The goal of the Very Open Data Project is to provide a software-technical foundation for this exchange of data, more specifically to provide an open database platform for data from the raw data coming from experimental measurements or models through intermediate manipulations to finally published results. The sheer expanse of the amount data involved creates some unique software-technical challenges. One of these challenges is addressed in the part of the study presented here, namely to characterize scientific data (with the initial focus being detailed chemistry data from the combustion kinetic community), so that efficient searches can be made. A formalization of this characterization comes in the form of schemas of descriptions of tags and keywords describing data and ontologies describing the relationship between data types and the relationship between the characterizations themselves. These will be translated to meta-data tags connected to the data points within a non-relational data of data for the community. The focus of the initial work will be on data and its accessibility. As the project progresses, the emphasis will shift on not only having available data accessible for the community, but that the community itself will be able to, with emphasis on minimal effort, will be able contribute their own data. This will involve, for example, the concepts of the ‘electronic lab notebook’ and the existence and availability of extensive concept extraction tools, primarily from the chemical informatics field.

1. RESEARCH POSTER PRESENTATION DESIGN © 2012
www.PosterPresentations.com
Goal
and
Challenges
Ontologies
and
Metadata:
RelaAonships
between
the
data
Interdependencies: A data point is not isolated
EvoluAon
of
data
exchange
Degrees of Sharing and quality
Data
Use
and
QualificaAon
Interdependecies within a model:
REACTION,
Lund,
Sweden
Blurock
Consul/ng
AB
Edward
S.
Blurock
The
Very
Open
Data
Project:
Characterizing
CombusAon
KineAc
Data
with
ontologies
and
meta-­‐data
Derived
from
Derived
from
Used
in
Complex
Models
Experiments
Derived
from
A
data
point
is
not
isolated
When
the
other
data
changes,
It
effect
can
cascade
through
the
derived
data
History: Models and data evolve
Time
History
Proprietory
Public
Shared
Private
Mechanism
ReacAon
Molecule
Substructures
Energy
Energy
ProperAes
Ontology:
a
formal
naming
and
defini/on
of
the
types,
proper/es,
and
interrela/onships
of
the
en//es
that
really
or
fundamentally
exist
for
a
par/cular
domain.
The
pillar
of
the
seman/c
web.
The
network
of
rela/onships
enrich
the
data
points
giving
them
more
context.
Open
Data:
The
movement
of
Open
Data
stems
from
a
priority
at
the
na/onal,
European
and
interna/onal
levels
to
make
scien/fic
data,
especially
that
that
stems
from
public
funding,
freely
accessible.
SemanAc
Web:
The
term
“Seman/c
Web”
refers
to
W3C’s
vision
of
the
Web
of
linked
data.
Seman/c
Web
technologies
enable
people
to
create
data
stores
on
the
Web,
build
vocabularies,
and
write
rules
for
handling
data.
Linked
data
are
empowered
by
technologies
such
as
RDF,
SPARQL,
OWL,
and
SKOS.
Big
Data
is
an
evolving
term
that
describes
any
voluminous
amount
of
structured,
semi-­‐structured
and
unstructured
data
that
has
the
poten/al
to
be
mined
for
informa/on.
Beyond
just
Accessing
Data
PublicaAons
and
Conferences
(Scien/fic
papers)
Data
exchanged
between
Researchers
(data
files,
emails,
….)
Virtual
Research
Environment
(Clouds,
infrastructures)
Store
the
data
so
as
to
facilitate
answering
ques/ons
about
the
data:
EvoluAon:
How
has
the
data
point
evolved?
Has
the
data
point
evolved
to
a
stable
value?
Origins
and
uses:
On
what
other
data
does
this
point
depend?
In
which
other
models
or
deriva/ons
is
this
data
point
used?
What
are
the
rela/onships
between
the
models
where
the
data
was
used?
Not
just
the
‘standard
value’:
What
is
the
range
of
values
that
the
data
point
can
take?
In
which
contexts
were
these
values
used?
Intermdiate
data
Published
Standard
Value
Not
confirmed
Limited
Usage
exploratory
exploratory
Not
just
one
‘standard’
value
Make
ALL
scien/fic
(kine/c)
data
available,
not
just
standard
published
values,
but
those
at
all
stages
of
development
Challenge:
Goal:
Create
an
extensive
database
Handle
the
immense
amount
of
data
in
terms
of
storage,
security
and
efficient
access
Challenge:
Goal:
Accessability
of
the
data
To
define
meta-­‐data
(through
ontological
rela/onships)
to
quan/fy
the
rela/onship
between
the
data
and
the
use
of
the
data.
Data
is
not
just
a
data
structure.
To
be
able
to
efficiently
access
data
from
mul/ple
contexts,
from
data
type,
to
related
values,
to
how
it
is
used
in
models,
its
history,......
The
movement
of
Open
Data
stems
from
a
priority
at
the
na/onal,
European
and
interna/onal
levels
to
make
scien/fic
data,
especially
that
that
stems
from
public
funding,
freely
accessible.
Beyond
poli/cal
and
financial
considera/ons
is
that
science
thrives
on
interac/on.
With
modern
science,
especially
with
the
explosive
use
and
availability
of
electronic
media,
this
translates
to
sharing
electronically
data
between
groups.
The
goal
of
the
Very
Open
Data
Project
is
to
provide
a
soiware-­‐
technical
founda/on
for
this
exchange
of
data,
more
specifically
to
provide
an
open
database
plajorm
for
data
from
the
raw
data
coming
from
experimental
measurements
or
models
through
intermediate
manipula/ons
to
finally
published
results.
Scalability
Goal:
Challenge:
To
be
able
to
handle
an
immense
amount
of
data
and
rela/onships
between
the
data
to
sa/sfy
present
needs
and
future
expansive
needs
To
find
and
use
the
necessary
soiware
technical
tools
that
will
not
break
down
as
the
amount
of
data
increases.
Course
and
fine
grain
informaAon
A
model,
,
for
example
kine/c
mechanisms,
is
not
just
a
single
en/ty.
Extract
and
store
individual
elements
of
the
model
and
create
rela/onships
on
how
they
were
derived,
developed
and
used
in
other
contexts
and
other
models.
For
the
study
of
reac/on
mechanisms,
the
database
can
help
in
the
study
of
rela/onships
between
the
mechanisms
in
terms
of
species,
reac/ons
and
pathways.
Ontologies
can
be
used
to
beker
access
data
through
the
path
of
connec/ons
derive
informa/on
that
is
not
directly
stored
in
the
database.
A
single
data
point
is
not
just
a
data
structure
or
mathema/cal
construct.
Consists
of