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.”