The Neuroscience Information Framework:A Data and Knowledge Environment for Neuroscience

Daniel Gardner & Huda Akil & Giorgio A. Ascoli &Douglas M. Bowden & William Bug &

Duncan E. Donohue & David H. Goldberg &

Bernice Grafstein & Jeffrey S. Grethe &

Amarnath Gupta & Maryam Halavi &David N. Kennedy & Luis Marenco &

Maryann E. Martone & Perry L. Miller &

Hans-Michael Müller & Adrian Robert &Gordon M. Shepherd & Paul W. Sternberg &

David C. Van Essen & Robert W. Williams

# The Author(s) 2008. This article is published with open access at Springerlink.com

Abstract With support from the Institutes and Centersforming the NIH Blueprint for Neuroscience Research, wehave designed and implemented a new initiative forintegrating access to and use of Web-based neuroscience

resources: the Neuroscience Information Framework. TheFramework arises from the expressed need of the neuro-science community for neuroinformatic tools and resourcesto aid scientific inquiry, builds upon prior development of

NeuroinformDOI 10.1007/s12021-008-9024-z

D. Gardner (*) :D. H. Goldberg :B. Grafstein :A. RobertLaboratory of Neuroinformatics and Department of Physiology,Weill Medical College, Cornell University,1300 York Avenue,New York, NY 10065, USAe-mail: dan@med.cornell.edu

H. AkilMolecular and Behavioral Neuroscience, University of Michigan,Ann Arbor, MI 48109, USA

G. A. Ascoli :D. E. Donohue :M. HalaviCenter for Neural Informatics, Structure, and Plasticityand Molecular Neuroscience Department,Krasnow Institute for Advanced Study, George Mason University,Fairfax, VA 22030, USA

D. M. BowdenNational Primate Research Center, University of Washington,Seattle, WA 98195, USA

W. Bug : J. S. Grethe :M. E. MartoneDepartment of Neurosciences, University of California,San Diego, CA 92093, USA

A. GuptaSan Diego Supercomputer Center, University of California,San Diego, CA 92093, USA

D. N. KennedyDepartments of Neurology and Radiology,Harvard Medical School,Boston, MA 02129, USA

L. Marenco : P. L. Miller :G. M. ShepherdDepartment of Neurobiology and Yale Center for MedicalInformatics, School of Medicine, Yale University,New Haven, CT 06510, USA

H.-M. Müller : P. W. SternbergHoward Hughes Medical Institute and Division of Biology,California Institute of Technology,Pasadena, CA 91125, USA

D. C. Van EssenDepartment of Anatomy and Neurobiology, School of Medicine,Washington University,St. Louis, MO 63110, USA

R. W. WilliamsDepartment of Anatomy and Neurobiology and Departmentof Pediatrics, University of Tennessee Health Science Center,Memphis, TN 38163, USA

neuroinformatics by the Human Brain Project andothers, and directly derives from the Society forNeuroscience’s Neuroscience Database Gateway. Part-nered with the Society, its Neuroinformatics Committee,and volunteer consultant-collaborators, our multi-siteconsortium has developed: (1) a comprehensive, dynam-ic, inventory of Web-accessible neuroscience resources,(2) an extended and integrated terminology describingresources and contents, and (3) a framework acceptingand aiding concept-based queries. Evolving instantiationsof the Framework may be viewed at http://nif.nih.gov,http://neurogateway.org, and other sites as they come online.

Keywords Neurodatabases . Data sharing . Terminologies .

Portals

Introduction to This Special Issue of Neuroinformatics

This special issue of Neuroinformatics, edited by D.Gardner and M. Martone, informs the neuroscience andneuroinformatics communities of our plans and progressdesigning the Neuroscience Information Framework(NIF). We begin with this White Paper, which summa-rizes the project, briefly analyzes the present and futureof neuroinformatics, introduces the work we haveconducted under phases I and II of the Frameworkproject, and discusses the challenges of serving theentire neuroscience community. Gardner et al. (2008)outline the rationale for, and the community-deriveddesign of, the NIF core terminologies: a set of controlled-vocabulary terms for describing neuroscience data, theexperiments that generate them, neuroscience Webresources, and their areas of interest. Müller et al.(2008) describe a parallel terminology effort, Textpresso,which marks up and provides new ways to search foran increasingly large fraction of the contemporaryneuroscience literature. Bug et al. (2008) integrate NIFand other terminologies toward the NIFSTD, a stan-dardized semantic framework and ontology bridgingscales and areas. Gupta et al. (2008) describe thearchitecture, rationale and functions of the NIF informa-tion federation system, providing examples from thecurrent release. Marenco et al. (2008a, b) present twoenabling components, the NIF LinkOut Broker and aconcept-based query interface. Finally, Halavi et al.(2008) use NeuroMorpho.Org, an integrated NIF reposi-tory for digitally reconstructed neurons, as an example ofdesigning, creating, populating, and curating a neurosci-

ence digital resource. With this issue, we all—as a team—offer to the neuroscience community and to the NIH ourdesign for the Neuroscience Information Framework—and for its evolution.

Introduction to the Neuroscience InformationFramework

The Neuroscience Information Framework Derives From,and Is Designed To Serve, the Neuroscience Community

The NIF is a new initiative for integrating access to—and thereby promoting use of—Web-based neuroscienceresources. Working as a team, we and colleagues havedesigned and implemented the NIF under contract fromthe Institutes and Centers forming the US NIH Blue-print for Neuroscience Research.

In the initial phase, constrained by the enabling contractto exploratory work, we:

& Surveyed the web for neuroscience information resour-ces: databases, literature, gene, tool, and material sites,and built an inventory,

& Developed terminologies to characterize and describethese resources and their contents,

& Convened expert terminology workshops,& Converged on a feasible design for our initial release

compatible with future extensions, and& Prepared an initial version of this White paper.

Once extension to a technical implementation phase wasapproved by NIH, we:

& Constructed the Framework as a dynamic inventory ofneuroscience data,

& Incorporated a user interface accepting and aidingconcept-based queries that span resources across mul-tiple levels of biological function, and

& Developed an underlying terminology for the Frame-work, brought together from multiple sources includingTextpresso, other biomedical terminologies and ontolo-gies, and a total of 18 neuroscience terminologyworkshop meetings.

All the above is being delivered to the NIH andoffered under Open Source (OS) licensing to theneuroinformatics and neuroscience communities.

This is a US national project with contributions frombeyond the authorship of this document. Figure 1 shows thepaid and volunteer performance sites, emphasizing thegeographic spread as well as the intellectual breadth of

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neuroinformatic contributors to the Framework. An Appendixprovides a more extensive list of participants.

The Neuroscience Information Framework Will AdvanceNeuroscience Research

The Framework is being designed to serve neuroscienceinvestigators by:

1. Facilitating directed and intelligent access to data andfindings,

2. Aiding integration, synthesis, and connectivity acrossrelated data and findings,

3. Stimulating new and enhanced development of neuro-informatic resources, and

4. Enabling new and enhanced analyses of data.

The Framework and its query tools are beingdesigned to directly implement the first end and therebyenable informed investigators to achieve the second.The Framework, its components, and its satellites willsupport accessibility, interoperability, and integration;

exploration and reasoning will continue to be performedby members of the research community.

We envision that Framework development will furtheradvance neuroinformatics and links among neuroinfor-matics, bioinformatics, and the terminologies and ontol-ogies relating them, supporting the third goal. Theexistence of the Framework will spur development ofneuroinformatic resources in each of two ways. Manydisease- technique- or preparation-focused communitiesmay be reluctant to develop a database or otherneuroinformatic resource. By offering a portal and entrypoint to be used by the entire neuroscience community,the Framework provides a much larger potential audi-ence than a single community can muster. Largernumbers of viewers with broad expertise can addsignificant value to resources. As the Framework andits tools are Open Source, development will also beaided by making available modules useful for describ-ing, archiving, and sharing data and findings. Frame-work terminologies, built with the support of manydomains of neuroscience, will also aid development of afuture semantic web of biomedical ontologies.

Fig. 1 Framework contributors include both contract sites and volunteer consultant-collaborators. An Appendix lists contributors in greater detail

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The fourth end is not a direct function of theFramework; rather, development of the Framework andeasier access to data should spur development andutilization of analytic tools. The many tools indexedby the Internet Accessible Tool Resource, now accessi-ble via the Framework, and the computational neuro-informatic resources at neuroanalysis.org provide twosuch examples.

The Neuroscience Information Framework is Designedto Advance the Mission and Goals of the NIH Blueprintfor Neuroscience Research

The Blueprint “confronts challenges that transcend anysingle institute or center and serves the entire neurosciencecommunity” and includes procedures that “focus on cross-cutting scientific issues.” These summarize the goal andmethodology of the Neuroscience Information Frameworkas well.

The Decade of the Brain (1990–1999; see http://www.loc.gov/loc/brain/) and the years beyond have continuedto demonstrate the complexity of nervous systems, intheir development, structure, function, and susceptibilityto disease. Each individual technique, insight, scale ofexamination and depth of analysis, each individualdisorder advances our understanding of neuroscience asa whole, informed by neuroscience as a whole. Neuro-informatics has served neuroscience well, but no neuro-informatic project has—until now—been designed toserve “the entire neuroscience community.” New neuro-informatic tools and resources are needed to “focus oncross-cutting scientific issues” by facilitating access todata and findings that cut across traditional boundarieswithin neuroscience.

The Framework Will Enable New Paradigmsfor Neuroinformatics

The Neuroinformatic Ecosystem

Science is an ecosystem: its roots and soil are theexperiments that support or disprove hypotheses, and thefindings garnered from them. Its sun is the applicationand creativity of its investigators; their work tills andcultivates. Whether drip irrigation or heavy precipitation,the moisture needed for healthy growth is its funding.The product of all these is data—findings—and the goalis insight. The scientific ecosystem would fail withoutone other essential component: cross-fertilization. Sciencefocuses on specific details, but gains significance inrelation to the whole. Communication among scientists

and between scientists and other interested individuals isnecessary to relate, to inform, to explain, and to planthe conduct of science.

When techniques were few, direct observation by theunaided eye the only means of data acquisition, and thescale unitary, then words, numbers, and pictures weresufficient for scientific communication. As the scope andmethods of science have expanded, and continue to expand,new and far more complex methods of communication andrelation of results are needed for the scientific ecosystem toflourish. Bioinformatics is only the latest of these, a productof the fortuitous co-development of affordable computationand universal networking.

Neuroscience is among the most complex scientificactivities the world has known. No other area uses moredifferent techniques, develops more different models,explores across more scales: from Ångstrom units topopulations. Just as no other contemporary area of sciencepresents a more complex picture, so no other contemporaryarea of bioinformatics presents as many challenges asneuroinformatics. Our Neuroscience Information Frame-work is not, cannot be, a complete solution. It is, however,an essential first step towards an integrated ecosystem forneuroscience.

The Neuroinformatic Ecosystem Needs More Data, BetterAccess to Data, and Easier Re-use of Data

The amount of neuroscience data currently shared, althoughcontinuing to increase, is a tiny fraction of what exists andis potentially useful. To form a rich neuroinformaticecosystem, what is needed is a greatly increased numberof data and related resources, resources supporting manymore techniques and areas, and a larger number of datasetsfor existing resources. This does not require significanttechnical breakthroughs: techniques exist or are beingrefined for receiving, archiving, describing, supplying, anddisplaying, and utilizing most types of data relevant toneuroscience. What is needed is recognition and commit-ment by many disparate neuroscience communities toannotate these data and make them freely and readilyavailable both within their community and also to otherdomains of neuroscience.

Kennedy (2006) has identified data sparseness as arelated important issue. If a resource is only sparselypopulated with respect to the potentially available data, itloses both utility and credibility. If a researcher looks fordata in an archive, fails to find it, and then discovers textpartially describing the same data available through othermeans (e.g. Google, supplementary materials of papers,personal web pages of individual investigators), the archiveis failing at a central task. The greater the fraction of thepotentially available data of a given type that is accessible

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through a database, even if the absolute amount of data issmall, the more likely that database is to become a useful,credible, and valued resource for those data.

Even in those areas where resources make dataavailable, we find a notable continuum in the utility ofthe available data (Kennedy 2004). Data best suited tointegration and re-analysis are the ones that neuroinformaticresources should leverage for development of links andterms. Sites that provide actual data have utility distinctfrom those that include statements about data, or figuresdisplaying data, and have an essential role in the neuro-informatic ecosystem.

Interoperability is a Continuing Need

Potential utility and availability of web-accessible neuro-science data are not enough. Just as different components ofa natural ecosystem interact in multiple and complex ways,so must components of the neuroinformatic ecosystem. Weillustrate some of these interactions in Fig. 2, whichrepresents interoperability of data, findings, and theresources that make them available, as a multidimensionalset of vectors. For every dimension, distance from theorigin gives increasing capacity for interoperability. Basicavailability is indicated by the vertical axis, which spansclosed data to data freely available via an open, public,resource. Use of standard open protocols and platform- and

software-independence is indicated by the technical axis.From the Framework perspective, the domain and datacompatibility axes are the most significant: these stress theneed for common formats that permit data re-use beyondthe immediate community that generated it, and the needfor common or relatable descriptors for data, tools,methods, and materials that span different domains ofneuroscience. The presence of the temporal axis serves as areminder that the Framework itself, as well as the resourcesaccessed through it, must incorporate methods for itsgraceful, scalable, evolution as datasets and resourcesmultiply and techniques, our understanding of neurosci-ence, and the terminology used to characterize them evolveand expand.

Methods for Post-Hoc Analysis are a Needed Componentof the Ecosystem

The value of data for enabling multiscale integration via re-analysis, meta-analysis, or comparison depends upon boththe availability of actual datasets themselves, the adoptionof common or convertible data formats, and their charac-terization by metadata sufficient to permit post-hoc analy-sis. The Framework is designed to aid these, as well as tofacilitate access to such data.

What is also needed, and must similarly be supported bythe Framework, is the availability of analytic tools enablingthe methods noted above. Such tools need to be robust,general, and characterized—just as data need to becharacterized—using precise, neuroscience-aware descrip-tive terms. Such methods are now available for neuro-imaging and some areas of neurophysiology, and need to beexpanded, characterized, and made more widely available.

Foundations

The Framework Addresses Needs of the NeuroscienceCommunity

Neuroscience investigators themselves have the greatestneed for, and present the primary call for, intelligentlydirected access to data. As noted above, some of these dataare not available outside the laboratory in which they weregenerated or recorded, others are available but notaccessible to public search, and some are in existing web-accessible databases (see the data sparseness problemabove). Neuroscientists welcome methods for describingand organizing their own data, and facilitating data sharingtoward collaborative and citation-generating re-use of data(Gardner et al. 2003; Liu and Ascoli 2007). Investigatorswant their data to inform and be informed by others’ data.Every database developer is familiar with requests from

DOMAINDATA COMPATIBILITY

TECHNICAL

TEMPORAL

USER

Open

Private

Future

Legac

y

Curren

t

High

Low

InterdisciplinaryFocusedProprietary

Open

Fig. 2 Vector representation of interoperability dimensions forneuroinformatic resources. For each dimension, increasing interoper-ability is represented by distance from the origin. User interoperabilityis enhanced by open access to data, findings, or tools, and zero orminimal cost and licensing requirements. Technical interoperabilitymeasures openness of architecture and utility of standards for dataformat specification and for data and data model exchange. Domaininteroperability includes the scope of a resource and the ease withwhich it interfaces with resources representing different subfields ordomains of neuroscience. The data dimension measures relatedness ofdata and intersection of data models; the domain and data dimensionsare thus non-orthogonal. Temporal interoperability reflects ease ofmigration and of incorporation of both future and legacy data (figureand legend modified from Gardner et al. 2001, © 2001 AMIA)

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individual investigators for laboratory systems that organizedata and potentially ready the data for sharing. Informaticsystems for textual access are powerful and becoming moreso, as illustrated by the report on Textpresso in this issue(Müller et al. 2008). However, as we note in a later section,access to and descriptions of datasets, images, tools, andsyntheses transcend the capabilities of resources such asGoogle or PubMed.

The Framework Builds Upon Prior Developmentof Neuroinformatics

We acknowledge with gratitude but without explicitcitation a very large and important body of neuro-informatics development, much of it funded by theNIH’s Human Brain Project, that forms the necessarysubstrate for our Framework development (De Schutter etal. 2006; Koslow and Hirsch 2004). A representative set ofprojects that directly informed our work includes: Sense-Lab, Neurodatabase.org, the Internet Accessible ToolRegistry (IATR), the Surface Management System Data-base (SumsDB), the Cell-Centered Database, GeneNet-work/WebQTL, and the Biomedical Informatics ResearchNetwork (BIRN) (Gardner 2004; Gardner et al. 2005;Kennedy and Haselgrove 2006; Marenco et al. 2005;Martone et al. 2005; Van Essen et al. 2005; Wang et al.2003).

The Framework Derives from the Neuroscience DatabaseGateway

The Neuroscience Database Gateway (NDG) began in 2004as a pilot project developed by the Society of Neuroscienceto investigate the integration of federated neuroscienceinformation on the Web (Gardner and Shepherd 2004). Thistask was initiated by the Society’s Brain InformationGroup. It is now coordinated by the Society’s standingNeuroinformatics Committee, supported through theFramework project, and located at http://ndg.sfn.org, hostedby the Yale Center for Medical Informatics.

This New White Paper Reflects Advancesin Neuroinformatics

We here report significant advances in the state of the fieldpresented in an earlier neuroinformatics White Paper, aproject of the Society for Neuroscience Brain InformationGroup led by Floyd Bloom. That paper, available at: http://web.sfn.org/index.cfm?pagename=NDG_whitepapers,highlighted information infrastructure needs of neurosci-ence research and offered three specific and highly relevantgoals for the proposed White Paper and the other threeobjectives as well: an inventory of neuroscience databases,

creation of a database portal, and to “promote broader andmore integratable information infrastructural tools toplace…neuroscience data in the public domain.”

We note the close alignment between these goals, thoseof the subsequent Neuroinformatics Committee, and theFramework project, as well as our adoption of OpenSource. We additionally note that the earlier work’s authorsincluded team members Huda Akil, Douglas Bowden,Daniel Gardner, Gwen A. Jacobs, Luis Marenco, MaryannMartone, Gordon Shepherd, David Van Essen, and RobertW. Williams.

Challenges for Framework Development

The Framework Project Began with an Inventory of WebNeuroscience Databases and Related Resources

To provide a representative sample of web-accessibleneuroinformatic resources, and a testbed for syntactic andsemantic tags distinguishing among available Web-basedneuroinformatic resources, the Framework established a testsite at http://neurogateway.org. Figure 3 shows one view ofthis working development site. We emphasize that this isnot the Framework: the other reports in this special issuedescribe multiple facets of the current NIF (Bug et al. 2008;Gardner et al. 2008; Gupta et al. 2008, Halavi et al. 2008;Marenco et al. 2008a, b; Müller et al. 2008).

The Framework can incorporate only the data orknowledge that are made available; it can integrate theseonly if sufficient metadata are provided. We note above thatin spite of the vigorous development of neuroinformatics,and the many techniques for data collation, archiving,annotation, and distribution developed over the last decade,the amount of neuroscience data available is only a smallfraction of the total. The solution depends upon commit-ments from both data providers across neuroscience andfunding agencies to encourage the open archiving andsharing of data. We have also noted that it is important todistinguish between available data—publicly accessible,often via a web archive—and potentially-available data—residing locally in a laboratory or Department willing toshare, but not web-accessible or lacking essential metadata(Kennedy 2004). For an example leveraging the Frameworkcomponent NeuroMorpho.Org see Halavi et al. (2008) inthis issue.

Inventoried resources differ in their potential forinteroperability. Global neuroscience web resources includeexperimental, clinical, and translational neurodatabases,knowledge bases, atlases, genetic/genomic and materialresources, and tool and modeling sites for processing,analysis, or simulation of brain data. This diversity of sitesspans multiple biological scales, techniques, and data

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models, serving communities of neuroscientists with spe-cific conventions, individual terminologies, and distinctfoci. The potential for interoperability among resourcesdepends upon design decisions and practices of theinventoried resources, including data model, user interface,and adoption of standard formats and terminologies. Someresources are accessible only via a proprietary or special-ized interface, some allow browsing but not query, someallow query using non-intuitive indices or descriptors.Some do not provide sufficient metadata to allow theirdata or findings to be integrated or analyzed. Some toolsites do not clearly indicate the scope or applicability oftheir tools, provide verification, or facilitate pipelining.

Disparate neuroscience resources have areas of inter-section that allow their findings to be compared andextended. The breadth of contemporary neuroscienceensures that the neuroinformatic resources accessed viathe framework will be disparate, but like neuroscience itselfthese will have areas of intersection that allow findings tobe related or extended. Such areas of intersection cannot bepredicted in advance; they depend upon both what ques-tions are being asked and how new findings enableconnections to be bridged across previously-disparate sub-fields. The potential for intersection depends upon thescope and type of data or finding in each resource (or the

applicability of tools in each toolkit). Identifying such areaswas a key goal of Framework design, and we believe, asdescribed below, that common or relatable terminologies,whether detectors describing resources as a whole orselectors that narrowly specify a cell type, gene, antibody,or protocol, will aid such connectivity.

Framework Design Must Facilitate Maintenance,Expansion, Extension, and Evolution

Neuroscience continues to grow and evolve and this is thegreatest challenge to the Framework stability. Here we layout specific features of this challenge; in the section onFramework design we briefly outline the reasons why OpenSource development best meets this challenge.

The Framework must be a stable, reliable, yet extendableresource. This key requirement needs careful planning toaccommodate extension of our initial version-1 Framework—NIFv1. Were NIFv1 to be merely a static software systemthat would require little to no extension or bug-fixing,then the requirements would be minimal. Instead, both thetechnology required to create a functional and effectiveFramework and the inevitable expansion of the domain ofneuroscience requires long-term support, maintenance, andevolution. We envision that this evolution will also

Fig. 3 This working development site was established initially to assemble an inventory towards assessing the state of the neuroinformaticecosystem; later uses included testing ‘detector’ controlled vocabularies

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encompass specialization so that groups will be able totailor the Open Source Framework for their sub-commu-nity or special use. Both design methodology andcommunity agreements should ensure that this diversityis accommodated and these additions and extensions arefed back into the Framework in general.

Framework Open Design Specifications

This section presents design choices for a dynamic, scalableFramework capable of degrees of integration from multiplesources. In particular, we detail our adoption of OpenSource, suggest that Open Source design and broad scopewill aid efficient access to and use of data, and brieflydiscuss the needs of and solutions toward interoperable andadoptable terminologies.

Overall planning for the technical implementation wasagreed upon at a meeting of the Principal Investigator,Project Directors (with P. Miller representing G.M. Shep-herd), and selected team members at Caltech on 16 and 17April, 2007, following NIH approval of the developmentphase. Also at that meeting, the team selected the goals thatwere possible given the time and resources available, madea list and detailed plan for development beyond NIFv1, andagreed to remain a consortium for future work. The otherreports in this special issue detail the NIFv1 Frameworkdevelopment agreed upon at that time, and carried out inthe following year.

Framework Design Combines Specific Technical Choicesand Broad Community Support

Open data, access and exchange, via open source andplatform, aid Framework-enabled open discovery forneuroscience. Perhaps the most important design principlewe have adopted for the Framework is openness. The originalNIH proposal for Framework development specified transferof copyright to the U.S. government. At the insistence ofthe P.I., this was modified to allow the NIF consortium tosubstitute Open Source (OS) development. The goal of theFramework is open access to data, facilitating open discoverythroughout and across neuroscience and bridging neurosci-ence with complementary areas of biomedicine. Open Sourcedevelopment methodology supports the informatic ecosystemjust as the Framework is designed to aid the neuroinformaticecosystem. Open Source is implemented through releaseof all code, terminology, and algorithms under a copyrightlicense that permits unlimited re-use, adoption, andextension of the material, requiring only the continuedincorporation of the OS license permitting such use. TheFramework is offered under BSD and MIT compatible OSlicenses (http://opensource.org/licenses).

In practical terms, this means that the Framework isavailable to any group that wishes to establish a mirror site,focused subset, or extension of the Framework, or tomodify it for a complementary purpose. As we detail below,we also believe that Open Source development willsignificantly reduce maintenance and versioning costs bypromoting multi-site and multi-organization replication andadoption of the Framework and related tools.

Framework Design is Projected to Reduce Costsand Enhance Benefits of Data and Knowledge

We envision the NIF as not only a resource in itself, but asa nucleus and an exemplar to aid bioinformatic develop-ment across neuroscience and potentially to linked fields ofbiomedicine. We project that the Framework will not onlypromote data sharing and utilization in neuroscience, butalso reduce the cost/benefit ratio for data acquisition andutilization, in each of several ways. These include provid-ing Open Source neuroinformatic tools and code that otherscan leverage, as well as stimulating development by others.Some of these reduce costs that other groups would have toexpend to develop resources centered upon their subfieldsof neuroscience. Others increase the benefit of suchdevelopment by expanding audience, utility, and opportu-nities to collaborate and to leverage findings outside theimmediate subfield.

Framework inventory and content-aware queries willdisseminate and relate neuroscience data and knowledge.We justify our commitment to Framework development—including the many contributions of time, code, tools,insights, and findings from neurobiological and neuro-informatic investigators—by projecting that access via theFramework will increase the distribution, utility, andsignificance of data and other findings. The content-basedquery tool will enable more investigators to ask morequestions, and will make more easily available theresources capable of providing answers. Just as a paperwith a greater number of citations increases the value andtherefore decreases the cost/benefit ratio of data containedwithin, so Framework-enabled examination, coordination,and possible re-analysis of data does the same.

Framework availability and scope will spur developmentof additional neuroinformatic resources. As noted in theIntroduction, we believe that the existence of a singleFramework query point for a very wide range of Web-basedneuroscience will itself encourage the growth of theneuroinformatic ecosystem. The potential is great foradditional communities in neuroscience, whether centeredon specific areas of function, disease, technique, orpreparation, to develop terminologies and methods formaking available data, findings, or tools useful for theirdomain and beyond. By providing a portal and query point

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to the entire neuroscience community, the Frameworkexpands the potential audience, increasing exposure of thesite’s contents and offering the possibility for collaborationsand informative links to related areas. This can motivatecommunities to support the neuroinformatic ecosystem andthereby reduce the data sparseness problem.

Framework Terminology Integrates Multiple Streams

The NIFv1 Framework and content-based query tooldevelopment include multiple neuroscience terminologythrusts, detailed in Gardner et al. (2008) Bug et al. (2008),and Müller et al. (2008) in this volume. Good design alsofavors adoption of existing terminologies, both to easeintegration of neuroscience knowledge with that of otherfields and also to reduce the magnitude of lexicaldevelopment. We recognize that interoperability and effi-ciency would both be aided by our adoption of terms takenfrom existing standards, subject to relevance for neurosci-ence and availability under Open Source licensing. Obviouschoices include BIRNLex and the NCBI taxonomy. Wealso acknowledge the first neuroscience-centric keyworddevelopment, established more than a decade ago byFramework team member Bernice Grafstein. The Frame-work adoption of XML for future terminology representa-tion, and parallel Human Brain project efforts to placeFramework terms in BrainML format, allow incorporationof other XML-based terminologies in whole or in part usingthe namespace feature of XML.

Implementation and Core Functionality of the NIFv1

We have implemented NIFv1 as a Web resource availableto any neuroscientist user with a contemporary Web-accessible computer; all functionality is available on anyplatform and operating system compatible with currentJava. Supporting this goal required adherence to standardspermitting current use and future evolution, and of courseadministrative tools aiding content management and updateof the system. The NIFv1 was developed followingstandard commercial-grade techniques for Web-accessiblecode development, tracking, and testing. Delivered under anon-contaminating Open Source license, it includes soft-ware components and terminologies needed to establish aWeb-based Framework application on any contemporarymulti-processor or multi-core Unix server with gigabyte(GB) or better memory and 250 GB or larger disc, standardOpen Source gnu compilers and library, Java 1.5, MySQLor PostgreSQL database, and Apache web server compo-nents including Tomcat.

Details of Framework design and implementation areprovided in the accompanying papers, especially Gupta etal. (2008). An overview of major system components of the

NIF is shown in Fig. 4. Implementation of the systemdelivering core NIFv1 functionality includes four mainmodules. At the top level of Fig. 4 are the NIFv1 interfaces:the NIFv1 Query Interfaces supporting neuroscientist usersand administrative interfaces, including those for register-ing and maintaining entries specifying interoperable NIFresources. At the middle level in Fig. 4 are the NIFDatabase Resource Directory, the NIF Database Mediator,and the NIF Document Archive. Additional NIFv1 compo-nents include NeuroMorpho.Org as well as multi-tieredback-end data resources and NIFv1 services which providespecific functionality.

Why Nothing Else Does What the Framework Will Do

The Framework is Neuroscience-Specificand Neuroscience-Generated

Neuroscience does not at present have a central, generalsource for relevant data. Geneticists, structural biologists,and molecular biologists have universally-accessed data-bases that emphasize gene and protein sequence andstructure data (e.g., NCBI Entrez, PDB, and others).Because there is no site that directly addresses their needs,neuroscientists by default make use of a variety of searchengines (e.g., Google, Google Scholar, and PubMed) thatare largely literature-oriented.

We are designing NIFv1 to change this. The Frameworkpresents neuroscientists with a single starting point for theirsearches, one that can be a portal that students start using atthe dawn of their training and continue to utilize as theirprimary access to multiple and complex sets of dataavailable from a growing number of neuroscience-specificdatabases. No other site or tool is comparable because thisapproach has never before been attempted for neuroscience.This will not echo material available through other sources,but will complement it.

& The Framework is focused on neuroscience, with accessto resources that individually address key specific areasor techniques, that supply data in addition to knowl-edge, and that in aggregate span the breadth ofneuroscience.

& The Framework derives from the neuroscience commu-nity itself; many of the authors are developers but weare all in addition neuroscientists and users.

& The Framework has the Society for Neuroscience asa resource (Kennedy 2007). Three SfN Presidentshave said: ‘The Society for Neuroscience stronglysupports the joint effort by members of the Society’sNeuroinformatics Committee to spearhead establish-ment of a Neuroscience Information Framework’,

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‘Development of the NIF has benefited and willcontinue to benefit greatly from the volunteer contri-butions from SfN membership, particularly frommembers of the NeuroInformatics Committee’ and:‘this partnership with the SfN is pivotal, because theSfN can promote the power of the NIF in presenta-tions, courses, on its web site and even provide avenue for training and demonstrations. The goal is tofully integrate neuroinformatics into the daily life ofthe average neuroscientist, and none of the existingdatabases, search engines or entities have ever suc-ceeded in doing that.’

& The Framework builds on a broad series of neurosci-ence expert terminology workshops. These workshopsare to our knowledge the only coordinated unifiedefforts to assemble working neuroscientist-users repre-senting focused communities within the breadth ofneuroscience and derive collegial consensus terminolo-gies broadly characterizing the questions they ask, thedata they collect, and the techniques they use (Gardneret al. 2008).

& The Framework allows users to specify both the typesof resource to query and whether data or literaturereferences are required; this capability may in the future

Fig. 4 Overview of the NIFv1implementation core architecture

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be expanded to allow synthesizing information frommultiple sources and ranking by value.

NIF Functionalities Relative to Other Tools

We offer comparisons to popular search tools:

Google: Compared to Google, the Framework enablesneuroscientists by offering content-based queries, access todata, and a focus on neuroscience:

& Framework neuroscience concept-based queries, pro-vide a more comprehensive, yet focused search resultthan Google and thereby reduce the number of falsenegative results. Unlike Google, the Framework allowsusers to clarify, specify, or modify search terms,reducing the number of false positive items in theresponse, and so increasing the signal to noise ratio.

& Google indexes existing Web pages. However, manyneuroscience datasets are contained in databases acces-sible only via query interfaces, and only presenteddynamically (often not in HTML or PDF) in response toan ad-hoc query. This provision of data, rather than textdescribing data or pictures showing a static representa-tion of some feature of data, further distinguishes manyFramework-accessible resources from those that Googlecan find.

& Unlike Google, the Framework specifically referencesneuroscience resources that are known to providemeaningful, useful data or other information. This isbecause the Framework only links to Web resourcesthat members of the Framework team have visited andapproved as relevant and reliable.

Entrez-PubMed: Compared to Entrez, the Frameworkagain enables neuroscientist users by its focus on neuroscienceand its use of content-based queries:

& The NIF is a portal to a rapidly growing body ofneuroscience information on the web, much as Entrezprovides a portal to a curated set of biomedicalresources, largely built around genomics and proteo-mics (although expanding to other areas). ThoughEntrez does provide combined searching against docu-ments plus data repositories, it does so in a manner thatcan’t fully tap the conceptual inter-relatedness of theindividual elements. Indexing all NIF entities with theNIF terminology/ontology specifically enriched forconcepts relevant to neuroscientists makes it possibleto provide a much more contextually-relevant andthorough correlated concept analysis to drive queryresolution and to organize query results.

& As a literature service, PubMed provides somewhatbetter focus than Google by, (1) limiting citations to

documents related to biomedicine, (2) enabling users tonarrow their searches by language, species, age, type ofdocument, etc., (3) utilizing Boolean logic, and (4)indexing literature citations using MeSH; however, itremains largely a search-by-key-word service. Thus, itis vulnerable to both false negatives and false positiveswhen users’ terminology differs from that used forindexing.

Information Sharing Statement

Lector, si monumentum requiris, Circumspice.

Acknowledgements This project has been funded in whole or inpart through the NIH Blueprint for Neuroscience Research withFederal funds from the National Institute on Drug Abuse, NationalInstitutes of Health, Department of Health and Human Services, underContract No. HHSN271200577531C. The Neuroscience InformationFramework team gratefully acknowledges the support of volunteerconsultant-collaborators and friends, and The Society for Neurosci-ence. Early development of the SfN Neuroscience Database Gatewaywas supported by the Society for Neuroscience by means of agenerous gift from Paul Allen and Jody Patton and by contract (NIHOrder No. 263-MD-409125-1) from NIMH, NINDS, and NIDA. Wethank C. Wren for the epitaph adaptable as the Information SharingStatement. The Advisory Committee consists of Huda Akil, GiorgioAscoli, Daniel Gardner, Bernice Grafstein, Maryann E. Martone,Gordon Shepherd, Paul Sternberg, David C. Van Essen, and Robert W.Williams.

Appendix

The Framework TeamThe Framework Team includes many individuals, repre-

senting many nodes of a collegial network for neuro-informatic development.

The Contractor for Phases I and II, described in thisWhite paper and the special issue it introduces, is WeillMedical College of Cornell University, Daniel Gardner, PI,and subcontractors (with the PD at each) are:

& Yale University (Gordon Shepherd, PD)& Caltech (Paul Sternberg, PD)& University of California, San Diego (Maryann Martone,

PD)& George Mason University (Giorgio Ascoli, PD), and& Capital Meeting Planners Inc

Team members supported via Framework Contractor orSubcontractor sites include: Giorgio A. Ascoli, VadimAstakhov, William Bug, Fabien Campagne, Mark Ellisman,Ronit Gadagkar, Daniel Gardner, Bernice Grafstein, JeffreyGrethe, Amaranth Gupta, Erdem Kurul, Luis Marenco,Maryann E. Martone, Perry L. Miller, Hans-MichaelMüller, Thien Nguyen, Xufei Qian, Adrian Robert, Ruggero

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Scorcioni, Gordon M. Shepherd, Paul W. Sternberg, WillyWoong, and Ilya Zaslavsky

The team also includes a set of consultant-collaborators.None received direct support from the Framework project;each is pleased to make available, towards supporting theneuroinformatic ecosystem, code, products, or expertisethat aid Framework development:

& The Society for Neuroscience& Huda Akil, Univ. of Michigan Med School& Douglas Bowden, Univ. of Washington& Kristen M. Harris, Univ. of Texas at Austin& Gwen A. Jacobs, Montana State Univ.& David N. Kennedy, Massachusetts General Hospital& Ken Smith, MITRE Corp.& David C. Van Essen, Washington Univ.& John D. Van Horn, UCLA& Robert W. Williams, Univ. of Tennessee

As this work was being submitted for publication, theteam learned of the sudden and untimely death of ourvalued colleague William Bug. Untiring in his vision,enthusiasm for the project, and ability to bridge commu-nities of biomedicine, he will be greatly missed. In hishonor we echo his invariable signoff from hundreds ofinspiring e-mails: Cheers, Bill.

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