The possibility and probabilityof establishing a globalneuroscience informationframework:lessons learned from practical ex...
“Neural Choreography”“A grand challenge in neuroscience is to elucidate brain function in relationto its multiple layers o...
On the other hand... In that same issue of Science Asked peer reviewers from last year about the availability and use of...
We speak piously of takingmeasurements and making smallstudies that will add another brickto the temple of science. Mostsu...
TheEncyclopediaof LifeA…Access to data haschanged over theyearsTim Berner-s Lee: Web of dataWikipedia defines Linked Data ...
Are we there yet?We’d like to be able to find: What is known****: What is the average diameter of a Purkinjeneuron Is G...
 NIF is an initiative of the NIH Blueprint consortium of institutes What types of resources (data, tools, materials, ser...
How many resources are there?•NIF Registry: Acatalog ofneuroscience-relevantresources•> 3500 currentlydescribed•> 1700 dat...
But we have Google! Current web is designedto share documents Documents areunstructured data Much of the content ofdigi...
A tip of the “resourceome”Microarray9, 535, 440Model organisms246, 639Connectivity26, 443Antibodies890, 571Pathways43, 013...
But we have Pub Med! Bulk of neuroscience datais published as part ofpapers > 20,000,000 Structured vsunstructured info...
The Neuroscience Information Framework: Discovery andutilization of web-based resources for neuroscience A portal for fin...
Neuroscience is unlikely to beserved by a few large databaseslike the genomics and proteomicscommunityWhole brain data(20 ...
NIF Data Federation Too many databases to visit Registry not adequate for finding and using them Capturing content in a...
What are the connections of thehippocampus?
HippocampusOR “CornuAmmonis” OR“Ammon’s horn” Query expansion: Synonymsand related conceptsBoolean queriesData sourcescate...
What are the connections of thehippocampus?Connects toSynapsed withSynapsed byInput regioninnervatesAxon innervatesProject...
NIF: Minimum requirements to use shareddata You (and the machine) have to be able to find it Accessible through the web...
Is GRM1 in cerebral cortex? NIF system allows easy search over multiple sources of information But, we have difficulty f...
Cerebral CortexAtlas Children ParentGenepaint Neocortex, Olfactory cortex (Olfactorybulb; piriform cortex), hippocampusTel...
What is an ontology?BrainCerebellumPurkinje Cell LayerPurkinje cellneuronhas ahas ahas ais a Ontology: an explicit, forma...
What can ontology do for us? Express neuroscience concepts in a way that is machine readable Synonyms, lexical variants...
Linking datatypes to semantics: What isthe average diameter of a Purkinjeneuron dendrite? Branch structure not atree, not...
“A rose by any other name...”: Identity: Entities are uniquely identifiable Name is a meaningless numerical identifier ...
Comprehensive Ontology NIF covers multiple structural scales and domains of relevance to neuroscience Aggregate of commu...
Query across resources: Sncaand striatumNIF uses the NIFSTD ontologies to query across sources that use verydifferent term...
Entity mappingBIRNLex_435 Brodmann.3Explicit mapping of database content helps disambiguate non-unique andcustom terminology
Concept-based search: search by meaning Search Google: GABAergic neuron Search NIF: GABAergic neuron NIF automatically ...
Data mining throughinterrogation What genes are upregulated by drugs of abuse in the adultmouse?MorphineIncreasedexpressi...
Integration of knowledge based onrelationshipsLooking for commonalities and distinctions among animalmodels and human cond...
And now, the literature The scientific article remains the currency of science Vast majority of neuroscience data is pub...
 Neuroscience is fundamentally reliant on antibodies Neuroscientists spend a lot of time searching for antibodiesthat wi...
 Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9)and from subjects with AD (n11) were obtain...
Try this Watson!• 95 antibodies were identified in 8 articles• 52 did not contain enough information to determine theantib...
 NIF along with several other large informaticsprojects recommends that all authors providevendor and catalog # for all r...
NIF Antibody Registry• We have created an antibodyregistry database• Assigns each antibody apersistent identifier to bothc...
“Find studies that used a rabbit polyclonal antibodyagainst GFAP that recognizes human inimmunocytochemisty”Paz et al,J Ne...
Demo 2: Extracting data fromtables and supplementarymaterial Challenge: Extract data on gene expression in brain fromstud...
Gene for tyrosinehydroxylase hasincreasedexpression in locuscoeruleus of mousecompared to controlwhen given chronicmorphin...
Challenges working with tables andsupplemental data Difficult data arrangements PDF, JPG,TXT,CSV, XLS Difficult styles:...
What affects SMN1 expression?Researchers often report results in a way where curators cannotextract full information from ...
Common theme•We are not publishing data in aform that is easy to integrate•What we mean isn’t clear to asearch engine (or ...
When I talk to neuroscientists (and journal editors)...
Collaboration, competition,coordination, cooperation The diversity and dynamism of neuroscience will make dataintegration...
Hopeful signs...•Means for sharing data on the webbecoming more routine•With availability, growing recognition for a roleo...
We don’t know everything but wedo know some things1. Register your resourcewith NIF!!!!3: Be mindful Resource providers: ...
Learn about neuroinformatics
Many thanks to...Amarnath Gupta, UCSD, Co InvestigatorJeff Grethe, UCSD, Co InvestigatorAnita Bandrowski, NIF CuratorGordo...
Register your resource to NIF!
How old is an adult squirrel? Definitions can bequantitative Arbitrary but defensible Qualitative categoriesfor quantit...
But there are no databases forsiRNANIF Registry is probably the most complete accounting we have of what is outthere
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The possibility and probability of a global Neuroscience Information Framework

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Maryann Martone
Plenary Talk, Neuroinformatics 2010, Kobe, Japan
August 31, 2010
http://www.neuroinformatics2010.org/

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  • The possibility and probability of a global Neuroscience Information Framework

    1. 1. The possibility and probabilityof establishing a globalneuroscience informationframework:lessons learned from practical experiencesin data integration for neuroscienceMaryann Martone, Ph. D.University of California, San Diego
    2. 2. “Neural Choreography”“A grand challenge in neuroscience is to elucidate brain function in relationto its multiple layers of organization that operate at different spatial andtemporal scales. Central to this effort is tackling “neural choreography” --the integrated functioning of neurons into brain circuits--their spatialorganization, local and long-distance connections, their temporalorchestration, and their dynamic features. Neural choreography cannotbe understood via a purely reductionist approach. Rather, it entails theconvergent use of analytical and synthetic tools to gather, analyze andmine information from each level of analysis, and capture the emergenceof new layers of function (or dysfunction) as we move from studyinggenes and proteins, to cells, circuits, thought, and behavior....However, the neuroscience community is not yet fully engaged in exploiting therich array of data currently available, nor is it adequately poised to capitalizeon the forthcoming data explosion. “Akil et al., Science, Feb 11, 2011
    3. 3. On the other hand... In that same issue of Science Asked peer reviewers from last year about the availability and use ofdata About half of those polled store their data only in theirlaboratories—not an ideal long-term solution. Many bemoaned the lack of common metadata and archives as amain impediment to using and storing data, and most of therespondents have no funding to support archiving And even where accessible, much data in many fields is too poorlyorganized to enable it to be efficiently used. “...it is a growing challenge to ensure that data produced during thecourse of reported research are appropriatelydescribed, standardized, archived, and available to all.” Lead Scienceeditorial (Science 11 February 2011:Vol. 331 no. 6018 p. 649 )
    4. 4. We speak piously of takingmeasurements and making smallstudies that will add another brickto the temple of science. Mostsuch bricks just lie around thebrickyard.Platt,J.R. (1964)Strong Inference.Science. 146: 347-353."We now have unprecedentedability to collect data aboutnature…but there is now a crisisdeveloping in biology, in thatcompletely unstructured informationdoes not enhance understanding”-Sidney Brenner
    5. 5. TheEncyclopediaof LifeA…Access to data haschanged over theyearsTim Berner-s Lee: Web of dataWikipedia defines Linked Data as "a term usedto describe a recommended best practice forexposing, sharing, and connecting pieces ofdata, information, and knowledge on theSemanticWeb using URIs and RDF.”http://linkeddata.org/GenbankPDB
    6. 6. Are we there yet?We’d like to be able to find: What is known****: What is the average diameter of a Purkinjeneuron Is GRM1 expressed In cerebral cortex? What are the projections of hippocampus What genes have been found to beupregulated in chronic drug abuse in adults What studies used my monoclonal mouseantibody against GAD in humans? Find all instances of spines thatcontain membrane-bound organelles ****by combining data from differentsources and different groups What is not known: Connections among data Gaps in knowledgeWe’d like it to be really simple toimplement and use:– Query interface– Search strategies– Data sources– Infrastructure– Results display– Trust– Context– Analysis tools– Tools for translating existingcontent into linkable form– Tools for creating new data readyto be linked
    7. 7.  NIF is an initiative of the NIH Blueprint consortium of institutes What types of resources (data, tools, materials, services) areavailable to the neuroscience community? How many are there? What domains do they cover? What domains do they not cover? Where are they? Web sites Databases Literature Supplementary material Who uses them? Who creates them? How can we find them? How can we make them better in the future? http://neuinfo.orgA look into the brickyard• PDF files• Desk drawers
    8. 8. How many resources are there?•NIF Registry: Acatalog ofneuroscience-relevantresources•> 3500 currentlydescribed•> 1700 databases•Another 3000awaiting curation•And we are findingmore every day
    9. 9. But we have Google! Current web is designedto share documents Documents areunstructured data Much of the content ofdigital resources is part ofthe “hidden web” Wikipedia: The DeepWeb(also called Deepnet, theinvisibleWeb, DarkNet, Undernetor the hiddenWeb) referstoWorldWideWeb contentthat is not part of theSurfaceWeb, which isindexed by standardsearch engines.
    10. 10. A tip of the “resourceome”Microarray9, 535, 440Model organisms246, 639Connectivity26, 443Antibodies890, 571Pathways43, 013Brain ActivationFoci56, 59165 databases
    11. 11. But we have Pub Med! Bulk of neuroscience datais published as part ofpapers > 20,000,000 Structured vsunstructured information“...it is a growing challenge to ensure thatdata produced during the course of reportedresearch are appropriatelydescribed, standardized, archived, andavailable to all.” Lead Science editorial(Science 11 February 2011: Vol. 331 no. 6018 p.649 )Author, year,journal, keywordsContent
    12. 12. The Neuroscience Information Framework: Discovery andutilization of web-based resources for neuroscience A portal for finding andusing neuroscienceresources A consistent framework fordescribing resources Provides simultaneoussearch of multiple types ofinformation, organized bycategory Supported by an expansiveontology for neuroscience Utilizes advancedtechnologies to search the“hidden web”http://neuinfo.orgUCSD,Yale, CalTech, George Mason, Washington UnivSupported by NIH BlueprintLiteratureDatabaseFederationRegistry
    13. 13. Neuroscience is unlikely to beserved by a few large databaseslike the genomics and proteomicscommunityWhole brain data(20 ummicroscopic MRI)Mosiac LMimages (1 GB+)Conventional LMimagesIndividual cellmorphologiesEM volumes &reconstructionsSolved molecularstructuresNo single technology serves these allequally well.Multiple data types; multiplescales; multiple databasesA data federation problem
    14. 14. NIF Data Federation Too many databases to visit Registry not adequate for finding and using them Capturing content in a few keywords is difficult if not impossible Access to deep content; currently searches over 30 million records from > 65different databases Flexible tools for resource providers to make their content available as easily andmeaningfully as possible Organized according to level of nervous system and data type, e.g., brainactivation foci Link to host resource: these databases are independent! Provides simplified and unified views to help users navigate very differentresources Common vocabularies Common data models for basic neuroscience data Laying the foundations for data integration for neuroscience
    15. 15. What are the connections of thehippocampus?
    16. 16. HippocampusOR “CornuAmmonis” OR“Ammon’s horn” Query expansion: Synonymsand related conceptsBoolean queriesData sourcescategorized by“data type” andlevel of nervoussystemSimplified views ofcomplex datasourcesTutorials for usingfull resource whengetting there fromNIFLink back torecord inoriginalsource
    17. 17. What are the connections of thehippocampus?Connects toSynapsed withSynapsed byInput regioninnervatesAxon innervatesProjects toCellular contactSubcellular contactSource siteTarget siteEach resource implements a different, though related model;systems are complex and difficult to learn, in many cases
    18. 18. NIF: Minimum requirements to use shareddata You (and the machine) have to be able to find it Accessible through the web Structured or semi-structured Annotations You (and the machine) have to be able to use it Data type specified and in a usable form You (and the machine) have to know what the datamean Semantics Context: Experimental metadataReporting neuroscience data within a consistent framework helps enormously
    19. 19. Is GRM1 in cerebral cortex? NIF system allows easy search over multiple sources of information But, we have difficulty finding data Well known difficulties in search Inconsistent and sparse annotation of scientific data Many different names for the same thing The same name means many things “Hidden semantics”: 1 = male; 1 = present; 1=mouseAllen Brain AtlasMGDGensat
    20. 20. Cerebral CortexAtlas Children ParentGenepaint Neocortex, Olfactory cortex (Olfactorybulb; piriform cortex), hippocampusTelencephalonAllen Brain Atlas Cortical plate, Olfactory areas,Hippocampal FormationCerebrumMBAT (cortex) Hippocampus, Olfactory, Frontal,Perirhinal cortex, entorhinal cortexForebrainGENSAT Not defined TelencephalonBrainInfo frontal lobe, insula, temporal lobe,limbic lobe, occipital lobeTelencephalonBrainmapsEntorhinal, insular, 6, 8, 4, A SII 17,Prp, SITelencephalon
    21. 21. What is an ontology?BrainCerebellumPurkinje Cell LayerPurkinje cellneuronhas ahas ahas ais a Ontology: an explicit, formalrepresentation of conceptsrelationships among themwithin a particular domain thatexpresses human knowledge in amachine readable form Branch of philosophy: a theoryof what is e.g., Gene ontologies
    22. 22. What can ontology do for us? Express neuroscience concepts in a way that is machine readable Synonyms, lexical variants Definitions Provide means of disambiguation of strings Nucleus part of cell; nucleus part of brain; nucleus part of atom Rules by which a class is defined, e.g., a GABAergic neuron is neuron that releasesGABA as a neurotransmitter Properties Provide universals for navigating across different data sources Semantic “index” Perform reasoning Link data through relationships not just one-to-one mappings Provide the basis for concept-based queries to probe and mine data As a branch of philosophy, make us think about the nature of thethings we are trying to describe, e.g., synapse is a site
    23. 23. Linking datatypes to semantics: What isthe average diameter of a Purkinjeneuron dendrite? Branch structure not atree, not a set of bloodvessels, not a road map but aDENDRITE Because anyone who usesNeurolucida uses the sameconcepts: axon, dendrite, cellbody, dendriticspine, information systemscan combine the datatogether in meaningful ways Neurolucidadoesn’t, however, tell you thatdendrite belongs to a neuronof a particular type or whetherthis dendrite is a neuraldendrite at all( (Color Yellow) ; [10,1](Dendrite)( 5.04 -44.40 -89.00 1.32) ; Root( 3.39 -44.40 -89.00 1.32) ; R, 1(( 2.81 -45.10 -90.00 0.91) ; R-1, 1( 2.81 -45.18 -90.00 0.91) ; R-1, 2( 1.90 -46.01 -90.00 0.91) ; R-1, 3( 1.82 -46.09 -90.00 0.91) ; R-1, 4( 0.91 -46.59 -90.00 0.91) ; R-1, 5( 0.41 -46.83 -92.50 0.91) ; R-1, 6(( -0.66 -46.92 -88.50 0.74) ; R-1-1, 1( -0.74 -46.92 -88.50 0.74) ; R-1-1, 2( -2.15 -47.25 -88.00 0.74) ; R-1-1, 3( -2.15 -47.33 -88.00 0.74) ; R-1-1, 4( -3.06 -47.00 -87.00 0.74) ; R-1-1, 5( -4.05 -46.92 -86.00 0.74) ; R-1-1, 6Output of Neurolucida neuron trace
    24. 24. “A rose by any other name...”: Identity: Entities are uniquely identifiable Name is a meaningless numerical identifier (URI: Uniform resource identifier) Any number of human readable labels can be assigned to it Definition: Genera: is a type of (cell, anatomical structure, cell part) Differentia: “has a” A set of properties that distinguish among members of thatclass Can include necessary and sufficient conditions Implementation: How is this definition expressed Depending on the nature of the concept or entity and the needs of theinformation system, we can say more or fewer things Different languages; can express different things about the concept that can becomputed upon OWLW3C standard, RDF
    25. 25. Comprehensive Ontology NIF covers multiple structural scales and domains of relevance to neuroscience Aggregate of community ontologies with some extensions forneuroscience, e.g., Gene Ontology, Chebi, Protein Ontology Simple, basic “is a : hierarchies that can be used “as is” or to form the building blocksfor more complex representationsNIFSTDOrganismNS FunctionMolecule InvestigationSubcellularstructureMacromolecule GeneMolecule DescriptorsTechniquesReagent ProtocolsCellResource InstrumentDysfunction QualityAnatomicalStructure
    26. 26. Query across resources: Sncaand striatumNIF uses the NIFSTD ontologies to query across sources that use verydifferent terminologies, symbolic notations and levels of granularity
    27. 27. Entity mappingBIRNLex_435 Brodmann.3Explicit mapping of database content helps disambiguate non-unique andcustom terminology
    28. 28. Concept-based search: search by meaning Search Google: GABAergic neuron Search NIF: GABAergic neuron NIF automatically searches for types ofGABAergic neuronsTypes of GABAergicneurons
    29. 29. Data mining throughinterrogation What genes are upregulated by drugs of abuse in the adultmouse?MorphineIncreasedexpressionAdult Mouse
    30. 30. Integration of knowledge based onrelationshipsLooking for commonalities and distinctions among animalmodels and human conditions based on phenotypesSarah Maynard, Chris Mungall, Suzie Lewis NINDSThalamusCellular inclusionMidline nucleargroupLewy BodyParacentral nucleusCellular inclusion
    31. 31. And now, the literature The scientific article remains the currency of science Vast majority of neuroscience data is published inthe literature Computational biologists like to consume data Neuroscientists like to produce it Two NIF projects: 1) Resource identification from the literature Identifying antibodies used in scientific studies fromtext 2) Extracting data from tables and supplementarymaterial
    32. 32.  Neuroscience is fundamentally reliant on antibodies Neuroscientists spend a lot of time searching for antibodiesthat will work in their system for the target of interest andtroubleshooting experiments that didn’t work The scientific literature is a major source of information onantibodies Proposal Use text mining strategies to identify antibodies, protocoltype and subject organism from materials and methodssection of J. NeuroscienceProblem: antibodies
    33. 33.  Midfrontal cortex tissue samples from neurologically unimpaired subjects (n9)and from subjects with AD (n11) were obtained from the Rapid AutopsyProgram Immunoblot analysis and antibodies The following antibodies were used for immunoblotting:-actinmAb (1:10,000dilution, Sigma-Aldrich); -tubulinmAb (1:10,000,Abcam);T46 mAb (specific to tau 404–441, 1:1000, Invitrogen);Tau-5 mAb (human tau 218–225, 1:1000, BD Biosciences) (Porzig etal., 2007);AT8 mAb (phospho-tau Ser199, Ser202, andThr205, 1:500, Innogenetics); PHF-1mAb (phospho-tau Ser396 and Ser404, 1:250, gift from P. Davies); 12E8 mAb(phospho-tauSer262 and Ser356, 1:1000, gift from P. Seubert); NMDA receptors 2A, 2B and 2D goat pAbs (Cterminus, 1:1000, Santa Cruz Biotechnology)…Semantic annotation: Entity mapping byhumanSato et al., J. Neurosci. 2008 Subject isHumanAntibody #7"12E8" is a Monoclonal antibody birnlex_2027Antibodyreagent has target human PHF tauWaiting forNeurolex IDProtein product ofAntibodyreagent has provider Peter SeubertAntibodyreagent has catalog #Antibodyreagenthas sourceorganism Mouse birnlex_167 NCBI Taxonomic ID: 10090Antibodyreagent has id "12E8"Provider has locationElan Pharmaceuticals, South SanFrancisco, CAProvider has url
    34. 34. Try this Watson!• 95 antibodies were identified in 8 articles• 52 did not contain enough information to determine theantibody used• Some provided details in another paper• And another paper, and another...• Failed to give species, clonality, vendor, or catalog number• But, many provided the location of the vendor becausethe instructions to authors said to do so• no antibodies had lot numbers associatedWe never got to test the algorithms!
    35. 35.  NIF along with several other large informaticsprojects recommends that all authors providevendor and catalog # for all reagents use But...vendors merge and sell each other’santibodies, making it difficult to track down exactlywhich reagent was used in some cases Catalog numbers get replaced; many variants on thesame product, e.g., HRP-conjugated, 200 ulvs 500 ul Clone names are not unique Universal antibody IDPublishing for the 21st Century
    36. 36. NIF Antibody Registry• We have created an antibodyregistry database• Assigns each antibody apersistent identifier to bothcommercial and non-commercial antibodies• ID will persist even if companygoes out of business or theantibody is sold by multiplevendors• The data model is being formalizedinto a rigorous ontology incollaboration with others:• We negotiated with antibodyaggregators to pull data for over800,000 commercial antibodies,200 vendors• Can be used to register homegrownantibodies as well• http://antibodyregistry.org
    37. 37. “Find studies that used a rabbit polyclonal antibodyagainst GFAP that recognizes human inimmunocytochemisty”Paz et al,J Neurosci, 2010(AB_310775)
    38. 38. Demo 2: Extracting data fromtables and supplementarymaterial Challenge: Extract data on gene expression in brain fromstudies relevant to drug abuse Workflow:Find articlesExtract resultsfrom tablesStandardizeresultsLoad into NIFCurrent DB: 140 tables from 54 articlesAndrea Arnaud-Stagg, Anita Bandrowski
    39. 39. Gene for tyrosinehydroxylase hasincreasedexpression in locuscoeruleus of mousecompared to controlwhen given chronicmorphineTranslations:Upregulatedp< 0.05 =increased expressionLC = locus coeruleusProbe ID = gene nameExtract data and meaning of datafrom tables
    40. 40. Challenges working with tables andsupplemental data Difficult data arrangements PDF, JPG,TXT,CSV, XLS Difficult styles: colors, symbols, data arrangements (resultscombined into one column, multiple comparisons in one table,legends defining values, unclearly described data (eg., unclearsignificance) Not clear what tables/values represent nothing in paper about the supplementary data file and table has no heading Probe ID’s are given but not gene identifiers No link from supplemental material back to article; loseprovenance Results are presented but values of significance unclear Neither curator (nor machine) could distinguish between no differenceand not reported
    41. 41. What affects SMN1 expression?Researchers often report results in a way where curators cannotextract full information from a study
    42. 42. Common theme•We are not publishing data in aform that is easy to integrate•What we mean isn’t clear to asearch engine (or even to ahuman)•We use many different datastructures to say the samething•We don’t provide crucialinformation•Searching and navigating acrossindividual resources takes aninordinate amount of human effortTempus PecuniaEst Painting by RichardHarpum
    43. 43. When I talk to neuroscientists (and journal editors)...
    44. 44. Collaboration, competition,coordination, cooperation The diversity and dynamism of neuroscience will make dataintegration challenging always Neural space is vast: No one group or individual can doeverything We don’t have to solve everything to make it better Global partnership with room for everyone: Neuroscientists Curators Resource developers Funders Computational biologists Text miners Computer scientists Watson
    45. 45. Hopeful signs...•Means for sharing data on the webbecoming more routine•With availability, growing recognition for a roleof standards and curation•For neuroscience, we now haveorganizations that can helpcoordinate•NIF, NITRC (http://nitrc.org)•NeuroimagingTools and ResourceClearinghouse•International NeuroinformaticsCoordinating Facility•Educate neuroscientists on what isnecessary•Bring together stakeholders todefine what is necessary forinteroperation•Implement structures andprocedures for developingneuroscience resources within aframeworkhttp://incf.org
    46. 46. We don’t know everything but wedo know some things1. Register your resourcewith NIF!!!!3: Be mindful Resource providers: Mindfulness that yourresource is contributing data to a globalfederation Link to shared ontology identifiers wherepossible Stable and unique identifiers for data Explicit semantics Database, model, atlas Researchers: Mindfulness when publishingdata that it is to be consumed by machinesand not just your colleagues Accession numbers for genes and species Catalog numbers for reagents Provide supplemental data in a form where it isis easy to re-use2. Become involved with NIFand INCF
    47. 47. Learn about neuroinformatics
    48. 48. Many thanks to...Amarnath Gupta, UCSD, Co InvestigatorJeff Grethe, UCSD, Co InvestigatorAnita Bandrowski, NIF CuratorGordon Shepherd,Yale UniversityPerry MillerLuis MarencoDavidVan Essen,Washington UniversityErin ReidPaul Sternberg, CalTechArunRangarajanHans Michael MullerGiorgioAscoli,George Mason UniversitySrideviPolavarumFahimImam, NIF Ontology EngineerKaren Skinner, NIH, Program OfficerMark EllismanLee HornbrookKara LuVadimAstakhovXufeiQianChris ConditStephen LarsonSarah MaynardBill Bug
    49. 49. Register your resource to NIF!
    50. 50. How old is an adult squirrel? Definitions can bequantitative Arbitrary but defensible Qualitative categoriesfor quantitativeattributes Best practice toprovide ages ofsubjects, but forquery, need totranslate intoqualitative conceptsJonathan Cachat, Anita Bandrowski
    51. 51. But there are no databases forsiRNANIF Registry is probably the most complete accounting we have of what is outthere

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