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
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
NITRD Big Data Interagency Working Group Workshop: Pioneering the Future of Federally Supported Data Repositories Jan 13, 2021 - Opening comments on where we are and one suggestion of where we might go with an International Data Science Institute (IDSI) - A blue sky view.
SCUP 2016 Mid-Atlantic Symposium: Big Data: Academy Research, Facilities, and Infrastructure Implications and Opportunities. John Hopkins, May 13, 2016
PSB2014 A Vision for Biomedical ResearchPhilip Bourne
Some preliminary thoughts about my role as Associate Director for Data Science at the NIH so as to have a discussion with attendees at the Pacific Symposium on Biocomputing on Jan 4, 2014, The Big Island of Hawaii.
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
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and RealityPaul Courtney
Presentation made by Paul Courtney (Dana-Farber Cancer Institute, Boston, MA and OHSL, MD) and Anil Srivastava (OHSL) at the 2013 VIVO conference in St. Louis, MO. Material contributed by Rubayi Srivastava (OHSL), Swati Mehta (Centre for Development of Advanced Computing, India), Juliusz Pukacki (Poznan Supercomputing and Network Center, Poland) and Devdatt Dubhashi (Chalmers Institute of Technology, Sweden).
Presented on May 7, 2015 to the TechChange Technology for M&E course. The aim of the presentation was to highlight key considerations in designing visualizations as part of international development programs, and includes both challenges of visualization in development programs and six things to consider when designing visualizations.
Training Quantitative Scientists for Biomedical Science Through the BD2K Init...Philip Bourne
The NIH remains committed to training the next generation of biomedical scientists reflecting the scientific needs of the funded research. The NIH agrees that the training of quantitative scientists is critical to continuing the advancement of biomedical science, especially in the era of big data and complex genomic data. We will describe efforts in this direction through the Big Data to Knowledge BD2K Initiative.
Presented at the AAAS Meeting, San Jose, CA, February 16, 2015.
NITRD Big Data Interagency Working Group Workshop: Pioneering the Future of Federally Supported Data Repositories Jan 13, 2021 - Opening comments on where we are and one suggestion of where we might go with an International Data Science Institute (IDSI) - A blue sky view.
SCUP 2016 Mid-Atlantic Symposium: Big Data: Academy Research, Facilities, and Infrastructure Implications and Opportunities. John Hopkins, May 13, 2016
PSB2014 A Vision for Biomedical ResearchPhilip Bourne
Some preliminary thoughts about my role as Associate Director for Data Science at the NIH so as to have a discussion with attendees at the Pacific Symposium on Biocomputing on Jan 4, 2014, The Big Island of Hawaii.
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
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and RealityPaul Courtney
Presentation made by Paul Courtney (Dana-Farber Cancer Institute, Boston, MA and OHSL, MD) and Anil Srivastava (OHSL) at the 2013 VIVO conference in St. Louis, MO. Material contributed by Rubayi Srivastava (OHSL), Swati Mehta (Centre for Development of Advanced Computing, India), Juliusz Pukacki (Poznan Supercomputing and Network Center, Poland) and Devdatt Dubhashi (Chalmers Institute of Technology, Sweden).
Presented on May 7, 2015 to the TechChange Technology for M&E course. The aim of the presentation was to highlight key considerations in designing visualizations as part of international development programs, and includes both challenges of visualization in development programs and six things to consider when designing visualizations.
Training Quantitative Scientists for Biomedical Science Through the BD2K Init...Philip Bourne
The NIH remains committed to training the next generation of biomedical scientists reflecting the scientific needs of the funded research. The NIH agrees that the training of quantitative scientists is critical to continuing the advancement of biomedical science, especially in the era of big data and complex genomic data. We will describe efforts in this direction through the Big Data to Knowledge BD2K Initiative.
Presented at the AAAS Meeting, San Jose, CA, February 16, 2015.
Regional Student Group NBIC Career Presentation April 18, 2011Philip Bourne
Talk given to the Regional Student Group (RSG) of the Netherlands BioInformatics Center annual meeting on April 18, 2011. It is one scientists career path in bioinformatics.
Presentation at the Department of Health and Human Services October 17, 2014 to introduce other agencies outside of NIH the development of the Commons concept.
A Big Picture in Research Data ManagementCarole Goble
A personal view of the big picture in Research Data Management, given at GFBio - de.NBI Summer School 2018 Riding the Data Life Cycle! Braunschweig Integrated Centre of Systems Biology (BRICS), 03 - 07 September 2018
Presentation given by Sarah Jones and Joy Davidson to a group of South African librarians at a webinar organised by LIASA HELIG. http://www.liasa.org.za/node/977
Access the webinar: http://goo.gl/p08pTz
These slides were presented in a webinar by Denodo in collaboration with BioStorage Technologies and Indiana Clinical and Translational Sciences Institute and Regenstrief Institute.
BioStorage Technologies, Inc., Indiana Clinical and Translational Sciences Institute, and Regenstrief Institute (CTSI) have joined Denodo to talk about the important role of technological advancements, such as data virtualization, in advancing biospecimen research.
By watching this webinar, you can gain insight into best practices around the integration of biospecimen and research data as well as technology solutions that provide consolidated views and rapid conversions of this data into valuable business insights. You will also learn how data virtualization can assist with the integration of data residing in heterogeneous repositories and can securely deliver aggregated data in real-time.
Why is the NIH investing $100M at the intersection of data science and health research? The NIH seeks to invest in ways to help researchers easily find, access, analyze, and curate research data. Researchers want visual analytics, and to build the database into a “social network” – being able to “friend” or “like” the data.
Presented online as part of the NASM series in Advancing Drug Discovery see https://www.nationalacademies.org/event/40883_09-2023_advancing-drug-discovery-data-science-meets-drug-discovery
For a panel discussion at the Associate Research Libraries Spring meeting April 27, 2022, Montreal https://www.arl.org/schedule-for-spring-2022-association-meeting/
Frontiers of Computing at the Cellular and Molecular ScalesPhilip Bourne
3 basic points when establishing a new biomedical initiative. Presented at Frontiers of Computing in Health and Society, George Mason University, September 21, 2021.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
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.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
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.
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.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How libraries can support authors with open access requirements for UKRI fund...
Yale Day of Data
1. NIH’s Day, Months, Four Years of
Data
Philip E. Bourne Ph.D.
Associate Director for Data Science
National Institutes of Health
2. What is NIH’s Overall Approach to
Data and What Does It Mean to You?
3. Some Context: NIH Data Science History
6/12 2/14 3/14
• Findings:
• Sharing data & software through catalogs
• Support methods and applications development
• Need more training
• Need campus-wide IT strategy
• Hire CSIO
• Continued support throughout the lifecycle
4. My Bias
Still a scientist
A funder who still thinks like a PI
Not yet attuned to the federal system
Big supporter of open science through prior work with
the Public Library of Science, FORCE11 etc.
5. Data – A Few Observations …
We talk about the promise of big data, but we don’t
even know the value of little data (aka could “Big
Data” be the new “AI”)
Good data is expensive in terms of time and money
Looking at data retroactively is really expensive
Good data begats trust; trust begats community;
community is God
The way we support scientific data currently is not
sustainable
That is, is no business model currently for scientific
data
6. Data – A Few NIH Observations …
1. We have little idea how much we spend on data –
estimated over $1bn per year
2. We have even less idea how much we should be
spending
Point 2 is part of a culture clash between the more
observational history of biomedicine and the new
analytical approach to discovery
7. Data – An Academic Medical Center
Observation
A digital enterprise exists when data connections are
made across different areas of the organization such
that productivity and competitiveness are improved
For example, between education and research
I am not aware that any such academic institutions
exist?
Many are starting to wake up to the idea of getting
there
JAMIA 2014, 21(2), 194
8. ADDS Mission
Statement
To foster an ecosystem that enables
biomedical research to be conducted
as a digital enterprise that enhances
health, lengthens life and reduces
illness and disability
9. What Problems Are We Trying to Solve?
One Possible Solution
Sustainability – 50% business model
Efficiency – sharing best practices in longitudinal
clinical studies; “trusted investigator”
Collaboration - identification of collaborators at the
point of data collection not publication
Reproducibility – data accessible with publication
Integration – phenotype homogenization
Accessibility – clinical trials registration
Quality – sharing CDEs across institutes
Training – keeping trainees in the ecosystem
10. The Data Ecosystem
Community Policy
Infrastructure
• Sustainable
business
model
• Collaboration
• Training
11. The Data Ecosystem
Community Policy
Infrastructure
• Sustainable
business
model
• Collaboration
• Training
Virtuous
Research
Cycle
13. Raw Materials to Seed the Ecosystem
NIH mandate & support
ADDS team of 8 people
Intramural participation of over 100 team members
across ICs
Funding through BD2K:
– ~$30M in FY14
– ~$80M in FY15
– ....
15. Associate Director for Data Science
Scientific Data Council External Advisory Board
Programmatic Theme
Sustainability Education Innovation Process
Deliverable
Commons Training
BD2K Efficiency
Example Features • IC’s
• Cloud – Data &
Compute
• Search
• Security
• Reproducibility
Standards
• App Store
• Coordinate
• Hands-on
• Syllabus
• MOOCs
• Community
• Centers
• Training Grants
• Catalogs
• Standards
• Analysis
• Data
Resource
Support
• Metrics
• Best
Practices
• Evaluation
• Portfolio
Analysis
Collaboration
Partnerships
• Researchers
• Federal
Agencies
• International
Partners
• Computer
Scientists
The Biomedical Research Digital Enterprise
16. Associate Director for Data Science
Scientific Data Council External Advisory Board
Programmatic Theme
Sustainability Education Innovation Process
Deliverable
Commons Training
BD2K Efficiency
Example Features • IC’s
• Cloud – Data &
Compute
• Search
• Security
• Reproducibility
Standards
• App Store
• Coordinate
• Hands-on
• Syllabus
• MOOCs
• Community
• Centers
• Training Grants
• Catalogs
• Standards
• Analysis
• Data
Resource
Support
• Metrics
• Best
Practices
• Evaluation
• Portfolio
Analysis
Collaboration
Partnerships
• Researchers
• Federal
Agencies
• International
Partners
• Computer
Scientists
The Biomedical Research Digital Enterprise
17. Example Communities
– NIH
• 27 ICs
– Agencies
• NSF
• DOE
• DARPA
• NIST
– Government
• OSTP
• HHS HDI
• ONC
• CDC
• FDA
– Private sector
• Phrma
• Google
• Amazon
– Organizations
• PCORI, GA4GH
• RDA, ELIXIR
• CCC
• CATS
• FASEB, ISCB
• Biophysical Society
• Sloan Foundation
• Moore Foundation
18. Example Policies
– Clinical data harmonization
– DbGaP in the cloud
– Data citation
– Machine readable data sharing plans on all grants
– New review models, audiences etc.
• Open review
• Micro funding
• Standing data committees to explore best
practices
• Crowd sourcing
19. Example Infrastructure: The Commons
Data
The Long Tail
Core Facilities/HS Centers
Clinical /Patient
The Why:
Data Sharing Plans
The How:
NIH Knowledge
The
Government
Commons
Data
Discovery
Index
The End Game:
Scientific
Discovery
Usability
Quality
Security/
Privacy
Sustainable
Storage
Awardees
Private
Sector Metrics/
Standards
Rest of
Academia
Software Standards
Index
BD2K
Centers
Cloud, Research Objects,
Business Models
20. What Does the Commons Enable?
Dropbox like storage
The opportunity to apply quality metrics
Bring compute to the data
A place to collaborate
A place to discover
http://100plus.com/wp-content/uploads/Data-Commons-3-
1024x825.png
21. One Possible Commons Business Model
[Adapted from George Komatsoulis]
HPC, Institution …
22. Pilots Around A Virtuous Cycle
Expect a FY15 Funding Call to Work in
the Commons
23. TTrraaiinniinngg && DDiivveerrssiittyy
Training & Diversity Goals:
– Develop a sufficient cadre of diverse researchers skilled in
the science of Big Data
– Elevate general competencies in data usage and analysis
across the biomedical research workforce
– Combat the Google bus
How:
– Traditional training grants
– Work with IC’s on a needs assessment
– Standards for course descriptions with EU
– Work with institutions on raising awareness
– Partner with minority institutions
– Virtual/physical training center(s)?
24. Closing Question
Calls for increased NIH funding has so
far gone unheeded, what can the
ADDS do (that you have not heard
about) to improve data science
activities?