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.
A description of software as infrastructure at NSF, and how Apache projects may be similar. What lessons can be shared from one organization to the other? How does science software compare with more general software?
A description of software as infrastructure at NSF, and how Apache projects may be similar. What lessons can be shared from one organization to the other? How does science software compare with more general software?
Where are all the Semantic Web agents? There are billions of "machine readable" open facts on the Semantic Web, i.e. Linked Open Data (LOD), isn't that enough? It looks like it's not. We're still far from seeing Lucy's and Pete's agents brilliantly solving their tasks with the help of other Semantic Web agents they can trust (Tim Berners Lee et al., The Semantic Web, Scientific American (2001) ). Despite its technological impact on many applications and areas, the Semantic Web promised to cause a breakthrough that we didn't yet experience. One issue is that LOD ontologies are not as linked as they should be. Another issue is that formalising only semi-structured Web pages or databases is not enough for making them able to operate. They also need to reason with commonsense knowledge, the encoding of which is a long-standing challenge in Artificial Intelligence. A third consideration is that most existing commonsense knowledge bases lack formal semantics and situational constraints. In this talk I will advocate the role of the Semantic Web as a provider of a knowledge graph of commonsense to Artificial Intelligence, and discuss ways and obstacles towards the achievement of this goal.
Functional and Architectural Requirements for Metadata: Supporting Discovery...Jian Qin
The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.
This presentation was provided by Libbie Stephenson, UCLA Social Science Data Archive, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016
HathiTrust Research Center Secure CommonsBeth Plale
Introduces HTRC secure commons, expanded secure infrastructure and services for text mining of HT digital data. Shows results comparing n-gram discovery using Solr full text index and a framework using mapReduce. Compute time over 1 million digital volumes is 1 day with 1024 cores. Weaknesses of Solr in n-gram identification are explored.
The Jeopardy match between the two best human players of all time and the IBM Deep Q/A software, “Watson,” captured the spotlight and stimulated the imagination of the entire world. The subsequent announcement of IBM’s involvement in the creation of “Dr. Watson” has created a high level of interest in the healthcare community about the potential of this breakthrough technology as well as the potential pitfalls of the use of “artificial intelligence” in medicine. Dr. Siegel is currently working together with IBM engineers to explore how Dr. Watson can work together with physicians and medical specialists. His presentation, which was delivered on March 28th, provided a high level overview of the uniqueness of Deep Q/A Software and how it differs from other previous artificial intelligence applications.
Genome sharing projects around the world nijmegen oct 29 - 2015Fiona Nielsen
Genome sharing projects across the world
Did you ever wonder what happened to the exponential increase in genome sequencing data? It is out there around the world and a lot of it is consented for research use. This means that if you just know where to find the data, you can potentially analyse gigabytes of data to power your research.
In this talk Fiona will present community genome initiatives, the genome sharing projects across the world, how you can benefit from this wealth of data in your work, and how you can boost your academic career by sharing and collaboration.
by Fiona Nielsen, Founder and CEO of DNAdigest and Repositive
With a background in software development Fiona pursued her career in bioinformatics research at Radboud University Nijmegen. Now a scientist-turned-entrepreneur Fiona founded DNAdigest and its social enterprise spin-out Repositive Ltd. Both the charity and company focus on efficient and ethical sharing of genetics data for research to accelerate diagnostics and cures for genetic diseases.
Case Study Big Data: Socio-Technical Issues of HathiTrust Digital TextsBeth Plale
Invited talk at TRUST Women’s Institute for Summer Enrichment (WISE), Cornell, NY Jun 16, 2014. Infrastructure support for text mining research of big data repository like HathiTrust raises challenges in access and security when the bulk of the repository is protected by copyright.
Applying machine learning techniques to big data in the scholarly domainAngelo Salatino
Slides of the Lecture at the 5th International School on Applied Probability Theory,Communications Technologies & Data Science (APTCT-2020)
12 Nov 2020
Sensors, Signals and Sense-making in Human-Energy RelationshipsMartha Russell
Presentation by Martha G Russell to Wireless World Research Forum in Vancouver BC on October 21, 2013. Smart meters and related sensing technologies promise that energy information will change energy use. However, information complexity, poorly designed interfaces, and lack of engagement jeopardize billion dollar infrastructure investments because sensors, signals and sense-making are not designed to modify behavior and because context is ignored. Information and resources flow through human relationships when context and values are shared. Using social media to harvest Twitter data about energy use and online press release type information about business innovation, social network analysis provides insights about issue framing, public engagement, and innovation ecosystems. These signals are seen in the larger context of the Stanford ARPA-E Sensor and Behavior Initiative to develop a comprehensive human-centered solution that leverages the anticipated widespread diffusion of energy sensors to significantly reduce and shift energy use.
Big Data Analytics of Software Ecosystem Health: Presentation during INFORTECH Scientific Day (23 May 2018) by Professor Tom Mens. The talk reports on ongoing research of the Software Engineering Lab of the University of Mons (UMONS) on health aspects of evolving software ecosystems. This research was conducted in collaboration with postdoctoral researchers Alexandre Decan and Eleni Constantinou, as well as the external partners of two ongoing research projects: SECOHealth (https://secohealth.github.io) and the Excellence of Science research project SECO-ASSIST (https://secoassist.github.io).
Where are all the Semantic Web agents? There are billions of "machine readable" open facts on the Semantic Web, i.e. Linked Open Data (LOD), isn't that enough? It looks like it's not. We're still far from seeing Lucy's and Pete's agents brilliantly solving their tasks with the help of other Semantic Web agents they can trust (Tim Berners Lee et al., The Semantic Web, Scientific American (2001) ). Despite its technological impact on many applications and areas, the Semantic Web promised to cause a breakthrough that we didn't yet experience. One issue is that LOD ontologies are not as linked as they should be. Another issue is that formalising only semi-structured Web pages or databases is not enough for making them able to operate. They also need to reason with commonsense knowledge, the encoding of which is a long-standing challenge in Artificial Intelligence. A third consideration is that most existing commonsense knowledge bases lack formal semantics and situational constraints. In this talk I will advocate the role of the Semantic Web as a provider of a knowledge graph of commonsense to Artificial Intelligence, and discuss ways and obstacles towards the achievement of this goal.
Functional and Architectural Requirements for Metadata: Supporting Discovery...Jian Qin
The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.
This presentation was provided by Libbie Stephenson, UCLA Social Science Data Archive, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016
HathiTrust Research Center Secure CommonsBeth Plale
Introduces HTRC secure commons, expanded secure infrastructure and services for text mining of HT digital data. Shows results comparing n-gram discovery using Solr full text index and a framework using mapReduce. Compute time over 1 million digital volumes is 1 day with 1024 cores. Weaknesses of Solr in n-gram identification are explored.
The Jeopardy match between the two best human players of all time and the IBM Deep Q/A software, “Watson,” captured the spotlight and stimulated the imagination of the entire world. The subsequent announcement of IBM’s involvement in the creation of “Dr. Watson” has created a high level of interest in the healthcare community about the potential of this breakthrough technology as well as the potential pitfalls of the use of “artificial intelligence” in medicine. Dr. Siegel is currently working together with IBM engineers to explore how Dr. Watson can work together with physicians and medical specialists. His presentation, which was delivered on March 28th, provided a high level overview of the uniqueness of Deep Q/A Software and how it differs from other previous artificial intelligence applications.
Genome sharing projects around the world nijmegen oct 29 - 2015Fiona Nielsen
Genome sharing projects across the world
Did you ever wonder what happened to the exponential increase in genome sequencing data? It is out there around the world and a lot of it is consented for research use. This means that if you just know where to find the data, you can potentially analyse gigabytes of data to power your research.
In this talk Fiona will present community genome initiatives, the genome sharing projects across the world, how you can benefit from this wealth of data in your work, and how you can boost your academic career by sharing and collaboration.
by Fiona Nielsen, Founder and CEO of DNAdigest and Repositive
With a background in software development Fiona pursued her career in bioinformatics research at Radboud University Nijmegen. Now a scientist-turned-entrepreneur Fiona founded DNAdigest and its social enterprise spin-out Repositive Ltd. Both the charity and company focus on efficient and ethical sharing of genetics data for research to accelerate diagnostics and cures for genetic diseases.
Case Study Big Data: Socio-Technical Issues of HathiTrust Digital TextsBeth Plale
Invited talk at TRUST Women’s Institute for Summer Enrichment (WISE), Cornell, NY Jun 16, 2014. Infrastructure support for text mining research of big data repository like HathiTrust raises challenges in access and security when the bulk of the repository is protected by copyright.
Applying machine learning techniques to big data in the scholarly domainAngelo Salatino
Slides of the Lecture at the 5th International School on Applied Probability Theory,Communications Technologies & Data Science (APTCT-2020)
12 Nov 2020
Sensors, Signals and Sense-making in Human-Energy RelationshipsMartha Russell
Presentation by Martha G Russell to Wireless World Research Forum in Vancouver BC on October 21, 2013. Smart meters and related sensing technologies promise that energy information will change energy use. However, information complexity, poorly designed interfaces, and lack of engagement jeopardize billion dollar infrastructure investments because sensors, signals and sense-making are not designed to modify behavior and because context is ignored. Information and resources flow through human relationships when context and values are shared. Using social media to harvest Twitter data about energy use and online press release type information about business innovation, social network analysis provides insights about issue framing, public engagement, and innovation ecosystems. These signals are seen in the larger context of the Stanford ARPA-E Sensor and Behavior Initiative to develop a comprehensive human-centered solution that leverages the anticipated widespread diffusion of energy sensors to significantly reduce and shift energy use.
Big Data Analytics of Software Ecosystem Health: Presentation during INFORTECH Scientific Day (23 May 2018) by Professor Tom Mens. The talk reports on ongoing research of the Software Engineering Lab of the University of Mons (UMONS) on health aspects of evolving software ecosystems. This research was conducted in collaboration with postdoctoral researchers Alexandre Decan and Eleni Constantinou, as well as the external partners of two ongoing research projects: SECOHealth (https://secohealth.github.io) and the Excellence of Science research project SECO-ASSIST (https://secoassist.github.io).
Anita Bandrowski explains how the uniform resource layer of the Neuroscience Information Framework allows several interesting questions about the state of scientific research to be answered.
RDAP14: Maryann Martone, Keynote, The Neuroscience Information FrameworkASIS&T
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Maryann Martone, Principal Investigator, Neuroscience Information Framework, University of California, San Diego
Data Landscapes: The Neuroscience Information FrameworkMaryann Martone
Overview of how to use the Neuroscience Information Framework for data discovery presented at the Genetics of Addiction Workshop, held at Jackson Lab Aug 28- Sept 1, 2014.
Data-knowledge transition zones within the biomedical research ecosystemMaryann Martone
Overview of the Neuroscience Information Framework and how it brings together data, in the form of distributed databases, and knowledge, in the form of ontologies to show the mapping of the dataspace and places where there are mismatches between data and knowledge.
Reproducibility in human cognitive neuroimaging: a community-driven data sha...Nolan Nichols
Access to primary data and the provenance of derived data are increasingly recognized as an essential aspect of reproducibility in biomedical research. While productive data sharing has become the norm in some biomedical communities, human brain imaging has lagged in open data and descriptions of provenance. The overarching goal of my dissertation was to identify barriers to neuroimaging data sharing and to develop a fundamentally new, granular data exchange standard that incorporates provenance as a primitive to document cognitive neuroimaging workflow.
For my dissertation research, I led the development of the Neuroimaging Data Model (NIDM), an extension to the W3C PROV standard for the domain of human brain imaging. NIDM provides a language to communicate provenance by representing primary data, computational workflow, and derived data as bundles of linked Agents, Activities, and Entities. Similar to the way a sentence conveys a standalone thought, a bundle contains provenance statements that parsimoniously express the way a given piece of data was produced. To demonstrate a system that implements NIDM, I developed a modern, semantic Web application platform that provides neuroimaging workflow as a service and captures provenance statements as NIDM bundles. The course of this work necessitated interaction with an international community, which adopted and extended central elements of this work into prevailing brain imaging software. My dissertation contributes neuroinformatics standards to advance the current state of computational infrastructure available to the cognitive neuroimaging community.
Kennisalliantie Nieuwjaarsreceptie 31 januari 2013:
Prof. dr. Jacob de Vlieg: “Taming the Big Data Beast Together”
CEO en wetenschappelijk directeur van het Netherlands eScience Center (NLeSC)
Vince smith-delivering biodiversity knowledge in the information age-notextVince Smith
Smith, V.S. 2013. Delivering biodiversity knowledge in the information age. Hellenic Botanical Society, Thessaloniki, Greece, 3-6 Oct. 2013. [Delivered via video link through Google Hangouts]
Maryann Martone
Making Sense of Biological Systems: Using Knowledge Mining to Improve and Validate Models of Living Systems; NIH COBRE Center for the Analysis of Cellular Mechanisms and Systems Biology, Montana State University, Bozeman, MT
August 24, 2012
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
2. 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?
3. 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?
7. • 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
8. 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
9. 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)
10. 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
11. 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
12. 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
13. 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
15. 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
16. 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
17. 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
18. 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
19. 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. 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