Vivien Bonazzi leads the Data Commons efforts within NIH. She discussed how big data is characterized by volume, velocity, variety and veracity. She explained that data is becoming the central currency of a new digital economy and organizations must leverage their digital assets through platforms like the Data Commons to transform into digital enterprises. The Data Commons platform fosters development of a digital ecosystem by enabling interactions between producers and consumers of FAIR digital objects like data, software and publications.
NIH Data Commons - Note: Presentation has animations Vivien Bonazzi
Presented at the Data Commons & Data Science Workshop (University of Chicago - Centre for Data Intensive Science):
NB- there are animations in these slides so static slides might not view well
The NIH Data Commons - BD2K All Hands Meeting 2015Vivien Bonazzi
Presentation given at the BD2K All Hands meeting in Bethesda, MD, USA in November 2015
https://datascience.nih.gov/bd2k/events/NOV2015-AllHands
Video cast of this presentation:
http://videocast.nih.gov/summary.asp?Live=17480&bhcp=1
talk starts at 2hrs 40min (its about 55mins long) - includes video!
Document describing the Commons : https://datascience.nih.gov/commons
NIH Data Commons - Note: Presentation has animations Vivien Bonazzi
Presented at the Data Commons & Data Science Workshop (University of Chicago - Centre for Data Intensive Science):
NB- there are animations in these slides so static slides might not view well
The NIH Data Commons - BD2K All Hands Meeting 2015Vivien Bonazzi
Presentation given at the BD2K All Hands meeting in Bethesda, MD, USA in November 2015
https://datascience.nih.gov/bd2k/events/NOV2015-AllHands
Video cast of this presentation:
http://videocast.nih.gov/summary.asp?Live=17480&bhcp=1
talk starts at 2hrs 40min (its about 55mins long) - includes video!
Document describing the Commons : https://datascience.nih.gov/commons
ESIP Federation: Community-Driven, Collaborative Governance - Carol Beaton Me...ASIS&T
ESIP Federation: Community-Driven, Collaborative Governance
Carol Beaton Meyer
Presentation at Research Data Access & Preservation Summit
22 March 2012
Ginny Pannabecker, Life Science & Scholarly Communications Librarian at Virginia Tech, is an ACRL Science and Technology Section (STS) liaison to the American Institute of Biological Sciences (AIBS). This presentation shares key points for librarians and researchers from an AIBS workshop on "Changing Practices in Data Publications," which took place in December 2014 and involved representatives from federal funding agencies; publishers and librarians; scientific societies and journals; and data services / providers.
NIH Data Initiatives: Harnessing Big (and small) Data to Improve Health
Presentation at the internet2 Global Forum, April 28, 2015
Session NIH Perspectives
Presentation given at Supercomputing 2007 on the progress of data sharing models, specifically highlighting the collision of data grid / data service and Web 2.0 worlds.
RDAP 16: Sustainability of data infrastructure: The history of science scienc...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Part of Panel 2, Sustainability
Presenter:
Kristin Eschenfelder, University of Wisconsin-Madison
Panel Leads:
Kristin Briney, University of Wisconsin-Milwaukee & Erica Johns, Cornell University
Big Data in Biomedicine – An NIH PerspectivePhilip Bourne
Keynote at the IEEE International Conference on Bioinformatics and Biomedicine, Washington DC, November 10, 2015.
https://cci.drexel.edu/ieeebibm/bibm2015/
RDAP13 Mark Parsons: The Research Data Alliance: Making Data WorkASIS&T
Mark Parsons, Rensselaer Polytechnic Institute
Mark A. Parsons and Francine Berman: "The Research Data Alliance: Making Data Work"
Panel: Global scientific data infrastructure
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
RDAP 16: DMPs and Public Access: An NIH Perspective (Panel 5, DMPs and Public...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Part of Panel 5, "DMPs and Public Access: Agency and Data Service Experiences"
Presenter:
Lisa Federer, National Institutes of Health
Panel Lead:
Margaret Henderson, Virginia Commonwealth University
What is Data Commons and How Can Your Organization Build One?Robert Grossman
This is a talk that I gave at the Molecular Medicine Tri Conference on data commons and data sharing to accelerate research discoveries and improve patient outcomes. It also covers how your organization can build a data commons using the Open Commons Consortium's Data Commons Framework and the University of Chicago's Gen3 data commons platform.
ESIP Federation: Community-Driven, Collaborative Governance - Carol Beaton Me...ASIS&T
ESIP Federation: Community-Driven, Collaborative Governance
Carol Beaton Meyer
Presentation at Research Data Access & Preservation Summit
22 March 2012
Ginny Pannabecker, Life Science & Scholarly Communications Librarian at Virginia Tech, is an ACRL Science and Technology Section (STS) liaison to the American Institute of Biological Sciences (AIBS). This presentation shares key points for librarians and researchers from an AIBS workshop on "Changing Practices in Data Publications," which took place in December 2014 and involved representatives from federal funding agencies; publishers and librarians; scientific societies and journals; and data services / providers.
NIH Data Initiatives: Harnessing Big (and small) Data to Improve Health
Presentation at the internet2 Global Forum, April 28, 2015
Session NIH Perspectives
Presentation given at Supercomputing 2007 on the progress of data sharing models, specifically highlighting the collision of data grid / data service and Web 2.0 worlds.
RDAP 16: Sustainability of data infrastructure: The history of science scienc...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Part of Panel 2, Sustainability
Presenter:
Kristin Eschenfelder, University of Wisconsin-Madison
Panel Leads:
Kristin Briney, University of Wisconsin-Milwaukee & Erica Johns, Cornell University
Big Data in Biomedicine – An NIH PerspectivePhilip Bourne
Keynote at the IEEE International Conference on Bioinformatics and Biomedicine, Washington DC, November 10, 2015.
https://cci.drexel.edu/ieeebibm/bibm2015/
RDAP13 Mark Parsons: The Research Data Alliance: Making Data WorkASIS&T
Mark Parsons, Rensselaer Polytechnic Institute
Mark A. Parsons and Francine Berman: "The Research Data Alliance: Making Data Work"
Panel: Global scientific data infrastructure
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
RDAP 16: DMPs and Public Access: An NIH Perspective (Panel 5, DMPs and Public...ASIS&T
Research Data Access and Preservation Summit, 2016
Atlanta, GA
May 4-7, 2016
Part of Panel 5, "DMPs and Public Access: Agency and Data Service Experiences"
Presenter:
Lisa Federer, National Institutes of Health
Panel Lead:
Margaret Henderson, Virginia Commonwealth University
What is Data Commons and How Can Your Organization Build One?Robert Grossman
This is a talk that I gave at the Molecular Medicine Tri Conference on data commons and data sharing to accelerate research discoveries and improve patient outcomes. It also covers how your organization can build a data commons using the Open Commons Consortium's Data Commons Framework and the University of Chicago's Gen3 data commons platform.
Meeting Federal Research Requirements for Data Management Plans, Public Acces...ICPSR
These slides cover evolving federal research requirements for sharing scientific data. Provided are updates on federal agency responses to the 2013 OSTP memo, guidance on data management plans, resources for data management and curation training for staff/researchers, and tips for evaluating public data-sharing services. ICPSR's public data-sharing service, openICPSR, is also presented. Recording of this presentation is here: https://www.youtube.com/watch?v=2_erMkASSv4&feature=youtu.be
Palestra, em inglês, "Publishing Data on the Web" sobre o documento Data on the Web Best Practices, apresentada na Semana de Metodologia NIC.br, em São Paulo, dia 12 de abril de 2016.
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville
FAIR Data Management and FAIR Data SharingMerce Crosas
Presentation at the Critical Perspective on the Practice of Digiral Archeology symposium: http://archaeology.harvard.edu/critical-perspectives-practice-digital-archaeology
PAARL's 1st Marina G. Dayrit Lecture Series held at UP's Melchor Hall, 5F, Proctor & Gamble Audiovisual Hall, College of Engineering, on 3 March 2017, with Albert Anthony D. Gavino of Smart Communications Inc. as resource speaker on the topic "Using Big Data to Enhance Library Services"
FAIR data: what it means, how we achieve it, and the role of RDASarah Jones
Presentation on FAIR data, the FAIR Data Action Plan developed by the European Commission Expert Group and the role of the Research Data Alliance on implementing FAIR. The presentation was given at the RDAFinland workshop held on 6th June - https://www.csc.fi/web/training/-/rda_and_fair_supporting_finnish_researchers
Trust and Accountability: experiences from the FAIRDOM Commons Initiative.Carole Goble
Presented at Digital Life 2018, Bergen, March 2018. In the Trust and Accountability session.
In recent years we have seen a change in expectations for the management and availability of all the outcomes of research (models, data, SOPs, software etc) and for greater transparency and reproduciblity in the method of research. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for stewardship [1] have proved to be an effective rallying-cry for community groups and for policy makers.
The FAIRDOM Initiative (FAIR Data Models Operations, http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards and sensitivity to asset sharing and credit anxiety. Our aim is a FAIR Research Commons that blends together the doing of research with the communication of research. The Platform has been installed by over 30 labs/projects and our public, centrally hosted FAIRDOMHub [2] supports the outcomes of 90+ projects. We are proud to support projects in Norway’s Digital Life programme.
2018 is our 10th anniversary. Over the past decade we learned a lot about trust between researchers, between researchers and platform developers and curators and between both these groups and funders. We have experienced the Tragedy of the Commons but also seen shifts in attitudes.
In this talk we will use our experiences in FAIRDOM to explore the political, economic, social and technical, social practicalities of Trust.
[1] Wilkinson et al (2016) The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
[2] Wolstencroft, et al (2016) FAIRDOMHub: a repository and collaboration environment for sharing systems biology research Nucleic Acids Research, 45(D1): D404-D407. DOI: 10.1093/nar/gkw1032
dkNET Office Hours: NIH Data Management and Sharing Mandate 05/03/2024dkNET
Presenter: Jeffrey Grethe, PhD, Principal Investigator of NIDDK Information Network (dkNET), Center for Research in Biological Systems, University of California San Diego
For all proposals submitted on/after January 25 2023, NIH requires the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
Similar to Data commons bonazzi bd2 k fundamentals of science feb 2017 (20)
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
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spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
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z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
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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.
Richard's aventures in two entangled wonderlandsRichard Gill
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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.
The ASGCT Annual Meeting was packed with exciting progress in the field advan...
Data commons bonazzi bd2 k fundamentals of science feb 2017
1. Section 3:
Commons:
Lessons Learned, current state
The Big Data to Knowledge (BD2K)
Guide to the Fundamentals of Data Science
Vivien Bonazzi
Senior Advisor for Data Science & the Data Commons
National Institutes of Health, Bethesda
February 3, 2017
2. Vivien Bonazzi
• Leads the Data Commons efforts within the NIH.
• Serves on the NIH Big Data to Knowledge (BD2K) executive
committee
• Dr. Bonazzi received a B.Sc. in Medical Laboratory Science from
the University of Canberra, Australia, a M.Sc. (prelim) in
Pharmacology from the University of Melbourne, Australia and
a Ph.D. in Molecular Pharmacology and Computational Biology
also from the University of Melbourne.
• Served as a Program Director for the computational biology and
bioinformatics program for National Human Genome Research
Institute (NHGRI)
• Was part of the Human Microbiome Project (HMP) a trans-NIH Common Fund Initiative.
She was responsible for the bioinformatics & computational aspect of the project as well as
managing several of the computational tools awards.
• She has held positions as the R&D Director for Bioinformatics at Invitrogen and Director of
Gene Discovery at Celera Genomics where she was part of the team that sequenced and
annotated the human, mouse and drosophila genomes.
5. What Makes Big Data Big?
VOLUME
VELOCITY
VARIETY
VERACITY
6. It’s a signal of the coming Digital Economy
DATA has VALUE
DATA is CENTRAL to the Digital Economy
But its more than this…..
7. An economy characterized by
using data to gain a business
advantage
(yes, institutions are a business)
Organizations that are not born
digital will be at a disadvantage in
the new economy
8. Organizations will be defined by their digital assets
Scientific digital assets
Data
Software
Workflows
Documentation
Journal Articles
9. The most successful organizations of the future will be
those that can leverage their digital assets and transform
them into a digital enterprise
12. Challenges Biomedical Data
The Journal Article is the end goal
Data is a means to an ends (low value)
Data is not FAIR
Findable, Accessible, Interoperable, Reproducible
Limited e-infrastructures to support FAIR data
15. FAIR principles drive data to become the currency
Policies that promote data sharing via FAIR help change
the culture
16. We also need a digital ecosystem that allows
transactions to occur on FAIR data
at scale
17. The Data Commons
is a platform
that fosters the development of a digital ecosystem
18. The Data Commons platform that fosters development of a digital
ecosystem
Treats products of research – data, software, methods, papers etc as
digital asset (object)
Digital objects need to conform to FAIR principles
Digital objects exist in a shared virtual space
- Find, Deposit, Manage, Share and Reuse: digital assets
Enables interactions between Producers and Consumers of digital assets
Gives currency to digital assets and the people who develop and support
them
19. The Data Commons
is a platform?
that fosters the development of a digital ecosystem
20. “A platform is a plug and play model that
allows multiple participants (producers and consumers)
to connect to it, interact with each other and create
value”
Sangeet Paul Choudary – Platform Scale
21. A lot of what see today uses a platform approach ”
Sangeet Paul Choudary – Platform Scale
22. The goal of the a Data Commons Platform is to enable
interactions between producers and consumers
Sangeet Paul Choudary – Platform Scale
23. To understand the
Data Commons Platform
(and how it works for biomedical data) we
need to use a Platform stack
to help visualize the concept
27. Initial Phase
Unique digital object identifiers of resolvable to original authoritative source
Machine readable
A minimal set of searchable metadata
Clear access rules (especially important for human subjects data)
An entry (with metadata) in one or more indices
Future Phases
Standard, community based unique digital object identifiers
Conform to community approved standard metadata and ontologies for
enhanced searching
Digital objects accessible via open standard APIs
NIH Data Commons: Digital Asset Compliance
Making things FAIR
32. The NIH Data Commons Pilot
Co-location of large and/or highly utilized
NIH funded data with
storage and computing infrastructure +
Commonly used tools for analyzing and
sharing digital objects
to create an interoperable resource for the
research community.
Investigators will be able to collaborate and
share digital objects within this
environment and connect with others
39. Considerations
• Metrics – Understanding and accounting of data usage patterns
• Cost
• Cloud Storage
• Pay for use cloud compute (NIH credits pilot)
• Indirect costs for cloud
• Hybrid Clouds – Institution (private) and commercial (public) clouds
• Managing Open vs Controlled access data
• Auth: single sign on - dreams/nightmares?
• Archive vs Working and versioning Copies of data
• Interoperability with other Commons (clouds)
40. • Standards – Metadata, UIDs, APIs
• Discoverability – Finding digital objects across clouds
• Interfaces – For users with different needs and capabilities
• Consent – Re-consenting data
• Policies
• Data sharing policies that are useful and effective
• Keep pace with use of technology (e.g. dbGAP data in the Cloud)
• Incentives
• Access to, and shareability of FAIR Data as part of NIH grant review criteria
• Governance – Community involvement in governance models
• Sustainability – Long term support
Considerations
41. Acknowledgments
• ADDS Office: Jennie Larkin, Phil Bourne, Michelle Dunn,Mark Guyer, Allen Dearry, Sonynka Ngosso,
Tonya Scott, Lisa Dunneback, Vivek Navale (CIT/ADDS), Ron Margolis
• NCBI: George Komatsoulis
• NHGRI: Valentina di Francesco, Ajay Pillai,
• NIGMS: Susan Gregurick
• CIT: Andrea Norris, Debbie Sinmao
• NIH Common Fund: Jim Anderson , Betsy Wilder, Leslie Derr
• NCI: Ian Fore, Sean Davis, Warren Kibbe, Tony Kerlavage, Tanja Davidsen
• NIAID: Maria Giovanni, Alison Yao, Eric Choi, Claire Schulkey
• NHLBI: Weiniu Gan, Alastair Thomson
• NIH Clinical Centre: Elaine Ayres, (BITRIS),
• NIBIB: Vinay Pai (DK),
• OSP: Dina Paltoo, Kris Langlais, Erin Luetkemeier, Agnes Rooke,
• Research and Industry: Mathew Trunnell (FHC), Bob Grossman (Chicago), Toby Bloom (NYGC)
42. Stay in Touch
QR Business Card
LinkedIn
@Vivien.Bonazzi
Slideshare
Blog
(Coming soon!)
Vivien Bonazzi
bonazziv@mail.nih.gov
Editor's Notes
Currencies don’t exist in a vacuum
Buy and sell Goods
A nascent platform
Platforms that utilize data as a central currency – enable transactions between producers and consumers
Producers of digital objects - data, tools, workflows - used by consumers
The Platform enables these transactions –
Accommodates bioinformatics and non bioinformatics users
Framework helps visualize the concept of the platform