A decadal view of biodiversity informatics: challenges and priorities
Identifieur interne : 000092 ( Pmc/Corpus ); précédent : 000091; suivant : 000093A decadal view of biodiversity informatics: challenges and priorities
Auteurs : Alex Hardisty ; Dave RobertsSource :
- BMC Ecology [ 1472-6785 ] ; 2013.
Abstract
Biodiversity informatics plays a central enabling role in the research community's efforts to address scientific conservation and sustainability issues. Great strides have been made in the past decade establishing a framework for sharing data, where taxonomy and systematics has been perceived as the most prominent discipline involved. To some extent this is inevitable, given the use of species names as the pivot around which information is organised. To address the urgent questions around conservation, land-use, environmental change, sustainability, food security and ecosystem services that are facing Governments worldwide, we need to understand how the ecosystem works. So, we need a systems approach to understanding biodiversity that moves significantly beyond taxonomy and species observations. Such an approach needs to look at the whole system to address species interactions, both with their environment and with other species.
It is clear that some barriers to progress are sociological, basically persuading people to use the technological solutions that are already available. This is best addressed by developing more effective systems that deliver immediate benefit to the user, hiding the majority of the technology behind simple user interfaces. An infrastructure should be a space in which activities take place and, as such, should be effectively invisible.
This community consultation paper positions the role of biodiversity informatics, for the next decade, presenting the actions needed to link the various biodiversity infrastructures invisibly and to facilitate understanding that can support both business and policy-makers. The community considers the goal in biodiversity informatics to be full integration of the biodiversity research community, including citizens’ science, through a commonly-shared, sustainable e-infrastructure across all sub-disciplines that reliably serves science and society alike.
Url:
DOI: 10.1186/1472-6785-13-16
PubMed: 23587026
PubMed Central: 3843378
Links to Exploration step
PMC:3843378Le document en format XML
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<author><name sortKey="Roberts, Dave" sort="Roberts, Dave" uniqKey="Roberts D" first="Dave" last="Roberts">Dave Roberts</name>
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<front><div type="abstract" xml:lang="en"><p>Biodiversity informatics plays a central enabling role in the research community's
efforts to address scientific conservation and sustainability issues. Great strides
have been made in the past decade establishing a framework for sharing data, where
taxonomy and systematics has been perceived as the most prominent discipline
involved. To some extent this is inevitable, given the use of species names as the
pivot around which information is organised. To address the urgent questions around
conservation, land-use, environmental change, sustainability, food security and
ecosystem services that are facing Governments worldwide, we need to understand how
the ecosystem works. So, we need a systems approach to understanding biodiversity
that moves significantly beyond taxonomy and species observations. Such an approach
needs to look at the whole system to address species interactions, both with their
environment and with other species.</p>
<p>It is clear that some barriers to progress are sociological, basically persuading
people to use the technological solutions that are already available. This is best
addressed by developing more effective systems that deliver immediate benefit to the
user, hiding the majority of the technology behind simple user interfaces. An
infrastructure should be a space in which activities take place and, as such, should
be effectively invisible.</p>
<p>This community consultation paper positions the role of biodiversity informatics, for
the next decade, presenting the actions needed to link the various biodiversity
infrastructures invisibly and to facilitate understanding that can support both
business and policy-makers. The community considers the goal in biodiversity
informatics to be full integration of the biodiversity research community, including
citizens’ science, through a commonly-shared, sustainable e-infrastructure
across all sub-disciplines that reliably serves science and society alike.</p>
</div>
</front>
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</TEI>
<pmc article-type="other" xml:lang="en"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Ecol</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Ecol</journal-id>
<journal-title-group><journal-title>BMC Ecology</journal-title>
</journal-title-group>
<issn pub-type="epub">1472-6785</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">23587026</article-id>
<article-id pub-id-type="pmc">3843378</article-id>
<article-id pub-id-type="publisher-id">1472-6785-13-16</article-id>
<article-id pub-id-type="doi">10.1186/1472-6785-13-16</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Correspondence</subject>
</subj-group>
</article-categories>
<title-group><article-title>A decadal view of biodiversity informatics: challenges and priorities</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" corresp="yes" id="A1"><name><surname>Hardisty</surname>
<given-names>Alex</given-names>
</name>
<xref ref-type="aff" rid="I1">1</xref>
<email>hardistyar@cardiff.ac.uk</email>
</contrib>
<contrib contrib-type="author" corresp="yes" id="A2"><name><surname>Roberts</surname>
<given-names>Dave</given-names>
</name>
<xref ref-type="aff" rid="I2">2</xref>
<email>dmr@nomencurator.org</email>
</contrib>
<on-behalf-of>The Biodiversity Informatics Community <xref ref-type="corresp" rid="d33e30"></xref>
</on-behalf-of>
</contrib-group>
<aff id="I1"><label>1</label>
School of Computer Science and Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, CF24 3AA, UK</aff>
<aff id="I2"><label>2</label>
Department of Zoology, The Natural History Museum, Cromwell Road, London, SW7 5BD, UK</aff>
<aff id="I3"><label>3</label>
Detailed in Appendix 1</aff>
<author-notes><corresp id="d33e30">The Biodiversity Informatics Community <email>Biodiversity-community-list@nhm.ac.uk</email>
</corresp>
</author-notes>
<pub-date pub-type="collection"><year>2013</year>
</pub-date>
<pub-date pub-type="epub"><day>15</day>
<month>4</month>
<year>2013</year>
</pub-date>
<volume>13</volume>
<fpage>16</fpage>
<lpage>16</lpage>
<history><date date-type="received"><day>31</day>
<month>10</month>
<year>2012</year>
</date>
<date date-type="accepted"><day>4</day>
<month>3</month>
<year>2013</year>
</date>
</history>
<permissions><copyright-statement>Copyright © 2013 Hardisty et al.; licensee BioMed Central Ltd.</copyright-statement>
<copyright-year>2013</copyright-year>
<copyright-holder>Hardisty et al.; licensee BioMed Central Ltd.</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.0"><license-p>This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/2.0">http://creativecommons.org/licenses/by/2.0</ext-link>
), which
permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited. The authors also waive their right of attribution under
the terms of CC0 (http://creativecommons.org/about/cc0), so that text may be
excerpted and reused in governmental or other authoritative reports or documents without
the need for attribution.</license-p>
</license>
</permissions>
<self-uri xlink:href="http://www.biomedcentral.com/1472-6785/13/16"></self-uri>
<abstract><p>Biodiversity informatics plays a central enabling role in the research community's
efforts to address scientific conservation and sustainability issues. Great strides
have been made in the past decade establishing a framework for sharing data, where
taxonomy and systematics has been perceived as the most prominent discipline
involved. To some extent this is inevitable, given the use of species names as the
pivot around which information is organised. To address the urgent questions around
conservation, land-use, environmental change, sustainability, food security and
ecosystem services that are facing Governments worldwide, we need to understand how
the ecosystem works. So, we need a systems approach to understanding biodiversity
that moves significantly beyond taxonomy and species observations. Such an approach
needs to look at the whole system to address species interactions, both with their
environment and with other species.</p>
<p>It is clear that some barriers to progress are sociological, basically persuading
people to use the technological solutions that are already available. This is best
addressed by developing more effective systems that deliver immediate benefit to the
user, hiding the majority of the technology behind simple user interfaces. An
infrastructure should be a space in which activities take place and, as such, should
be effectively invisible.</p>
<p>This community consultation paper positions the role of biodiversity informatics, for
the next decade, presenting the actions needed to link the various biodiversity
infrastructures invisibly and to facilitate understanding that can support both
business and policy-makers. The community considers the goal in biodiversity
informatics to be full integration of the biodiversity research community, including
citizens’ science, through a commonly-shared, sustainable e-infrastructure
across all sub-disciplines that reliably serves science and society alike.</p>
</abstract>
<kwd-group><kwd>Biodiversity</kwd>
<kwd>Informatics</kwd>
<kwd>Grand challenge</kwd>
<kwd>Decadal vision</kwd>
<kwd>Research infrastructure</kwd>
<kwd>e-Infrastructure</kwd>
<kwd>Data sharing</kwd>
<kwd>Systems approaches</kwd>
</kwd-group>
</article-meta>
</front>
<body><sec><title>The grand challenge</title>
<p>The grand challenge for biodiversity informatics is to develop an infrastructure to
allow the available data to be brought into a coordinated coupled modelling
environment<sup>a</sup>
able to address questions relating to our use of the natural
environment that captures the ‘<italic>variety, distinctiveness and complexity of all
life on Earth</italic>
’<sup>b</sup>
.</p>
<p>Biodiversity processes are complex and can have a large, long-term impact at the
macro-scale, even if they have occurred rapidly at the sub-cellular molecular level
e.g., the Phosphate cycle [<xref ref-type="bibr" rid="B1">1</xref>
,<xref ref-type="bibr" rid="B2">2</xref>
]. Processes taking place in seconds over scales of nanometres, are crucial to
processes that take years and at scales of many hectares and ultimately to planetary
processes in geological time. Capturing such inter-dependent processes, across such a
breadth of scales, is beyond the capability of current information management and
modelling methods. To have an impact on biodiversity conservation, sustainability or our
environment, we need to consider all aspects of biodiversity, from genes to ecosystems,
in a holistic approach. We need to be able to assess global biodiversity changes and
make predictions about ecosystems. We need to be able to integrate different facets of
past and present environmental and biodiversity observations and embed them in models
with predictive power [<xref ref-type="bibr" rid="B3">3</xref>
]. We will need to develop new models to address socially urgent questions.
Such an approach will take biodiversity science far beyond a collection of taxon names,
capturing data about different facets of biodiversity, both by their absolute position
and their relative position, together with their observational and temporal context.
Most importantly, through biodiversity informatics, biodiversity scientists will be able
to understand, to measure and predict how change affects the actual functioning of the
ecosystem.</p>
</sec>
<sec><title>Recommendations</title>
<p>As well as addressing practitioners with an interest in and knowledge of informatics and
how it can be applied to support biodiversity science, our recommendations are intended
to inform funders, project managers and institutions whose remit includes at least some
aspect of biodiversity science. Our recommendations are intended to establish a
background against which decisions can be made when making and evaluating proposals,
allocating funds or directing work to build infrastructures. For long-term success,
geographically distributed infrastructure involving multiple stakeholders depends on the
commitment of those stakeholders to support a vision and to adhere to standards agreed
by the community. Stakeholders each have to fund their part in the endeavour to make the
whole thing sustainable. Long-term sustainability will be achieved by integrating
services provided by key players as part of their core mission.</p>
<p>The first 3 recommendations should apply to all activity in this area. They are
necessary to reduce duplication and to enhance collaboration. The intended consequence
is to facilitate the creation of new knowledge by synthesis activities using the data
and tools thus generated.</p>
<p>1. Open Data [<xref ref-type="bibr" rid="B4">4</xref>
], should be normal practice and should embody the principles of being
accessible, assessable, intelligible and usable [see Context].</p>
<p>2. Data encoding should allow analysis across multiple scales, e.g. from
nanometers to planet-wide and from fractions of a second to millions of years, and such
encoding schemes need to be developed. Individual data sets will have application over a
small fraction of these scales, but the encoding schema needs to facilitate the
integration of various data sets in a single analytical structure [see Paragraph 19 et
seq.].</p>
<p>3. Infrastructure projects should devote significant resources to market the
service they develop, specifically to attract users from outside the project-funded
community, and ideally in significant numbers. To make such an investment effective,
projects should release their service early and update often, in response to user
feedback. [see paragraphs 10 and 31].</p>
<p>While several technologies have already been developed, they are not widely embraced by
the community, often due to reasons related to the ‘human factor’. The
following 4 recommendations on technological foundations focus on enhancing the
usability and better deployment of existing technologies:</p>
<p>4. Build a complete list of currently used taxon names with a statement of
their interrelationships (e.g. this is a spelling variation; this is a synonym; etc.).
This is a much simpler challenge than building a list of valid names<sup>c</sup>
, and an
essential pre-requisite [see paragraph 1].</p>
<p>5. Attach a Persistent Identifier (PID) to every resource so that they can be
linked to one another. Part of the PID should be a common syntactic structure, such as
‘DOI: …’ so that any instance can be simply found in a free-text
search [see paragraph 7].</p>
<p>6. Implement a system of author identifiers so that the individual
contributing a resource can be identified. This, in combination with the PID (above),
will allow the computation of the impact of any contribution and the provenance of any
resource [see paragraph 11].</p>
<p>7. Make use of trusted third-party authentication measures so that users can
easily work with multiple resources without having to log into each one separately [see
paragraph 12].</p>
<p>The foundational technologies described above all exist to some degree, but need to be
integrated. The next steps will require developing new structures by exploiting existing
technologies in novel ways.</p>
<p>8. Build a repository for classifications (classification bank) that will
allow, in combination with the list of taxonomic names, automatic construction of
taxonomies to close gaps in coverage [see paragraph 2].</p>
<p>9. Develop a single portal for currently accepted names - one of the priority
requirements for most users [see paragraph 3].</p>
<p>10. Standards and tools are needed to structure data into a linked format by
using the potential of vocabularies and ontologies for all biodiversity facets,
including: taxonomy, environmental factors, ecosystem functioning and services, and data
streams like DNA (up to genomics). [see paragraphs 16 and 17].</p>
<p>11. Mechanisms to evaluate data quality and fitness-for-purpose are required
[see paragraphs 20 and 23].</p>
<p>Looking to the future, it is clear that new techniques, such as observatories employing
novel sensors are delivering data in unprecedented volumes, especially molecular data,
as the Genomic Observatories Network [<xref ref-type="bibr" rid="B5">5</xref>
,<xref ref-type="bibr" rid="B6">6</xref>
] has emphasised. This will require development of new technologies, or
adaptation of technologies from related fields, new information systems, and platforms
offering overviews of detectors and experimental setups for biodiversity research to
facilitate exploitation of the opportunities presented.</p>
<p>12. A next-generation infrastructure is needed to manage ever-increasing
amounts of observational data [see Paragraph 13, 19, 21 and Appendix 2].</p>
</sec>
<sec><title>Preface</title>
<p><italic>“The Hubbell paper</italic>
[<xref ref-type="bibr" rid="B7">7</xref>
]<italic>made it into BBC</italic>
[<xref ref-type="bibr" rid="B8">8</xref>
]<italic>. It is sad to see where we stand after 20 years. We have done more work,
we developed an impressive array of biodiversity informatics, we have tools to
capture specimens in our collections and make the data accessible, but the basic we
are missing: A strategy to explore the living planet, and even less a strategy to
measure the change of species based at least on a basic count of what's out
there.”</italic>
Donat Agosti, Plazi.</p>
<p><italic>“When writing my electronic monography (e-monograph) in 2007–9 I wished
to link the plant species to other organisms within the ecological food chains / food
web. However, I could not even find an e-monograph on birds at the time or have the
software programming knowledge to create interspecies relationships between
electronic monographs and / or electronic floras. Ultimately I wish to see a
‘virtual life on Earth’ where cross-linking of data can be explored, for
example, how shifting species distribution in light of climate change will affect
food webs. Consequently the results can be used to drive conservation management and
placement on the IUCN Red Data List.”</italic>
Fiona Young, University of Reading,
UK</p>
<p>The two quotes above illustrate the challenges and associated shortcomings facing
biodiversity informatics today. Despite considerable progress, biodiversity science is
still reliant on data that is not as fully available, linkable, discoverable and
accessible as it should be. Services and tools to process those data are not yet
‘plug and play’. Models of different parts of the overall biodiversity
system from the molecular to the planetary are not yet linked across time, space and
scales. We are still unable to understand the complex behaviour of the entire system
because until now we have reduced it, only taken account of some of its parameters and
analysed only parts of it, and just by summing those different parts we cannot
understand how the entire system functions.</p>
<p>Biodiversity science is part of the broader drive towards managing our planetary
environment, particularly moving to a sustainable pattern of use in the face of a
growing human population. Related questions to pose over the next decade include: Will
we need an organismal inventory to understand and monitor ecosystem function? Will we be
able to monitor functional diversity directly? Can we measure fluxes as a metric of
ecosystem health [<xref ref-type="bibr" rid="B9">9</xref>
]? Will we be able to develop better mechanisms to represent organism
interactions, for instance, the microbiome of multicellular organisms [<xref ref-type="bibr" rid="B10">10</xref>
], viruses in plants or the composition of the rhizosphere? These are
comparatively new areas of research not yet represented by a significant body of data or
services, but essential for managing our planet in the long-term [<xref ref-type="bibr" rid="B3">3</xref>
,<xref ref-type="bibr" rid="B11">11</xref>
,<xref ref-type="bibr" rid="B12">12</xref>
].</p>
<p>To scale up and understand the whole system, we need new approaches, data types and
services. Access to these larger data resources are largely to be found through
informatics, but the application of those resources will be made by domain specialists.
Our ultimate goal is an understanding of the whole Earth system, so we must retain a
broad range of biodiversity monitoring sites, but at the same time we should also focus
research effort on key model ecosystems where we can achieve the intensity of outcomes
the biomedical research community has with the model organism approach. Only by looking
at vast databases that describe the whole of the system will we be able to understand
the big picture, find correlations and patterns of activities. Knowing how such patterns
and processes of biodiversity change will further help in more targeted experimentation,
resulting in new key datasets. Enhancing the biodiversity informatics infrastructure we
have today is therefore indispensable.</p>
</sec>
<sec><title>Context</title>
<p>The EC Commissioners Máire Geoghegan-Quinn (Research and Innovation), Neelie Kroes
(Digital Agenda), and Connie Hedegaard (Climate Action) have emphasised<sup>d</sup>
the
crucial nature of infrastructures for achieving their respective political agendas. In
particular, Commissioner Neelie Kroes on 11th April 2012 [<xref ref-type="bibr" rid="B13">13</xref>
], emphasised the importance of open e-Infrastructures, sharing of raw data and
results, and collaboration to enable more open science. Open science is the direction
that the European Commission (EC) promotes for project proposals under the Horizon 2020
funding initiative, also in accordance with the Nagoya protocol [<xref ref-type="bibr" rid="B14">14</xref>
], adopted at the 10th meeting of the Parties to the Convention on Biological
Diversity [<xref ref-type="bibr" rid="B15">15</xref>
].</p>
<p>The UK’s Royal Society published a report called ‘Science as an open
enterprise’ [<xref ref-type="bibr" rid="B4">4</xref>
] that highlights the need for a paradigm shift away from traditional practices
and mindsets. To quote the report, “… although scientists do routinely
exploit the massive data volumes and computing capacity of the digital age, the approach
is often redolent of the paper age rather than the digital age”. Key to their
vision is the concept of ‘Intelligent Openness’ (Table <xref ref-type="table" rid="T1">1</xref>
), a standard that Biodiversity Informatics must attain before attempting
more complex linkage of services.</p>
<table-wrap position="float" id="T1"><label>Table 1</label>
<caption><p>Intelligent openness as defined by the UK’s Royal Society</p>
</caption>
<table frame="hsides" rules="groups" border="1"><colgroup><col align="left"></col>
<col align="left"></col>
</colgroup>
<thead valign="top"><tr><th align="left"><bold>Intelligent openness terms</bold>
</th>
<th align="left"><bold>Definition</bold>
</th>
</tr>
</thead>
<tbody valign="top"><tr><td align="left" valign="bottom">Accessible<hr></hr>
</td>
<td align="left" valign="bottom">Data must be located in such a manner that it can readily be found and in
a form that can be used.<hr></hr>
</td>
</tr>
<tr><td align="left" valign="bottom">Assessable<hr></hr>
</td>
<td align="left" valign="bottom">In a state in which judgments can be made as to the data or
information’s reliability. Data must provide an account of the
results of scientific work that is intelligible to those wishing to
understand or scrutinise them. Data must therefore be differentiated for
different audiences.<hr></hr>
</td>
</tr>
<tr><td align="left" valign="bottom">Intelligible<hr></hr>
</td>
<td align="left" valign="bottom">Comprehensive for those who wish to scrutinise something. Audiences need
to be able to make some judgment or assessment of what is communicated.
They will need to judge the nature of the claims made. They should be
able to judge the competence and reliability of those making the claims.
Assessability also includes the disclosure of attendant factors that
might influence public trust.<hr></hr>
</td>
</tr>
<tr><td align="left">Useable</td>
<td align="left">In a format where others can use the data or information. Data should be
able to be reused, often for different purposes, and therefore will
require proper background information and metadata. The usability of data
will also depend on those who wish to use them.</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Within this context of a more open and transparent future where both scientific results
and the data needed for the conduct of science are easily accessible, linked and
properly attributed and preserved, we consider the challenges and priorities in a
decadal vision for biodiversity informatics<sup>e</sup>
at the European level.</p>
<p>Such a vision is of global interest<sup>f</sup>
and should be the result of a
comprehensive strategic roadmapping exercise, like the one recently undertaken in the
health informatics domain [<xref ref-type="bibr" rid="B16">16</xref>
]. It is necessary to engage with the biodiversity community and use mixed
methods to elaborate likely future scenarios from which to derive the required strands
of future informatics development. On the other hand, we can build on substantial
reflective work that already exists.</p>
<p>In “The big questions for biodiversity informatics” [<xref ref-type="bibr" rid="B17">17</xref>
] Peterson et al. assert that biodiversity informatics currently exists
“without major guiding scientific goals that represent intellectual frontiers and
challenges”, and fear this gap leaves biodiversity informatics without a framework
for effective thinking, resulting in a disjoint set of resources – both data and
tools – that cannot be effectively harnessed together yet. They posit a future
where biodiversity informatics enables biodiversity science to become a predictive
exploration of space, time and form. In “Evolutionary Informatics: Unifying
knowledge about the diversity of life” [<xref ref-type="bibr" rid="B18">18</xref>
], Parr et al. propose the grand goal to "Link together evolutionary data
across the great Tree of Life by developing analytical tools and proper documentation
and then use this framework to conduct comparative analyses, studies of evolutionary
process and biodiversity analyses". Five challenges to realising that goal are also
discussed.</p>
<p>In this white paper we must establish a stronger focus and a direction to guide the
development of biodiversity informatics in Europe over the next decade whilst at the
same time allow for serendipity. Clearly, the rate of change in the technology
environment around us is dramatic: tools like Facebook, Twitter, Foursquare, Google
(Earth/Scholar/…), Mendeley and Dropbox have penetrated our working lives and
techniques like MapReduce [<xref ref-type="bibr" rid="B19">19</xref>
] impact greatly our ability to manipulate and analyse massively large
datasets. Smartphones, digital cameras, GPS positioning and progress in geospatial
analysis offer possibilities for ‘apps’ and techniques that were hardly
imagined just a few years ago. Workflows as a tool for in-silico processing of data and
the concept of Virtual Laboratories where scientists carry out digital experiments,
hardly known few years ago, offer today enormous opportunities in virtually reproducing
our environment. Similarly in genomics, the rapidly decreasing costs of sequencing
technologies combined with the emergence of increasingly sophisticated alignment and
inferencing algorithms is leading to huge increases in our knowledge of life as a
system.</p>
<p>This ‘disruptive’ innovation trend will continue with ‘cloud’,
‘big data’, ‘linked data’ and ‘open access’ leading
to new ideas, products and services. The biodiversity informatics community is adapting
to the increasing rate of change by adopting dynamic solutions freed from rigid
technologies that may be obsolete tomorrow. We need to demonstrate how we are joining
up. Collectively we need to see the big picture, understand the jigsaw of challenges and
decode all the complexity that exists within populations and species.</p>
<p>To put this in context, two recent initiatives have, respectively, examined the
challenges facing biodiversity science research in Europe, and espoused the
architectural principles of the Biodiversity Observation Network (GEO BON) within the
Global Earth Observation System of Systems (GEOSS) initiative. The first initiative led
to the LERU report [<xref ref-type="bibr" rid="B20">20</xref>
] that discusses 18 building blocks for the biodiversity research agenda,
necessary to implement the EU’s 2050 vision for biodiversity and ecosystem
services<sup>g</sup>
, and to reach the EU’s 2020 targets<sup>h</sup>
for
halting biodiversity loss. Principally, the report points to the need for a common
e-Science infrastructure for biodiversity research (sub-clause 13). Several of its key
recommendations involve informatics playing a substantial, enabling role. With their
clause numbers from the LERU report in brackets, these recommendations are:</p>
<p>• Investing in a European infrastructure for biodiversity data and
research (sub-clause 32)</p>
<p>• Investing strongly in enhancing fundamental knowledge on biodiversity
drivers and threats (sub-clause 33)</p>
<p>• Supporting effective translation of scientific knowledge into
biodiversity practice (sub-clause 34)</p>
<p>• Supporting multidisciplinary collaborative networks (sub-clause
41)</p>
<p>• Supporting the science-policy interface in biodiversity protection,
and in particular supporting the needs of the Intergovernmental Panel on Biodiversity
and Ecosystem Services (IPBES) (sub-clause 42)</p>
<p>• Delivering education and awareness (sub-clause 43).</p>
<p>The second initiative is GEO BON [<xref ref-type="bibr" rid="B21">21</xref>
-<xref ref-type="bibr" rid="B23">23</xref>
] that, building on existing networks and initiatives, proposes "an informatics
network in support of the efficient and effective collection, management, sharing, and
analysis of data on the status and trends of the world’s biodiversity, covering
variation in composition, structure and function at ecosystem, species and genetic
levels and spanning terrestrial, freshwater, coastal, and open ocean marine
domains".</p>
<p>GEO BON fits in the broader conceptual framework of GEOSS [<xref ref-type="bibr" rid="B24">24</xref>
] to deliver a decentralised and distributed informatics infrastructure. The
GEO BON system will have a Service Oriented Architecture (SOA) and will be built largely
from contributing systems that have their genesis at regional, national or sub-national
scales. At the European level, the planned ESFRI LifeWatch research infrastructure [<xref ref-type="bibr" rid="B25">25</xref>
], along with EBONE [<xref ref-type="bibr" rid="B26">26</xref>
] and EU BON [<xref ref-type="bibr" rid="B27">27</xref>
] projects, eventually forms the European contribution to realising GEO
BON.</p>
<p>Key among the technical objectives of GEO BON is the need to promote the use of
multidisciplinary interoperability standards, and to define and update interoperability
solutions – applying the System of Systems approach promoted by GEOSS. GEO BON
will also help to promote data publication principles in support of full and open
availability of data and information, recognising relevant international instruments and
national policies and legislation. One of the main tasks for GEO BON contributors is
thus to identify the main contributing components, list the services they provide and
also the standards or special interoperability solutions they use. Central to the
success of GEO BON is increasing cooperation among the standards organisations with
interests in the biodiversity science domain, notably: the Genomics Standards Consortium
(GSC) for standards at the genomic / genetic level; TDWG for biodiversity information
standards at the organism level; and the Long-Term Ecological Research network (LTER)
for standards concerned with populations and ecosystems. Better cooperation leads to
better coherence among standards and better interoperability.</p>
<p>It is clear, then, that the landscape of biodiversity informatics is already
complicated, but is understood. Moving forward must take account of, and build upon,
what has already been achieved.</p>
</sec>
<sec><title>The taxonomic impediment</title>
<p>The term Taxonomic Impediment [<xref ref-type="bibr" rid="B28">28</xref>
] was coined by IUBS/Diversitas to refer to the gaps in our taxonomic
knowledge, the shortage of taxonomic expertise and the impacts that these have on the
progress of biodiversity science. In this context, taxonomy is the knowledge that allows
the actors in a process to be identified and for inference to be drawn from the presence
of a particular organism. Taxonomic services refer to the means of delivering that
knowledge and present three basic problems:</p>
<p>• Taxonomic services rely on highly educated personnel and hence are
very expensive;</p>
<p>• The data delivered by traditional taxonomic services have a limited
application potential, largely because species identification is expensive and therefore
typically carried out with a limited spatio-temporal and taxonomic scope, unsuited to
address ecological questions at larger spatio-temporal scales or more complex
patterns;</p>
<p>• Taxonomic expertise is shifting away from traditional practices of
producing morphological descriptions and identification keys towards phylogenetic,
especially molecular, studies.</p>
<p>The Taxonomic Impediment is, in part, a reflection of the need to use these expensive
taxonomic services for all studies in the natural world. Alternative approaches that
could address some biodiversity-related problems could help to relieve the currently
perceived bottleneck and allow taxonomists to focus on those groups where their skill
delivers greatest return. Necessary tools include semi-automated image-based species
identification services based on techniques such as those described in [<xref ref-type="bibr" rid="B29">29</xref>
] and citizen reporting systems such as the Swedish Species Gateway [<xref ref-type="bibr" rid="B30">30</xref>
]. Enhancing taxonomic services with DNA-based identification tools (e.g. the
DNA Barcode of Life standard [<xref ref-type="bibr" rid="B31">31</xref>
]) for example will not only improve the quality of identifications
(objectivity, data interoperability), but will also deliver high-throughput approaches
for environmental monitoring, species intense ecosystem research (e.g. Moorea Biocode
Project [<xref ref-type="bibr" rid="B32">32</xref>
]), and better ecosystem-based management.</p>
<p>Biodiversity informatics can help by liberating the taxonomic scientist from the
clerical labour of locating comparative materials, both specimens and
literature<sup>i</sup>
[<xref ref-type="bibr" rid="B33">33</xref>
].</p>
<p>A more radical way to overcome the taxonomic impediment might be to use biodiversity
informatics without traditional taxonomy. Molecular studies can generate characteristic
sequences, to identify organisms, or more radically still, identify particular enzymes
central to the process of interest. It could be argued that the current thinking of
species name and location is paper-based and not embracing the informatics potential.
Research projects exploring such innovative approaches should be encouraged. In
particular genomic observatories are in a position routinely to sequence DNA and link
this foundational layer of biodiversity to its biological, ecological, environmental and
social context.</p>
</sec>
<sec><title>Changing the landscape - a decadal vision</title>
<p>The key component needed to develop biodiversity informatics further is effective
integration of the available resources, to ensure that the practice of publishing
biodiversity information becomes widely adopted in the scientific community and leads to
scientific synthesis. Synthesis is increasingly recognised as an essential component of
the scientific endeavour. Scientific synthesis refers to the integration of diverse
research in order to increase the generality and applicability of the results. At its
core, synthesis is about blending disparate information and knowledge in ways that yield
novel insights or explanations [<xref ref-type="bibr" rid="B34">34</xref>
]. Synthesis occurs both within and across disciplines and the implementation
of an effective biodiversity informatics infrastructure would greatly enhance this type
of activity. Such an enhanced integration of all related information, including raw
data, processed data, algorithms (code, workflows) and publications can be achieved
through the implementation of an effective biodiversity informatics infrastructure: a
shared and maintained multi-purpose network of computationally-based processing services
sitting on top of an open (published, registered and linked) data domain. Together,
these deliver a stable, broad portfolio of biodiversity information and analytical
services that can be used by user communities to investigate problems of interest.</p>
<p>The vision is to develop the concept of 'services' delivering either data or analysis of
information using a small set of interchange standards. New services can be introduced
into such an environment and be generally accessible without special effort. This vision
implies a number of significant details, which are elaborated in more detail in the
remainder of this white paper.</p>
</sec>
<sec><title>Realising the vision</title>
<p>Effective realisation of the decadal vision relies on achieving a balance of top-down
and bottom-up approaches by making appropriate funding decisions. Top-down approaches
include thinking and acting at the European level, encouraging community adoption of
standards within the EU (part of a worldwide effort in which the EU is a key player),
setting direction and goals through targeted funding calls, workshops and meetings.
Bottom-up approaches derive from the motivation of individuals, their ideas and
enthusiasms and their need to solve specific problems. Both approaches together
recognise the role that individuals and groups have to play in the decadal vision by
encouraging islands of infrastructure to emerge, grow organically and fuse with one
another over time.</p>
</sec>
<sec><title>Leveraging existing projects</title>
<p>Numerous biodiversity informatics projects have been funded in Europe by, amongst
others, the Framework Programmes. Globally, there are already more than 650 projects
known [<xref ref-type="bibr" rid="B35">35</xref>
]. Examples from Europe include the networks of excellence (ALTER-Net,
LTER-Europe, EDIT/PESI, MarBEF/Mars, EuroMarine etc.) and other projects such as
4D4Life/i4Life, agINFRA, Aquamaps, iMarine, BioFresh, BioVeL, ENVRI, EU-BON,
EUBrazilOpenBio, Fauna Iberica, MicroB3, OpenUp!, pro-iBiosphere and ViBRANT among
others<sup>j</sup>
. Many of these projects directly address the challenges of
deploying e-Infrastructure for biodiversity science<sup>k</sup>
. They seem to share
similar characteristics, such as scientific field, integration and interoperation of
resources, open access, service orientation, e-Infrastructure and e-Science virtual
environments. They differ substantially, however, in their architectures and
technological approaches. These largely technical differences illustrate a larger
problem: the lack of a common understanding about how best to deploy e-Infrastructures
for biodiversity and ecosystem research. None of the projects can solve the problem
alone nor hope to provide all the functionalities that will be needed in the future.
Working on non-converging agendas, understandable given the imperative to push
boundaries for innovation and academic advancement, does not lead to a coherent
infrastructure with all necessary capabilities and capacities to support scientific
research. There are overlaps, dead-ends and often, complete lack of mainstream
industrial involvement. It is for such reasons that community consensus around a decadal
vision, combined with effective selection of projects to be funded and their subsequent
interactions and management within a coherent programme, is so important.</p>
<p>The decadal vision provides the means by which the complementary aspects of multiple
projects can be combined in a common roadmap forward. Achieving this requires an
increased awareness from all projects of the architectural approaches and construction
steps to be adopted. Multiple projects contributing to that infrastructure need to get
aligned because no single project can solve all problems alone. Separate projects need
to achieve greater coherence and coordination to maximise the benefit from substantial
investments of the past, present and future.</p>
<p>Within the Horizon 2020 framework it is therefore required to develop an effective and
continuing coordination, dissemination, education and training capability providing and
re-distributing help, technical guidance and examples of best practice. This capability
will inform individuals and groups about the top-down strategies, the priorities and
progress made, leading towards greater community understanding of the overall
vision.</p>
<p>Project proposals developed bottom-up for Horizon 2020 funding should fit under the
umbrella of the community’s decadal vision. They should leverage completed and
existing funded projects to gain the maximum benefit for the future biodiversity
infrastructure. Proposals should explain how they have taken earlier and current project
results into account and demonstrate that they are building on them rather than offering
incompatible alternatives. Re-inventing the same (or different) solutions is not
cost-effective. Letters of support from other projects should be used to demonstrate
that community-wide discussion and acceptance of proposals has taken place prior to
submission for funding. Project proposals should show clearly where and how they
contribute towards the decadal vision. They should devote a significant portion of their
resources to networking with other projects, to demonstrating compatibility and
added-value as a key performance indicator at an early stage, and to marketing the
services, technologies and approaches being developed to potential users.</p>
</sec>
<sec><title>Section 1: the fundamental backbone (getting the basics right)</title>
<sec><title>Why are names important?</title>
<p>1. Until the recent application of molecular technologies to biodiversity
studies, almost all information has been labelled with scientific names. Names have a
special significance to link information elements and as such, it is important to use
them knowingly and to build tools that work with names [<xref ref-type="bibr" rid="B36">36</xref>
]. As they reflect concepts that change between individuals and over time,
names may refer to many different concepts, making them equivocal identifiers. In
addition, information is often available only in local databases. The challenge is to
find it, harmonise the way it is accessed and make it available in computer-readable
formats. Nomenclature, taxonomy, taxa and their biology together constitute a large
challenge requiring novel infrastructure and change of usual practices by
stakeholders. Numerous initiatives exist to deal with these aspects but progress will
require a common agenda to bring about a virtual infrastructure that will reduce the
apparent diversity of web resources without reducing the diversity of services
required by a diverse user community. While content about taxon names must be
assembled by nomenclaturalists, taxonomists and managers of biodiversity information,
there is an urgent need for vision-driven architectural and engineering solutions.
The GNA’s (Global Name Architecture) [<xref ref-type="bibr" rid="B37">37</xref>
] current priority is on name-strings (Global Names Index GNI [<xref ref-type="bibr" rid="B38">38</xref>
]) and name-usage instances<sup>l</sup>
(Global Names Usage Bank GNUB [<xref ref-type="bibr" rid="B39">39</xref>
]). The latter does not yet exist but will provide the essential semantic
relationships (cross-links) at the nomenclatural level. This focus is entirely
appropriate because universal coverage is tractable in the short to medium term. The
resolution of names to concepts (see paragraph 3, below) is far more difficult and is
likely to be intractable for universal coverage.</p>
</sec>
<sec><title>How are names organised?</title>
<p>2. A long unorganised list of names is not particularly helpful. Since
Linnaeus biologists have used latinised binomial names where the first part (the
genus) is shared by a group of similar organisms and the second part, the epithet,
differentiates between members of the group (e.g. oak trees belong to the genus
<italic>Quercus</italic>
that contains around 600 species). A similar hierarchical
classification is followed for genera that are grouped into families, families into
orders, orders into classes and classes into phyla. As science advances, however,
these relationships change with greater understanding. While it is possible to build
hierarchies from instances of name-strings, it is inefficient. The solution required
includes a classification bank combined with a name list (see Paragraph 1) to produce
a taxonomic hierarchy automatically for groups that have not recently received
taxonomic attention.</p>
</sec>
<sec><title>Which is the right name?</title>
<p>3. The Species2000 / ITIS Catalogue of Life [<xref ref-type="bibr" rid="B40">40</xref>
] is a global taxonomic reference system drawing on content from more than
100 sources. It provides a composite expert view on taxonomic information, providing
an authoritative but mutable framework. Names within CoL represent concepts, but
there is no link to the concepts themselves and therefore an identification cannot be
unequivocally verified. Other classifications with names, such as NCBI taxonomy [<xref ref-type="bibr" rid="B41">41</xref>
] or the WoRMS systems [<xref ref-type="bibr" rid="B42">42</xref>
] can also be used as organisational frameworks. Yet each serves its own
audiences, revealing the need for multiple systems that are however interoperable.
Initiatives such as the Global Names Architecture (GNA) [<xref ref-type="bibr" rid="B37">37</xref>
] promote the development of an infrastructure capable of linking available
information about biological names. iPlant’s Taxonomic Name Resolution Service,
TNRS 3.0 [<xref ref-type="bibr" rid="B43">43</xref>
] corrects and standardises plant scientific names against particular
taxonomies. ZooBank [<xref ref-type="bibr" rid="B44">44</xref>
] is a new initiative to move the process by which new names become
recognised into the digital age. Tools for alignment and cross-mapping of taxonomies
can only be partially automated, since the domain knowledge held by taxonomists is
very difficult fully to codify. Some projects such as i4Life [<xref ref-type="bibr" rid="B45">45</xref>
] have developed tools that exploit characteristics of biological
nomenclature to detect relationships between taxonomies, providing a useful "first
draft" cross-map. Nevertheless to be authoritative, future environments must link
nomenclators (like ZooBank, IPNI and Index Fungorum, Mycobank), taxonomic compendia
(such as CoL and WoRMS), other classifications (a classification bank, perhaps),
literature sources (describing species, their attributes, distributions, and common
names), and phylogenies, covering the whole spectrum of biodiversity complexity. The
taxon name is an access key, but it is essential that it can be linked to other
resources, such as descriptions, traits and habitat. Ultimately names are the bridge
to the accumulated information built over the past 300 years and trapped in the paper
world.</p>
</sec>
<sec><title>What is the name of that organism?</title>
<p>4. The practical identification of an organism relies on the construction
of a circumscription of the taxon to be identified, which in turn requires the
examination of a range of specimens agreed to belong to the taxon. Before the digital
era, the only ways to identify the name of an organism were to use a paper
identification key or to consult an expert. New identification techniques have
emerged to get to the name of an organism, including matrix keys and ‘smart
keys’ that use the locality and time of the year to reduce the number of
identification choices. These identification techniques however are labour-intensive
and depend on experts to create the necessary circumscriptions, keys and the link to
the list of accepted names. Automated identification techniques, like image
recognition or <italic>in situ</italic>
DNA analysis, are not yet sufficiently developed to
be used routinely and reliably for most organisms. Identification keys always cover a
small part of biodiversity and may also be difficult to discover. Developing
morphological keys to all organisms is not achievable because no global organisation
can establish a central repository, or even coordinate, prioritise and fund the
creation of keys. The major priority therefore is to make the necessary descriptive
data with their associated range and habitat information freely available. Services
can then be created, for instance as 'apps' for the mobile phone market.</p>
</sec>
<sec><title>Can biodiversity studies be done without names?</title>
<p>5. Almost all of our accumulated knowledge about biodiversity has been
gathered and organised using species names. According to a recent exhaustive review
by Costello [<xref ref-type="bibr" rid="B33">33</xref>
], taxonomists think that about 1.5 million living species have been
described, but lacking a single authoritative list of names, this is only an
approximation and many species may be invalid [<xref ref-type="bibr" rid="B46">46</xref>
]. The number of species left to be discovered are substantial [<xref ref-type="bibr" rid="B47">47</xref>
] and, given that current taxonomy is the product of more than 250 years of
effort, it is unrealistic to have a complete catalogue if we adhere to currently
accepted methods [<xref ref-type="bibr" rid="B33">33</xref>
,<xref ref-type="bibr" rid="B48">48</xref>
]. Solutions include modern molecular techniques, such as DNA barcoding and
massively parallel high throughput sequencing, effective at revealing much of the
undescribed diversity. Such systems based on environmental genomics
(‘metagenomics’) are already well established in microbial ecology where
DNA sequences act as tags identifying organisms in the ecosystem. These techniques,
being inherently destructive, cannot yield a traditional specimen, so cannot be used
to name a new species, but they are promising in assessing ecosystem biodiversity
without the requirement to name every species present. Challenges in the deployment
of such techniques include:</p>
<p>• Ensuring that the data, information and knowledge emerging from
this new paradigm become integrated with traditional taxonomy so that we continue to
benefit from the efforts of taxonomists over the previous 250 years;</p>
<p>• Curating the species’ names that have been attributed to
sequences in databases of the International Nucleotide Sequence Database
Collaboration (INSDC);</p>
<p>• Devising a framework to integrate the specimen-centric
observations encoded by the Darwin Core standard with the environmental and
ecological context of metagenomics;</p>
<p>• Putting these different layers of information together so as to
identify the response of the ecosystem to environmental change;</p>
<p>• Being able to access covariate data, for example concurrent
observable chemical, biological and other environmental variables from the target
ecosystem, especially for environmental metagenomic studies.</p>
<p>Ecological research will largely benefit from such new approaches classifying and
understanding genomic biodiversity based on functions, their evolution and
distribution.</p>
</sec>
<sec><title>Biodiversity data beyond names</title>
<p>6. Names are an access key to biodiversity information, including
information on the occurrence of species in time and space. Ultimately we need to be
able to integrate biodiversity information indexed by names with information on:</p>
<p>• Functional diversity;</p>
<p>• Diversity at various levels of organisation: genes, organisms,
ecosystems, landscapes;</p>
<p>• Relationships between facets of biodiversity and ecosystem
functioning and services;</p>
<p>• Those variables and data that describe the physical
environment;</p>
<p>• Fluxes through environments, such as phosphorus into and out of a
system.</p>
<p>Crucial to this endeavour will be our ability to devise methods to link such data and
to define data standards to make such linkage straightforward (see Paragraph 15 et
seq. below).</p>
</sec>
<sec><title>To link resources we need identifiers</title>
<p>7. Biodiversity science needs to adopt a system of persistent and
universally unique identifiers (PIDs or UUIDs) that will allow resources to be
located and linked. An identifier can be attached to a resource of any kind,
including data (e.g. specimens in a collection), taxonomic concepts, genetic
sequences (e.g. INSDC accession numbers), publications (e.g. DOIs), or data sets and
services, (e.g. workflows, computational services or computer code). Identifiers must
be stable and unique but they should also:</p>
<p>• conform to some widely-used syntactic definition;</p>
<p>• their initial part should be consistent (e.g. http:// or DOI:), so
that they can be recovered in a free-text search;</p>
<p>• ideally, be resolvable (resolution = where to find a particular
resource);</p>
<p>• be archived together with the resource in a sustainable manner,
ideally in multiple locations (if the GUID is not resolvable, the resource can be
found by searching for the GUID).</p>
<p>PIDs do not protect against duplication, i.e. a single resource carrying multiple
PIDs, but if they were used, then resources could be linked, so that discovery of any
item in a chain of connections would permit the discovery of all the rest of the
resources as well as allow for consequent credit allocation (see paragraph 11). There
is no technical challenge in the use of PIDs. DOIs, for example, are now familiar in
many publications and although the DataCite [<xref ref-type="bibr" rid="B49">49</xref>
] initiative has significantly reduced both the cost and complexity of using
DOIs, their direct application by the biodiversity informatics community remains
rare. The community, then, seems reluctant to use PIDs. It is not clear whether this
is due to reluctance to change current working practice or whether it is due to lack
of suitable tools - either for linking or for following links. Note that a well-known
resolver, comparable to CrossRef for DOIs, is only necessary for resolvability (point
and click). PIDs, by their unique nature can be discovered with standard search tools
such as Google. More elaborate linkage mechanisms are possible, and could deliver
much greater benefit, but introducing the community to simple linkage is a challenge,
so Linked Data [<xref ref-type="bibr" rid="B50">50</xref>
] is considered a “next step” (Section 2) rather than a
foundational technology in this white paper.</p>
</sec>
<sec><title>Centralised or networked services?</title>
<p>8. Networked services refers to the use of a resource directly over the
web, so that one website may call another site for information necessary to carry out
its function. Centralised services, on the other hand, concentrate all the associated
resources at a single site. Although networked services are desirable to maintain
consistency and to focus resources for maintenance (i.e. an authoritative master
copy), in practice they are often unable to deliver the speed of response necessary
for usability and creating a local copy (a snapshot of a dynamic resource) or
developing an independent resource is often the only realistic solution available.
Local copies are often the only practical solution for computation but there is no
mechanism by which alterations to the primary resource can be effectively propagated
to all copies. This inevitably leads to differences between copies. Copies should,
therefore, be used over a short timeframe and, if necessary, refreshed. A feedback
mechanism is required so that a data user can report an inconsistency to the data
owner and, where a correction is suggested, can be easily incorporated into the
original dataset. Automated workflows crucially require Web services but working with
large datasets in networked services poses technical challenges in the ability to
move large volumes of data, the provision of suitable search facilities that minimise
the number of host-client interactions, and the bandwidth necessary to keep response
times short. Centralised services, such as VertNet [<xref ref-type="bibr" rid="B51">51</xref>
] assemble large collections of data in a common structure, submitted by
individual data creators. The benefit that this brings is economy of scale and the
ability to tune the performance of the system. In the context of data cleaning, for
example, having the data centrally makes it much easier to compare across data sets
and discover inconsistencies. The gain of economy of scale is very important since
once a given type of error is identified, rules can be applied for cleaning across
the whole data set, therefore avoiding overloading remote services. Once established,
though, it is difficult to change the structure and change the purpose, but for
large-scale data generation systems, logically centralised services offer significant
advantages. One drawback, however, now receiving attention in the genomics and other
fields is the issue of time taken to move large datasets to where the resources for
computational analysis and modelling are located. Strategies are presently being
considered for how to move computation to the data.</p>
</sec>
<sec><title>How to balance professional and non-professional contributions</title>
<p>9. Engagement of the biodiversity expert community is undoubtedly a key
factor in advancing knowledge. Citizen science projects have been remarkably
successful in advancing scientific knowledge by providing data primarily on species
occurrence and distribution around the world [<xref ref-type="bibr" rid="B52">52</xref>
]. These engage the public in the collection and analysis of data sets from
multiple habitats and can span long periods of time. The big scientific issue tackled
by these large data sets is how biodiversity varies through space and time, including
biodiversity loss and detection of trends, such as shifting distribution boundaries.
Citizen science projects represent a massive effort spent on biodiversity monitoring
that could not otherwise be covered by the professional community alone without huge
sustained financial investment. The primary challenge for the biodiversity
informatics community is to develop a framework to address the currently multiple,
cross-cutting requirements of citizen science projects, such as:</p>
<p>• Covering all steps in the development and implementation model of
such projects, from the choice of scientific question to the evaluation of the
outcomes;</p>
<p>• Automating validation (quality assurance, quality control and data
cleansing processing) and annotation of the data produced [<xref ref-type="bibr" rid="B53">53</xref>
];</p>
<p>• Developing incentives to encourage participation in processing,
analysis and use of data;</p>
<p>• Developing virtual research and teaching environment(s) for
citizen scientists, to develop their skills to answer basic scientific questions;</p>
<p>• Improving systems for automated image recognition based on
existing technologies (e.g. TinEye Reverse Image Search [<xref ref-type="bibr" rid="B54">54</xref>
]) to harvest the vast repositories of amateur naturalists' photos;</p>
<p>• Promoting best practices by disseminating successful examples of
actions on nature conservation;</p>
<p>• Ensuring continuous economic viability for the services through
the linking of such citizen science projects with the relevant economic
sector’s stakeholders [<xref ref-type="bibr" rid="B55">55</xref>
].</p>
</sec>
<sec><title>Engagement of Users</title>
<p>10. A great deal of high-quality software, services and resources have
been created over the past decade, but much remains underused, even within the
biodiversity informatics community itself. Many projects have relied on traditional
routes to publicise their products, primarily through academic publications. It would
be undesirable to impose standard applications or resources upon the community.
Better to allow users to decide, to select which products best match their
requirements. Projects should invest significant resource into marketing their
products, engaging with real users and refining the product from user feedback,
following the dictum of "release early and release often". Such marketing need not
absorb a significant fraction of a project's budget but should be a clear strategy
and an integral part of project management.</p>
</sec>
<sec><title>Who's who?</title>
<p>11. Traditionally, experts have published their observations and
conclusions in peer-reviewed paper publications, a tradition that has been
effectively transferred to the digital age through e-publication. The tradition has
several consequences. First, it has created a system of citations by which
individuals are assessed for career development. Second, the cost of print-on-paper
has driven data presentation to a compact, often summarised, format. Third, the
financial interests of the publisher have restricted the availability of the data for
re-use. Two aspects of the citation mechanism are important:</p>
<p>• Provenance, meaning that a data user can easily discover who
generated the data, which can attach a level of reliability to the data; and</p>
<p>• Impact, by providing a hyperlink that allows a user to see where a
particular data set has been used, both how often and for what, which could easily be
incorporated into the managerial assessment of an individual’s career.</p>
<p>Modern digital publication could effectively remove the typographical restrictions [<xref ref-type="bibr" rid="B4">4</xref>
] making data more easily available for re-use. Some publishers, e.g.
Pensoft [<xref ref-type="bibr" rid="B56">56</xref>
], are already introducing publication in parallel formats (paper, PDF,
HTML, XML). The new paradigm is about evolving new methods to identify contributors
and users consistently, where identification can be carried from one environment to
another, including the popular social networking environments like Facebook and
Twitter. Approaches to this are being developed in the ORCID consortium [<xref ref-type="bibr" rid="B57">57</xref>
] and VIAF [<xref ref-type="bibr" rid="B58">58</xref>
], designed for those who publish scientific articles (scholarly authors),
but also need to include other users, such as compilers of reports and assessments.
This transition to reusable data identifiably associated with an individual or group
of individuals is a common call within the Open Science movement, relevant for all
scientific disciplines. Note that the US National Science Foundation now requires
applicants for funding to list his or her research “products” rather than
“publications”, implicitly recognising the value of contributions beyond
paper publication [<xref ref-type="bibr" rid="B59">59</xref>
].</p>
</sec>
<sec><title>User identification</title>
<p>12. Open access data and services allow users to remain anonymous for some
level of access. Some forms of interaction however, such as posting comments,
corrections and some types of services, such as download, often require that users
identify themselves. Social media tools like Facebook, Google and Twitter offer
common 3rd-party authentication mechanisms that can be used for access control. This
has two main advantages: first, it makes every resource easy to access; and second,
it is a stronger security check compared to inventing a username and password for
each site visited. Nevertheless, some resources will require a stronger form of
authentication, for instance where payment is required. As a general principle,
access to biodiversity data should normally be unrestricted except where it is
essential to protect, for example, location data for rare bird nesting sites.</p>
</sec>
<sec><title>How do we ensure the right metadata are created at the point of data
generation?</title>
<p>13. The scientific process requires the collection of observations from
which hypotheses can be formed and, when necessary, more data to be collected to test
them. Adding metadata represents an overhead on current practice but it is essential
if data are to be discoverable and re-usable. Metadata are the key to discoverability
and provide the context for linking resources. To improve current practices there is
an urgent need for i) community agreement on metadata standards for specific purposes
and ii) mechanisms to collect and append the necessary metadata, automatically
whenever possible, such as the design of workflows that make use of standard services
to create data-recording templates. In the short term, the extra effort of metadata
production will have to be borne by the data producer, especially in the context of
data journals, but tools to automate the production of metadata are conceivable,
essentially eliminating the burden of production. A move to Linked Open Data is
expected to obviate the need for enlarged metadata by making data more easily
discoverable through concept linkage (see paragraph 15 et seq. below on Linked
Data).</p>
</sec>
<sec><title>Sustaining the physical infrastructure</title>
<p>14. Appropriate biodiversity informatics tools will generate greater
impact than is currently possible from the physical infrastructure of natural history
collections, mesocosms, other experimental facilities, long-term ecological
monitoring sites and genomics facilities, through much greater digital and on-line
access to the facilities than is physically practical. This will enhance the
sustainability of the infrastructure, since a large user base is critical for
political sustainability.</p>
</sec>
</sec>
<sec><title>Section 2: the next steps</title>
<sec><title>Data sharing</title>
<p>15. Two relatively large surveys were conducted to understand how data are
treated by scientists across different disciplines: by the PARSE.Insight project [<xref ref-type="bibr" rid="B60">60</xref>
], with 1202 respondents, and by Science Magazine [<xref ref-type="bibr" rid="B61">61</xref>
], with 1700 respondents, both with multidisciplinary international
responses. From what researchers say about where they store and manage data, it can
be deduced that data are not often shared openly. The results show that across all
disciplines only between 6-8% of the researchers deposit datasets in an external
archive of the discipline/research domain. The most common environment for storing,
managing and re-using data is the lab and/or individual working environment, down to
PCs and portable storage carriers. The category “server” is probably best
understood as a file server of the research organisation behind a firewall and with
restricted access for defined groups of registered users. According to Science
Magazine, most of the respondents (80.3%) said that they do not have sufficient
funding available for data curation. Other reports [<xref ref-type="bibr" rid="B4">4</xref>
,<xref ref-type="bibr" rid="B62">62</xref>
-<xref ref-type="bibr" rid="B65">65</xref>
] share more insight into data sharing practices by research area and
highlight the importance of data sharing becoming normal practice.</p>
</sec>
<sec><title>Why do we need vocabularies and ontologies?</title>
<p>16. Common vocabularies are the foundation for both human and machine
communication (e.g. in data sharing, in automated workflows, data integration and
analysis). By agreeing on a set of concepts and their definitions within a domain, a
community of practice can share data and information unambiguously. Data integration
and analysis critically requires semantic consistency as well as syntactic
standardisation, the former being more challenging to achieve than the latter.
Initially communities will accept a small controlled vocabulary - terms supported by
human-readable text definitions. As terms are rarely independent of one another, the
vocabulary list evolves into a thesaurus and, as formal relationships between terms
are agreed, an ontology [<xref ref-type="bibr" rid="B66">66</xref>
]. There are lessons to be learnt by looking elsewhere, for example,
Google’s "Knowledge Graph" [<xref ref-type="bibr" rid="B67">67</xref>
], the Unified Medical Language System (UMLS), medical informatics) [<xref ref-type="bibr" rid="B68">68</xref>
], AGROVOC (agriculture) [<xref ref-type="bibr" rid="B69">69</xref>
] and OBO (plant and animal phenotypes) stable of ontologies [<xref ref-type="bibr" rid="B70">70</xref>
]. AGROVOC covers many of the terms relevant to biodiversity and is modular
enough to be extended. There are other ontologies useful for capturing biodiversity
data, such as the environment ontology, EnvO [<xref ref-type="bibr" rid="B71">71</xref>
], and the more general DAML [<xref ref-type="bibr" rid="B72">72</xref>
]. There is a pressing need for ontologies that span multiple communities,
implying domains, and at present, such over-arching technologies do not seem to
exist. Individual community ontologies tend to isolate communities rather than enable
more open sharing, but community ontologies are with us now and need to be
integrated. Some systems, such as UMLS are not structured to support reasoning or
subsumption, so are not necessarily a good model for further development.
Nevertheless, establishment of community standard terminologies and ontologies
presents problems that are familiar to other communities, such as human genetics and
model organism functional genomics, and some of these lessons have already been
learned:</p>
<p>• Terminologies / ontologies need to be owned by the community but
their maintenance is an ongoing requirement which requires stable funding and a
degree of community coordination and interaction;</p>
<p>• tools that biologists find intuitive need to be developed for both
data coding and analysis, making the process efficient and effectively invisible;</p>
<p>• ongoing terminology and syntax development need expert
construction and are not just problems of computer science;</p>
<p>• a significant problem exists in the communication of changes in
those lists to sites that consume the data and a central catalogue / source is
required, such as currently provided by OBO or the NCBO (National Centre for
Biomedical Ontology);</p>
<p>• mapping of data coded by legacy terminologies and integration of
data coded by different species-specific ontologies are problems already addressed by
some communities.</p>
<p>There is potential in semantic interoperability for biodiversity data, but this
requires quite basic research and IT development to enter new paradigms supporting
open semantic approaches. The provision of a strategy for transferring
“legacy” data models into semantic-aware technologies is clearly
desirable because existing data models are often accurate, comprehensive and
represent a great deal of effort from the scientific community. We need a pragmatic
strategy for mobilising this knowledge. Such mobilisation may also assist in
achieving broad user acceptance, a greater problem than are the associated technical
issues. Developing and applying vocabularies is clearly hard and requires the
existence of persistent identifiers (paragraph 7 above) to be effective. It will
require organisation and cooperation, or to put it simply, it takes goodwill but also
cash.</p>
</sec>
<sec><title>How would knowledge organising systems help?</title>
<p>17. The term ‘knowledge organisation system’ (KOS) covers any
system for organising information, ranging from the traditional library subject
headings to newer approaches like semantic networks and ontologies. Recognising the
need for a standards architecture to provide basic interoperability across open
systems, the TDWG Technical Roadmaps in 2006, 2007 and 2008 all identified
community-supported vocabularies and ontologies, expressing shared semantics of data,
as one of three required components; the other two are common exchange protocols and
use of persistent identifiers for the data. The TDWG Darwin Core [<xref ref-type="bibr" rid="B73">73</xref>
] glossary of terms is amongst the most widely deployed biodiversity
vocabularies and both its management and relation to the TDWG Ontologies can be used
as a model for other vocabularies. The GBIF LSID-GUID Task Group [<xref ref-type="bibr" rid="B74">74</xref>
] highlighted the need for GBIF to identify sustainable support mechanisms
for essential shared vocabularies and commissioned White Paper Recommendations on the
use of Knowledge Organisation Systems. GBIF [<xref ref-type="bibr" rid="B75">75</xref>
] separated the need for ontology management from the lifecycle management
of flat vocabularies in such tools as BioPortal. The development, management and
governance of such vocabularies remain a challenge for the biodiversity community. As
concluded in paragraph 16 and discussed in section 4, the core technologies are
available and well understood, but uptake by the community is not ideal. The
challenge is to develop and deploy tools within the overall biodiversity informatics
infrastructure that make the implementation of knowledge organisation systems
effectively invisible. GBIF’s Integrated Publishing Toolkit [<xref ref-type="bibr" rid="B76">76</xref>
] is one example of a step in this direction. Put simply, what would it take
to make knowledge organisation work effectively and what would it achieve if it
did?</p>
</sec>
<sec><title>How easy is it to integrate data?</title>
<p>18. Biodiversity informatics is inherently a global initiative. With a
multitude of organisations from different countries publishing biodiversity data, our
foremost challenge is to make the diverse and distributed participating systems
interoperable in order to support discovery and access to those data. A common
exchange technology, e.g. XML or JSON, may allow the syntactic exchange of data
blocks, but both systems also need to understand the semantics of the data being
delivered to process it meaningfully. If the data do not share a common reference
model, then the exchange requires some brokering or other semantic processing (using
tools described in paragraphs 3, 7, 12 and 16 above). For instance, the widely used
standard Darwin Core is predicated on the occurrence (either a physical specimen or
an observational record) as the unit of information, so is of limited value in the
context of metagenomics for example, that may contain information about environmental
function without mention of a named taxonomic entity, or information about
communities of taxa. It is crucial that future efforts in this area take account of
major global initiatives, especially GEO BON, GBIF and Genomics Standards Consortium,
as well as novel approaches in eco-informatics, but it is likely that the data models
used in these initiatives will also need to be extended [<xref ref-type="bibr" rid="B77">77</xref>
]. Existing data must either be transformed in a semantically-aware manner
to conform to such standards, or software that is aware of the semantic heterogeneity
must work with multiple standards.</p>
</sec>
<sec><title>Beyond sharing and Re-use: the problem of scale</title>
<p>19. It should be straightforward to assemble a dataset on biodiversity and
reach conclusions by linking available information. To understand and model
processes, such as the phosphate cycle, requires information at the molecular level
over seconds (solubility, diffusion and uptake), kilometre level over years
(transportation and availability) and planetary level over geological time (mineral
formation, extractability). The integration of all these data resources is necessary
to model the cycle, from which policy decisions can be made for the time when cheap
mineral phosphate (a fertiliser) is no longer available (in the next few decades) [<xref ref-type="bibr" rid="B2">2</xref>
]. This example illustrates the complexity of the natural world, and how
‘grand’ is the challenge faced by biodiversity informatics to create a
coordinated coupled modelling environment to address health, sustainability and
environmental questions [<xref ref-type="bibr" rid="B78">78</xref>
].</p>
</sec>
<sec><title>How reliable are the data?</title>
<p>20. Science is, by its nature, a sceptical process. Data are received at
face-value, examined and tested. If the user is satisfied, then the data will be
applied. This process is crucial in biodiversity since information can rarely be
generated by simple measurements. Concepts (like species), observations (based on
human interpretations), proxy data (often originating from sensors) or algorithms
(models fit for specific cases) constitute most biodiversity data with their inherent
uncertainties and fuzziness. It is vital, then, that information about how the
measurement was taken, to the minimum data standard, is included in the associated
metadata. Judgement of quality involves an assessment of fitness-for-purpose and
therefore cannot be an absolute measure. Data can of course contain both errors of
fact, e.g. typographical errors, or errors of design, e.g. collecting data under a
flawed methodology. Errors of fact can be detected by various means, e.g. duplicate
entry or proof-reading whereas errors of design are more difficult to find
automatically. A more significant problem is the accuracy of the data, meaning how
precise and complete they are. In measurement it is accepted that a balance might
weigh to the nearest 5 g, being a characteristic of the balance. In information
terms, lacking a standard for generating the datum, it is harder. For instance,
bibliographic citations can have diverse formats that humans can easily resolve to
the same publication however computers, by and large, cannot unless given a PID as an
information standard. The challenge for biodiversity informatics is to provide
appropriate tools for data cleaning<sup>m</sup>
and to automate procedures for
reading data for consistency [<xref ref-type="bibr" rid="B79">79</xref>
], particularly against standard lists (see paragraph 16 above). Ultimately
it is a case of <italic>caveat emptor</italic>
. Users will develop trust in an information
supplier and sites may wish to use a voting mechanism, e.g. similar to the supplier
rating system on eBay. A system is required for data publishers to display comments
from identifiable users (see paragraph 12 above), providing a feedback mechanism,
essentially an open peer-review. Exposure to users is the best way to validate
data.</p>
</sec>
<sec><title>What will the physical infrastructure look like?</title>
<p>21. Plummeting cost of hardware, increasing use of virtualisation and
blurring between fixed / mobile computing and work / domestic environments for
computing makes the prediction of preferred computing environments of dubious value.
Compiling this white paper has identified no apparent need for bespoke ICT
technologies. A continued use of a wide variety of platforms and approaches is to be
expected. Biodiversity informatics has many requirements in common with other
informatics domains and it is noteworthy that biodiversity research, as in other
disciplines has the potential to produce very large and rapidly growing data sets
from, for example automated digitisation, remote sensing and genetic sequencing.
Although the configuration of existing and planned cross-domain infrastructure such
as LifeWatch supports biodiversity informatics well, the domain will place heavy
capacity demands on the computing infrastructure in the medium-term. Hardware
associated with sensor and data logging is addressed in Appendix 2. Like other
domains, biodiversity informatics will require robustness, stability and persistence,
so will likely rely on key institutions with long-term funding. Over the core
hardware infrastructure lies a spatial data information infrastructure, the
biodiversity component of which is largely the topic of this white paper. The
leveraging of information from distinct but adjacent domains will be increasingly
necessary in the future, such as digital literature resources, image, environmental
and climatic information databases. As molecular methods find ever greater uptake,
one particular set of resources will become increasingly important to biodiversity
informatics: these are the many biomolecular resources that, within Europe, lie
within the purview of the ELIXIR infrastructure [<xref ref-type="bibr" rid="B80">80</xref>
]. While many of the core resources themselves may be sustained with
comparatively long-term support, the services built upon these resources must be
configured to include biodiversity science use cases. A unified voice in specifying
these use cases is required from the biodiversity community. Building the
‘social infrastructure’, however, is a major challenge: we have the
technological capability but we need to increase its uptake by the community. For
that we need to strengthen considerably the socially connected network of experts
spanning the two communities: ICT and biodiversity science.</p>
</sec>
</sec>
<sec><title>Section 3: new tools</title>
<sec><title>How much of the legacy collections can be digitised?</title>
<p>22. The world’s biological collections represent the hard core of
biodiversity information. All other uses, from identification and naming onwards, are
anchored in them. The collections contain an estimated 2–3 billion specimens
but less than 10% have been catalogued in databases and much less captured as digital
images [<xref ref-type="bibr" rid="B81">81</xref>
,<xref ref-type="bibr" rid="B82">82</xref>
]. This means that more than 90% of the collections are essentially
unavailable for use through the Internet. Manually digitising collections represents
an effort estimated at up to one million person years, but, with today’s
mass-digitisation methodologies, the task is feasible. As shown by multiple virtual
herbarium projects [<xref ref-type="bibr" rid="B83">83</xref>
,<xref ref-type="bibr" rid="B84">84</xref>
], the process can be partly automated through imaging techniques. With
gazetteer services such as GeoLocate [<xref ref-type="bibr" rid="B85">85</xref>
], georeferencing can also be computer-assisted. Another good example is the
Volunteer site of the Atlas of Living Australia [<xref ref-type="bibr" rid="B86">86</xref>
] whereby, when a backlog of digital images is available, their
transcription is distributed through crowd-sourcing to a large number of volunteers.
With help from initiatives like iDigBio [<xref ref-type="bibr" rid="B87">87</xref>
], we envisage that distributed digitisation infrastructures will become
essential parts of most major natural history collections and that dedicated services
will be developed for outsourcing this task. A major challenge however is that
collections still grow faster than they are being digitised (e.g. through
endowments). As private collections must also be digitised by their owners, this
requires a new suite of easy and inexpensive tools that can be deployed at large
scale. To effectively deliver this research infrastructure service, digitisation
requires prioritisation and its own funding channels.</p>
</sec>
<sec><title>How to generate more targeted and reliable data?</title>
<p>23. Gathering information about the world around us has been a priority
for biodiversity science for many years (see Appendix 3). Observatories will soon
operate throughout the biosphere capturing different kinds of data over multiple
scales, from microns to planet-wide, from parts of a second to years. It is very
important to know the relative and also the absolute position of observed objects and
events. This brings special challenges when observing the desired phenomenon and
operating in extreme environments, such as the deep sea. The infrastructure for
biodiversity data urgently needs more advanced informatics, support - not only
mainstream ICT development but also the ability to deal with the specifics of
biodiversity features and data<sup>n</sup>
. It requires informatics to support
observations, event detections, species identification, data transfer, storage,
filtering and other kinds of data processing. New data-gathering tools that will
allow new observatories at all biological scales and sensor networks covering the
globe need to be designed, created and tested. There should be automatic processes
allowing for feedback from data interpretation back to the observation or detection
at site. This combination of techniques and related biodiversity informatics tools is
expected to herald a revolution in biodiversity research, resolving much of our
current fragmented data coverage and knowledge. Public-private partnerships should be
encouraged to enter pre-competitive research and development in this evolution.</p>
</sec>
<sec><title>What role do mobile devices play?</title>
<p>24. Developments in mobile communications offer numerous opportunities for
innovation (see Appendix 2). Smartphones and tablet PCs with on-board GPS location
can be easily taken into the field, creating opportunities both for innovative data
collection and user information services. They are also particularly innovative for
reference products such as identification keys<sup>o</sup>
. Apps like these can be
used to generate image-vouchered, location-tagged observations uploaded to central
databases<sup>p</sup>
. Performing science in large virtual communities, where
participants have varied levels of expertise requires new techniques for data
harvesting, processing, cleansing and validation.</p>
</sec>
<sec><title>How do you find the data you need?</title>
<p>25. Most biodiversity data that now exists are semi-structured and can be
searched with typical search tools (Google, GBIF, etc.). However, these are often
designed for use by humans rather than for automated data retrieval tasks and may
have in-built limitations or constraints. To make better use of general purpose
tools, users may need to use more specialised resources as well. GBIF, for example,
supports retrieval by species name but the user may also need to use resources such
as the Catalogue of Life to provide alternative names for species-based searching.
The volume of data now being searched is so large that it is often not possible to
refine keyword-type searches sufficiently to recover the needle buried in the
haystack, especially in the absence of widely-used vocabularies. Contextualising
information (establishing relationships between data elements) in a resource is
possible<sup>q</sup>
, but currently difficult and slow. The implementation of PIDs
(see Paragraph 7) would make the construction of metadata portals much easier. A
mature search mechanism that contextualises rather than simply indexing would be far
more powerful. A number of newly developed techniques exist, and some are under
development, that make extensive use of visualisation methods to detect patterns and
issues in data collections. These could be useful for quality and fitness-for-use
assessment, especially in very large datasets such as the LTER-Europe data index or
the GBIF index and taxonomical nomenclators. Data publishers need to go further in
helping users find the data that match their requirements, with the use of PIDs,
vocabularies and KOS (see paragraph 17).</p>
</sec>
<sec><title>How do you extract the data you need?</title>
<p>26. In publications, either paper or PDF, information is often embedded in
text blocks or tables in a way that inhibits its re-use. Semantic technologies (data
mining) offer potential for liberating such data, but have not yet demonstrated the
necessary flexibility or speed needed for broad uptake in the verbose, descriptive
disciplines of taxonomy and ecology. Perversely, it is also difficult to extract
information from highly condensed scientific writing such as taxonomic descriptions
because this style of writing relies on implicit context in order to be
understandable. New tools will be needed that use the vocabularies, ontologies and
KOS described above to establish context between data elements, then to extract and
assemble those elements into a format suitable for the user's purposes. Copyright
held by commercial publishers remains a serious obstacle to recovering the
non-copyright factual data. Some older publications that are not in copyright are
being digitised, but serious issues remain over the rate at which the historic legacy
literature is being captured, the completeness of the digitised literature and ease
of access to this literature. Errors in the Optical Character Recognition (OCR)
process mean that search of these archives will return an incomplete result set [<xref ref-type="bibr" rid="B88">88</xref>
]. This presents an additional level of difficulty over and above the
problems with extracting information from the born-digital literature.</p>
</sec>
<sec><title>How do you aggregate the data you need?</title>
<p>27. For many analyses it is often necessary to aggregate data from several
sources. Several data-aggregating initiatives have emerged in the last two decades
for various areas of biodiversity informatics. Some of these initiatives were done on
a project basis, while others were embedded in national structures, making them more
reliable sources of information in the long-term. Examples include GBIF for primary
data, Encyclopedia of Life (EOL) [<xref ref-type="bibr" rid="B89">89</xref>
] for species descriptions, uBio [<xref ref-type="bibr" rid="B90">90</xref>
] and Global Names Index [<xref ref-type="bibr" rid="B38">38</xref>
] for names usages, CoL [<xref ref-type="bibr" rid="B40">40</xref>
] for validated species names and Europeana [<xref ref-type="bibr" rid="B91">91</xref>
] for multimedia resources. These data-aggregation initiatives have
important beneficial side effects for biodiversity informatics such as enhancing the
availability, standardisation and duplication (‘backup’) of data.
Aggregation problems remain, similar for all these initiatives, such as hidden
duplication, proper attribution, harvesting and storage. Each aggregrator has tended
to solve these on their own by developing their own data provider network and
internal infrastructure. Increasingly, though they are recognising the need to
streamline and to avoid duplication of effort (for some examples see [<xref ref-type="bibr" rid="B18">18</xref>
]). To facilitate integration, it will be valuable to develop a well-known
catalogue of large-scale resources, with associated metadata, including a concept
map.</p>
</sec>
<sec><title>How complete are the data?</title>
<p>28. When combining data from different sources and domains,
interoperability is clearly not the only obstacle to analysing complex patterns of
biodiversity. The accessible biodiversity data today come largely from repositories
and individual researchers, and are generally of high quality with respect to
reliability. The quality is rather low however, with respect to consistency. In other
words the data aggregated today has been collected for different purposes and on
different spatio-temporal scales leaving significant gaps in the assembled data sets
and seriously hampering the analysis of complex patterns with data from diverse
domains. Gaps can be filled by developing more comprehensive biodiversity observatory
networks (BONs) and associated e-tools to support the collection, aggregation, and
discovery of data from these observatories. There is also a fundamental need to
re-consider what we already have – to ‘invert the infrastructure’ [<xref ref-type="bibr" rid="B92">92</xref>
],p.20] – to re-examine and re-formulate existing data to make it more
homogeneous, to remove non-biodiversity factors (e.g. compensating for differing
observation technique) and to make it suited to the kinds of analyses we foresee for
the future.</p>
</sec>
<sec><title>How can we encourage virtual research environments?</title>
<p>29. Virtual research environments (VREs), or Virtual Laboratories are
online systems helping researchers to carry out their work. They include environments
both to publish data (e.g. Scratchpads [<xref ref-type="bibr" rid="B93">93</xref>
]) and to execute operations on data (e.g. myExperiment [<xref ref-type="bibr" rid="B94">94</xref>
]) or both (e.g. AquaMaps [<xref ref-type="bibr" rid="B95">95</xref>
] and iMarine [<xref ref-type="bibr" rid="B96">96</xref>
]). VREs also include facilities to support collaboration between
individuals. The challenge is to build integrative flexible e-Science environments
using standardised building blocks and workflows, with access to data from various
sources. Just as with physical laboratories, different kinds of VREs are possible,
ranging from general-purpose to the highly specialised. A general VRE for wetland
studies can be customised to a specific geographical area and populated with relevant
datasets. A VRE specialised for a single scientific objective e.g. to find an optimal
way of sequestering carbon in a forest would be equipped with workflows based on
highly specialised simulation tools such as Biome-BGC [<xref ref-type="bibr" rid="B97">97</xref>
]. For a successful uptake of VREs, they must generate immediate benefit for
their users. For casual users the interface(s) must be simple and intuitive. For
developers, there must be a usable pool of services and other resources that can be
linked simply (e.g. BioVeL [<xref ref-type="bibr" rid="B98">98</xref>
]). VREs must perform functions that people find useful. VREs as envisaged
here, also act as social networking applications and have a central role in making
some of the available technology described above usable or better, invisible to the
majority of users.</p>
</sec>
<sec><title>What can you do with your data in the future?</title>
<p>30. A biodiversity e-infrastructure should go much further than the
generation, transfer, storage, and processing of biodiversity data. Applications
demand that the infrastructure supports deploying the data in analysis, predictive
modelling and decision support. The complexity of biodiversity includes systems
interacting in chaotic and non-linear processes, extreme system effects and
interactions between microscopic and macroscopic global levels, as well as on
multiple time-scales. Understanding biodiversity is far more complex than
understanding either meteorology or climate-change. To address problems that we
cannot now handle we need:</p>
<p>• User friendly VREs with:</p>
<p>• interoperable and easily configurable components;</p>
<p>• access to real-time data (sensor, earth observation, weather,
etc.);</p>
<p>• pre- and post-processing capabilities.</p>
<p>• Predictive multi-scale models;</p>
<p>• Feedback mechanisms to prompt new data generation (remote
observations and measurements);</p>
<p>• Methods for integrated interaction between data, parameters,
models and visualized results (“fine tuning” experiments, computational
steering);</p>
<p>• New approaches for decision support when model outcomes result in
various scenarios.</p>
</sec>
</sec>
<sec><title>Section 4: the human interface</title>
<sec><title>How can we give users confidence?</title>
<p>31. Experience suggests that for an effective outcome in biodiversity
informatics, a balance of top-down and bottom-up approaches is required. It is also
important to remember that there is small benefit to asking end-users for their
requirements, when they may not be aware of the benefits that new technologies can
bring. The FP5 funded project ENBI (European Network for Biodiversity Information,
funded 2003–2005) [<xref ref-type="bibr" rid="B99">99</xref>
] concluded that a modular infrastructure could provide both the
architecture and the sustainability to overcome the partial and ad hoc solutions
developed in the past twenty years, and designed the LifeWatch infrastructure for
biodiversity and ecosystem research (an ESFRI project [<xref ref-type="bibr" rid="B100">100</xref>
]). This approach, followed for a decade, has not led to the development of
trusted repositories or stable funding, and therefore has not generated user
confidence. Thus far, all biodiversity information projects share a common problem,
viz. how to keep the service running after the funding period. If people don't have
confidence that an environment will last essentially indefinitely, or at least as
long as it is relevant to enter information, then why would they invest their time
and effort contributing to the common system? Paper publication is perceived,
unrealistically, to last indefinitely and is the yardstick by which people judge any
new approach. Publishing in PDF format is conceptually equivalent to paper, although
it’s easier, faster and cheaper to distribute copies and its longevity has not
been demonstrated. New paradigms are making data available in forms that can be
readily re-used, e.g. Pensoft Publishers [<xref ref-type="bibr" rid="B56">56</xref>
] and GBIF's Integrated Publishing Toolkit [<xref ref-type="bibr" rid="B76">76</xref>
]. It may be possible that international organisations, such as GBIF, or
large national institutions, such as natural history museums, will agree to
underwrite data services on a care-and-maintenance basis, while the underlying
software goes open source but with institutional oversight. Ultimately, to build the
crucial user confidence, service managers need to invest far more than they have thus
far in marketing to create new social norms.</p>
</sec>
<sec><title>Who owns what?</title>
<p>32. The traditional citation system provides a mechanism to measure impact
and provenance but it applies to a publication unit rather than code from a software
library or data from a repository. Systems are required that will generate comparable
metrics from the new open science resources (see paragraph 15). Data contributors
would benefit by knowing the number of people and/or projects who used those data
(impact). Code developers will benefit by knowing how widely their code is being used
(impact). Users want to be able to drill down and find who wrote the code or
generated the data (provenance). The ability to credit the data creator or code
author is the primary basis for trust in the quality of the data or service. The
following challenges need to be resolved: first, we need a system of attribution that
is robust in a distributed network, easily achieved by the use of PIDs and author
identities (see paragraphs 7 and 11). Second, licensing is poorly understood in the
community, both by producers and consumers. For data, the flavours of Creative
Commons licenses that involve "non-commercial" clauses make risk-averse consumers
wary of using material, even when free use was the intention of the original
contributor. For software, the terms of open source licencing and free-use are
similarly subtle. In both cases, there is a widespread failure to understand the
distinction between licensing and copyright. Third, copyright often creates a barrier
to data use and re-use, although in academic work no instances of case law have been
identified, so guidance is based on commercial publishing case law, predicated on
financial loss. The wider Open Science movement is pushing hard to clarify this
situation and biodiversity data should benefit from the increasing widespread
liberalisation.</p>
</sec>
<sec><title>What benefits come to contributors?</title>
<p>33. Career progression is enormously influenced by citation metrics as a
proxy for impact and, more than anything else, this keeps us tied to a paper
publication model. Products that users want, e.g. identification keys, are often used
without citation and contribute nothing to career progression. People are too often
not sharing their data freely, but save it for their close collaborators: they need
to be given new tools that facilitate data sharing in the long run, but keep them in
control while the research is still active. New metrics need to be defined that
measure how often a data set is used and where conclusions based (in part) on those
data appear. This through-tracking requires, at the very least, two of the
fundamentals discussed above in Section 1, the use of PIDs to track the data and the
development of a system to identify contributors (= authors). Ultimately, this is the
single largest problem we face in persuading people share their data.</p>
</sec>
</sec>
<sec><title>Endnotes</title>
<p><sup>a</sup>
By a "coordinated coupled modelling environment" we mean a technological
framework of interoperability that allows researchers to bring together different data
and algorithms without undue difficulty for analysis, modelling and prediction. Such a
framework could assist us to better understand biodiversity as a comprehensive, complex,
integrated system rather than as an assemblage of species (or any other biological
organisation). This comprehensive systems-oriented framework would be built from diverse
but interlinked data and tools for data discovery and analysis across dimensions of
scale of phenomena, time, space and disciplines (biology, chemistry, climatology,
economics, sociology, geography). The effect of impacts and processes can then be
assessed across temporal, spatial, and organisational (e.g. gene, individual, species,
ecosystems) dimensions. For an alternative impression, refer to Virtual Physiological
Human (VPH) for an analogous objective, as described by [<xref ref-type="bibr" rid="B101">101</xref>
]:</p>
<p>“… a technological framework that aims to be descriptive, integrative
and predictive.</p>
<sec><title>Descriptive</title>
<p>The framework should allow observations made in laboratories, in hospitals and in
the field, at a variety of locations situated anywhere in the world, to be
collected, catalogued, organized, shared and combined in any possible way.</p>
</sec>
<sec><title>Integrative</title>
<p>The framework should enable experts to analyse these observations
collaboratively, and develop systemic hypotheses that incorporate the knowledge of
multiple scientific disciplines.</p>
</sec>
<sec><title>Predictive</title>
<p>The framework should facilitate the interconnection of predictive models defined
at different scales, with different methods and with different levels of detail,
producing systemic networks that breathe life into systemic hypotheses;
simultaneously, the framework should enable their validity to be verified by
comparison with other clinical or laboratory observations.”</p>
<p><sup>b</sup>
Based on the Lister definition of biodiversity, [<xref ref-type="bibr" rid="B102">102</xref>
]: ‘Biodiversity is the variety, distinctiveness and complexity of all
life on Earth, including its structures, functions, cultures, and information at all
scales (from genetic to global) and in all its contexts (from DNA to self
organization)’.</p>
<p><sup>c</sup>
A valid name is the correct biological name of a taxon, determined
according to the relevant rules of nomenclature.</p>
<p><sup>d</sup>
At the International Conference on Research Infrastructures (ICRI2012),
Copenhagen, 21–23 March 2012.</p>
<p><sup>e</sup>
For a working definition of biodiversity informatics see
<ext-link ext-link-type="uri" xlink:href="http://en.wikipedia.org/wiki/Biodiversity_informatics">http://en.wikipedia.org/wiki/Biodiversity_informatics</ext-link>
</p>
<p><sup>f</sup>
Related ‘future’ initiatives are presently being coordinated
at the global level by the FP7 funded CReATIVE-B project
(<ext-link ext-link-type="uri" xlink:href="http://creative-b.eu/">http://creative-b.eu/</ext-link>
) and by GBIF (<ext-link ext-link-type="uri" xlink:href="http://www.gbif.org/">http://www.gbif.org/</ext-link>
)
through its Global Biodiversity Informatics Conference (Copenhagen, 2–4 July
2012) and subsequent Global Biodiversity Informatics Outlook publication (in
preparation).</p>
<p><sup>g</sup>
The EU vision for 2050 is: “Biodiversity and ecosystem services
– the world’s natural capital – are preserved, valued and, insofar
as possible, restored for their intrinsic value and so that they can continue to
support economic prosperity and human well-being as well as avert catastrophic
changes linked to biodiversity loss.”
[<ext-link ext-link-type="uri" xlink:href="http://ec.europa.eu/environment/nature/biodiversity/policy/">http://ec.europa.eu/environment/nature/biodiversity/policy/</ext-link>
].</p>
<p><sup>h</sup>
The EU target for 2020 is to: “halt the loss of biodiversity and
ecosystem services in the EU by 2020 and restore them insofar as possible, and step
up the EU’s contribution to averting global biodiversity loss.”
[<ext-link ext-link-type="uri" xlink:href="http://ec.europa.eu/environment/nature/biodiversity/policy/">http://ec.europa.eu/environment/nature/biodiversity/policy/</ext-link>
].</p>
<p><sup>i</sup>
BiSciCol project [<ext-link ext-link-type="uri" xlink:href="http://biscicol.blogspot.co.uk/p/home.html">http://biscicol.blogspot.co.uk/p/home.html</ext-link>
]
is one example of an attempt to do that.</p>
<p><sup>j</sup>
At the non-European and global levels important projects include:
DataONE, iDigBio, Atlas of Living Australia, Catalogue of Life, COOPEUS, CReATIVE-B,
EOL, GBIF, GSC Biodiversity WG, TreeBase, CBOL and many more.</p>
<p><sup>k</sup>
BioVeL in particular is a pilot implementation following the
architecture and technical approach envisaged for the ESFRI LifeWatch Research
Infrastructure for biodiversity science and ecosystems research.</p>
<p><sup>l</sup>
A name usage is a statement that includes a name. The GNUB connects
names with their usage in the literature, collections, etc.</p>
<p><sup>m</sup>
See for example, how Atlas of Living Australia approaches this problem:
<ext-link ext-link-type="uri" xlink:href="http://www.ala.org.au/aboutthe-atlas/how-we-integrate-data/data-quality-assurance/">http://www.ala.org.au/aboutthe-atlas/how-we-integrate-data/data-quality-assurance/</ext-link>
.</p>
<p><sup>n</sup>
The situation today can be likened to that which existed in the fields
of meteorology and climatology in the 1960’s and 70’s when the emergence
of numerical weather prediction drove the demand for new observations and the
emergence of a global infrastructure for acquiring data.</p>
<p><sup>o</sup>
The EC KeyToNature project (<ext-link ext-link-type="uri" xlink:href="http://www.keytonature.eu">http://www.keytonature.eu</ext-link>
)
developed a series of apps for identifying species in the field.</p>
<p><sup>p</sup>
For example Artportalen in Sweden
(<ext-link ext-link-type="uri" xlink:href="http://www.artportalen.se/default.asp">http://www.artportalen.se/default.asp</ext-link>
), Ornitho in Italy
(<ext-link ext-link-type="uri" xlink:href="http://www.ornitho.it/">http://www.ornitho.it/</ext-link>
) and Project Noah in the USA
(<ext-link ext-link-type="uri" xlink:href="http://www.projectnoah.org/">http://www.projectnoah.org/</ext-link>
).</p>
<p><sup>q</sup>
For example sig.ma (<ext-link ext-link-type="uri" xlink:href="http://sig.ma/">http://sig.ma/</ext-link>
).</p>
</sec>
<sec><title>Appendix 1</title>
<p>A moderated mailing list has been established for the Biodiversity Informatics
Community. To join the list, e-mail DR (dmr@nomencurator.org). Contributing authors
to this White Paper are, in alphabetical order:</p>
<p>Wouter Addink, ETI Bioinformatics, NL</p>
<p>Bart Aelterman, Research Institute for Nature and Forest (INBO), BE</p>
<p>Donat Agosti, Plazi, CH</p>
<p>Linda Amaral-Zettler, Marine Biological Laboratory, US</p>
<p>Arturo H. Ariño, Universidad de Navarra, ES</p>
<p>Christos Arvanitidis, Hellenic Center for Marine Research, GR</p>
<p>Thierry Backeljau, Royal Belgian Institute for Natural Sciences, BE</p>
<p>Nicolas Bailly, WorldFish Center, PH</p>
<p>Lee Belbin, Atlas of Living Australia, AU</p>
<p>Walter Berendsohn, Botanischer Garten und Botanisches Museum Berlin-Dahlem, Freie
Universität Berlin, DE</p>
<p>Nic Bertrand, Centre for Ecology and Hydrology, Lancaster, UK</p>
<p>Neil Caithness, Oxford University, UK</p>
<p>David Campbell, The Paleontological Research Institution, US</p>
<p>Guy Cochrane, EMBL - European Bioinformatics Institute Hinxton, UK</p>
<p>Noël Conruyt, Université de la Réunion, FR</p>
<p>Alastair Culham, University of Reading, UK</p>
<p>Christian Damgaard, Aarhus University, DK</p>
<p>Neil Davies, UC Berkeley, US</p>
<p>Bruno Fady, INRA, UR629 Ecologie des Forêts Méditerranéennes (URFM)
and CESAB (CEntre de Synthèse et d'Analyse sur la Biodiversité), FR</p>
<p>Sarah Faulwetter, Hellenic Center for Marine Research, GR</p>
<p>Alan Feest, Bristol University, UK</p>
<p>Dawn Field, Oxford University, UK</p>
<p>Eric Garnier, UMR5175 Centre d'Ecologie Fonctionnelle & Evolutive and CESAB
(CEntre de Synthèse et d'Analyse sur la Biodiversité), FR</p>
<p>Guntram Geser, Salzburg Research Forschungsgesellschaft, AT</p>
<p>Jack Gilbert, University of Chicago, US</p>
<p>Bernd Grosche, Federal Office for Radiation Protection, DE</p>
<p>David Grosser, Université de la Réunion, FR</p>
<p>Alex Hardisty, Cardiff University, UK</p>
<p>Bénédicte Herbinet, Fondation pour la Recherche sur la Biodiversité,
FR</p>
<p>Donald Hobern, GBIF Secretariat, DK</p>
<p>Andrew Jones, Cardiff University, UK</p>
<p>Yde de Jong, Universiteit van Amsterdam, NL</p>
<p>David King, The Open University, UK</p>
<p>Sandra Knapp, Natural History Museum, London, UK</p>
<p>Hanna Koivula, Finnish Museum of Natural History, FI</p>
<p>Wouter Los, University of Amsterdam, NL</p>
<p>Chris Meyer, Smithsonian Institution, US</p>
<p>Robert A. Morris, UMASS-Boston and Harvard University Herbaria, US</p>
<p>Norman Morrison, University of Manchester, UK</p>
<p>David Morse, The Open University, UK</p>
<p>Matthias Obst, University of Gothenburg, SE</p>
<p>Evagelos Pafilis, Hellenic Center for Marine Research, GR</p>
<p>Larry M. Page, Florida Museum of Natural History, US</p>
<p>Roderic Page, University of Glasgow, UK</p>
<p>Thomas Pape, Natural History Museum of Denmark, DK</p>
<p>Cynthia Parr, Smithsonian Institution, US</p>
<p>Alan Paton, Royal Botanic Gardens, Kew, UK</p>
<p>David Patterson, Marine Biological Laboratory, Woods Hole, US</p>
<p>Elisabeth Paymal, Fondation pour la Recherche sur la Biodiversité, FR</p>
<p>Lyubomir Penev, Pensoft Publishers, BG</p>
<p>Marc Pollet, Research Institute for Nature and Forest (INBO), BE</p>
<p>Richard Pyle, Bishop Museum, Honolulu, US</p>
<p>Eckhard von Raab-Straube, Botanischer Garten und Botanisches Museum Berlin-Dahlem,
Freie Universität Berlin, DE</p>
<p>Vincent Robert, Centraalbureau voor Schimmelcultures, NL</p>
<p>Dave Roberts, Natural History Museum, London, UK</p>
<p>Tim Robertson, GBIF Secretariat, DK</p>
<p>Olivier Rovellotti, Natural Solutions, FR</p>
<p>Hannu Saarenmaa, Finnish Museum of Natural History, FI</p>
<p>Peter Schalk, ETI Bioinformatics, NL</p>
<p>Joop Schaminee, Wageningen UR and Radboud University Nijmegen, NL</p>
<p>Paul Schofield, University of Cambridge, UK</p>
<p>Andy Sier, Centre for Ecology & Hydrology, UK</p>
<p>Soraya Sierra, Stichting Naturalis Biodiversity Center, NL</p>
<p>Vince Smith, Natural History Museum, London, UK</p>
<p>Edwin van Spronsen, ETI Bioinformatics, NL</p>
<p>Simon Thornton-Wood, University of Reading, UK</p>
<p>Peter van Tienderen, Universiteit van Amsterdam, NL</p>
<p>Jan van Tol, Stichting Naturalis Biodiversity Center, NL</p>
<p>Éamonn Ó Tuama, GBIF Secretariat, DK</p>
<p>Peter Uetz, Virginia Commonwealth University, US</p>
<p>Lea Vaas, Centraalbureau voor Schimmelcultures, NL</p>
<p>Régine Vignes Lebbe, University Pierre et Marie Curie, FR</p>
<p>Todd Vision, University of North Carolina at Chapel Hill, US</p>
<p>Duong Vu, Centraalbureau voor Schimmelcultures, NL</p>
<p>Aaike De Wever, Royal Belgian Institute for Natural Sciences, BE</p>
<p>Richard White, Cardiff University, UK</p>
<p>Kathy Willis, University of Oxford, UK</p>
<p>Fiona Young, University of Reading, UK</p>
</sec>
<sec><title>Appendix 2</title>
<sec><title>Mobilising economic benefits</title>
<p>At present, 87% of the world’s population have mobile phone subscriptions
and 1.2 billion of these are mobile Web users. In 2011, almost half a billion
smartphones were shipped globally, exceeding sales of PCs [<xref ref-type="bibr" rid="B103">103</xref>
]. In 2010, 300,000+ Smartphone apps were downloaded 10.9 billion times.
Prediction is that in 2014 some 77 billion apps will be downloaded representing an
estimated US$35 billion market [<xref ref-type="bibr" rid="B104">104</xref>
]. With the 10 times faster 4G mobile networks as successor of 3G already
available in some countries, high speed bandwidth to mobile devices will
facilitate online use of services demanding bandwidth such as video-streaming.</p>
<p>Next generation apps, incorporating stable content, smart algorithms and
location-awareness in combination with multiple layers of online data delivered
over 4G bandwidth (not yet available in Europe), offer the promise of highly
innovative information products that can serve markets in both the science and
social domains, provided the data are made available to serve these needs.</p>
<p>The EC KeyToNature project [<xref ref-type="bibr" rid="B105">105</xref>
] developed a series of apps for identifying species in the field
demonstrating that there is a market for quality taxonomic reference works that
can contribute to cost recovery. This approach however does not come without risk.
The mobile devices’ field is evolving extremely fast and apps developed for
a device are out of business only one or two years later.</p>
</sec>
</sec>
</sec>
<sec><title>Appendix 3</title>
<sec><title>Gathering biodiversity data</title>
<p>Gathering biodiversity data can be divided into 3 main routes:</p>
</sec>
<sec><title><italic>Remote sensing</italic>
</title>
<p>Earth observation at multiple wavelengths by aeroplane, satellite and ground-based
sensors are in the early stages of development for biodiversity observation. They are
largely based on surveillance technologies and require the development of new
techniques to process the type of data they produce, both in routine monitoring and
the detection of rare events. New generations of sensors designed for biodiversity
observation are needed. Camera traps today and DNA chip sensors tomorrow could
transmit data wirelessly, and be linked directly to researcher’s desks. Even
with existing technology, it is becoming economically feasible to collect large
amounts of environmental data automatically. This approach will undoubtedly present a
significant new challenge in handling very large data volumes [<xref ref-type="bibr" rid="B106">106</xref>
].</p>
</sec>
<sec><title><italic>Environmental metagenomics</italic>
</title>
<p>"Grind and find" techniques allows the study of many organisms in a sample at the
same time, presenting the challenge of scaling biodiversity observation from the
molecule to the planet [<xref ref-type="bibr" rid="B107">107</xref>
]. For example in November 2011, the Beijing Genomics Institute (BGI)
launched its “Three Million Genomes Project”, an ambitious effort
consisting of three sub-projects: “Million Plant and Animal Genomes
Project”, “Million Human Genomes Project” and “Million
Micro-Ecosystem Project”. In the latter, genomes of more than 600 microbial
species, including over 3,500 strains and 1,800 metagenomes have already been
completed. Projects like these are generating in the order of 20 petabytes of data
per year. With the unlimited influx of sequence data being a real possibility,
archives operating under the INSDC (International Nucleotide Sequence Database
Collaboration) face a new situation in which it is no longer possible to archive all
components of all datasets. Indeed, a community discussion is underway around
decision-making that is informed by scientific and economic arguments about the
aggressiveness to which different classes of sequence data should be compressed [<xref ref-type="bibr" rid="B108">108</xref>
]. Given the value of samples from temporal environmental genomic studies
with time-point specific elements (e.g. Ocean Sampling Day 2014
<ext-link ext-link-type="uri" xlink:href="http://www.microb3.eu/work-packages/wp2">http://www.microb3.eu/work-packages/wp2</ext-link>
) and limited or no opportunity
to resample, contributions to the sequence compression debate from the biodiversity
informatics community are essential.</p>
</sec>
<sec><title><italic>Human observation</italic>
</title>
<p>Informatics should empower the human observer in the field and in the laboratory,
improving observational data quality and providing for data transfer with automatic
feedback mechanisms. Laboratory-based studies are increasingly being supported by
electronic tools that are replacing the traditional paper laboratory notebook and
increasingly, instruments are producing data feeds that can be directly integrated.
It is often necessary to prepare baseline sample information that is used to
interpret field information, for example use micro-CT scanning [<xref ref-type="bibr" rid="B109">109</xref>
] to reveal details of three-dimensional structure. In field sites the
infrastructure is either based on long term monitoring of selected parameters, or
consists of small experimental plots where the response of controlled biodiversity
systems on parameter change can be detected. Examples of the latter are mesocosms or
plant communities in laboratory conditions. Long term monitoring is quite well
developed in the LTER-Europe network (Long term Ecological Research monitoring), the
MARS network of marine stations, GLEON (Global Lake Ecological Observatory Network) [<xref ref-type="bibr" rid="B110">110</xref>
], NEON (National Ecological Observatory Network) [<xref ref-type="bibr" rid="B111">111</xref>
] and the Swedish Taxonomy Initiative [<xref ref-type="bibr" rid="B112">112</xref>
]. These monitoring networks produce vast amounts of biodiversity data and a
common data infrastructure is yet only developed for the metadata.</p>
</sec>
<sec><title>Author’s contributions and acknowledgements</title>
<p>This article was complied from contributions to a public GoogleDoc
(<ext-link ext-link-type="uri" xlink:href="http://is.gd/WhitePaperChapters">http://is.gd/WhitePaperChapters</ext-link>
) and notice of the consultation was
widely circulated through mailing lists and presentation at conferences.
Contributions were either additions to the text or in the form of comments, some by
e-mail. Continuity editing was performed jointly by AH and DR. The fundamental
structuring of the article has been determined by AH, NM, DR, HS and PvT. The
unnumbered sections (Grand Challenge, Recommendations, Preface, Context, Taxonomic
Impediment, Changing the landscape - A decadal vision, Realising the vision) have
been written by AH and DR. Many individuals have helped with the overall creation of
this article, either by providing contributions and comments on the sections
previously referred to or by contributing content for the numbered paragraphs 1
– 33. This has been by direct contribution of text or as the source of material
on specific points or by providing comments and points of clarification.</p>
<p>We thank each one of them. The editing authors would also like to extend particular
thanks to Niobe Haitas for her invaluable help for proof-reading and editing some
late drafts of this article.</p>
<p>The work reported in this article forms part of the coordination and support
activities (dissemination, outreach, community building) being carried out by the
BioVeL and ViBRANT projects. BioVeL is funded by the European Union 7th Framework
Programme within the Research Infrastructures group, grant no. 283359. ViBRANT is
funded by the European Union 7th Framework Programme within the Research
Infrastructures group, grant no. 261532.</p>
</sec>
<sec><title>Hardisty, Roberts & biodiversity informatics community</title>
<p>See Appendix 1 for a complete list of the 80 contributors and their affiliations.</p>
</sec>
</sec>
</body>
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