[Infovis] 2016 New York Scientific Data Summit (NYSDS)
mhaley at sfu.ca
Fri Apr 15 19:21:15 CEST 2016
2016 New York Scientific Data Summit (NYSDS)
The New York Scientific Data Summit (NYSDS) aims to accelerate data-driven discovery and innovation by bringing together researchers, developers and end-users from academia, industry, utilities and state and federal governments. Jointly organized by Brookhaven National Laboratory (BNL), Stony Brook University (SBU), New York University (NYU) and the IEEE Computer Society Long Island Chapter the conference will take place at NYU Kimmel Center for University Life, 60 Washington Square South,
New York, NY 10012 USA on August 14-17, 2016.
The theme of this year's conference is "Data-Driven Discovery."
Website https://www.bnl.gov/nysds16/ <https://www.bnl.gov/nysds16/reg/step1.php>
With keynote speakers from industry and international big-science projects, the 2-1/2 day conference is organized into five sessions with topics including streaming data analysis, long term data storage, curation, and sharing, experimental data, industry solutions and challenges for big data and the convergence of data and HPC.
Sponsors include BNL, the Institute for Advanced Computational Science at SBU, the NYSTAR High-Performance Computing Consortium (HPC2), New York University Center for Data Science, the Moore-Sloan Data Science Environment and the IEEE Computer Society Long Island Chapter.
We are looking for original research paper or poster contributions related to analysis or visualization of scientific data. Two–four page extended abstracts describing the paper or poster must be received by April 25, 2016 and can be submitted through:
Registration to the conference can be made through: https://www.bnl.gov/nysds16/reg/step1.php#form <https://www.bnl.gov/nysds16/reg/step1.php#form>
Topics of interest include, but are not limited to:
• Scientific data management
• Visual analytics, and scientific visualization
• Streaming methods for analysis, collection and visualization of scientific data
• Real time interaction with scientific data
• Distributed, parallel or MapReduce based methods applied to scientific data
• Scientific data case studies
• Industry solutions for handling large scientific data
For more information contact conference coordinator:
lperagine at bnl.gov <https://mail.bnl.gov/owa/14.3.279.2/scripts/premium/redir.aspx?REF=Tl5-e7Bl2NS46zZCNIXBNXPyRNIew0zpljdXg2_pxUV1sS_6emTTCAFtYWlsdG86bHBlcmFnaW5lQGJubC5nb3Y.>
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