Zoomorphology (2009) 128:201–217

DOI 10.1007/s00435-008-0081-5

REVIEW

The future role of bio-ontologies for developing a general data standard in biology: chance and challenge for zoo-morphology

Lars Vogt

Received: 15 April 2008 / Revised: 30 October 2008 / Accepted: 31 October 2008 / Published online: 20 December 2008© Springer-Verlag 2008

Abstract Due to lack of common data standards, thecommunicability and comparability of biological dataacross various levels of organization and taxonomic groupsis continuously decreasing. However, the interdependencebetween molecular and higher levels of organization is ofgrowing interest and calls for co-operations between biolo-gists from diVerent methodological and theoretical back-grounds. A general data standard in biology would greatlyfacilitate such co-operations. This article examines the rolethat deWned and formalized vocabularies (i.e., ontologies)could have in developing such a data standard. I suggestbasic criteria for developing data standards on grounds ofdistinguishing content, concept, nomenclatural, and formatstandards and discuss the role of data bases and their use ofbio-ontologies in current activities for data standardizationin biology. General principles of ontology development areintroduced, including foundational ontology properties (e.g.class–subclass, parthood), and how concepts are deWned.After addressing problems that are speciWc to morphologi-cal data, the notion of a general structure concept for mor-phology is introduced and why it is required for developinga morphological ontology. The necessity for a general mor-phological ontology to be taxon-independent and free ofhomology assumptions is discussed and how it can solvethe problems of morphology. The article concludes with anoutlook on how the use of ontologies will likely establishsome sort of general data standard in biology and why thedevelopment of a set of commonly used foundational ontol-

ogy properties and the use of globally unique identiWers forall classes deWned in ontologies is crucial for its success.

Keywords Bio-ontology · Data standard · Linguistic problem of morphology · Morphology · RDF

Introduction

In the past, the Weld of biological research and knowledgeexperienced a continuous process of diVerentiation anddiversiWcation into diVerent disciplines and communities ofresearchers to a degree that is unrivaled within natural sci-ences, having led to signiWcant diVerences in traditions andschools of thought, methods and techniques applied.

One obvious reason for the strong impact of this processis the fact that organisms exhibit a complex hierarchicalorganization (see ‘scalar hierarchy’ Salthe 1985, 1993;‘levels of organization’ Wimsatt 1976, 1994; ‘cumulativeconstitutive hierarchy’ Valentine and May 1996; ‘Theoriedes Schichtenbaus der Welt’ Riedl 2000). One can distin-guish for instance a molecular and a cellular level of orga-nization, a level of tissues and a level of organs, up to thelevel of organization of multicellular organisms or the orga-nization of eco-systems. Thereby, an increase in organiza-tional level is usually accompanied by an increase instructural diversity—the higher the organizational level, themore complex the structures and the higher the degree ofstructural diversity.

Biological objects of diVerent organizational levels diVerfrom one another quite substantially, having their ownessential structural properties and functional relationships.Thus, it is not surprising that each level has its own typicalscientiWc questions and research programs, and the methodsfor producing data for one level of organization are often

L. Vogt (&)FU Berlin, Fachbereich Biologie Chemie Pharmazie, Systematik und Evolution der Tiere, Königin Luise Str. 1-3, 14195 Berlin, Germanye-mail: lars.vogt@zoosyst-berlin.de

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completely diVerent from the methods used for producingdata for another level. As a consequence, in correlationwith the diVerent levels of organization one has to distin-guish fundamentally diVerent types of data in biology.

While each level of organization possesses a set ofunique and characteristic structural and functional proper-ties that distinguishes it from another level, objects ofdiVerent levels of organization are yet functionally andmaterially interlinked and constrain one another by up- anddownward causation (Trewavas 2006). Objects of diVerentlevels therefore inter-depend on one another with respect tocausal processes in which they are involved as well as withrespect to their compositional integrity. Experience hasshown that due to emergent properties (sensu Mahner andBunge 1997) the knowledge of all relations, properties, andprocesses of all the objects of one level not necessarilyenables one to deduce comprehensive knowledge for allobjects of another level of organization—knowing thenucleotide sequence of a particular gene and all its chemi-cal properties as such tells us nothing about its origin orabout the function of its transcript (see e.g., Polyani 1968;Schutt and Lindberg 2000; Trewavas 2006). Thus, in orderto gain comprehensive knowledge of a given biologicalsystem, it is insuYcient to take in a reductionist approachand exclusively investigate only the molecular level—quitecontrary research into all organizational levels of the bio-logical system in question is required.

Due to increasing methodological specialization amongbiologists, however, a biologist often produces data refer-ring only to a single level of organization and has no experi-ence with methods and techniques that are required forproducing data and no knowledge and experience withestablished quality standards for interpreting and analyzingdata referring to other levels of organization. This is unfortu-nate since the functional interlinks between objects and pro-cesses of diVerent levels of organization, as for instancebetween the DNA and the protein level or between the pro-tein and the cellular and organ level, become more and moreinteresting for biologists, especially with regard to systemsbiology (e.g., Trewavas 2006). Thus, in order to success-fully study these interlinks, it is essential that biologists fromvarious backgrounds co-operate within interdisciplinaryresearch programs. However, the progressive specializationof biologists signiWcantly complicates the inquiry of func-tional interlinks, as it requires biologists with such diVerentmethodological and theoretical backgrounds as for instancemolecular biologists and morphologists to co-operate andwork together. In order to compensate the lack of practicalexperience with parts of the data and the methods and tech-niques applied for their production, communication aboutthe diVerent data types and their corresponding quality stan-dards becomes very important for the biologists who areinvolved in such co-operations.

Thus, modern biology is situated within a Weld of tensionbetween diVerences in (a) methods and techniques appliedduring research practice, (b) quality standards required forcorrectly interpreting and analyzing data, (c) types of expla-nations sought, (d) types of data produced and analyzed,and (e) philosophies and research strategies applied, all ofwhich somehow depend on the choice of the level of orga-nization studied. All this resulted in the development ofspecialized languages for communicating data and meta-data within specialized communities—biologists workingwith diVerent model organisms, diVerent techniques, orwith structures of diVerent levels of organization oftenspeak diVerent (scientiWc) languages, to a degree thatmakes their co-operation diYcult.

Unfortunately, biology is lacking a commonly acceptedgeneral data standard, which could facilitate such co-opera-tions. Most types of biological data are lacking such a stan-dard. For instance on the morphological level biologistshave to deal with the linguistic problem of morphology,causing fundamental ambiguities regarding morphologicalterminology and resulting in a serious slowdown of scien-tiWc progress in morphology (Vogt et al. 2008). But also onthe genetic level, biologists had to deal with a lack of stan-dards regarding gene names and spellings (Brazma 2001;Stein 2003), not to speak of the conceptual problems ofagreeing on a common gene concept (e.g., Beurton et al.2000; Wilson 2005; Gerstein et al. 2007; GriYths and Stotz2007; Scherrer and Jost 2007; Prohaska and Stadler 2008).As a consequence, communication of data is not trivial andcomparing data sometimes even impossible, which consid-erably hampers co-operations between biologists. Compa-rability and compatibility of data can best be accomplishedby standardization of data production and representation,which would at the same time enable their reliable commu-nication. The standardization of biological data would sig-niWcantly increase eVectiveness of the performance ofintegrated analyses over diVerent types of data and particu-larly co-operations among biologists of diVerent methodo-logical backgrounds, not to speak of all biological researchthat is based on comparison. The standardization of biolog-ical data could provide all biologists with a common (meta-)language; and a common language is one of the mostimportant prerequisites for the integration of a group.

Considering that even the diVerent types of biologicaldata are lacking a commonly accepted standard, the devel-opment of a general data standard for all types of biologicaldata represents a real challenge. But it also represents animportant task—at least as long as biology does not want tocontinue to waste a signiWcant fraction of its resources forthe correction of incorrectly used data and the translation ofindividual data formats, which is very time-consuming. Is itpossible to develop such a general data standard for alltypes of biological data? What are the components of such

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a standard? Would not such a standard also constrain futuredevelopments within biological research? These are thequestions I am addressing in this article.

Recognizing the need for data standardization in biology

Standardization and data quality

A standardization of data is usually accompanied by a qual-ity increase, which is the reason for developing a data stan-dard in the Wrst place. But what does quality of data meanin that respect?

Besides research dependent speciWc criteria for theassessments of data quality, such as various statistical mea-sures of support of data for a given explanatory hypothesis(i.e., evidential weight), there exist some general criteria ofdata quality that are common to all empirical sciences.These usually involve reference to transparency and repro-ducibility of methods and techniques applied during dataproduction and are relevant for comparability and compati-bility of data. Biologists usually have this latter type of dataquality in mind when they develop data standards in theirrespective Weld of research.

Assuming that the ultimate structure of reality is inde-pendent of humankind and human culture (ontologicalobjectivity sensu Daston and Galison 1992), increasingthe communicability of scientiWc data increases theirobjectivity. This is referred to as aperspectival objectivity(see Daston 1992, 1998; Daston and Galison 1992; proce-dural objectivity sensu Heintz 2000; for a critique of theclaim for aperspectival objectivity in science see, e.g.,Kukla 2006). Aperspectival objectivity claims that some-thing is more objective than something else if it relies lesson the speciWc individual who generated the results, theirsocial position and character. While increasing aperspecti-val objectivity is either implicitly or explicitly part ofmost data standardization eVorts in biology, what isreferred to as mechanical objectivity (Daston and Galison1992; methodical objectivity sensu Heintz 2000) isanother part. Mechanical objectivity requires ruling outall individual and subjective inXuences of body and mindand forbids judgment and interpretation in documentationand reports of observation (Daston and Galison 1992). Itinvolves the establishment of speciWc experimental andmeasurement standards, with the purpose to rule out thefallibility (in terms of deceivability) and deWciency ofhuman cognition. Thus, most data standardization eVortsare aimed at increasing aperspectival and mechanicalobjectivity of data, and when I refer to data quality withinthis paper it is always in reference to these two types ofobjectivity.

Minimum information checklists

The need for standardizing biological data has been recog-nized in various Welds of biological research—especiallywithin molecular biology and the model organism researchcommunities. The introduction of the so called OMICStechnologies (i.e., technologies from genomics and proteo-mics, including DNA microarray technologies for expres-sion, genetic and epigenetic analysis, metabolic proWlingtechniques, etc.) resulted in an exponentially growing bodyof raw data produced by high through-put methods andrequired the development of new techniques and approachesfor data management that enable easy sifting through oflarge amounts of data in order to be analyzed, compared,and disseminated through public data bases (e.g., Brazma2001; Field and Sansone 2006). Especially the variation inthe results of OMICS experiments, which are usually associ-ated with details of the processing of samples and which arehighly dependent on the condition, age, and history of thesamples (Field and Sansone 2006), required transparencyregarding such contextual information in order to guaranteeunlimited usability of the respective data. Without corre-sponding annotations most OMICS analyses would be hardto understand and very diYcult to interpret correctly. WithinOMICS, these annotations, which represent data about data(i.e. metadata), usually refer to experimental design, thesource, preparation, and treatment of the biological materialbeing studied, the parameters and values of instrumentsused, and other information (Field and Sansone 2006). Thus,standardizing data to require the inclusion of (standardized)metadata became a crucial condition for the success ofOMICS research programs. This became especially appar-ent in the context of multi-OMICS approaches, whichattempt to understand complete biological systems andrequire data from various Welds of biological research.

It requires the development of a commonly acceptedreporting structure that speciWes which annotations (i.e.,metadata) in which form should be included for publicationand dissemination of data. A Wrst step towards such areporting structure is to identify and agree upon whichinformation must be included in a data report. This hasbeen referred to as minimum information convention andthe minimum information about a microarrray experiment(MIAME) checklist (Brazma et al. 2001), developed by theMicroarray Gene Expression Data Society, has been theWrst of its kind, specifying which minimum informationshould be reported about a microarray experiment. TheMIAME checklist had a great impact on other OMICS stan-dardization eVorts and subsequently became a benchmarkfor the development of various minimum informationchecklists (see table 1 in Field and Sansone 2006).

Unfortunately, within zoo-morphology respectiveattempts are usually restricted to standardization of taxonomic

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information (e.g. Kennedy et al. 2006) or to speciWc modelorganisms and their corresponding data bases—there is notaxon-independent standard for morphological metadataestablished so far and all attempts are in their beginnings.Much worse, it seems that the need for such a standard isnot commonly recognized in the community of morpholo-gists. Consequently, the motivation for participating andcontributing to a standardization eVort is very limitedwithin the community. This is very unfortunate, since mor-phology, compared to other biological disciplines, suVerssigniWcantly from a low degree of aperspectival andmechanical objectivity (see Vogt et al. 2008, Vogt 2008).

Biological data bases

Data bases become more and more popular in biology.Besides general data bases for molecular data, a lot of databases have been developed that are restricted to data refer-ring to a speciWc model organism, as for instance FlyBasefor Drosophila and Arabidopsis Information Resource forArabidopsis thaliana. Other data bases have been devel-oped restricted to a speciWc taxonomic group, as forinstance Antbase, Fishbase, or AmphibiaWeb (seeTable 1).

Every data base has to deWne what information can beuploaded by whom in which way, and it necessarily has itsown standardized way of storing and presenting data andmetadata. Thus, each data base inevitably sets its own dataand metadata standard. In the beginning of the developmentof biological data bases the focus was on deWning what typeof information should be stored (this is comparable to mini-mum information checklists). However, terminology prob-lems such as the lack of standards of gene names andspellings (Brazma 2001; Stein 2003) resulted in fundamen-tal problems regarding the comparability and compatibilityof the content of a data base. Thus, although a data basemight have speciWed which type of information has to beincluded when uploading new data, relevant informationconcerning a speciWc gene can be present in a data base butstill, due to terminological or conceptual ambiguities, notbe found reliably, which turns the initial purpose of thedevelopment of the data base upside down. Thus, termino-logical and conceptual ambiguities made a change of focusnecessary, and more and more data bases started to put a lotof eVort into the development of deWned and controlledvocabularies in order to deal with these problems.

The development of a deWned and controlled vocabularywhich also incorporates relationships (i.e., an ontology)requires the explication of the meaning of terms and con-cepts and the rules of their application. Using an ontologywithin a data base has the potential to signiWcantly increasethe formalization and standardization of data presentation,storage, and documentation.

In the past, various eVorts have been made to imposeontologies on data bases. Some of the projects focus on tax-onomic classiWcation and nomenclature, attempting to pro-vide a comprehensive list of all known species togetherwith a validation of their respective taxonomic names andrelevant metadata such as taxonomic ranks, vernacularnames, and synonyms (see e.g., Bisby et al. 2002; Gewin2002; Godfray 2002; Wilson 2003; see also taxonomicindexing, Patterson et al. 2006). Such eVorts are important,since often the scientiWc past acts like a heavy weight oneverybody who attempts to revise a taxonomic group. Thisweight is the result from hundreds of years of sedimenta-tion of taxonomic and nomenclatural revisions, bringingabout a dubious complex of synonymy and scattered typematerial (Godfray 2002). Many of the projects listed inTable 1 aim at bringing stability and unambiguousness oftaxonomic names and aYliated metadata into this dubiouscomplex. This is insofar a very important task, as taxonomyand nomenclature provide the fundamental reference sys-tem for all biology.

However, general terminological ambiguities become anincreasingly severe problem with increasing amounts ofdata that have to be sighted and with diVerent types of bio-logical data that have to be analyzed simultaneously.

Morphological data bases

Within the last years, some interesting morphological databases and projects started. MorphBank (http://morph-bank.csit.fsu.edu) is an open web repository of images forthe documentation of specimens and vouchers for sharingresearch results in taxonomy, morphometrics, morphology,and phylogenetics. Another project, MorphoBank (http://morphobank.com), is a repository for storing digital images(Pennisi 2003). It catalogues images and allows the label-ing of structures on the images and the display of editablephylogenetic matrices, to which the images can be linked.A diVerent project, Digital Morphology (DigiMorph; http://www.digimorph.org), is an archive of digital morphologi-cal images and 3D models. None of the abovementionedmorphological data bases, however, have their focus onmanaging morphological descriptions and, thus, morpho-logical data in the strict sense (see Vogt et al. 2008).Instead, they mainly manage all kinds of diVerent morpho-logical media Wles.

Bio-ontologies, RDF, and zoo-morphology

Bio-ontologies and knowledge bases

By now, many biological data bases use ontologies (not tobe mistaken with Ontology in philosophy, which is the

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study of being or existence) for providing deWned and con-trolled vocabularies that incorporate also relationships.Ontologies consist of a vocabulary of terms and some spec-iWcations of their meaning that is used to describe a certainreality. A bio-ontology is a formal way of representingknowledge of a particular scientiWc Weld of biology throughconcepts and represents a data standard (Wang et al.

2005)—“a speciWcation of a conceptualization” (Gruber1993). An ontology is structured and formalized through aset of formal rules and assertions that describe the relation-ship between the concepts in a computer parsable form.

An ontology is build by a set of statements each ofwhich is composed of two types and their relation to oneanother: ‘Type_X relation Type_Y’. The relations of an

Table 1 List of taxonomy and phylogeny projects and model organism data bases

Project URL Description

Species 2000 and the Integrated Taxonomic Information System (ITIS)

http://www.sp2000.orghttp://www.itis.gov

Two of the most prominent taxonomic projects with the aim to create an electronic global framework for taxonomy. They joined their forces in the catalogue of Life consortium with the aim to catalogue all known organisms by establishing a federation of interoperable data bases for documenting the world’s taxonomic knowledge

All Species Foundation

http://www.all-species.org They bring together taxonomists and high-tech people in order to name and describe all living species within the next 25 years by utilizing web-based approaches and recruiting ‘parataxonomists’

Universal Biological Indexer and Organizer (uBio)

http://www.ubio.org uBio is an initiative gathering all names for taxonomic indexing purposes (for taxonomic indexing see Patterson et al. 2006)

Global BiodiversityInformation Facility (GBIF)

http://www.gbif.org GBIF develops an interoperable network of diVerent biodiversity data bases and information technology tools that require deWned vocabularies in order to Wnd and utilize the information stored in the various data bases, therewith developing standards for interoperability and digitizing biodiversity data

Species Analyst http://Speciesanalyst.net Species Analyst represents a research project developing standards and software tools for access to the world’s natural history collections and observation data bases

Taxonomic Search Engine (TSE)

http://darwin.zoology.gla.ac.uk/» rpage/portal TSE provides a platform for searching several data bases including ITIS, Index Fungorum, and NCBI

Taxonomic Databases Working Group (TDWG)

http://www.tdwg.org TDWG attempts to establish international collaboration among biological data base projects by developing, adopting, and promoting standards and guidelines for the recording and exchange of data about organisms

Tree of Life web project http://www.tolweb.org A data base of phylogenies

TreeBase http://www.treebase.org TreeBase is a data base for storing phylogenetic trees and phylogenetic character matrices

Projects developed by specialized taxonomic communities

Index Fungorum (http://www.speciesfungorum.org/Names/Names.asp), AlgaeBase (http://www.algaebase.org), ILDIS LegumeWeb (http://www.ildis.org), the International Plant Names Index (http://www.ipni.org), FishBase (http://www.fishbase.org), AntBase (http://antbase.org), AmphibiaWeb (http://amphibiaweb.org), some of which are aYliated to the Catalogue of Life consortium

Database URL Description

FlyBase http://flybase.bio.indiana.edu A data base of genetic and molecular data for Drosophila

Saccharomyces Genome Database (SGD)

http://www.yeastgenome.org A data base of the molecular biology of the yeast Saccharomyces cerevisiae

Mouse Genome Informatics (MGI)

http://www.informatics.jax.org Provides integrated access to data on the genetics, genomics, and biology of the laboratory mouse

Arabidopsis Information Resource (TAIR)

http://www.arabidopsis.org A data base of genetic and molecular biology data for Arabidopsis thaliana

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ontology play an important role since they carry all thesemantic content. The concepts of an ontology aredescribed both by their meaning and their relationship toeach other (see also Bard 2003; Bard and Rhee 2004).Therefore, by its rules and assertions, an ontology imposesa structure on a knowledge domain that constraints the pos-sible interpretations of terms (Stevens et al. 2000). Anontology is usually intended to be explicit and complete. Itspossible applications outclass those of an indexed set ofterms and deWnitions as it is common for dictionaries orglossaries. The concepts of an ontology represent classes ofdeWned terms and their inter-relationships and should notcontain empirical data (i.e. instances) in principle. If empir-ical data and an ontology are combined in a data base onereceives what is called a knowledge base (Stevens et al.2000).

Within the last decade research on ontologies hasincreased tremendously, and, as a result, more and morebio-ontologies become available. While most of them arerestricted to one speciWc model organism, one of the mostimportant bio-ontologies, the Gene Ontology (GO), repre-sents an exception. GO is an ontology that integratesgenetic data about gene products with knowledge of theirproperties (Bard 2003). GO provides a standardized, con-trolled vocabulary for genome annotation systems, catalog-ing information about the structural and cellular location ofgene products, about the processes to which these productscontribute, and the functions that they fulWll (Stevens et al.2000; Bard 2003). Currently, GO contains over 1.6 millionannotated gene products (Gene Ontology Consortium2006), which are connected by class–subclass and parthoodrelationships. GO is also used for data analysis (Blake2004). GO was initiated by the model organism data baseinformatics community and involved the yeast (SGD), Xy(FlyBase), and mouse (MGI) data base and soon expandedto include other model organism data bases, such as forinstance the Arabidopsis Information Resource (TAIR)(Blake 2004). In the mean time, other genome centers suchas NCBI have also incorporated the GO annotation sets intotheir systems (Blake 2004).

Morphological ontologies

The Wrst bio-ontologies referring to higher levels of organi-zation have been used in data bases specialized for data ofspeciWc model organisms and are, thus, customized for han-dling model organism speciWc data. By now, many modelorganism ontologies exist, for example: for human data theGALEN CORE Model of the openGalen project (http://www.opengalen.org), the Foundational Model of Anatomyontology (FMA, http://sig.biostr.washington.edu/projects/fm), the Human developmental anatomy ontology, and theHuman disease ontology for various medical data bases; the

mouse adult gross anatomy, gross anatomy and develop-ment, and pathology ontologies for the Mouse GenomeInformatics data base (MGI; http://www.informatics.jax.org), the edinburgh mouse atlas project (emap; http://genex.hgu.mrc.ac.uk), and Pathbase (http://www.pathbase.net); the ZebraWsh anatomy and development ontology forthe ZebraWsh Model Organism Database (ZFIN; http://zfin.org); the C. elegans development, gross anatomy, and phe-notype ontologies for WormBase (http://www.wormbase.org); the Drosophila development, gross anatomy, and Xytaxonomy ontologies for FlyBase (http://flybase.org); andthe yeast phenotypes ontology for the SaccharomycesGenome Database (SGD; http://www.yeastgenome.org).All these ontologies are available from the Open BiomedicalOntologies website (OBO; http://www.obofoundry.org).

Unfortunately, these specialized ontologies cannot beapplied to a broader taxonomic range, and none of theabovementioned morphological data bases (MorphBank,MorphoBank, and Digital Morphology) has a deWned andformalized, taxon-independent morphological ontologyimplemented so far. This is owed to the fact that cur-rently there is no anatomy ontology available that is com-parable to GO in terms of GO’s degree of taxonomicindependence and terminological depth. However, mor-phologists solely start to recognize the requirement andutility of ontologies in morphology and respective pro-jects on the way (e.g., Ramírez et al. 2007). Anyway, sofar no morphological ontology has been developed thatcovers large zoological taxonomic units as for instancethe Metazoa or the Eukaryota. The only anatomy ontol-ogy referring to a larger taxonomic group is the Bilateriaanatomy ontology, which is currently under developmentand which will be implemented in 4DXpress (4dx.embl.de/4DXpress), a data base that does not focus on manag-ing morphological data but that has been developed forcomparing gene expression data from already existingdata bases such as the abovementioned ZebraWsh ModelOrganism Database.

Other morphology centered initiatives focus on mediat-ing between existing species-speciWc anatomy ontologies,making them comparable and compatible: while the Uberanatomy ontology is a multi-species anatomy ontology forcomparison across multiple species and is generated semiautomatically from the union of existing species anatomyontologies, therefore suVering from limited applicability(http://www.bioontology.org/wiki/index.php/UBERON:Main_Page), the Common Anatomy Reference Ontology(CARO; http://www.bioontology.org/wiki/index.php/CARO:Main_Page), on the other hand, is currently being developedto facilitate interoperability between existing species-speciWcanatomy ontologies, which requires the development ofthese species speciWc ontologies in the Wrst place (to whichit might actually provide some guidance).

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The Morphological Descriptions Data Base (MorphD-Base; http://www.morphdbase.de) is a relational data base,which attempts to provide a platform for uploading diVer-ent types of phenotypic information including all kinds ofmedia Wles and morphological descriptions based on a mor-phological ontology (i.e. MorphOntology) in the nearfuture. MorphOntology is currently being developed and isaccessible through the MorphDBase website. It will pro-vide taxon-independent concepts for morphological struc-tures that do not refer to homology assumptions and willstandardize morphological descriptions.

The lack of a general zoological anatomy ontology isvery unfortunate, since the generation of morphologicaldata has a long lasting history, going even back to ancienttimes, and a rich deposit of valuable information lies buriedin the grounds of the extensive literature of past centuries.Unfortunately, due to the linguistic problem of morphology(see below), the old literature has to be thoroughly studiedin order to evaluate what part of the enclosed informationmeets modern scientiWc quality standards. The relevantmorphological information then has to be translated intopresent day terminology. As a consequence, the extractionof morphological data from literature is often extremelytime-consuming—and as long as the results of these eVortsare not documented in detail and not made publicly avail-able, morphologists will have to repeat extracting themagain and again. In the near future, MorphDBase mightprovide an appropriate platform for documenting legacydata that has been extracted and translated from older mor-phological literature, which would make valuable dataavailable to every body without having to repeatedly putthe same time-consuming eVort into its evaluation andtranslation.

Resource description framework (RDF)

The method of representing an ontology takes in a crucialrole since it has to be highly standardized and formalized inorder to be applicable with description logics and utilizablefor many diVerent software applications. The ResourceDescription Framework (RDF) (http://www.w3.org/RDF)has become the most accepted general method for modelingknowledge (with OWL and RDFS as its most commonimplementations—see below). In RDF, relationshipsbetween resources are described by connecting oneresource to another through a property. A resource is any-thing that is identiWable by a uniform resource identiWer(URI) reference (Manola and Miller 2004).

The basic information unit in RDF is an RDF statementconsisting of the triple Subject property Object (in theremainder of the paper, Subject and Object will be writtenin italics while the property will be in bold font) and repre-sents a special case of the ‘Type_X relation Type_Y’ statement

mentioned above. The Subject represents the object that isbeing described, the property speciWes the relationship orproperty type between Subject and Object, and the ObjectspeciWes the value of the property and is either anotherresource or a literal string. For example: ‘Coelom has_partCoelothel’, ‘Arenicola_marina is_synonymous_with Lum-bricus_marinus’, ‘Protonephridium actively_participates_inExcretion’, ‘Epithelial_Cell is_a Polarized_Cell’, and‘Arenicola_marina has_maxlength(m) 0.25’ represent WveRDF statements—the former four connect two resources,the latter a resource with a string.

Each RDF statement can be modeled as a graph compris-ing two nodes connected by a directed arc (Fig. 1). A col-lection of such RDF graphs can jointly form a directedlabeled graph (DLG) (see Fig. 2). Such a DLG at its turncan, in theory, model most domain knowledge (Wang et al.2005) and is a useful tool for analysis and equivalence cal-culation using graphs logics. A collection of RDF state-ments can be used to represent an ontology.

RDF is a (meta-)data model and not a speciWc descrip-tion language for metadata. In order to make RDF com-puter parsable it requires syntax. Typically, RDF uses adeWned XML syntax (Beckett 2004) or N3 (Berners-Lee2005) and the semantics via reference to RDF Schema lan-guage (RDFS) (Brickley 2004) or Ontology Web Language(OWL) (McGuinness and van Harmelen 2004). RDFS andOWL represent languages that are based upon RDF andoVer support for machine processing and inferences (Wanget al. 2005).

The advantage of RDF lies in its ability to provide a wayof explicitly describing semantic relationships. Moreover,since RDF statements can be modeled as a DLG, addingfurther RDF statements (i.e. adding nodes and edges to aDLG) does not change the structure of the existing state-ments. In other words, extending the data structure inducesno fundamental change of the entire data structure—newRDF statements neither change nor negate the validity ofalready existing RDF statements.

Since an existing RDF statement can serve as subject orobject in another RDF statement, even reiWcation is possi-ble. This allows the documentation of both consensus anddisagreement about the same resource. It further allowsadditional commenting and supplementing of any RDFstatement. Since all these features meet common require-ments in scientiWc communities, RDF seems to be perfectlysuited for academic use (Wang et al. 2005).

Fig. 1 A RDF statement modeled as a directed labeled graph (DLG).Subject and Object represent the nodes and the property the edge thatconnects the nodes

propertySubject Object

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General principles of ontology development

Foundational ontology properties

Within an ontology, a term has to be deWned by means ofits relationship to other terms. This is achieved through theproperty part of the RDF statement. Thus, properties takein a central role in every ontology, since all classes deWnedin an ontology refer to them. There is a variety of possibleproperties to choose from, and one should be very carefulin deciding which properties one incorporates in an ontol-ogy and how to deWne/characterize them to avoid redun-dancies and inconsistencies.

Ontology properties describe the interactions or relationsbetween concepts or a concept’s properties (Stevens et al.2000). The instance_of relationship, the class–subclassrelationship is_a, and the parthood relationship part_ofbelong to the most foundational properties in an ontology.

A particular individual object or event (i.e. a datum) isreferred to via the instance_of property with which it isassociated to a concept of the ontology. It thus relates aninstance to a class. With this property empirical data can be

associated to an ontology implemented in a data base,thereby forming a knowledge base.

The is_a property, on the other hand, relates a class (i.e.concept) to another class. With the is_a property one canspecify hierarchical relationships of classes and their sub-classes, which, from class to subclass, results in a taxon-omy of more and more specialized concepts, implying ahierarchical organization of terms (i.e. taxonomic inclusion,Bittner et al. 2004). This hierarchy can be modeled as asimple directed graph with nodes and edges representingparent-child relationships, relating concepts across a treestructure (Fig. 2a). In the class–subclass relationship everychild has only a single parent, with the deWning propertiesof a parent being inherited downstream to its children (i.e.downward propagation).

The parthood property part_of describes relationshipsbetween two or more individual instances (i.e. objects orprocesses) in which one instance represents part of anotherinstance. Parthood relations (and all topological relations)never cross the dichotomy between objects and processes—an object is never part of a process and vice versa (Grenonet al. 2004). Thus, following that basal dichotomy one

Fig. 2 DiVerent types of graphs. a A unidirectional rule that allowsonly a single parent (e.g., has_subclass, has_part). It can be modeledas a simple directed graph representing a tree. b A unidirectional rulethat allows for more than one parent (e.g., derives_from) can be

modeled as a directed acyclic graph in which the graph itself can be tra-versed in several ways, with more than one path linking two nodes. cA bidirectional rule that imposes no directional constraints (e.g., adja-cent_to), resulting in an undirected graph

has_subclasshas_part

ot_tnecajdamorf_sevired

directed with 1 parent directed with > 1 parent undirected with parent = children

a Simple directed graph b Directed acyclic graph c Undirected Graph

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distinguishes parthood relations between objects (e.g., spa-tial_part_of) from parthood relations between processes(e.g., phase_of). Processes are partitioned in time, resultingin a sequential order of sub-processes of a lower level ofcomplexity (i.e. phases of a process). The life history of anorganism, for instance, can be partitioned into the sequen-tial order of larval/embryonic phase, juvenile and adultphase. On the other hand, spatial parthood is deWned on thebasis of topological inclusion (i.e. mereological inclusion).All the spatial parts of one level of structural organizationhave spatio-topological relations to one another that aremultidimensional and, unlike temporal parts, cannot beordered into a sequence—my brain, my nose, and my earsare all spatial_part_of of my head, but I cannot specifyany natural sequential or hierarchical order for them.Though both types of parthood properties represent rela-tions between individual instances, considering certainrules (e.g., Smith et al. 2005; Bittner 2004) one can alsotalk about parthood relations between universals (i.e. con-cepts, classes).

Parthood relations between individual instances andbetween concepts can also be modeled as a simple directedgraph. Contrary to the class–subclass relation, however,properties are inherited upstream (i.e. upward propagation),from children to parent, and not downstream as in theclass–subclass relationship. The resulting hierarchy of clas-ses based on the parthood relation is usually called a par-tonomy (Smith and Rosse 2004), and one can distinguishtemporal and spatial partonomies.

There are, of course, many more properties which arecommonly used in ontologies but cannot be discussed here,as for instance adjacent_to, derives_from, actively_par-ticipates_in, is_synonymous_with, and others (e.g., Ste-vens et al. 2000; Smith 2004). Unfortunately, currentlymany ontologies use their own properties and property deW-nitions/characterizations, which considerably hamperscomparisons of data across diVerent ontologies and thetransfer of data between data bases that use diVerent ontolo-gies (but see Smith et al. 2005).

Logical properties

Each property can be classiWed according to its logicalproperties. For instance is the is_a property transitive (if A1

is_a A2 and A2 is_a A3, then A1 is_a A3), reXexive (A1 is_aA1), and antisymmetric (if A1 is_a A2 and A2 is_a A1, thenA1 and A2 are identical); it is not symmetric. Both, the part-hood relation between individual instances and the onebetween concepts are transitive, reXexive, and antisymmet-ric as well. Antisymmetry implies a direction and can bemodeled as a directed graph (Fig. 2a, b) in which the nodesand edges represent parent–child relationships (for symme-try see Fig. 2c). The instance_of property, on the other

hand, is neither transitive, nor reXexive, nor symmetric, norantisymmetric.

Besides transitivity, reXexivity, and symmetry or anti-symmetry, further logical properties can be speciWed forevery RDF property. Regarding a speciWc RDF property onecan deWne the concepts that are allowed to be used as Subject(i.e. the domain of the property) and as Object (i.e. the rangeof the property) in a RDF triple in connection with this prop-erty. The speciWcation of the domain and the range of a RDFproperty constraints its applicability with the intention tominimize logical inconsistencies within sets of RDF triples.The property actively_participates_in, for instance, speci-Wes a material object that participates in a process. Thus, thedomain of actively_participates_in has to be restricted tomaterial objects and its to range processes.

DeWning concepts in an ontology

The deWnition of a term/concept takes in an important func-tion in every ontology. One can distinguish two types ofterms or concepts. On the one hand primitive concepts thathave only necessary conditions, and on the other hand deW-ned concepts that have necessary and suYcient conditions(Stevens et al. 2000). Regarding explicitness and clearnessone should always prefer deWned over primitive conceptswhen developing an ontology.

Necessary conditions are speciWed via the class–subclassrelationship is_a. The speciWcation of class–subclass rela-tionships represents an essential part of what is commonlyreferred to as Aristotelian deWnitions and includes, due todownward propagation which is inherent to the is_a prop-erty, all deWning properties of all of its parent classes. AnAristotelian deWnition consists of two sets of deWning prop-erties (Fig. 3): genus, necessary for assigning an entity to aclass (properties inherited from the parent class), and diVer-entiae, necessary for distinguishing the subclass (child) fromother subclasses assigned to the same class (e.g., Smith2004; Rosse and Mejino 2003). By connecting a class toanother class through the class–subclass relationship is_aand restricting it through additional properties that are spe-ciWcally required for the subclass, diVerent subclasses can bedistinguished and deWned. An individual object necessarilyhas to meet the requirements of the class to qualify as aninstance of its subclass. This allows for a convenient way ofdeWning a new term by relating it through the class–subclassrelationship to an already deWned term (necessary condition)and than adding its distinguishing property (in combinationresulting in the suYcient condition). The human kidney, forinstance, can be deWned as: Polarized_junctioned_cell is_aJunctioned_cell AND Polarized_junctioned_cell orientationApico-basal. Here ‘is_a Junctioned_cell’ represents thegenus part of the deWnition and ‘orientation Apico-basal’the diVerentia part (Fig. 4).

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Zoo-morphology

The linguistic problem of morphology

Lately, the representation of phenotypic information repre-sents an area of intense discussion (see Blake 2004).

Especially in the context of inquiring causal/functionalinterlinks between objects belonging to diVerent levels oforganization, such as gene products and morphologicalstructures, the demand of high quality phenotypic dataincreases constantly. But also for all kinds of comparativestudies over a large taxonomic range, phenotypic data withhigh degree of comparability is highly requested.

Morphological data constitutes a very large proportion ofphenotypic data. In morphology, only descriptions of thestructures or the functions of morphological features repre-sent data in the strict sense (see Vogt et al. 2008; Vogt 2008).Compared to molecular data, morphological descriptionsseem to be inferior with respect to transparency and repro-ducibility of their production. The apparent weakness of mor-phological characters has been addressed in a series of papersby Wiens and Jenner within the framework of phylogeneticmethodology (e.g., Wiens 1995; Hillis and Wiens 2000; Poeand Wiens 2000; Wiens 2001, 2004; Jenner 2002, 2004a,2004b, 2004c; see also Pimentel and Riggins 1987; Stevens1991; Thiele 1993; Kesner 1994;). According to theseauthors, morphological character matrices build by one phy-logeneticist are often treated like a black box by other phy-logeneticists, and it becomes a question of authority ratherthan scientiWc argumentation whether one should rely on aphylogenetic character statement of another phylogeneticist.

The problems of phylogenetic morphological charactersaddressed by Wiens and by Jenner within a phylogenetic

Fig. 3 Aristotelian deWnition: the set of deWning properties (i.e. es-sence) of a ‘parent’ class are passed on to all its ‘child’ classes (down-ward propagation) and provides the Genus part of their deWnitions. Allinstance of every ‘child’ class necessarily has to posses all of theseproperties. The ‘child’ classes are distinguished from one another by

their respective essential properties, which provide the DiVerentia partof their deWnitions. Only the combination of DiVerentia and Genus issuYcient for membership to a class and represents the essence of akind, and thus the deWnition of the respective class

Fig. 4 RDF graph of a deWnition of ‘polarized junctioned cell’: a‘polarized junctioned cell’ can be deWned as a ‘junctioned cell’ thatpossesses an apico-basal orientation. In this case, all deWning proper-ties of ‘junctioned cell’ as well as those of all of its parent classeswould provide the genus part of the deWnition of ‘polarized junctionedcell’, while the property of possessing an apico-basal orientation wouldrepresent the diVerentia part. Understood that way, ‘polarized junc-tioned cell’ would represent a special kind of ‘junctioned cell’

Differentia

Genus

Apico-basalorientation

intermolecular_bond_with

has_part

is_a

is_a

Polarized_junctioned_cell

Junctioned_cell

Cell

Cell_organelle

Cell_membrane

Cell_junction

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framework result from a more fundamental and generalproblem concerning morphological descriptions, the lin-guistic problem of morphology (Vogt et al. 2008). The lin-guistic problem of morphology results from the lack of astandardized and commonly accepted morphological termi-nology, the lack of a rational for the delimitation of mor-phological traits, and a standardized and formalizedmethodology of morphological description. As a conse-quence, morphological terminology and morphologicaldescriptions vary from author to author, the meaning of aterm often changes through time, and morphological termi-nology is often restricted to a speciWc taxonomic group andcannot be easily adapted to other groups. This non-stan-dardization and therefore diverse usage of morphologicalterminology and the lack of a commonly accepted methodof description can, in scientiWc practice, lead to identicallydescribed structures that are in fact not identical or diver-gent descriptions of one and the same morphological struc-ture (Vogt et al. 2008; see also Ramírez et al. 2007).

The linguistic ambiguities in morphological descriptionsturned out to pose fundamental problems for comparativemorphological studies, since it is the source for repeatedmisunderstandings among morphologists, undermining thepossibility to reliably communicate morphological data.Reliable communication of data, however, represents a nec-essary prerequisite for division of labor not only amongmorphologists conducting comparative studies over a broadtaxonomic range, but also for all kinds of co-operations andstudies in which morphologists are involved or morpholog-ical data is analyzed.

Another problem, which is somehow related to the lin-guistic problem of morphology, is often found in compar-ative studies like for instance phylogenetics. Inphylogenetics morphological descriptions are often notclearly separated from hypothetical conclusions andexplanatory hypotheses based on them, with the conse-quence that observational data and hypotheses blend andcannot be diVerentiated anymore. This happens forinstance whenever morphological descriptions use termsthat imply homology of the described structures (Vogt2008; Vogt et al. 2008). Homology statements representhypotheses that transcend the perceptually given by pro-viding a possible explanation for the sameness of struc-tures through the implicit assumption of a commonevolutionary origin and should not be mistaken with mor-phological descriptions, which are by nature descriptiveand not explanatory.

RDF ontology provides a promising solution to the linguistic problem

Hitherto, no traditional dictionary or thesaurus for morpho-logical terminology solved the problems resulting from the

linguistic problem. Apart from the fact that no taxon-inde-pendent dictionary/thesaurus is available, this may be dueto the fact that diVerent traditional dictionaries and thesauripropose diVerent terms and concepts and that they often useterms that imply homology. In any case, the most importantreason for their failure is that they are not commonlyaccepted and therefore not used by a majority of morpholo-gists.

Instead of the traditional dictionaries and thesauri, thecombination of a data base for morphological/phenotypicdata and the use of a taxon-independent RDF ontology formorphology could have the potential to provide a regionalsolution to the linguistic problem by explicitly imposing adeWned and controlled vocabulary (Vogt et al. 2008). Thiscombination would provide an integrative platform—although restricted to the particular data base—withinwhich comparative morphological/phenotypic studieswould be possible. Within life sciences such a morphologi-cal data base could take in a central methodological func-tion comparable to GenBank (http://www.ncbi.nlm.nih.gov/Genbank) for molecular data.

The necessity for a taxon-independent data standard

So far, a lot of eVort and resources went into the develop-ment of bio-ontologies with a molecular focus or with afocus on modeling knowledge for a speciWc model organ-ism (see above). Some of these ontologies reached a levelof sophistication that, by now, allows their successful appli-cation within respective data bases (e.g., FlyBase, MouseGenome Database, Arabidopsis Information Resource,WormBase, etc.). On the other hand, data bases that aredesigned for the management of comparative data over alarge taxonomic range are still rare. This is particularly truefor morphological data. For the use of data in comparativestudies across a broad taxonomic range a taxon-indepen-dent data standard would be required. Thus, comparativebiology requires ontologies to be as much as possibletaxon-independent. Taxon-independence in this contextmeans that the applicability of terms and concepts referringto speciWc types of structures should not be restricted to aparticular taxonomic group—the deWnition of terms shouldbe free of conditional predicates such as ‘insect’-head or‘arthropod’-segmentation, but should provide taxon-inde-pendent criteria for their application—even though someterms might in practice only be applied to a certain taxo-nomic group.

A general structure concept for morphology

The development of a taxon-independent anatomy ontol-ogy that does not rest on homology assumptions presup-poses a general structure concept for morphology (see

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Vogt 2008). In this context, structure should be under-stood to represent a way to conceive properties and rela-tions of a complex whole: the set of relations betweendiVerent parts and aspects of a given complex wholedetermines its structure. A structure concept is requiredfor the conceptualization of a complex whole, and shouldfacilitate in generating data of a speciWc type and qualitythat are relevant for a speciWc scientiWc discipline orresearch program. Every structure concept is thus neces-sarily context-dependent (Vogt 2008). As such, the role ofa structure concept regarding data in the strict sense (datas.str.) is comparable to the role of minimum informationchecklists regarding metadata.

On a very basic level, morphological data s.str., likemany other types of data s.str. in natural sciences, repre-sent existence statements. In morphology they are calledmorphological descriptions and they represent hypothe-ses about the existence of entities and their properties,which are based on observational judgments—just likeanswers to questions about the entities properties andrelations. A general morphological structure concept(like any other structure concept) should provide ascheme for standardizing and formalizing such descrip-tions by providing a list of relevant perceptual categories(Fig. 5)—non-redundant questions to be answered on thebasis of morphological investigations. Morphologicaldata would than consist of a list of answers, which can beunderstood to represent the values to each category thathas been evaluated in a morphological investigation. Ide-ally, the structure concept also speciWes which answers(i.e., values) are possible, thereby restricting the possiblevalue-space (Vogt 2008).

A general structure concept for morphology would thushave to consist of a standardized set of relevant empiricalcategories (questions like: what is directly adjacent to theentity to be described; is it continuous with some otherentity; out of what parts is it composed; does it activelyparticipate in a speciWc type of biological process)together with their respective value-spaces (to each ques-tion a standardized set of possible answers). A value-space can be either a Boolean YES or NO, numerical val-ues like for instance the natural numbers or a speciWcnumeral interval, or a limited set of deWned terms. It isobvious that a morphological ontology presupposes thedevelopment of a general structure concept for morphol-ogy, and Wrst steps in this direction have already beentaken (see Vogt 2008).

The future of zoo-morphology

Until the late nineteenth century, morphology was consid-ered to be the most important and proliWc scientiWc Weld ofwhat would later become ‘biology’ (e.g., Nyhart 1995).

However, with the advent of physiology as an independentscientiWc discipline and its rapid development in the twenti-eth century, and not least with the rise of genetics, morphol-ogy more and more lost its prominent role.

The diVerence in data quality in terms of aperspectivaland mechanical objectivity might be the reason why somebiologists question the relevance of organismic biology ingeneral and morphology in particular with respect to theirpotential to signiWcantly contribute to future developmentsin biology. I am convinced that the linguistic problem ofmorphology accounts for a signiWcant proportion of thepopularity of this opinion. Apparently, if morphology man-ages to come up with a solution to the linguistic problem, itis likely that it will continue to make valuable contributionsto important future developments in biology. In doing so, itmay actually become indispensable and could take in anessential role, since it has the potential to provide high-quality phenotypic data that is highly demanded for variousinteresting biological research programs.

Standardized morphological data would not onlyimprove comparability of morphological data. It would alsofacilitate many co-operations of morphologists with forinstance developmental biologists or geneticists. Veryinteresting research programs concerning for instancemechanismic interrelationships of morphogenesis andunderlying gene organization could beneWt from a morpho-logical data standard. The development of a RDF ontologyfor morphology represents a Wrst and important steptowards that direction.

Fig. 5 Using a structure concept in RDF statements: The entity to bedescribed (e.g., a morphological trait) is represented by the ‘Subject’.The ‘property’ corresponds with one perceptual category taken fromthe structure concept and functions as an empirical question that canonly be answered by investigating the entity to be described. The an-swer to such a question can be represented by the ‘Object’ of the RDFtriple. It corresponds with one of the ‘values’ that the structure conceptallows as a possible value (‘value-space’) for this particular category

Structure Concept

propertyperceptualcategory

Subject ?Trait

propertyperceptualcategory

SubjectTrait

ObjectValue

The perceptual category poses a question that a morphologist

studying a specimen has to answer in order to complete

the RDF triple

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A general data standard in biology

Standardizing bio-ontologies

The general use of RDF ontologies within biological databases facilitates interoperability across diVerent data basesand provides the formal basis for the Wnal goal of establish-ing a general and uniWed data standard for all types of bio-logical data. A uniWed data standard, at its turn, wouldprovide the formal basis for an integrative approach to biol-ogy (Bard and Rhee 2004), enabling communication andintegrated comparison of all kinds of biological data. Theestablishment of commonly accepted general criteria forthe development of well-structured and standardized ontol-ogies is essential for ontologies to be successful in thisrespect.

Unfortunately, although all well constructed ontologiesare highly formalized, diVerent ontologies often diVerquite substantially in their use of properties and their hier-archy (i.e. taxonomy) of concepts. Practical demands ofresearchers for exchanging and integrating data require acoordinated development of shared ontologies. The OpenBiological Ontologies initiative (OBO; http://obo.source-forge.net) provides a virtual umbrella organization forincreasing transparency of ontology development andcoordinating standardization eVorts for ontologies forshared use across diVerent biological and medicaldomains. At the OBO website one can Wnd various bio-ontologies ranging from anatomy to development, genom-ics and proteomics, experimental conditions, metabolo-mics, phenotype, and taxonomic classiWcation. OBOspeciWes criteria that have to be met by an ontology inorder to be accepted as one of the OBO (obo.source-forge.net/crit.html). These criteria guarantee for instance(a) that all their ontologies are for sharing and areresources for the entire community, (b) that tools, as forinstance query machines, can be usefully applied to alltheir ontologies, facilitating shared software implementa-tions, and (c) the possibility of two diVerent ontologies(e.g., an anatomy and a process ontology) to be combinedthrough additional relationships.

OBO is showing the way of how to impose somethinglike a seal of approval for ontologies that meet certain cri-teria. Thus, the basis for the establishment of a generaldata standard in biology does already exist, and under theOBO umbrella it is actually already under development.Yet, at the current stage of development, the data standardis still very unspeciWc and cannot utilize all its potential.However, at present nobody can predict what exactly theWnal schema of a general standard for biological data willbe like, other than that it will most likely involve RDFontologies.

What is a data standard?

All standardization activities have to deal with many meth-odological as well as practical problems and any criteria orsets of questions that could guide this process are welcome.In analogy to manufacturing standards, Brazma (2001) sug-gests that the huge amounts of information produced byhigh throughput experiments have to be transformed intoexecutive summaries, and he identiWes three criteria for thegeneration of such summaries.

First, a summary should include all elements essentialfor understanding the studied phenomena, as for instanceall relevant metadata about the studied biological samples(e.g., species determination, sampling coordinates, etc.) andsome quality indicators. This is the objective of minimuminformation checklists. However, all minimum informationchecklists are restricted to metadata and thus only representthe metadata part of a content standard (for the data s.str.part of a content standard see structure concept).

A data base that has an ontology implemented and thatuses it for data representation and documentation providesits data in a computer parsable form. This corresponds withBrazma’s (2001) second criterion that the informationshould be available in a way that it can be parsed by a com-puter program. Since RDF statements can be modeled asdirected graphs and since RDF allows the application ofdescription logics, RDF ontologies provide a broad basisfor all kinds of computer applications, including very pow-erful query engines that enable a researcher to Wnd mostspeciWc peaces of information in very large data sets, with-out having the scientist to go through all available data her-self. One could, for instance, pick one morphologicaldescription from the data base and let the data base searchfor most similar descriptions, or search for all structuresdescribed as adjacent to heart (i.e. all RDF triples with theproperty adjacent_to and the object Heart).

The use of RDF ontologies within data bases results in asigniWcant increase not only of the transparency and repro-ducibility of the generation of data, but also the degree ofcomparability and communicability of biological data ingeneral. Thus, RDF ontologies have the potential to signiW-cantly increase aperspectival and mechanical objectivity ofbiological data. Understanding aperspectival and mechani-cal objectivity as an index for data quality, RDF ontologiescan establish a quality standard for biological data, whichrepresents Brazma’s (2001) third criterion.

Wang et al. (2005) suggest that before developing a datastandard one has to answer two questions. The Wrst questionconcerns the content: what should be standardizes? Thisquestion addresses the requirement of a content standardand thus corresponds with Brazma’s Wrst criterion, exceptthat it also includes the data s.str. part of a content standard.

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The second question of Wang et al. (2005) concerns themethodology: how should the standardization be format-ted? This corresponds with Brazma’s second criterion ofcomputer parsability. Agreeing upon a common formatstandard is essential for increasing aperspectival andmechanical objectivity, and RDF provides such a formatstandard. While the content standard speciWes, which typeof information is required, the format standard provides inform of a standardized Wle format the speciWc syntax fortransmitting and communicating data (Field and Sansone2006).

Anyhow, a very important distinction that one shouldalways keep in mind while developing data standards ismuch more basic: the establishment of a data standard notonly requires a detailed documentation of the methods andtechniques applied for data production (i.e., metadata stan-dard), but also the development of clear and transparentdeWnitions of scientiWc terminology for data representation(i.e., data s.str. standard). Thus, the abovementioned criteriaand types of standardizations apply to both data and meta-data. Most current standardization activities, especiallywithin the OMICS Welds of research, however, only focuson standardizing metadata. This might be due to the factthat what belongs to data s.str. and how to unambiguouslyrepresent it is less problematic in the OMICS Welds ofresearch, which already established standards for their datas.str. (e.g., DNA sequence data), than it is for instance inmorphology, where it represents the main challenge (seeVogt et al. 2008). Only in case one manages to standardizeboth, the expected increase in data quality will be reached.

Moreover, considering the terminology problems of lackof standards of gene names and spellings mentioned above,it is obvious that agreeing upon a content standard and aformat standard is not suYcient—biological data requiresfurther standardization in order to meet the demands forcommunicability and comparability of data. In order toavoid terminological problems, concepts standards andnomenclatural standards are required in addition to contentand format standards. The implementation of ontologieswithin data bases has proven to be very successful in thatrespect.

Do general standardization criteria exist that apply to all biological disciplines?

Developing a data standard actually requires the develop-ment of a whole set of diVerent standards: (a) what representsrelevant information (i.e. content standard), (b) what does aconcept mean (i.e., concept standard), (c) which label (i.e.,word, symbol, acronym) should we use to refer to this con-cept (i.e., nomenclatural standard), and (d) how is the syn-tax with which data should be transmitted andcommunicated.

Content standard

The content of what should be standardized has to be deW-ned by each ontology and data base separately, dependingon its scope and purpose. In case of metadata, content stan-dards are determined by minimum information checklists,which specify what types of annotations are required forestablishing a high degree of transparency and reproduc-ibility of data production. For data s.str., on other hand,content standards are determined by structure concepts,which specify what types of perceptual categories togetherwith their corresponding value-spaces should be used fordescribing data s.str., in order to establish a high degree ofcomparability of data.

Both, minimum information checklists as well asstructure concepts are research and data type speciWc.Depending on a given research program or theoreticalframework, they specify which empirical phenomena andprocedural information is relevant and which should beignored. Obviously, a general content standard that isuniversally applicable to all biological disciplines cannotexist. However, whenever information is relevant in sev-eral biological disciplines, as for instance specimeninformation, the same content standard should beapplied, thereby generalizing standardization across vari-ous biological disciplines and avoiding duplication ofeVorts (Field and Sansone 2006). Some standards, as forinstance inferential standards, entail other standards, asfor instance standards for proper experimentation. Stan-dards thus can have a hierarchical nature with some‘higher level’ standards entailing ‘lower level’ standards.Projects already exist that provide support for integratingvarious standardization eVorts, as for instance theReporting Structures for Biological Investigations(RSBI) project (Sansone et al. 2006).

Concept standard

Concept standards provide the meaning to scientiWc termsused for metadata and data s.str. They are essential, as theysolve terminological problems. Ontologies provide conceptstandards.

Although ontologies for data s.str. are based on speciWcstructure concepts that are necessarily research speciWc andvary with data type and with the theoretical background ofan investigation, they can still be based on a general taxonomyof foundational concepts, which can be commonly used inorder to develop more speciWc ontologies. This applies par-ticularly to the properties used for deWning concepts. If agiven property, as for instance actively_participates_in,has the same meaning throughout all bio-ontologies, wewould have a standardized terminology for deWning biolog-ical concepts within bio-ontologies. The necessity for stan-

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dardizing properties has been recognized and eVorts existfor developing a standard set of foundational properties(see, e.g., obo relationship types ontology; Smith et al.2005).

The use of RDF ontologies in data bases for the purposeof data management also improves possibilities for thetransfer or mapping of data across diVerent data bases,which increases aperspectival objectivity between the con-tents of these data bases.

Nomenclatural standard

The nomenclatural standard provides a stable link betweena term and its corresponding concept. Ontologies providenomenclatural standards. By providing local identiWers forthe concepts deWned in an ontology, each ontology estab-lishes a nomenclatural standard that is restricted to the databases in which it is implemented. However, what isrequired is a nomenclatural standardization across diVerentontologies and diVerent data bases. To provide a stable andresolvable identiWer for concepts across ontologies and databases it would be highly desirable to adopt globally uniqueidentiWers (GUIDs) or life science identiWers (LSIDs) forall biological concepts deWned within bio-ontologies.

Format standard

The format standard provides a standardized Wle format,which speciWes the syntax for transmitting and communi-cating metadata and data s.str. The current trend is to useXML based formats, like the resource description frame-work (RDF).

The advantage of XML based syntax standards is thatthey provide a formalized standard that is computer read-able but still allow a lot of freedom regarding the contentthat the syntax organizes. This is especially important con-sidering that all standardization activities in biologicalresearch necessarily represent an ongoing process (Brooks-bank and Quackenbush 2006). What seems to be importanttoday may be less important tomorrow and new technolo-gies and theoretical insights may bring about new types ofdata, which requires the development of new concepts andproperties for existing ontologies or even new ontologies.Thus, XML based syntax standards represent a goodanswer to Wang et al’s (2005) second question concerningdata format.

Conclusion

Looking into the history of biology one can observe thecontinuous diVerentiation and subsequent methodologicaldivergence of diVerent biological disciplines, with organismic

and molecular biology forming the most divergent poles.Besides all the obvious advantages of a diversiWcationwithin biological research, this process also holds its draw-backs, because it results in a diversiWcation of scientiWc lan-guages, to the eVect that communication between thevarious biological research communities becomes more andmore diYcult. But only co-operation involving organismicand molecular biologists will maximize output in biologicalsciences and will do justice to the biological diversity andorganizational integration that is caused by evolution. Inthat context focusing only on model organisms is problem-atic, since model organism studies usually generate knowl-edge with high explanatory force, but this knowledge has alimited explanatory range since it only refers to a very lim-ited portion of biological diversity (for ‘explanatory force’and ‘explanatory range’ see Platts 1997; Rieppel 2005).Thus, in order to give good estimates regarding larger por-tions of biological diversity, comparative approaches arerequired, which at their turn not only depend on the knowl-edge gained by model organism studies but also requiredata that is highly comparable across various species andtaxa.

The need for standardization has been recognized inmany Welds of biological research by now and all respectiveactivities are gaining credibility, in particular in the light ofemerging policies on open access and data sharing (NAS2003; NIH 2006; OECD 2003; Wellcome 2003; Field andSansone 2006). Unfortunately, many proposals for devel-oping data standards still do not well in traditional peer-review settings, which mostly favor hypothesis-drivenresearch (Brooksbank and Quackenbush 2006). However,funding agencies seem to start to realize that the expensesthat are generated by incorrect data interpretations andanalyses due to lack of data standards by far outweigh thecosts for developing standards—thus, also from a purelyWnancial point of view, investing into standardization activ-ities makes good sense. Moreover, a common biologicaldata standard would also provide instrument manufacturersand lab suppliers with common criteria for their products,reducing time and cost for implementing standard-compli-ance (Field and Sansone 2006).

To be successful as a data standard, RDF ontologiesmust gain widespread acceptance in their respective biolog-ical communities. Only if ontologies can overcome theresistance of the major generators of the corresponding dataso that they are willing to use the ontologies, they will be asuccess. While this has been recognized in the OMICSWelds of biological research, morphologists seem to beespecially reluctant (which might be due to the fact that theOMICS communities have already been more integrated tobegin with than for instance morphologists).

Further development of RDF ontologies is inevitable,also because the amount of data in life sciences will keep

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216 Zoomorphology (2009) 128:201–217

increasing exponentially in future. Ontology related toolswill enable easy sifting through the data bulks and the inte-gration of data from diVerent sources. Data bases in combi-nation with description logics and underlying bio-ontologies will provide the basis for the development ofsuch tools. This development provides a great opportunityfor morphologists to disseminate their data through variousbiological communities in a way that they are actuallyusable for non morphologists. And when more morphologi-cal data are used by other biologists, not only new knowl-edge and insights will be gained, but also moreopportunities for co-operations and maybe even more fund-ing for morphological research. Thus, the entire Weld ofmorphological research could beneWt from a morphologicaldata standard. Whether morphology will be part of thisongoing process of standardization and integration willdepend on morphologists managing to develop and agreeon a common data standard.

Acknowledgments I thank Thomas Bartolomaeus and Markus Kochfor discussion and valuable suggestions for an earlier draft of this pa-per. I also want to thank Ronald Jenner, as well as two other anony-mous referees, for reading, criticizing, and commenting on an earlymanuscript. It goes without saying, however, that I am solely respon-sible for all the arguments and statements in this paper. This study wassupported by the SPP 1174 of the German Research Foundation (DFG)(VO 1244/3-2). I am also grateful to the taxpayers of Germany.

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