Biodiversity data should be published, cited, and peer reviewed

Abstract

Concerns over data quality impede the use of public biodiversity databases and subsequent benefits to society. Data publication could follow the well-established publication process: with automated quality checks, peer review, and editorial decisions. This would improve data accuracy, reduce the need for users to 'clean' the data, and might increase data use. Authors and editors would get due credit for a peer-reviewed (data) publication through use and citation metrics. Adopting standards related to data citation, accessibility, metadata, and quality control would facilitate integration of data across data sets. Here, we propose a staged publication process involving editorial and technical quality controls, of which the final (and optional) stage includes peer review, the most meritorious publication standard in science.

Full text

Biodiversity
data
should
be
published,
cited,
and
peer
reviewed
Mark
J.
Costello
1
,
William
K.
Michener
2
,
Mark
Gahegan
3
,
Zhi-Qiang
Zhang
4
,
and
Philip
E.
Bourne
5
1
Institute
of
Marine
Science,
University
of
Auckland,
Auckland,
1142,
New
Zealand
2
University
Libraries,
University
of
New
Mexico,
Albuquerque,
NM
87131-0001,
USA
3
Centre
for
eResearch,
University
of
Auckland,
Auckland,
1142,
New
Zealand
4
Landcare
Research,
231
Morrin
Road,
Auckland,
1072,
New
Zealand
5
Skaggs
School
of
Pharmacy
and
Pharmaceutical
Sciences,
University
of
California,
San
Diego,
La
Jolla,
CA,
92093-0657,
USA
Concerns
over
data
quality
impede
the
use
of
public
biodiversity
databases
and
subsequent
benefits
to
socie-
ty.
Data
publication
could
follow
the
well-established
publication
process:
with
automated
quality
checks,
peer
review,
and
editorial
decisions.
This
would
improve
data
accuracy,
reduce
the
need
for
users
to
‘clean’
the
data,
and
might
increase
data
use.
Authors
and
editors
would
get
due
credit
for
a
peer-reviewed
(data)
publication
through
use
and
citation
metrics.
Adopting
standards
related
to
data
citation,
accessibility,
metadata,
and
quality
control
would
facilitate
integration
of
data
across
data
sets.
Here,
we
propose
a
staged
publication
process
involving
edito-
rial
and
technical
quality
controls,
of
which
the
final
(and
optional)
stage
includes
peer
review,
the
most
meritori-
ous
publication
standard
in
science.
The
importance
of
biodiversity
data
Biodiversity
data
In
today’s
digital
world,
all
biodiversity
information
and
data
should
be
available
online,
unless
there
are
sound
reasons
why
they
should
be
kept
confidential
(e.g.,
nesting
site
of
a
rare
bird).
Information
that
is
not
online
will
be
overlooked.
For
biodiversity
data,
the
requisite
storage
capacity
and
infrastructure
are
available,
and
there
are
continuing
improvements
in
data
management
tools
[1,2].
However,
quality
assurance
is
inconsistent
and
a
culture
of
data
publication
is
lacking.
Consequently,
few
scientists
use
biodiversity
databases
for
their
research,
and
fewer
still
contribute
data
back
to
the
community.
Meanwhile,
publicly
funded
data
are
‘lost’,
and
global
issues
that
threaten
human
food
sources
and
ecosystem
health
re-
main,
such
as
climate
change,
overfishing,
infectious
dis-
eases,
and
invasive
species.
Addressing
these
challenges
requires
that
existing
data
be
properly
maintained,
trusted,
and
unconditionally
accessible
[3,4].
Biodiversity
data
can
include
inventories
of
species
names
and
synonyms,
species
distributions,
images
and
sounds,
ecological
interactions,
behaviour,
data
set
descriptions,
and
analyses
and
interpretations
[5].
Here,
we
are
most
concerned
with
the
primary
biodiversity
data
rather
than
the
secondary
(e.g.,
modelled
or
simulated)
data
derived
from
them,
and
interpretations
and
descrip-
tions
around
data.
Thus,
data
can
be
numerical,
categorical
(e.g.,
species
or
place
names),
images,
or
sounds.
The
rate
at
which
new
data
are
published
through
the
Global
Biodiversity
Information
Facility
(GBIF)
(Box
1),
as
a
proportion
of
available
data,
is
declining
each
year
[6].
GBIF
was
established
to
make
biodiversity
data
publicly
available
and,
thus,
to
satisfy
a
key
aim
of
the
Convention
on
Biologi-
cal
Diversity.
Nonetheless,
more
data
are
continually
being
collected
[7–9].
Moreover,
centuries
of
irreplaceable
historic
data
on
biodiversity
and
the
environment
need
to
be
digi-
tised
to
provide
the
historical
context
for
present
observa-
tions,
and
enable
predictive
modelling
of
the
consequences
of
human
activities
for
the
environment
and
biodiversity
[10–
13].
This
historic
record
is
especially
important
for
taxono-
my,
because
the
first
description
of
a
species
has
legal
priority
for
the
name
of
that
species
[14,15].
Motivating
data
publication
It
is
necessary
to
motivate
and
reward
the
contribution
of
data
to
international
integrated
databases
by
bringing
such
data
into
the
mainstream
of
respected
scientific
pub-
lication
[5,9,16,17].
Data
publication
increases
the
visibili-
ty
of
scientists’
work
and
citation
rates
[18].
This
can
be
an
incentive
to
some
scientists,
but
still
less
than
half
of
authors
make
their
data
publicly
available
online
[18,19].
The
situation
in
ecology
may
be
worse;
a
survey
of
environmental
biology
publications
from
2005
to
2009
found
that
57%
had
not
released
their
data
and,
when
genetic
data
were
excluded,
only
8%
had
[20].
Even
in
those
journals
that
require
that
data
be
made
available,
one
study
found
that
most
(59%)
papers
did
not
submit
their
data
[21].
Most
scientists
(92%)
agree
that
data
sharing
is
important
[22].
Smit
[23]
found
that,
whereas
80%
of
scientists
wanted
access
to
data
created
by
others,
13%
did
not
want
to
share
their
data
and
only
20%
have
actually
shared
data.
Clearly,
data-sharing
agreements
and
poli-
cies
are
insufficient,
and
new
approaches
are
required
[5].
Data
publication
Decades
ago,
journals
frequently
published
species
in-
ventories,
ecological
survey
data,
and
data
appendices.
Opinion
0169-5347/$
–
see
front
matter
ß
2013
Elsevier
Ltd.
All
rights
reserved.
http://dx.doi.org/10.1016/j.tree.2013.05.002
Corresponding
author:
Costello,
M.J.
(m.costello@auckland.ac.nz).
Keywords:
databases;
species;
journals;
quality
control;
Global
Biodiversity
Information
Facility.
TREE-1699;
No.
of
Pages
8
Trends
in
Ecology
&
Evolution
xx
(2013)
1–8
1

However,
printing
and
postage
costs
led
to
journals
being
reluctant
to
publish
tables
and
appendices
of
primary
data.
Today,
the
availability
of
online
appendices
and
electronic
publication
means
that
this
should
no
longer
be
an
issue,
and
some
biodiversity
journals
(e.g.,
Zootaxa
and
Phytotaxa)
publish
species
inventories
both
in
print
and
online.
It
is
increasingly
acknowledged
that
data
created
using
public
funds
or
for
the
public
good
(e.g.,
environmental
monitoring)
should
be
publicly
available
[5,24,25].
Like-
wise,
many
publishers
expect
authors
to
make
their
data
publicly
available,
ideally
in
international
databases,
in
permanent
institutional
repositories,
or
as
online
supple-
mentary
material
(reviewed
in
[5]).
However,
peer
review
and
editorial
processes
generally
exclude
assessment
of
such
data.
Important
exceptions
include
Data
Papers
and
Ecological
Monographs
of
the
Ecological
Society
of
Amer-
ica,
the
Earth
System
Science
Data
Journal,
BioInvasions
Records,
and
Datasets
in
Ecology.
Also,
the
publisher
PenSoft
has
announced
the
introduction
of
‘data
papers’
in
six
of
its
journals
[26].
However,
unless
authors
publish
in
a
specialist
‘data
journal’,
there
is
often
no
oversight
to
ensure
that
the
data
set
adheres
to
accepted
standards,
has
adequate
metadata,
and
is
largely
error
free.
Published
online
appendices
are
not
ideal
because
they
are
not
usually
peer
reviewed
[27],
subject
to
independent
editorial
attention,
and
may
not
be
open
access.
Because
such
appendices
are
not
required
to
conform
to
data
and
metadata
standards,
their
reuse
can
be
problematic.
Fur-
thermore,
much
‘supplemental
material’
is
not
permanently
archived
and
can
become
inaccessible
over
time
[28,29].
Although
print
publications
with
an
ISSN
and
ISBN
are
archived
in
libraries,
this
is
not
the
case
for
online
supple-
mentary
material.
Institutional
repositories
can
be
prefera-
ble
where
they
provide
permanent
archiving,
but
most
lack
peer
review,
editorial
review,
and
alignment
with
emerging
disciplinary
standards.
A
better
option
is
to
deposit
data
in
Dryad
(http://datadryad.org)
because
it
is
a
centralised
open-access
repository
directly
linked
to
journals.
By
early
2013,
it
had
published
over
7000
data
files
from
articles
published
in
187
journals.
Some
journals
now
require
authors
to
pre-deposit
data
in
Dryad
rather
than
as
‘sup-
porting
material’
on
the
journal
website.
However,
the
data
are
not
subject
to
independent
quality
checks,
are
not
required
to
conform
to
particular
standards,
are
not
peer
reviewed,
and
are
limited
to
data
associated
with
published
papers.
By
comparison,
far
more
biodiversity
data
are
pub-
lished
through
GBIF
by
government
organisations,
of
which
only
fragments
may
be
associated
with
research
papers.
In
contrast
to
journals,
specialised
data
centres
are
most
familiar
with
data
standards,
and
in-house
staff
typically
provide
some
quality
assurance
of
data
and
metadata
(e.g.,
PANGAEA,
the
Distributed
Active
Archive
Centers
of
the
National
Aeronautics
and
Space
Administration,
Gen-
Bank,
and
Protein
Data
Bank).
Thus,
specialised
data
centres
are
preferable
for
data
publication.
The
problem
with
‘data
sharing’
Perhaps
the
primary
reason
why
data
publication
is
not
the
norm
is
that
most
data
policies
refer
to
‘sharing’
or
making
data
‘available’,
rather
than
‘publishing’
(e.g.,
[30,31]).
This
is
a
key
distinction,
because
making
data
available
suggests
a
negotiation
between
the
parties
in-
volved
as
to
the
terms
and
conditions
of
availability.
This
might
require
direct
payment,
joint
authorship,
or
part-
nership
in
research
contracts
(e.g.,
[5,24,32,33]).
Fortu-
nately,
this
is
not
the
case
for
scientific
papers,
and
should
also
not
be
so
for
data
sets
[34].
Calls
for
making
data
‘available’
can
be
counter-productive
because
they
pressure
scientists
to
do
something
outside
their
comfort
zone:
for
example,
they
may
not
have
clarified
data
owner-
ship
and
a
dissemination
policy
with
their
collaborators,
Box
1.
Biodiversity
data
publication
by
the
Global
Biodiversity
Information
Facility
Over
its
first
decade,
GBIF
published
over
370
million
records
of
species,
from
12
000
data
sets
supplied
by
400
organisations
from
over
40
countries,
with
over
4.5
million
names
(Figure
I).
The
names
include
scientific,
vernacular,
and
other
names,
and
amount
to
almost
1
million
species,
of
which
590
000
have
distribution
data
(Tim
Robertson,
personal
communication).
The
marine
component
of
GBIF,
OBIS,
contains
over
120
000
species,
which
is
over
half
of
all
described
marine
species
[61–63].
Approximately
80%
of
records
represent
species
observations
and
samples
rather
than
museum
specimens
[9].
The
data
from
each
source
are
integrated
into
a
large
searchable
database
[53].
Over
85%
of
animals
and
76%
of
plant
species
can
be
mapped
[6].
Thus,
the
sum
of
local
and
regional
data
can
be
used
to
examine
global-scale
phenomena.
Over
two-thirds
of
the
data
sets
in
GBIF
have
been
provided
by
government
organisa-
tions
whose
staff
are
directed
to
do
so.
Far
fewer
data
sets
are
delivered
from
the
academic
community,
although
it
publishes
approximately
75%
of
all
scientific
papers,
despite
comprising
only
15–50%
of
all
scientists
[38].
Nevertheless,
the
number
of
publications
that
has
used
data
from
GBIF
is
increasing
(Figure
I).
GBIF
needs
to
address
not
only
the
amount
of
data,
but
also
the
geographic,
temporal,
and
taxonomic
coverage,
and
accuracy
(quality).
Scientists’
concerns
over
data
accuracy
might
be
impeding
data
reuse
and
consequent
benefits
to
society
[22,64].
A
more
incentivised
publication
model
could
encourage
scientists
to
offer
data
sets
to
GBIF
for
publication,
just
as
they
now
offer
papers
to
journals
to
publish.
This
could
be
direct
to
GBIF,
through
one
of
the
GBIF
participants,
or
offered
through
a
biodiversity
journal.
This
does
not
exclude
the
present
process
of
data
publication
continuing,
but
offers
a
quality-assured
process
that
might
be
more
attractive
to
some
scientists
and
data
users.
0
50
100
150
200
250
300
350
400
450
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Data sets, georeferenced data or
publicaons
Year
Georeferenced
species records
(millions)
Publicaons
cing GBIF
Data sets (100's)
Publicaons
using GBIF data
TRENDS in Ecology & Evolution
Figure
I.
The
increasing
number
of
millions
of
species
distribution
records
published
by
the
Global
Biodiversity
Information
Facility
(GBIF)
(solid
circles),
hundreds
of
data
sets
(open
squares),
publications
that
use
data
from
GBIF
(open
triangles),
and
publications
that
cite
GBIF
(open
circles).
Data
from
GBIF
[65].
Opinion Trends
in
Ecology
&
Evolution
xxx
xxxx,
Vol.
xxx,
No.
x
TREE-1699;
No.
of
Pages
8
2

employer,
or
funding
sources.
Furthermore,
significant
work
can
be
required
to
get
the
data
into
a
well-described
format
that
others
can
use.
Whether
these
concerns
are
justified
is
immaterial,
because
there
is
little
incentive
for
the
scientist
to
spend
time
overcoming
them
when
their
success
is
primarily
judged
by
publications.
Biodiversity
data
publication
There
are
many
open-access
scholarly
biodiversity
data-
bases
(e.g.,
listed
in
[24]).
Most
provide
information
on
species
such
as
FishBase
[35],
AlgaeBase
[36],
and
the
Global
Invasive
Species
Database
(www.issg.org/data-
base),
and
images,
such
as
to
identify
individual
whales
from
their
photographs
(www.cetabase.info).
A
few
provide
standardised
distribution
data,
such
as
the
Ocean
Biogeo-
graphic
Information
System
(OBIS
[37]),
which
republishes
data
into
GBIF.
Both
GBIF
and
OBIS
have
inter-governmental
governance
and
funding
structures,
and
are
organised
as
a
global
network
of
nodes
that
publish
data
through
a
single
portal.
Quality
assurance
and
control
Data
sets
are
often
accompanied
by
statements
about
the
publisher
and
creators
not
being
responsible
for
the
use
others
might
make
of
the
data,
or
for
any
errors
contained
therein.
In
a
2011
survey
of
GBIF
participants,
57%
of
the
35
respondents
stated
they
could
not
guarantee
data
qual-
ity
and
43%
had
no
statement
about
data
quality.
Such
disclaimers
are
not
prominent
in
conventional
scientific
journals
because
authors
and
publishers
are
responsible
for
ensuring
high-quality
publications.
This
often
includes
a
prior
review
of
the
submission
by
editors
and
indepen-
dent
experts
(i.e.,
peer
review).
Furthermore,
if
after
publication,
some
errors,
plagiarism,
or
other
defects
are
found,
then
they
can
be
corrected
or,
in
extreme
cases,
the
publication
can
be
withdrawn.
It
is
clear
to
the
scientific
community
and
the
public
that
different
publications
have
been
subject
to
different
levels
of
quality
assurance
and
control
(QA/QC),
of
which
peer
review
is
the
highest
quality
mark
(Box
2).
Thus,
instead
of
having
a
disclaimer,
data
publishers,
similar
to
scientific
journals,
should
be
proac-
tive
and
use
transparent
QA/QC
procedures.
Peer
review
is
integral
to
science
[38].
Non-peer-
reviewed
publications
are
regarded
as
inferior
by
scien-
tists,
their
employers,
and
policy-makers
[39].
Publications
that
are
not
peer
reviewed
have
negligible
value
in
metrics
of
scientists’
productivity
and
reputation.
A
review
for
the
European
Union
recognised
the
rapidly
growing
data
vol-
ume,
but
made
little
mention
of
the
need
to
capture
past
data
and
methods
of
quality
control,
and
no
mention
of
the
need
for
peer
review
[17].
Although
the
question
of
peer
review
of
data
publications
has
not
been
considered
in
detail,
it
is
now
being
encouraged
[5,25,27,40,41].
The
fact
that
species
web
page
profiles
in
the
Marine
Life
Informa-
tion
Network
and
the
Global
Invasive
Species
Database,
Global
Species
Databases
published
by
Species
2000,
Data
Papers
published
by
the
Ecological
Society
of
America,
species
inventories
in
Zootaxa,
and
data
in
the
NASA
Planetary
Data
System
[27]
are
peer
reviewed
demon-
strates
that
it
is
possible.
Fitness
for
purpose
Whereas
in
the
early
stages
of
collating
data,
the
emphasis
might
be
on
the
quantity
of
data,
quality
must
be
checked.
For
example,
during
the
early
years
of
the
Protein
Data
Bank,
one-third
of
its
budget
was
spent
on
data
cleaning.
Considering
that
all
potential
data
uses
are
unlikely
to
be
predicted
and
that
most
data
might
be
useful
for
some
purposes,
there
can
never
be
too
much
biodiversity
data.
However,
establishing
that
data
are
fit
for
a
specific
pur-
pose
is
often
a
difficult
task,
and
can
entail
study
of
both
metadata
and
the
processes
(workflow)
used
to
create
the
Box
2.
Publishing
Issues
Implementing
data
peer
review
Peer
review
could
include
a
list
of
questions
for
referees,
such
as:
is
the
data
set
description
complete,
clear,
and
adequate
to
understand
the
taxonomic,
temporal,
and
geographic
scope
of
the
data?
Does
it
contain
appropriate
descriptions
and
citation
of
methods
and
data
analyses?
Do
the
data
adhere
to
community
standards?
How
might
the
data
be
used
by
other
researchers?
How
significant
is
the
data
set
in
terms
of
size,
scope,
and
uniqueness?
The
publisher
Pensoft
has
developed
its
own
guidelines
for
such
peer
review
[26].
A
concern
in
adopting
peer
review
is
the
availability
of
willing
referees,
given
that
this
is
already
a
problem
for
science
publications.
It
is
remarkable
that
there
are
few
incentives
used
to
attract
referees,
yet
most
scientists
provide
their
time
gratis.
Incentives
used
by
subscrip-
tion-paid
journals,
such
as
temporary
free
access
to
publications
online,
are
not
available
to
open-access
publications.
Nevertheless,
other
options
can
be
explored,
such
as
public
acknowledgement
of
the
referees,
invitations
to
write
special
articles
and
join
editorial
boards,
payment
of
honoraria,
and
employment
of
a
few
experts
as
‘in-house’
reviewers
instead
of
relying
on
many
unpaid
referees.
Open
access
‘Open
access’
indicates
that
biodiversity
data
are
freely
accessible
to
countries,
including
developing
countries,
where
the
data
might
have
been
collected.
It
means
that
third
parties
are
expected
to
use
the
data,
create
new
data
sets
from
it,
and
benefit
from
it
in
terms
of
their
research,
making
policy
decisions,
or
developing
commercial
applications
from
the
data.
Such
uses
should
be
seen
as
signs
of
success
and
justification
for
the
funds
that
enabled
its
publication.
Having
collective
databases
such
as
GBIF,
OBIS,
and
VertNet,
is
simpler
in
terms
of
user
access,
and
for
the
development
of
standards
and
analytical
tools
that
facilitate
data
integration
and
synthesis.
Such
collective
resources
will
also
be
more
cost
effective
to
support,
and
precedents
for
tiered
financial
contributions
have
already
been
established
(e.g.,
based
on
gross
domestic
product).
Authors
of
open-
access
publications
are
more
cited
[55,66],
and
the
open-access
business
model
is
more
cost
effective
for
society
[67,68].
The
standardised
Creative
Commons
licences
are
often
used
for
open-
access
databases
and
publications.
Journal
websites
are
not
suitable
repositories
for
data
because
they
are
not
permanent
archives,
do
not
require
standards,
do
not
subject
data
to
any
quality
checks,
and
most
are
not
open
access.
However,
somebody
has
to
pay
for
open
access.
Publication
costs
Who
pays
for
the
publication
process
and
long-term
maintenance
of
access
to
the
data?
Open-access
journals
typically
charge
the
authors,
thereby
excluding
those
who
cannot
pay.
This
cost
might
come
from
readers
(users),
institutional
libraries,
authors,
or
be
sponsored
by
organisations
such
as
government
institutions.
There
are
additional
costs
when
data
are
integrated
with
other
data
sets,
a
service
provided
by
government-funded
data
centres,
such
as
GBIF.
Con-
sidering
the
importance
of
biodiversity
data
being
open
access
to
society,
it
is
appropriate
that
data
publication
is
government
funded.
Opinion Trends
in
Ecology
&
Evolution
xxx
xxxx,
Vol.
xxx,
No.
x
TREE-1699;
No.
of
Pages
8
3

data,
as
well
as
data
content.
Enhancements
to
metadata
should
be
driven
by
the
need
to
help
users
understand
appropriate
uses
of
the
data.
Data
set
citation
Calls
for
data
sets
to
be
cited
in
a
conventional
manner
are
now
widespread
(e.g.,
[5,16,27,32–34,42–46]),
and
an
on-
line
register
that
links
data
sets
to
digital
object
identifiers
(DOI)
has
been
launched
by
DataCite
(http://datacite.org).
However,
few
data
sets
and
online
resources
have
been
cited
in
this
way
[39].
Nearly
all
scientists
want
credit
for
the
use
of
their
data
[23].
In
the
process
proposed
here,
editors
determine
the
citation
style
for
their
journal,
but
one
can
expect
it
to
include
the
common
elements
of
authors,
title,
publisher,
and
publication
date
[5,34,39].
Costello
[5]
listed
16
benefits
of
data
publication,
but
nine
of
these
can
only
be
realised
if
the
data
are
cited
in
this
way.
Following
an
established
publication
process
implies
standard
citation
of
data
sets,
citation
tracking,
permanent
archiving,
and
other
use
metrics
[1,5,16].
Journals
and
authors
presently
have
different
policies
on
how
to
cite
online
resources.
Some
include
a
universal
resource
locator
(URL)
in
the
paper
text,
rather
than
citation
in
the
bibliography
or
references.
Furthermore,
the
practice
of
citing
the
date
on
which
an
online
resource
was
accessed
is
only
appropriate
when
it
is
a
web
page
that
can
change
over
time
[34].
The
conventional
publication
of
data
sets,
paralleling
the
‘papers’
in
a
journal,
would
make
it
clear
that
they
should
be
cited
in
the
references.
Thus,
OBIS
proposed
a
citation
as
part
of
the
metadata
for
the
data
sets
it
published
in
2006
[47]
and
various
options
have
been
considered
by
GBIF
[48].
Citations
should
not
be
confused
with
codes
for
tracking
publications,
data,
or
parts
of
publications;
but
these
can
be
added
to
citations.
Such
codes
include
DOI,
Life
Science
Identifiers,
and
Uniform
Resource
Names
that
aid
data-
bases
in
tracking
publication
citations
(e.g.,
[49])
and
the
provenance
of
individual
data
items
[14,50].
This
provides
new
opportunities
to
develop
data-use
metrics
that
measure
the
impact
of
data
publication.
Various
data-use
metrics
might
be
necessary
because
data
sets
might
not
always
be
cited
and
tracked
by
scientific
abstracting
services
[1].
When
a
data
paper
links
to
the
source
of
a
data
set
or
database,
typically
a
URL
is
included.
However,
a
URL
can
Editorial + technical +
peer reviews (Table 1)
Database staff
Data centre staff
Data analysis experience
Author QC/QA
Data collected
Data analysis
(B) Data archive or repository
(A) Data on author’s
or journal website
(C) Data centre
(D) Specialist data
integrator
(E) Data paper
Data accessibility
Increasing quality assurance
Quality control measures
Research paper
published
More research
papers published
TRENDS in Ecology & Evolution
Figure
1.
The
workflow
involved
in
biodiversity
data
publication
has
multiple
opportunities
for
improving
data
and
metadata
quality
(left
panel)
and
accessibility
(right
panel).
First,
the
conventional
research
publication
process
from
data
collection,
to
analysis,
and
publication
of
the
interpretation
of
the
data
in
a
paper
is
illustrated
with
broken
lines.
Associated
data
may
be
published
online
(solid
lines)
through
(A)
the
author’s
website
or
that
of
the
journal;
(B)
an
archive
or
repository
without
any
independent
quality
checks
or
standards;
(C)
a
data
centre
with
staff
who
check
that
the
data
conform
to
particular
standards;
and/or
(D)
a
specialist
data
centre
that
integrates
the
data
with
similar
data
sets.
A
third
option,
is
to
publish
the
data
as
a
‘data
paper’
(E)
that
describes
the
data
but
may
not
analyse
them
beyond
some
descriptive
statistics.
In
this
example,
data
on
the
abundance
of
fish
observed
by
scuba
divers
is
collected,
including
sampling
locations
and
dates,
species
counts,
body
size,
and
habitat.
The
data
collectors
would
typically
do
some
quality
control
(QC)
on
their
data.
During
data
analysis,
any
inconsistencies
in
data
recording
between
observers
are
corrected,
providing
further
quality
assurance
(QA).
Following
publication
of
the
first
paper
from
these
analyses,
the
supporting
data
are
made
available
on
the
website
of
the
journal,
the
authors’
website,
and/or
a
digital
archive
(e.g.,
Dryad).
These
data
are
typically
not
independently
reviewed
or
required
to
conform
to
specific
standards.
The
authors
decide
to
submit
the
data
to
a
data
centre
(e.g.,
PANGAEA).
The
data
centre
staff
inspect
the
data
and
ask
the
author
to
correct
some
inconsistencies,
provide
additional
metadata,
and
reorganise
them
to
conform
to
domain
standards.
Data
on
species
distribution
are
published
in
the
Global
Biodiversity
Information
Facility
(GBIF),
where
they
are
integrated
with
millions
of
other
data
values
and
can
now
be
searched
and
reanalysed
by
others.
Alternatively,
the
author
could
have
submitted
the
data
set
for
publication
as
a
‘data
paper’,
whereby
the
journal
would
have
performed
the
full
suite
of
technical,
editorial,
and
peer-review
checks
to
ensure
the
data
and
metadata
are
of
the
highest
quality
and
maximised
and
may
be
easily
integrated
with
GBIF
and
other
specialist
databases.
Opinion Trends
in
Ecology
&
Evolution
xxx
xxxx,
Vol.
xxx,
No.
x
TREE-1699;
No.
of
Pages
8
4

change
when
data
sets
are
moved
or
domain
names
are
changed.
A
central
URL
registry
for
published
data
sets
(similar
to
what
CrossRef
is
doing
for
papers)
is
needed
so
that
when
the
URL
for
data
sets
change,
the
records
in
the
registry
are
updated.
Persistent
DOIs
are
already
being
assigned
to
data
sets
(e.g.,
by
DataCite
and
Dryad).
GBIF
and
other
data
publishers
should
lead
in
supporting
a
central
registry
for
resolving
various
digital
identifiers
for
permanent
linking
to
biodiversity
data
sets.
A
proposed
data
publication
process
Data
publication
should
follow
established
practices
of
other
scientific
publications
in
several
regards,
including
editorial
quality
control,
independent
peer-review,
pub-
lished
data
set
citation,
and
permanent
archiving
(Figure
1).
Metadata
descriptors
must
include
authors,
their
contact
details,
abstract,
keywords,
and
other
infor-
mation
necessary
to
enable
abstracting
services
to
include
the
publications
in
their
databases
[1,34].
This
more
formal
approach
to
data
publication
will
require
more
comprehen-
sive
metadata
that
are
essential
for
appropriate
data
use
[9],
such
as
data
provenance,
context,
precision,
and
refer-
ences
to
papers
that
used
the
data.
It
is
likely
to
require
the
use
of
metadata
standards
and
standardised
vocabularies.
Michener
et
al.
[9]
provided
a
list
of
metadata
descriptors
for
ecology
data
to
which
taxonomic
metadata
(e.g.,
taxa
included
in
the
study)
could
be
easily
added.
Data
prove-
nance
might
be
traceable
through
the
executable
workflow
of
how
the
data
were
produced
or
assembled.
Metadata
might
ultimately
become
a
domain
ontology,
with
rich
semantics
describing
the
various
categories
used,
and
their
inter-relations.
The
data
publication
process
proposed
here
may
war-
rant
establishment
of
online
open-access
biodiversity
data
journals,
and
the
adoption
by
existing
journals
of
data
publication.
We
emphasise
the
importance
of
open
access
for
biodiversity
data
and
recognise
this
requires
financial
support
(Box
2).
This
is
also
the
principle
behind
many
government
and
intergovernmental
policies
for
data
avail-
ability
(reviewed
in
[5]),
including
the
public
funding
of
GBIF,
GenBank,
and
other
databases.
Data
publishers
There
could
be
several
data
publication
journals.
Some
might
specialise
in
photographs
to
aid
species
identifica-
tion,
species
geographic
distribution,
or
population
abun-
dance
time-series
data,
and
to
develop
new
data
standards
(Box
3).
They
could
be
linked
through
portals
with
other
databases,
such
as
GBIF,
and
to
published
literature,
similar
to
the
Biodiversity
Hubs
created
by
the
Public
Library
of
Science.
ZooKeys
and
PhytoKeys
already
submit
data
to
GBIF
post-publication
of
their
accompanying
paper
[51].
One
possible
way
forward,
whereby
a
data
journal
could
be
widely
abstracted
and
get
an
Impact
Factor,
is
to
design
it
as
a
peer-reviewed
scholarly
journal
covering
more
than
only
‘data’
papers,
with
various
publications
of
scholarly
value
so
that
these
will
be
abstracted
and
included
in
journal
citation
rankings.
These
might
include
editorials
on
topics
of
interest
in
the
field,
best
practice,
case
studies,
invited
review
papers
on
current
topics
in
biodiversity
data
and
databases,
papers
on
methods
and
data
standards,
introductions
to,
and
reviews
of,
new
software
for
data
exploration,
presentation
or
analysis,
and
other
related
topics.
Data
paper
authors
could
receive
automated
messages
when
their
paper
is
downloaded
and
cited,
and
be
contactable
by
data
users
so
that
new
collab-
orative
publications
might
arise.
The
ideal
solution
might
be
for
data
publication
to
occur
through
data
journals
that
archive
data
in
integrated
databases
managed
by
data
centres,
such
as
GBIF.
Differences
between
print
and
data
publication
There
are
differences
between
conventional
print
journals
and
data
journals
that
must
be
addressed.
First,
a
data
set
must
be
published
to
rigorous
domain-specific
standards
of
formatting
and
structure
to
enable
it
to
be
combined
with
other
data
sets,
such
as
by
following
the
‘Darwin
Core
Archive’
data
schema
[52]
so
that
it
can
be
automatically
integrated
into
GBIF
[53]
(Box
3).
If
data
users
find
errors
in
data,
they
may
inform
the
data
aggregator
(e.g.,
GBIF),
who
may
inform
the
original
data
provider.
However,
this
rarely
results
in
the
data
being
corrected
at
all
its
locations.
Fol-
lowing
the
more
conventional
publication
model
proposed
here
(Table
1)
would
reduce
the
occurrence
of
such
errors,
and
allow
for
publication
of
‘Corrections’
and
‘Responses’.
Second,
data
sets
will
often
be
supplemented
by
additional
data
over
time.
For
example,
new
versions
of
data
sets
might
have
corrected
errors
and
omissions,
and
time-series
data
may
progressively
add
new
data
sets.
We
agree
with
others
[34,54]
that
these
should
be
published
as
new
publications
because
their
data
will
be
unique,
the
authors
and
metadata
can
change,
and,
for
time-series
data,
the
temporal
scope
of
Box
3.
The
need
for
biodiversity
data
standards
A
benefit
of
integrating
data
into
one
(possibly
virtual)
system
is
that
it
drives
standards
for
data
management.
Standardised,
quality
assured,
permanently
archived
databases
are
essential
to
manage
the
collection,
storage,
and
accessibility
of
this
growing
data
stream.
This
is
widely
recognised
[17]
with
significant
investments
in
new
data
infrastructures,
but
with
insufficient
attention
to
bringing
past
data
into
a
quality-controlled
digital
environment.
For
example,
an
expert-validated
inventory
of
all
species
that
reconciles
synonyms
and
nomenclatural
confusion
is
essential
for
integrating
high-quality
biodiversity
data
from
different
sources
and
years
because
there
are
many
names
for
the
same
species,
including
multiple
scientific
names
(i.e.,
synonyms)
[4,15].
Perhaps
40%
of
species
names
are
synonyms
and
the
application
of
a
name
can
change
over
time
(just
as
geographic
names
can)
[15,56,69].
This
master
inventory
of
species
names
is
critical
for
molecular-
to
ecosystem-level
studies,
but
is
not
yet
complete,
although
progress
is
being
made
[56,61,70].
Its
completion
is
feasible,
and
is
a
key
step
in
advancing
knowledge
of
life
on
Earth
[4].
Approximately
100
experts
have
contributed
the
non-
marine
components
to
the
Catalogue
of
Life
(CoL
[69]),
which
is
at
least
two-thirds
complete,
and
200
to
the
World
Register
of
Marine
Species
(WoRMS),
which
is
over
95%
complete
[56,61].
The
remaining
taxa
might
be
the
most
difficult
to
compile,
but
(given
resources)
these
figures
suggest
that
it
should
be
possible
to
engage
50–100
new
experts
to
complete
the
CoL
within
the
next
5
years.
The
quality
assurance
and
global
nature
of
such
a
taxonomic
inventory
will
make
it
the
standard
and
will,
in
turn,
promote
further
standards
in
data
management.
Similarly,
molecular
databases
drove
the
need
for
standards
to
aid
data
exchange
and
management
that
in
turn
facilitated
data
analysis
and
research
in
genomics
and
drug
discovery
(e.g.,
[71]).
Such
standards
then
enable
data
to
be
more
mobile
between
databases.
For
example,
WoRMS
provides
a
Webservice
that
is
used
by
at
least
34
organisations
[15,56].
Opinion Trends
in
Ecology
&
Evolution
xxx
xxxx,
Vol.
xxx,
No.
x
TREE-1699;
No.
of
Pages
8
5

each
publication
will
be
discrete.
However,
where
changes
are
limited
to
minor
corrections
or
amendments,
a
version-
ing
control
system
would
be
preferable.
Steps
in
data
publication
We
propose
a
staged
QA/QC
process
before
publication
(Table
1).
The
first
stage
could
use
automated
tools
to
test
that
data
are
parsable
and
the
geographical
coordinates
feasible.
Such
tools
have
already
been
developed
by
GBIF
and
others
[55].
Species
names
can
be
automatically
checked
against
authoritative
standards
(e.g.,
‘Match
Taxa’
tool
on
WoRMS
[56]).
The
next
stage
could
check
that
metadata
are
sufficiently
informative.
A
further
stage
could
check
the
validity
of
species
and
place
names
against
a
taxonomically
authoritative
nomenclature
and
a
gazet-
teer,
respectively.
A
final
stage
could
involve
manual
peer
review
by
experts,
followed
by
an
editorial
process
and
decision
similar
to
research
articles.
GEON,
the
Geos-
ciences
Network
(http://www.geongrid.org)
uses
a
similar
strategy
for
publishing
open
geological
data.
Data
could
be
visible
online
at
any
stage
in
the
process,
but
would
not
have
all
quality
indicators.
Thus,
the
added
quality-control
options
would
not
impede
data
publication.
Online
com-
ment
boxes
could
allow
users
to
comment
on
published
data,
and
enable
data-set
authors
and
editors
to
provide
subsequent
information
(e.g.,
announce
data
additions
and
new
publications
that
included
the
data).
Calls
for
quality
metrics
in
other
science
fields
include
a
proposed
reliability
score
for
mass
spectrometry
data
[57].
Considering
the
various
potential
uses
of
biodiversity
data
and
the
principle
of
fitness
for
purpose,
a
‘reliability’
index
might
be
difficult
to
implement.
However,
measures
such
as
the
number
of
records,
species
and
geographic
locations,
proportion
of
species
names
validated,
and
data
and
meta-
data
completeness
would
be
useful
to
potential
users
[1,5,16,58].
Examples
of
such
metrics
for
biodiversity
data
have
been
demonstrated
by
Species
Link
[59].
Concluding
remarks
A
new
initiative
to
foster
biodiversity
data
publication
is
required
because
the
present
model
cannot
cope
with
the
increasing
need
for
availability
of
high-quality
data.
For-
mal
publication
of
data
sets,
including
peer
review,
is
a
logical
step
in
scholarly
publication
and
will
enable
closer
integration
of
publications
and
databases
[60].
Indeed,
considering
that
data
underpin
information
and
knowl-
edge,
it
is
at
least
as
important
that
data
sets
are
peer
reviewed
as
for
the
papers
resulting
from
their
analysis.
Although
peer-reviewed
data
publication
might
be
novel
in
the
new
field
of
biodiversity
informatics,
it
is
not
radical.
Several
peer-reviewed
journals
publish
primary
environ-
mental
and
biodiversity
data,
and
primary
data
have
previously
been
published
in
monographs,
cruise
reports,
and
appendices
to
papers.
We
recognise
that
this
standard
of
quality
assurance
might
not
be
practical
in
all
situations.
Thus,
our
proposed
quality
assurance
tiers
allow
data
to
be
published
immediately
and
thereafter
be
subject
to
steps
of
automated,
semi-automated,
and
peer
scrutiny
(Table
1).
Furthermore,
instead
of
these
steps
being
an
impediment
to
data
publication,
the
fact
that
the
final
publication
will
be
peer
reviewed
and
published
in
the
style
of
a
conven-
tional
scientific
journal,
will
attract
scientists
whose
pri-
ority
is
to
‘publish’
and
for
whom
‘making
data
available’
is
not
a
priority.
Table
1.
A
proposed
procedure
for
the
publication
of
biodiversity
data
sets
with
a
high
standard
of
quality
control,
including
peer
review
a
Process
Quality
indicator
1.
Online
submission
of
data
set
for
publication
with
full
metadata
(title,
editors
or
authors,
contact
details,
abstract,
sampling
methods,
taxa
and
habitats
sampled,
keywords,
etc.)
$
2.
Editor
verifies
that
the
data
set
is
within
the
scope
of
the
journal
3.
Automated
tools
check
data
set
for
omissions
and
errors,
including
matching
species
names
against
a
master
list,
and
mapping
geographic
data
to
check
against
metadata
$$
4.
Online
tools
generate
tables
of
statistics
(e.g.,
how
many
species
per
higher
taxonomic
group,
or
a
species
inventory)
and
maps
of
data
locations
5.
Potential
errors
and
omissions
reported
to
data
set
author
and/or
editors
6.
Data
set
author
(or
editor
as
appropriate)
responds
to
report
on
initial
submission
technical
screening,
including
resubmitting
corrected
data
and
metadata
if
necessary
7.
Automated
data
checks
verify
that
data
set
is
complete
and
standardised.
Statistics
are
recalculated
and
maps
regenerated
8.
Data
set
author
or
editor
confirms
that
resubmitted
data
and
metadata
are
correct $$$
9.
Independent
experts
(who
might
be
members
of
an
editorial
board)
assess
(i.e.,
peer
review)
whether
the
data
set
is
of
sufficient
quality
for
publication
10.
The
journal
might
wish
to
expose
the
data
set
to
a
wider
scientific
audience
for
comments
at
this
time
11.
Author
responds
to
referees’
comments
12.
Editor
makes
a
decision
on
quality
standard
achieved
by
the
data
set,
and
can
ask
the
data
set
author
or
editor
to
revise
the
metadata
or
make
other
improvements
to
the
data
before
it
will
be
accepted
13.
Data
and
metadata
are
published
online
having
passed
several
technical
checks
and
peer
review.
The
data
set
has
its
own
webpage
that
tracks
the
metrics
of
its
use.
The
abstract,
citation,
authors’
contact
details,
statistics,
and
maps
of
the
data
set
will
be
on
this
page.
The
data
can
be
downloaded
as
tables,
comma-separated
values,
or
in
other
formats
as
appropriate.
Where
appropriate,
the
data
set
is
integrated
into
the
GBIF
database,
from
where
it
can
also
be
downloaded
$$$$
14.
Papers
are
published
that
analysed
most
of
the
data
and
any
errors
found
in
this
process
have
been
corrected $$$$$
a
Possible
stages
where
an
overall
quality
indicator
can
be
applied
are
indicated
where
five
stars
is
the
highest
quality.
Other
quantitative
metrics
are
also
recommended
(see
main
text).
Opinion Trends
in
Ecology
&
Evolution
xxx
xxxx,
Vol.
xxx,
No.
x
TREE-1699;
No.
of
Pages
8
6

Acknowledgements
We
thank
Vishwas
Chavan,
Rod
Page,
A.
Townsend
Petersen,
Hannu
Saarenmaa,
Tim
Hirsch,
Peter
Desmet,
and
the
referees
for
helpful
suggestions.
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