and as of yesterday we’re sitting on 50 6 terabytes of
we’re sitting on 50 6 terabytes of
we’re sitting on 50 6 terabytes of information I can’t tell you how unhappy
information I can’t tell you how unhappy
information I can’t tell you how unhappy I would be if those data were something
I would be if those data were something
I would be if those data were something I had to encompass that I had to
I had to encompass that I had to
I had to encompass that I had to surround and defend from prying eyes the
surround and defend from prying eyes the
surround and defend from prying eyes the best thing about these data is they are
best thing about these data is they are
best thing about these data is they are completely open access anyone can get
completely open access anyone can get
completely open access anyone can get access to all of these data and analyze
access to all of these data and analyze
access to all of these data and analyze it with free software anyone in the
it with free software anyone in the
it with free software anyone in the world anywhere in the world this
world anywhere in the world this
world anywhere in the world this completely democratizes the process of
completely democratizes the process of
completely democratizes the process of dealing with these really critical
dealing with these really critical
dealing with these really critical morphological data points so we’ve
morphological data points so we’ve
morphological data points so we’ve leveraged accessibility to get lots of
leveraged accessibility to get lots of
leveraged accessibility to get lots of people access and learn a lot more than
people access and learn a lot more than
people access and learn a lot more than we ever could we put these things up on
we ever could we put these things up on
we ever could we put these things up on a really neat website called Morpho
a really neat website called Morpho
a really neat website called Morpho source so you can go here and download
source so you can go here and download
source so you can go here and download any of the datasets and we’re already
any of the datasets and we’re already
any of the datasets and we’re already seeing the benefits I got a very
seeing the benefits I got a very
seeing the benefits I got a very apologetic email from a wonderful German
apologetic email from a wonderful German
apologetic email from a wonderful German neurobiologist Michael Hoffman who was
neurobiologist Michael Hoffman who was
neurobiologist Michael Hoffman who was who he was terribly embarrassed to tell
who he was terribly embarrassed to tell
who he was terribly embarrassed to tell me that he had downloaded all of my data
me that he had downloaded all of my data
me that he had downloaded all of my data and because he’s a web wizard and great
and because he’s a web wizard and great
and because he’s a web wizard and great at making surfaces he had taken all of
at making surfaces he had taken all of
at making surfaces he had taken all of my data and taken these slices and
my data and taken these slices and
my data and taken these slices and turned them into 3d printable rotatable
turned them into 3d printable rotatable
turned them into 3d printable rotatable models
models
models and he put them up on a website for
and he put them up on a website for
and he put them up on a website for anybody to download for any purpose and
anybody to download for any purpose and
anybody to download for any purpose and he was very sorry because he wasn’t sure
he was very sorry because he wasn’t sure
he was very sorry because he wasn’t sure I was getting enough credit for it
I was getting enough credit for it
I was getting enough credit for it please do more terrible things like this
please do more terrible things like this
please do more terrible things like this to me this is awesome so now you can go
to me this is awesome so now you can go
to me this is awesome so now you can go to this virtual Museum of Natural
to this virtual Museum of Natural
to this virtual Museum of Natural History and Michael’s put my name
History and Michael’s put my name
History and Michael’s put my name embarrassingly on the front page saying
embarrassingly on the front page saying
embarrassingly on the front page saying you know here’s a bunch of Adam’s fish
you know here’s a bunch of Adam’s fish
you know here’s a bunch of Adam’s fish they’re not my fish they’re everybody’s
they’re not my fish they’re everybody’s
they’re not my fish they’re everybody’s fish the availability of these data
fish the availability of these data
fish the availability of these data allowed a computer Sciences team
allowed a computer Sciences team
allowed a computer Sciences team interested in the concept of extracting
interested in the concept of extracting
interested in the concept of extracting an object from a field of data that was
an object from a field of data that was
an object from a field of data that was completely unknown it inspired them to
completely unknown it inspired them to
completely unknown it inspired them to try their tools on my fish burritos and
try their tools on my fish burritos and
try their tools on my fish burritos and so they produced this thing called topo
so they produced this thing called topo
so they produced this thing called topo angler and they took a job that we were
angler and they took a job that we were
angler and they took a job that we were doing manually over the course of six or
doing manually over the course of six or
doing manually over the course of six or seven hours and dropped it down to a
seven hours and dropped it down to a
seven hours and dropped it down to a 15-minute task because the data were
15-minute task because the data were
15-minute task because the data were there they could have it for any purpose
there they could have it for any purpose
there they could have it for any purpose they wanted so what’s next for science
they wanted so what’s next for science
they wanted so what’s next for science what’s next is a lot more collaboration
what’s next is a lot more collaboration
what’s next is a lot more collaboration a lot more sharing of data more open
a lot more sharing of data more open
a lot more sharing of data more open access data more open access publication
access data more open access publication
access data more open access publication of scientific results so that you’re not
of scientific results so that you’re not
of scientific results so that you’re not sitting there reading some I mean
sitting there reading some I mean
sitting there reading some I mean terrible excuse for science news you can
terrible excuse for science news you can
terrible excuse for science news you can follow a link and get to the scientific
follow a link and get to the scientific
follow a link and get to the scientific paper and most excitingly the
paper and most excitingly the
paper and most excitingly the availability of these huge data sets to
availability of these huge data sets to
availability of these huge data sets to everybody invite synergies across fields
everybody invite synergies across fields
everybody invite synergies across fields physicists chemists computer scientists
physicists chemists computer scientists
physicists chemists computer scientists and biologists all realizing that we
and biologists all realizing that we
and biologists all realizing that we work on very similar problems and often
work on very similar problems and often
work on very similar problems and often need very similar tools so these 3d data
need very similar tools so these 3d data
need very similar tools so these 3d data which you know for me
which you know for me
which you know for me are useful as inspiration for new
are useful as inspiration for new
are useful as inspiration for new engineering and new technologies and
engineering and new technologies and
engineering and new technologies and biomaterials for other people might be
biomaterials for other people might be
biomaterials for other people might be useful for something completely
useful for something completely
useful for something completely different in fact their highest purpose
different in fact their highest purpose
different in fact their highest purpose is certainly not the ideas that I have
is certainly not the ideas that I have
is certainly not the ideas that I have as I’m gathering these data the highest
as I’m gathering these data the highest
as I’m gathering these data the highest purposes are out there and by giving
purposes are out there and by giving
purposes are out there and by giving people access we ensure that the
people access we ensure that the
people access we ensure that the possibility that they fulfill these is
possibility that they fulfill these is
possibility that they fulfill these is there so what’s next is more access more
there so what’s next is more access more
there so what’s next is more access more collaboration thank you
collaboration thank you
collaboration thank you [Applause]
”
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