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.
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.
How Portable Are the Metadata Standards for Scientific Data?Jian Qin
The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.
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.
What is data discovery and how do people find out about data?
Metadata: What information helps potential users decide whether that data might be useful?
How and why do machines exchange information about research data?
Data without metadata and connections is useless:
Linked data
How Scholix is helping publishers and others to link data with publications and more
Metadata, controlled vocabularies, linked data and crosswalks
Things #11, #12, #13 of 23 Things
How do we make FAIR data? Finable, Accessible, Interoperable, Reusable?
Talk for the workshop on the Future of the Commons, November 18, 2015: http://cendievents.infointl.com/CENDI_NFAIS_RDA_2015/
Slides distributed under under CC-by license: https://creativecommons.org/licenses/by/2.0/
bioCADDIE Webinar: The NIDDK Information Network (dkNET) - A Community Resear...dkNET
The NIDDK Information Network (dkNET; http://dknet.org) is a open community resource for basic and clinical investigators in metabolic, digestive and kidney disease. dkNET’s portal facilitates access to a collection of diverse research resources (i.e. the multitude of data, software tools, materials, services, projects and organizations available to researchers in the public domain) that advance the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This webinar was presented by dkNET principle investigator Dr. Jeffrey Grethe.
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
A Lined Data Approach to Interoperability between Biomedical Resource Invento...Trish Whetzel
Overview of Resource Representation Coordination efforts to coordinate the representation of resources from Biositemaps, eagle-i, and the Neuroscience Information Framework.
How Portable Are the Metadata Standards for Scientific Data?Jian Qin
The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.
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.
What is data discovery and how do people find out about data?
Metadata: What information helps potential users decide whether that data might be useful?
How and why do machines exchange information about research data?
Data without metadata and connections is useless:
Linked data
How Scholix is helping publishers and others to link data with publications and more
Metadata, controlled vocabularies, linked data and crosswalks
Things #11, #12, #13 of 23 Things
How do we make FAIR data? Finable, Accessible, Interoperable, Reusable?
Talk for the workshop on the Future of the Commons, November 18, 2015: http://cendievents.infointl.com/CENDI_NFAIS_RDA_2015/
Slides distributed under under CC-by license: https://creativecommons.org/licenses/by/2.0/
bioCADDIE Webinar: The NIDDK Information Network (dkNET) - A Community Resear...dkNET
The NIDDK Information Network (dkNET; http://dknet.org) is a open community resource for basic and clinical investigators in metabolic, digestive and kidney disease. dkNET’s portal facilitates access to a collection of diverse research resources (i.e. the multitude of data, software tools, materials, services, projects and organizations available to researchers in the public domain) that advance the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This webinar was presented by dkNET principle investigator Dr. Jeffrey Grethe.
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
A Lined Data Approach to Interoperability between Biomedical Resource Invento...Trish Whetzel
Overview of Resource Representation Coordination efforts to coordinate the representation of resources from Biositemaps, eagle-i, and the Neuroscience Information Framework.
Creating Sustainable Communities in Open Data Resources: The eagle-i and VIVO...Robert H. McDonald
This is the slidedeck for my ACRL 2015 TechConnect Presentation with Nicole Vasilevsky (OHSU). For more on the program see - <a>http://bit.ly/1xcQbCr</a>.
Open Access and Research Communication: The Perspective of Force11Maryann Martone
Presentation at the National Federation of Advanced Information Services Workshop: Open Access to Published Research: Current Status and Future Directions, Philadelphia, PA USA November 22, 2013
Identifying and tracking research resources using RRIDs: a practical approachdkNET
At this presentation, you will learn (1) Why you need to use Research Resource identifier (RRID) (2) What is Resource Identification Initiative (3) How dkNET.org supports RRID (4) What can you do with RRID
NISO Two Day Virtual Conference:
Using the Web as an E-Content Distribution Platform:
Challenges and Opportunities
Oct 21-22, 2014
Maryann Martone, Ph.D., Professor of Neuroscience, University of California, San Diego
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch 06/02/2023dkNET
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch
Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego
Abstract
The dkNET (NIDDK Information Network) team is announcing an exciting new service - Biomed Resource Watch (BRW, https://scicrunch.org/ResourceWatch), a knowledge base for aggregating and disseminating known problems and performance information about research resources such as antibodies, cell lines, and tools. We aggregate trustworthy information from authorized sources such as Cellosaurus, Antibody Registry, Human Protein Atlas, ENCODE, and many more. In addition, BRW includes antibody specificity text mining information extracted from the literature via natural language processing. BRW provides researchers and curators an easy-to-use interface to report their claims about a specific resource. Researchers can check information about a resource before planning their experiments via BRW-enhanced Resource Reports. This new service aims to help improve efficiency in selecting appropriate resources, enhancing scientific rigor and reproducibility, and promoting a FAIR (Findable, Accessible, Interoperable, Reusable) research resource ecosystem in the biomedical research community.
Join us for a webinar to introduce the following resources & topics:
1. An overview of dkNET
2. How Resource Reports benefit you
3. Biomed Resource Watch
3.1 Navigating Biomed Resource Watch
3.2 How to Submit a Claim
Upcoming webinars schedule: https://dknet.org/about/webinar
5-14-13 An Introduction to VIVO Presentation SlidesDuraSpace
“Hot Topics: The DuraSpace Community Webinar Series, "Series Five: VIVO: Research Discovery and Networking.” Webinar #1: An Introduction to VIVO, May 14, 2013
Presented by: Dean Krafft, Chief Technology Strategist at Cornell University Library and Chair of the VIVO-DuraSpace Management Committee, Brian Lowe, Semantic Applications Programmer, Cornell and Jon Corson-Rikert, VIVO Development Lead, Cornell
Open Science, Open Data: towards a new transparent and reproducible ecosystemLIBER Europe
Presented at the Preforma Open Source Workshop 8 April 2016
As a library membership organization, LIBER works on addressing Open Science barriers. Standardisation of file formats can really help in overcoming some of these barriers: it enables us to process and preserve data in a controlled way, it helps ensure that outputs are really open and accessible in the long term and it improves interoperability of new tools and services. Making sure data is stored in a controlled way and can be (re) used today and in the future is an important element in Open Science. We see this as not only a technical challenge but also a social one: awareness, trust and community building is needed in order to ensure uptake of these standards. Libraries therefore have a valuable role to play in the development of good research data management throughout all phases of the Open Data lifecycle.
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.
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
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
1. Surveying the Biomedical Resource
Landscape
Maryann E. Martone, Ph. D.
Professor Emeritus
University of California, San Diego
and
Director of Biosciences
Hypothesis
3. Database
Software Application
Data Analysis Service
Topical Portal
Core Facility
Ontology
Software Resource
Years:
NIF is an initiative of the
NIH Blueprint
consortium of institutes
– NIF has been tracking
and cataloging the
biomedical resource
landscape since 2008
4. 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 was one of the first attempts to unite neuroscience information without
respect to domain, funding agency, institute or community
– Confront the scale, dynamism of domain and fluidity of technology
– Thought about global search across independently maintained
resources
– NIF is a library for scholarly output that is a web enabled resource and not a paper;
a Pub Med and Pub Med Central for things that aren’t articles
– Aggregates and tracks all the different databases, tools and resources now
produced by the scientific community
– Makes them searchable from a single interface
– Educate neuroscientists and students about effective data sharing
http://neuinfo.org
5. Organizing framework
and portal for data
dashed lines: mapping of
metadata, standards,
links to aggregators,
datasets
aggregators: repositories
or various indices whose
metadata are or can be
mapped into Commons
metadata
Data
Digital objects
A data discovery index for
biomedicineThereisworkforeveryone(andmore)
datamed.biocaddie.org (v0.5) alpha testing
6. Registry vs Data index: Metadata about
resource vs metadata/data in database
With the thousands of databases and other information sources
available, simple descriptive metadata will not suffice
Each source is
categorized
and presents
custom
facets;
integrated
views
7. SciCrunch: A “social network” for resources
• NIF is a general search engine
across neuroscience and
biomedicine
• Many communities want to
create more focused portals
• Own brand
• Own view
• How do we create a system that
satisfies community needs
without creating another silo?
• SciCrunch: Configurable portals
on top of shared resource pools
9. Semantic Information Framework
• Aggregate of community ontologies with some extensions for neuroscience
• Available as services through SciCrunch and BioPortal —> SciGraph Neo4J-based
Organism
Molecule InvestigationSubcellular
structure
Cell
Dysfunction
Quality
Anatomical
Structure
SciCrunch uses ontologies to enhance search and discovery but is not constrained by them
NS Function
NIFSTD
11. Domain
Knowledge
• Ontologies
• Atlases/Ma
ps
Claims,
assertions
• Registries
• Annotation
• Models and
simulations
• Analyses
Data
• Databases
• Data sets
• Derived
data
Literature
Search and Discovery
Cannot try to shoe-horn everything into a single representation or system, but figure
out how information (data + knowledge) can flow between them; Knowledge is fluid
and will continually update
Creating a Data and Resource Discovery
Environment
12. ORCID
RRID
Data
Digital world runs on globally unique and persistent identifiers; PID’s serve as a
“key” for identifying the same entity across different contexts
e-Science Ecosystem
Metadatastandards
People
Research resources
Ontology
Concepts
DOI
Protocols
Minimal Information Models
TranslationNon-digital
Repositories
and
Registries
CDE
No resource provider is an island: ensure your objects are FAIR
PID
Repositories,
Registries,
Aggregators, Social
platforms, Workflow
platforms
Searchanddiscovery
Citationstandards
articles
software
Digital
14. Making research objects FAIR
– You (and the machine) have to be able to
find it
• Accessible through the web
• Annotations
• Stable links and unique identifiers
– You have to be able to use it
• Data type specified and in a usable form
– You have to know what the data mean
• Some semantics
• Context: Experimental metadata
–You have to be able to cite it:
• Provenance: Where did the data come from?
Make your data FAIR: Findable, Accessible, Interoperable, Reusable
https://www.force11.org/group/fairgroup
X
Research Resource
15. Resource Identification Initiative: Linking
resources to literature
• Have authors supply appropriate
identifiers for key resources used
within a study such that they are:
– Machine processible (i.e., unique
identifier that resolves to a single
resource)
– Outside of the paywall
– Uniform across journals and publishers
• Pilot project: SciCrunch portal
serving identifiers for
– Software/databases (NIF RR)
– Antibodies (NIF AB Registry)
– Genetically modified organisms (NIF
aggregation)
Absolutely reliant on comprehensive registries to enforce uniqueness, persistence and
consistent metadata
16. What studies used...
Type RRID into
Google Scholar;
return a list of
papers that use
that resource
>700 papers
>90 journals
1000’s of RRID’s
17. Resource IDs from NIF aggregated databases
•A single portal for
authors
•>15 authoritative
databases
•One search interface
•Not just my research
resource
•Thinking globally
about infrastructure
RII Portal
http://scicrunch.org/resources
Utilized NIF/SciCrunch infrastructure-NIH
Blueprint; NIDDK
18. Linking data to Literature: Joint Declaration of
Data Citation Principles
• Synthesis of data
citation principles
– >25 groups
participating
• Designed to be high
level and easy to
understand
• Supplemented with a
glossary, references
and examples
http://www.force11.org/datacitation
1. Importance
2. Credit and attribution
3. Evidence
4. Unique Identification
5. Access
6. Persistence
7. Specificity and
verifiability
8. Interoperability and
flexibility
20. hypothes.is: Web annotation
• Works as an
independent layer
over any web page or
PDF *(images, video
and data coming)
• Open source
• Standards based
• Easy-to-use
https://hypothes.is/annotating-all-knowledge/
21. Neuroscientist annotating her own paper to provide updates and additional
information
An interactive knowledge layer
22. Conclusions
• Investments in infrastructures-successful and unsuccessful-
have laid the foundations for a functioning ecosystem
• Comprehensive registries, repositories and aggregators play
a key role in providing stable and useful representations of
key digital entities
• Persistence is a social contract
• Population is key
• i.e., people and organizations are in the mix!
• Need to think globally across the workflow
• FORCE11 coordinating, collating and organizing principles
that govern flow of research objects within the ecosystem
• New technologies are constantly arising
Figure X: Resource types and year added to the registry. Research resources are each tagged with one or more resource types, the most common are represented in this graph (for all data see http://neurolex.org/wiki/Resource_Type_Hierarchy). The year that a resource was added to the registry is denoted by the color, note that 2009 and earlier data are lumped into 2010.
The core of the DDI (Data Discovery Index) is indexing of data (digital objects) drawn from data repositories. These consist of individual repositories (e.g. PDB) but sources of data can also include aggregators (e.g. OmicsDI, LINCS) that draw from multiple repositories. bioCADDIE is not to replace existing indices. It is for community to work together to maximize the discoverability on top of the existing indices. BioCADDIE indexes the data and provides a search engine that users can access through a User Interface.