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Data Landscapes - Addiction
 

Data Landscapes - Addiction

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the Neuroscience Information Framework has over 100 big data databases indexed, allowing us to ask big data landscape questions. Anita Bandrowski presents an overview of the NIF system and provides ...

the Neuroscience Information Framework has over 100 big data databases indexed, allowing us to ask big data landscape questions. Anita Bandrowski presents an overview of the NIF system and provides insights into the addiction data landscape to JAX laboratories.

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Data Landscapes - Addiction Data Landscapes - Addiction Presentation Transcript

  • Data LandscapesData Landscapes neuinfo.orgneuinfo.org Anita Bandrowski, Ph. D. University of California, San Diego
  • Overview • Brief overview of NIF philosophy • Examples of data about addiction • Why you should never use google to answer any scientific question • How can we make google better?
  • Power! • How many subject/patients do we need to be relatively certain that we are correct? • More than you can afford? • If YFGM gave each of you 1B dollars, would that solve the problem? • But, what if: – Big data from small data?
  • Addiction is a large problem
  • Solving the large problems of science? • Observation • Experimentation • Modeling • Cooperative data intensive science
  • A shared understanding of the genetics of Addiction, How can everyone play?
  • • NIF is an initiative of the NIH Blueprint consortium of institutesNIF is an initiative of the NIH Blueprint consortium of institutes – What types of resources (data, tools, materials, services) are available to theWhat types of resources (data, tools, materials, services) are available to the neuroscience community?neuroscience community? – How many are there?How many are there? – What domains do they cover? What domains do they not cover?What domains do they cover? What domains do they not cover? – Where are they?Where are they? • Web sitesWeb sites • DatabasesDatabases • LiteratureLiterature • Supplementary materialSupplementary material – Who uses them?Who uses them? – Who creates them?Who creates them? – How can we find them?How can we find them? – How can we make them better in the future?How can we make them better in the future? http://neuinfo.org • PDF filesPDF files • Desk drawersDesk drawers
  • NIF: A New Type of Entity for NewNIF: A New Type of Entity for New Modes of Scientific DisseminationModes of Scientific Dissemination • NIF’s mission is to maximize the awareness of, access to and utility of digital resources produced worldwide to enable better science and promote efficient use – NIF unites neuroscience information without respect to domain, funding agency, institute or community – NIF is a library for scholarly output that is a web enabled resource and not a paper – Aggregates all the different databases, tools and resources now produced by the scientific community – Makes them searchable from a single interface – A practical approach to the data deluge – Educate neuroscientists and students about effective data sharing
  • Surveying the resource landscapeSurveying the resource landscape NIF resource registry: listing of > 6000 databases, tools, materials, services, websites (> 2500 databases) NIF resource registry: listing of > 6000 databases, tools, materials, services, websites (> 2500 databases)
  • NIF data federation: Pub Med Central for dataNIF data federation: Pub Med Central for data NIF was designed to accommodate the multiplicity of heterogeneous and distributed data resources, providing deep query of the contents and unified views NIF was designed to accommodate the multiplicity of heterogeneous and distributed data resources, providing deep query of the contents and unified views 200 sources > 360 M records 200 sources > 360 M records
  • NIF Semantic Framework: NIFSTD ontologyNIF Semantic Framework: NIFSTD ontology • NIF covers multiple structural scales and domains of relevance to neuroscience • Aggregate of community ontologies with some extensions for neuroscience, e.g., Gene Ontology, Chebi, Protein Ontology NIFSTDNIFSTD OrganismOrganism NS FunctionNS FunctionMoleculeMolecule InvestigationInvestigationSubcellular structure Subcellular structure MacromoleculeMacromolecule GeneGene Molecule DescriptorsMolecule Descriptors TechniquesTechniques ReagentReagent ProtocolsProtocols CellCell ResourceResource InstrumentInstrument DysfunctionDysfunction QualityQualityAnatomical Structure Anatomical Structure Ontologies provide the universals for integrating across disparate data by linking them to human knowledge models Ontologies provide the universals for integrating across disparate data by linking them to human knowledge models
  • Neurolex: Machine-processable concepts for neuroscience • Machine-processable lexical units • Connected via relationships • Identified by a unique identifier (URL) • Computable index for neuroscience • Framework for linking knowledge, claims and data Built using a semantic wikiBuilt using a semantic wiki
  • NIF Analytics: The Neuroscience Landscape Ontologies provide a semantic framework for understanding data/resource landscape Ontologies provide a semantic framework for understanding data/resource landscape Where are the data? Striatum Hypothalamus Olfactory bulb Cerebral cortex Brain Brainregion Data source Vadim Astakhov, Kepler Workflow Engine
  • A data homunculus?
  • Genetics of addiction? Gene Protein Subcellular components Cells Cell microcircuits Cell macrocircuits Networks Brain regions PNS Whole organism Behaving organism (environment) Networks of organisms Populations
  • Genetics of addiction? Gene Protein Subcellular components Cells Cell microcircuits Cell macrocircuits Networks Brain regions PNS Whole organism Behaving organism (environment) Networks of organisms Populations
  • Genetics of addiction? • Addiction is a disease of subpopulations of humans who take sociologically undesirable drugs or sociologically desirable drugs at undesirable concentrations • Drug is a molecule that does not exist in the body, an environmental factor • Drugs are metabolized by the digestive system and act after crossing the BBB • Drugs modify the activity of existing proteins on vastly different time scales • Drugs modify behaviors that depend on the actions of an orchestra of neurons acting within circuits that all have a purpose that is not to take drugs
  • The ecosystem is diverse and messy (and that’s OK)The ecosystem is diverse and messy (and that’s OK) NIF favors a hybrid, tiered, federated system • Domain knowledge – Ontologies • Claims and observations – Virtuoso RDF triples • Data – Data federation – Spatial data – Workflows • Narrative – Full text access NeuronNeuron Brain partBrain part DiseaseDisease OrganismOrganism GeneGene Caudate projects to Snpc Caudate projects to Snpc Grm1 is upregulated in chronic cocaine Grm1 is upregulated in chronic cocaine Betz cells degenerate in ALS Betz cells degenerate in ALS Data KnowledgeData Knowledge
  • Wish list: Cooperative science • A mission that will engage the entire neuroscience community and beyond • An active community contribution model where everyone is expected to contribute their outputs, not just a selected few – Diverse contributions are tracked and recognized – Spatial-semantic-genetic-temporal frameworks make data discoverable-usable-integratable and help fill in the gaps • A platform that moves neuroscience into the web – Networking data, knowledge, tools, models, efforts, people, compute resources, simulation – Supports digital research objects as first order contributions, not just narrative – Works through and with existing platforms to improve them where possible Cooperative system: “...individual components that appear to be “selfish” and independent work together to create a highly complex, greater-than-the-sum-of-its-parts system.” Cooperative system: “...individual components that appear to be “selfish” and independent work together to create a highly complex, greater-than-the-sum-of-its-parts system.”
  • 20 neurolex.org •INCF Community encyclopedia •Standardize vocabulary •Define all vocabulary, terms, protocols, brain structures, diseases, etc •Living review articles •Build and maintain working ontologiesLinks to data, models and literature •Semantic organization, search, analysis and integration •Global directory of all shared vocabularies, CDEs, etc Slide courtesy of Sean HillSlide courtesy of Sean Hill
  • Community Platforms: Researchers-tools-data-computing