Data Landscapes
neuinfo.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?
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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 institutes
– What types of resources (data, tools, materials, services) are available to the
neuroscience community?
– How many are there?
– What domains do they cover? What domains do they not cover?
– Where are they?
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Web sites
Databases
Literature
Supplementary material
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PDF files
Desk drawers
Who uses them?
Who creates them?
How can we find them?
How can we make them better in the future?
http://neuinfo.org
NIF: A New Type of Entity for New
Modes 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 landscape
NIF resource registry: listing of > 6000 databases, tools,
materials, services, websites (> 2500 databases)
NIF data federation: Pub Med Central for data
200 sources
> 360 M records
NIF was designed to accommodate the multiplicity of heterogeneous and distributed data
resources, providing deep query of the contents and unified views
NIF Semantic Framework: NIFSTD ontology
NIFSTD
Anatomical
Structure
Organism
Molecule Descriptors
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Dysfunction
Subcellular
structure
Molecule
Macromolecule
Cell
Gene
NS Function
Techniques
Resource
Reagent
Quality
Investigation
Instrument
Protocols
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
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 wiki
NIF Analytics: The Neuroscience Landscape
Where are the data?
Data source
Brain region
Brain
Striatum
Hypothalamus
Olfactory bulb
Cerebral cortex
Ontologies provide a semantic framework for understanding
data/resource landscape
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)
NIF favors a hybrid, tiered,
federated system
• Domain knowledge
Gene
Organism
Neuron
Brain part
Disease
– Ontologies
Caudate projects to
Snpc
• Claims and observations
Betz cells
degenerate in ALS
– Virtuoso RDF triples
Grm1 is upregulated in
chronic cocaine
• Data
– Data federation
– Spatial data
– Workflows
• Narrative
– Full text access
Data
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.”
•INCF Community encyclopedia
•Standardize vocabulary
•Define all vocabulary, terms, protocols,
neurolex.org
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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 Hill
Community Platforms: Researchers-tools-data-computing