data: the legacy of nees
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DATA: The Legacy of NEES. Shirley J. Dyke NEEScomm Center, Purdue University Professor of Mechanical Engineering Professor of Civil Engineering. Oregon State University. University of Minnesota. University of Illinois- Urbana. University of California Berkeley. University of California - PowerPoint PPT PresentationTRANSCRIPT
Shirley J. Dyke
NEEScomm Center, Purdue UniversityProfessor of Mechanical Engineering
Professor of Civil Engineering
University of CaliforniaSanta Barbara
University of CaliforniaSan Diego
University of CaliforniaLos Angeles
University of CaliforniaDavis
Lehigh University
Rensselaer Polytechnic Institute
Cornell University
University of Buffalo
University of Minnesota
University of Illinois- Urbana
Oregon State University
University of CaliforniaBerkeley
University of NevadaReno
University of TexasAustin
http://nees.org
Introduction The Legacy The Data The Challenges
Execute remote software tools as if they were here
View NEES project X
View NEES project X
N3DV launches on project X
data N3DV screen is inserted inUser’s web
browser
Each experiment and simulation performed constitutes an opportunity for us as a community to gain insight and reduce risk.
A data repository populated with high quality data is certain to be a valuable resource for the earthquake engineering community.
Data reuse must be available
Data from experiments and real-world systems provide information for improving modeling capabilities
The building codes that governdesign procedures aregrounded in experiments and the measurements thatare acquired
Often hundreds of tests areneeded to convince the codecommittees to make changes
Improved data collection and information management capabilities
Cyberinfrastructure resources to support the data structures and visualization methods
State-of-art capabilities to support innovative testing, data preservation, and collaboration
Instrumenting the built & natural environments
Courtesy of Jennifer Riceand Bill Spencer
Courtesy of Luca Giacosa
Open tools◦ multi-scale models ◦ hybrid simulation◦ human systems
Researchers Practicing Engineers (designers) Educators IT Managers Public-at-Large
Each user category has different ways to and reasons for using data!
Data security has been the norm
Measurement data from 1-1000 sensors◦ 1MB to 1GB◦ Multiple simultaneous records◦ 1-10,000 files per project, so far
Images from experiments Video captured during experiments Specimen information
Metadata◦ Testing conditions◦ Configurations◦ Sensor descriptions◦ Annotations about data
Model generation and analysis codes Analysis tools developed during research
The managing organization must deliver the tools for robust and versatile ingestion, storage, curation, visualization of these data.
The data repository must provide ◦ Quality data◦ Sharing capability ◦ Standards◦ Security◦ Provenance◦ Training
Sharing and preservation are not in the culture
Sites provide initial data upload within 48 hours
Much time and effort is required to enter metadata
Research teams have 12 months to use the data before it is released publically
Occasional confidentiality issues, no privacy issues
Recognition of project data as a scholarly contribution
Ensuring proper citations to the data generator
Policies will enforce data sharing, but this is a “stick” and we are working on the “carrot”
Community requires training in ◦ Data model◦ Making data accessible ◦ Standards and methods for data archiving◦ Data preservation
International◦ Language◦ Standards◦ Culture◦ Distance
Japan (largest shake table in the world) China Korea European Union
Data bring us all together
To achieve the 2020 Vision
“Cyberinfrastructure that will facilitate data collection and management to enable rapid and efficient access and distribution of experimental and simulated data is essential.”