1. 2 Quick Background I have an ecological background but I strayed……and ended up in computer science The good news is I have been able to blend the two.

Slides:



Advertisements
Similar presentations
INDIANAUNIVERSITYINDIANAUNIVERSITY GENI Global Environment for Network Innovation James Williams Director – International Networking Director – Operational.
Advertisements

Note: Lists provided by the Conference Board of Canada
Inside the Entrepreneurial Mind from ideas to reality.
Bill Gates at the 2008 SharePoint Conference “There is an incredible demand today for solutions that help businesses to harness the power of a global.
Presentation at WebEx Meeting June 15,  Context  Challenge  Anticipated Outcomes  Framework  Timeline & Guidance  Comment and Questions.
Shared Vision It all starts with a “Vision Statement”
Careers in CS & Engineering. CS & Engineering careers are not all this….
The Experience Factory May 2004 Leonardo Vaccaro.
Oklahoma Supercomputing Symposium 2008 Oct 7 th 2008 Mining for Science and Engineering Presented by: Kenji Yoshigoe.
What is Grid Computing? Grid Computing is applying the resources of many computers in a network to a single entity at the same time;  Usually to a scientific.
Scientific workflow systems are problem-solving environments designed to allow researchers to perform complex tasks simply by piecing together individual.
Fundamentals of Information Systems, Second Edition
Lecture 1.
Organizational Behavior, 9/E Schermerhorn, Hunt, and Osborn
Computing in Atmospheric Sciences Workshop: 2003 Challenges of Cyberinfrastructure Alan Blatecky Executive Director San Diego Supercomputer Center.
Chapter 1- Introduction Lecture 1 Ready, fire, aim (the fast approach to software development). Ready, aim, aim, aim, aim... (the slow approach to software.
Why use RequisitePro RequisitePro is a comprehensive tool that supports any of today's requirements management processes. The predominant requirements.
The Enterprise Map: See the unseen Enterprise Present to The Association of Enterprise Architects - DC May 4, 2011 John Chi-Zong Wu
Understanding the Mars Mission and soil studies by using the web A Web project.
TSTT ANALYTICS. CHANGING TELECOMS INDUSTRY CHALLENGES FOR TELCOs Rising customer sophistication and demand. Fast changing technological competitive industry.
CI Days: Planning Your Campus Cyberinfrastructure Strategy Russ Hobby, Internet2 Internet2 Member Meeting 9 October 2007.
SCIENTIFIC METHOD. 1.1 Observe. It is curiosity that breeds new knowledge. The process of observation, sometimes called "defining the question," is simple.
TEA Science Workshop #3 October 1, 2012 Kim Lott Utah State University.
Partnerships for Innovation Key Underlying Tenets ¬ Innovation happens locally - partnerships with state, regional and local governments and industry are.
Preserving Digital Collections for Future Scholarship Oya Y. Rieger Cornell University
U.S. Department of the Interior U.S. Geological Survey CDI Webinar Sept. 5, 2012 Kevin T. Gallagher and Linda C. Gundersen September 5, 2012 CDI Science.
Sharing Research Data Globally Alan Blatecky National Science Foundation Board on Research Data and Information.
Headwaters Communities in Action Building A Better Quality of Life Together.
API, Interoperability, etc.  Geoffrey Fox  Kathy Benninger  Zongming Fei  Cas De’Angelo  Orran Krieger*
Putting Research to Work in K-8 Science Classrooms Ready, Set, SCIENCE.
DISCIPLINARY PERSPECTIVE BIOLOGY/ECOLOGY Workshop on Cyberinfrastructure for Environmental Research and Education November 1, 2002.
2007. Software Engineering Laboratory, School of Computer Science S E Web-Harvest Web-Harvest: Open Source Web Data Extraction tool 이재정 Software Engineering.
17/9/2009 Nakato Ruth Chapter one Introduction and review of strategic management.
Geosciences - Observations (Bob Wilhelmson) The geosciences in NSF’s world consists of atmospheric science, ocean science, and earth science Many of the.
Futures Lab: Biology Greenhouse gasses. Carbon-neutral fuels. Cleaning Waste Sites. All of these problems have possible solutions originating in the biology.
Fundamentals of Information Systems, Second Edition 1 Systems Development.
Experts in numerical algorithms and High Performance Computing services Challenges of the exponential increase in data Andrew Jones March 2010 SOS14.
DataONE: Preserving Data and Enabling Data-Intensive Biological and Environmental Research Bob Cook Environmental Sciences Division Oak Ridge National.
Cyberinfrastructure What is it? Russ Hobby Internet2 Joint Techs, 18 July 2007.
Computing Fundamentals Module Lesson 6 — Using Technology to Solve Problems Computer Literacy BASICS.
Breakout # 1 – Data Collecting and Making It Available Data definition “ Any information that [environmental] researchers need to accomplish their tasks”
Group Science J. Marc Overhage MD, PhD Regenstrief Institute Indiana University School of Medicine.
Marv Adams Chief Information Officer November 29, 2001.
Differentiation What is meant by differences between learners?
Communimetrics and CQI Stephen Shimshock PhD Michael Martinez MSW Amy Edwards LMSW Yakiciwey Mitchell MSW Angelina Garcia MSW.
Got Data? A Guide to Data Preservation in the Information Age Written by Francine Berman Presented by Akadej Udomchaiporn.
The Business Relationship Management Toolkit - Relationship management essential part of your IT- Business Alignment Strategy Toolkit 1 The Secret Is Out!
Cyberinfrastructure: Many Things to Many People Russ Hobby Program Manager Internet2.
1 Power to the Edge Agility Focus and Convergence Adapting C2 to the 21 st Century presented to the Focus, Agility and Convergence Team Inaugural Meeting.
Chapter 1- Introduction Lecture 1. Topics covered  Professional software development  What is meant by software engineering.  Software engineering.
1 Supporting Ecological Analysis in 2100 Susan G. Stafford University of Minnesota Robert J. Robbins Fred Hutchinson Cancer Research Center.
Continuing the School Visit: Deepening the Next Level of Work East Boston High School December 9, 2014.
Identify, Develop and Retain High Performers
Minnesota’s Promise World-Class Schools, World-Class State.
Success on the Ground The State’s Role in Facilitative Leadership by Lauri Wilson, MS & Ron Chapman, MSW.
Data Mining With SQL Server Data Tools Mining Data Using Tools You Already Have.
Software Product Definition Fall, 2015 Week 3 Prof. Sheryl Root Prof. Tony Wasserman 1.
CompSci 280 S Introduction to Software Development
Organizational Behavior, 9/E Schermerhorn, Hunt, and Osborn
Chapter 1- Introduction
RDA US Science workshop Arlington VA, Aug 2014 Cees de Laat with many slides from Ed Seidel/Rob Pennington.
Fundamentals of Information Systems, Sixth Edition
Consumer technology is creating the smart home
Chapter 1- Introduction
Vocabulary Big Data - “Big data is a broad term for datasets so large or complex that traditional data processing applications are inadequate.” Moore’s.
Supporting an omnichannel strategy Enabling an omnichannel strategy
As we reflect on policies and practices for expanding and improving early identification and early intervention for youth, I would like to tie together.
OBHR 2P91 Organizational Behaviour
Fostering Critical and Creative Thinking
Sustaining Quality Curriculum
Presentation transcript:

1

2 Quick Background I have an ecological background but I strayed……and ended up in computer science The good news is I have been able to blend the two disciplines by working with researchers from many different science domains on their cyberinfrastructure needs, including the LTERNO, DataOne, OOI, SEEK, NEON, the and many more. Working at the INHS in the early 80s I was translating data from text files, oddball DBs, and handwritten notes & tags.

3 The Challenge Forecast what ecological science might look like 100 years from now and how LTER might prepare from a technological standpoint. Yet it is next to impossible to predict technologies 10 years into the future. So I believe we cannot worry about technology. However I do have a vision for what technologically we must do.

4 Quote from Dan Reed & Dennis Gannon The Fourth Paradigm (Jim Gray) Simply put, we are moving from data paucity to a data plethora, which is leading to a relative poverty of human attention to any individual datum and is necessitating machine-assisted winnowing. This ready availability of diverse data is shifting scientific approaches from the traditional, hypothesis(experiment)-driven scientific method to science based on (data)exploration. Researchers no longer simply ask, “What experiment could I construct to test this hypothesis?” Increasingly, they ask, “What correlations can I glean from extant data?” More tellingly, one wishes to ask, “What insights could I glean if I could fuse data from multiple disciplines and domains?” The challenge is analyzing many petabytes of data on a time scale that is practical in human terms.

5 100 Years Into the Future Data is being produced at an exponential rate. Imagine a world where everything is monitored Can you imagine the wealth of LTER data 100 years from today? What if it all was –Accessible –Searchable –Useable

6 To what extent should LTER today be preparing to support this kind data-driven investigation today, and in the far future? It is more than just supporting hypothesis-driven investigations, it is exploration of existing data, sometimes composing questions that can be asked or simply looking for correlations hiding in the complexities of LTER’s diverse and vast data collections. To what extent can LTER today truly support data-driven future studies, or can it only collect data that might be useful in a later hypothesis-driven study? 8/10ths of the answer lies in how the data is managed, how searchable is it, how accessible is it, how understandable is it, how translatable is it, to researchers that did not collect it. 2/10 th lies in the technology methods that support discovery, search, access, and analysis of the data How will LTER preserve it’s investment in data so it is accessible, searchable and useable?

7 What could LTER be doing differently today to better facilitate data-driven future analysis? Should ALL sites be doing this? –To the first order every site and every researcher on every project should be thinking about how to ensure that their data could be understood and potentially useful to other researchers. –Gone are the days when all your data exists at one site or in a single database. Supporting geographically, and organizationally distributed data is essential. Yet that does not mean there is not a place for centralized repositories. –Effort should continue to harmonize historical data through translation and or creation of metadata leveraging the EML standard. –Supporting use of data from other domains, and enabling other domains to utilize LTER’s data is essential in today’s multi-domain approach to scientific exploration.

8 Who are the partners for creating a usable data infrastructure who can help in terms of prototyping new technologies with LTER input? –DataOne IMO is the project leading the way. D1 is focused on solving the fundamental data access problems in a way that honors the data owners and stewards. –Unlike the other potential partners D1 does not “own” any data, they succeed only with broad community participation and they achieve that by offering mechanisms that facilitate data discovery and accessibility. –LTERNO is now rolling out PASTA – sharply focused on data preservation and accessibility. –Don’t wait! There may be more elegant textbook approaches promised, there are likely much more complicated approaches but PASTA and D1 are here today and are making great progress, embrace them and build upon them! Their approach to the data is what will be sustained, not necessarily the technology that serves it

9 The future will be one where there are sensors everywhere, and the ability to manipulate them from devices we all carry. What implications do these trends have for LTER? –As a security person I see a critical need to design the security for such systems right at the very beginning and to take very seriously the risks of enabling such capabilities. –From an IT perspective this further emphasizes the need to leverage commercial off-the-shelf solutions. The rate of technology change is much too rapid for the scientific software development teams to keep up with by building custom solutions. Think about what cell phone you were carrying 10 years ago, even 5 years ago and ask yourself if it could support the applications you have on your smart phone today? –There is another discussion we could have about handing this onslaught of sensed data.

10 END