Data Management and Data Sharing in Science and Technology Studies
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Hello, Everyone. My Name Is Amy Walton
>> Hello, everyone. My name is Amy Walton. I lead the data project in the Office of Advanced Cyberinfrastructure. I have to tell you a quick, funny story that, before that, for a long time, I was with the NASA's Jet Propulsion Laboratory, and it was my privilege at the time to have been at the, working with the Forest Service to try and do some of their first airborne detections of forest fires. The kinds of equipment that they had at the time was basically the size and weight of a refrigerator that they would roll onto the airplane. It would then take overflight infrared pictures, and they would have to drop the infrared film at a location, have it developed and taken to the fire camps overnight. The dramatic changes you are about to hear in how fires and many other data activities are now being done will be incredibly dramatic, so allow me to a take a moment to introduce one of my colleagues, Dr. Ilkay Altintas is the chief data science officer at the San Diego Supercomputer Center at the University of California San Diego, where she is also the founder and director for the Workflows for Data Science Center of Excellence. In her various projects, she leads collaborative, multidisciplinary activities with research objectives to deliver impactful results by making computational data science work more reusable, programmable, scalable, and reproducible. Since joining the San Diego Supercomputer Center in 2001, she has been a principal investigator and a technical leader in a wide range of cross-disciplinary projects. -
Download Program in PDF Format
www.sdsc.edu/research/IPP.html ________________________________________ Industry’s “Gateway” to SDSC The Industrial Partners Program (IPP) provides member companies with a framework for interacting with SDSC re- search-ers and staff, exchanging information, receiving education & training, and developing collaborations. Joining IPP is an ideal way for companies to get started collaborating with SDSC researchers and to stay abreast of new de- velopments and opportunities on an ongoing basis. The expertise of SDSC researchers spans many domains including computer science, cybersecurity, data management, data mining & analytics, engineering, geosciences, health IT, high performance computing, life sciences & ge-nomics, networking, physics, and many others. The IPP provides multiple avenues for consultation, networking, training, and developing deeper collaborations. The IPP is an annual fee-based program that provides member companies with a variety of mechanisms for interacting and collaborating with SDSC researchers. The IPP serves an important function in maintaining SDSC’s ties to industry and the high-technology economy. Membership fees fund a variety of preplanned and ad hoc activities designed to encourage the exchange of new ideas and “seed” deeper collaborations. ________________________________________ SDSC – A Long History of Applied R&D From its founding in 1985 by a private company, General Atomics, through to its present affiliation with UCSD, SDSC has a long history of collaborating with and delivering value to industry. SDSC has a strong culture of conducting applied R&D, leveraging science and technology to deliver cutting-edge solutions to real-world problems. From its roots in High Performance Computing to its present emphases in “Big Da-ta” and Predictive Analytics, SDSC has much to offer industry partners in terms of knowledge and experience that is relevant to their world and impactful to their business. -
Deep Learning on Operational Facility Data Relat
For citation, please use: A. Singh, E. Stephan, M. Schram, and I. Altintas, “Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows,” in 2017 IEEE 13th International Conference on e-Science (e-Science), 2017, pp. 586–591. https://ieeexplore.ieee.org/document/8109199/ Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows Alok Singh, Ilkay Altintas Eric Stephan, Malachi Schram San Diego Supercomputer Center Pacific Northwestern National Laboratory University of California, San Diego Richland, WA, USA La Jolla, CA, USA {eric.stephan, malachi.schram}@pnnl.gov {a1singh, ialtintas}@ucsd.edu Abstract—Distributed computing platforms provide a robust training/testing framework. Unsupervised learning techniques mechanism to perform large-scale computations by splitting the lend themselves to interpretation and can tackle uncertainty task and data among multiple locations, possibly located smoothly and provide mechanisms to infuse domain expertise. thousands of miles apart geographically. Although such Neural networks can be deployed to detect anomalies in distribution of resources can lead to benefits, it also comes with its dynamic environment with training. Deep Learning algorithms associated problems such as rampant duplication of file transfers involve development of multilayer neural networks to solve increasing congestion, long job completion times, unexpected site forecasting, classification and clustering solutions. Our crashing, suboptimal data transfer rates, unpredictable reliability approach leverages such Deep Learning algorithms to discover in a time range, and suboptimal usage of storage elements. In solutions to problems associated with having computational addition, each sub-system becomes a potential failure node that infrastructure that is spread over a wide area. -
Uva-DARE (Digital Academic Repository)
UvA-DARE (Digital Academic Repository) Collaborative provenance for workflow-driven science and engineering Altıntaş, İ. Publication date 2011 Link to publication Citation for published version (APA): Altıntaş, İ. (2011). Collaborative provenance for workflow-driven science and engineering. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:26 Sep 2021 147 Publications [1] Lin, Abel W., Ilkay Altintas, Chris Churas, Madhusudan Gujral, Jeffrey Grethe and Mark Ellisman (2011 (In print.)). REST: From Research to Practice (C. Pautasso and E. Wilde, Eds.). Chap. 16: Case Study on the Use of REST Architectural Principles for Scientific Analysis: CAMERA - Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis. Springer. [2] Altintas, Ilkay, Abel W. Lin, Jing Chen, Chris Churas, Madhusudan Gujral, Shulei Sun, Weizhong Li, Ramil Manansala, Mayya Sedova, Jeffrey S. -
Lightning Talks
Lightning Talks Leslie Ray Peter Rose Ilya Zaslavsky Senior Director, Director, Spatial Epidemiologist, Structural Information County of San Bioinformatics Systems Diego Laboratory, San Laboratory, San Diego Super Diego Super Introduction to COVID Computer Center, Computer Center, Epidemiology UC San Diego UC San Diego Sep. 1, 10:00 AM PT Register here COVID-19 Knowledge COVID-19 and Spatial Introduction to Graph and Jupyter Data Science COVID-19 Data in Notebooks Sep. 2, 11:00 AM PT California Sep. 2, 10:30 AM PT Register here Sep. 1, 10:30 AM PT Register here Register here Munseob Lee Melissa Floca Michele Gilman Assistant Border Solutions Venable Professor Professor of Alliance, UC San of Law, University Economics, Diego of Baltimore School of Global Policy and Strategy, UC San Diego The Cost of Privacy: Introduction to Data Justice and Welfare Effects of the COVID-19 and Automated Decision- Disclosure of COVID- Mortality Data in Making Systems 19 Cases (paper) Mexico Sep. 3, 11:30 AM PT Sep. 2, 11:30 AM PT Sep. 3, 10:30 AM PT Register here Register here Register here All talks are recorded and will be available online here Lightning Talks Onedeige Ilkay Altintas Ashok James Chief Data Science Srinivasan Civic Innovation Officer, San Diego William Nystul Apprentice, Super Computer Eminent Scholar BetaNYC Center Chair and Professor, University of West Florida An Intro to Story Fighting Disasters COVID-19 in Crowded Maps: Manhattan’s with Big Data and Locations: Leveraging Disappearing Predictive Modeling New Data Sources to Religious Facilities Sep. 8, 10:00 AM PT Analyze Risk Sep. -
Sharing Is Caring—Data Sharing Initiatives in Healthcare
International Journal of Environmental Research and Public Health Review Sharing Is Caring—Data Sharing Initiatives in Healthcare Tim Hulsen Department of Professional Health Solutions & Services, Philips Research, 5656AE Eindhoven, The Netherlands; [email protected] Received: 28 February 2020; Accepted: 24 April 2020; Published: 27 April 2020 Abstract: In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these ‘big data’ put together can be utilized to optimize treatments for each unique patient (‘precision medicine’). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the ‘valley of death’ of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. -
Digital Quality of Life Understanding the Personal & Social Benefits of the Information Technology Revolution
Digital Quality of Life Understanding the Personal & Social Benefits of the Information Technology Revolution Robert D. Atkinson & Daniel D. Castro October 2008 I Digital Quality of Life Acknowledgments The authors would like to thank the following individuals for providing input to the report: David K. Aylward, Peter Black, Shawn DuBravac, Jay Camdus, Dan Chenok, John Kamensky, Mary McCain, James Cortada, Angela Mickalide, Dawn Morehouse, Jon Peha, Karen Peltz Strauss, Valerie Rickman, Robert G. Rogers, Ken Salaets, Wayne Schroeder and Paul Taylor. In addition, we would like to thank ITIF staff Ameya Ananth, Stephen Ezell and Torey Liepa. Any errors or omissions are the authors’ alone. About the Authors Dr. Robert D. Atkinson is President of the Information Technology and Innovation Foundation. Daniel D. Castro is a Senior Analyst at the Information Technology and Innovation Foundation. Scott M. Andes is a Research Assistant at the Information Technology and Innovation Foundation. Daniel K. Correa is a former Research Analyst at the Information Technology and Innovation Foundation. Geoff Dailyis the editor of App-Rising.com. Jonathan L. Giffordis a professor in the School of Public Policy at George Mason University. Julie A. Hedlund is a former Senior Analyst at the Information Technology and Innovation Foundation. About the Information Technology and Innovation Foundation ITIF is a non-profit, non-partisan public policy think tank committed to articulating and advancing a pro-productivity, pro-innovation and pro-technology public policy agenda internationally, in Washington DC and the states. Recogniz- ing the vital role of technology in ensuring American prosperity, ITIF focuses on innovation, productivity, and digital economy issues. -
ORION OPEN SCIENCE FACTSHEETS Brief, Informative, and Easy-To-Understand One-Page Factsheets on Open Science Topics
ORION OPEN SCIENCE FACTSHEETS Brief, informative, and easy-to-understand one-page factsheets on Open Science topics Created by the Max-Delbrück-Centre for Molecular Medicine in the Helmholtz Association Design by Vetenskap & Allmänhet This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 741527 and runs from May 2017 to April 2021. CONTENTS 1. Biohacking and DIYBio Research .......................................................................................... 3 2. Career Benefits of Open Science ......................................................................................... 4 3. Citizen Science ....................................................................................................................... 5 4. Commercialisation of Research ............................................................................................ 6 5. Communicating Animal Research ......................................................................................... 7 6. Crowd Science ....................................................................................................................... 8 7. Research Data Management ................................................................................................. 9 8. Open Access ......................................................................................................................... 10 9. Open Research Data ........................................................................................................... -
Program Schedule Virtual Event
IEEE BigData 2020 Program Schedule Virtual Event Conference time zone: USA Eastern Standard Time A few days before the event, every registered participant will get an email from the company to provide the login information to access the virtual conference platform. Sponsored by 2020 IEEE International Conference on Big Data IEEE Big Data 2020 Program Schedule .............................................................................................................................. 2 Keynote Lectures ................................................................................................................................................................... 8 Conference Paper Presentations ........................................................................................................................................ 10 Industry and Government Paper Presentations ............................................................................................................... 16 Tutorials ............................................................................................................................................................................... 19 Workshops ............................................................................................................................................................................ 22 Special Symposiums ............................................................................................................................................................ 24 Special -
CURRICULUM VITAE JIANWU WANG EDUCATION Ph.D. 2007
CURRICULUM VITAE JIANWU WANG EDUCATION Ph.D. 2007 Chinese Academy of Sciences, Computer Software and Theory B.A. 2001 Tianjin University, Computer Science and Technology Experience in Higher Education 2015 - Present Assistant Professor, Information Systems Department, UMBC 2018 - Present Affiliated Faculty, the Joint Center for Earth Systems Technology (JCET), UMBC 2018 - Present Affiliated Faculty, the NSF Center for Accelerated Real Time Analytics, UMBC 2012 - 2017 Adjunct Professor, North China University of Technology 2014 - 2015 Assistant Director, Research of Workflows for Data Science Center of Excellence, San Diego Supercomputer Center, University of California, San Diego 2015 - 2015 Associate Project Scientist, University of California, San Diego 2014 - 2015 Visiting Assistant Research Scientist, University of Maryland, College Park 2010 - 2015 Assistant Project Scientist, University of California, San Diego 2013 - 2013 Summer Session Lecturer, University of California, San Diego 2008 - 2010 Postdoctoral Researcher, University of California, San Diego 2007 - 2008 Postdoctoral Researcher, Polytechnic University of Turin Honors Received 2020 CAREER Award, NSF 2019 Early-Career Faculty Excellence Award, UMBC Research Support and/or Fellowships A. Non-UMBC (total: $38,754) 2013 - 2014 Industrial Scale Demonstration of Smart Manufacturing Achieving Transformational Energy, $27,754, Department of Energy (DOE), Subcontract Co-PI. 2011 - 2013 Scientific Workflow Scheduling on Cloud, Amazon, $11,000, PI. B. External Funding (total: $4,211,803) 2020 - 2023 Developing Passive Satellite Cloud Remote Sensing Algorithms using Collocated Observations, Numerical Simulation and Deep Learning, $1,496,068, National Aeronautics and Space Administration (NASA), PI. 2020 - 2025 CAREER: Big Data Climate Causality Analytics, $542,295, National Science Foundation (NSF), PI. 2020 - 2021 Multi-layered Intrusion Tolerant Byzantine Architecture for Bulk Power System Protective Relays, $157,771, Department of Energy (DOE), Subcontract PI. -
Standards for Design and Measurement Would Make Clinical
PAPER Standards for design and measurement would make COLLOQUIUM clinical research reproducible and usable Kay Dickersina,1 and Evan Mayo-Wilsona aDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 Edited by David B. Allison, Indiana University Bloomington, Bloomington, IN, and accepted by Editorial Board Member Susan T. Fiske September 10, 2017 (received for review June 20, 2017) We find standards useful in everyday life and in science, although In research, standards facilitate cooperation and better overall we do not always follow them. Adopting new standards can be results—namely, good science. Across disciplines, scientists tacitly expensive, so there may be a strong incentive to maintain the status and formally agree to common standards for both the conduct and quo rather than adopt new standards. The scientific community has reporting of our work, ranging from units of measurement to many standards encompassing both doing clinical research and principles about research integrity (3). Standards may have im- reporting it, including standards for design and measurement. Al- portant benefits without incurring substantial costs or limiting though existing research standards have improved both research scientific creativity. For example, the American Heart Association and its reporting, we need to unify existing standards and to fill developed standards for measuring blood pressure (4); their use the gaps between steps throughout the research process. Existing may improve the consistency of data collection across times and gaps include implementation of standards and links between stan- places, thus improving the comparability of clinical trials. Failing dards for study registration (to know about all studies undertaken), to apply minimum standards can lead to calamitous errors. -
MURPA Seminar: Friday 24 August 2012, 9Am
MURPA Seminar: Friday 24 August 2012, 9am Distributed Workflow-Driven Analysis of Large-Scale Biological Data using bioKepler Speaker: Dr Ilkay Altintas Venue: Seminar Room 135, Building 26, Clayton Campus, and Seminar Room H7.84, Caulfield Campus, Monash University Abstract: Next-generation DNA sequencing machines are generating a very large amount of sequence data with applications in many scientific challenges, placing unprecedented demands on traditional single-processor bioinformatics algorithms. Technologies like scientific workflows and data-intensive computing promise new capabilities to enable rapid analysis of next- generation sequence data. Scientific workflow systems promote scientific discovery by supporting the scientific workflow design and execution. They can be instrumented to optimize execution of workflow graphs across a variety of distributed computing and data resources while capturing provenance (execution history) of workflows and related data. The provenance contains information about how the execution took place and the products were derived, and it is crucial for enabling scientists to easily understand, reproduce, and verify scientific results. Based on this motivation and our previous experiences in bioinformatics and distributed scientific workflows, we are creating a Kepler Scientific Workflow System module, called “bioKepler”, that facilitates the development of Kepler workflows for integrated execution of bioinformatics applications in distributed environments. This talk overviews scientific workflows with a focus on the open-source cross-disciplinary Kepler scientific workflow system, discusses the challenges related to next-generation sequencing data and explains the approaches taken in bioKepler to help with analysis of such data. Biography: Ilkay Altintas is the Director for the Scientific Workflow Automation Technologies Lab at the San Diego Supercomputer Center, UCSD where she also is the Deputy Coordinator for Research.