Video, slides available: “Advanced Research Computing at Michigan, An Overview,” Brock Palen, ARC-TS

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Video (http://myumi.ch/aAG7x) and slides (http://myumi.ch/aV7kz) are now available from Advanced Research Computing – Technology Services (ARC-TS) Associate Director Brock Palen’s presentation “Advanced Research Computing at Michigan, An Overview.”

Palen gave the talk on June 27, 2016, outlining the resources and services available from ARC-TS as well as from off-campus resource providers.

NSF Program Solicitation: Quantitative Approaches to Biomed. Big Data (QuBBD)

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The NIH Big Data to Knowledge Initiative (BD2K, https://datascience.nih.gov/bd2k), together with the Division of Mathematical Sciences at NSF, announces the release of a new program solicitation (NSF 16-573)

This program is designed to support novel mathematical, statistical, or computational approaches to biomedical big data challenges. Collaborative efforts that bring together quantitative scientists and biomedical researchers are a requirement for this program and must be convincingly demonstrated in the proposal. The program is designed to foster and support new inter- and multi-disciplinary teams of investigators. The due date for full proposals is September 28, 2016.

BD2K is a trans-NIH initiative that aims to support advances in data science, other quantitative sciences, and training that are needed for the effective use of big data in biomedical research. Interested applicants are encouraged to join the BD2K listserv (https://list.nih.gov/cgi-bin/wa.exe?SUBED1=bd2kupdates&A=1) to receive the most up-to- date information about BD2K events and funding opportunities. Please share this opportunity with your interested scientific communities.

Great Lakes Observing System Data Challenge: Call for Issue Experts, Sponsors

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CALL FOR ISSUE EXPERTS AND SPONSORS

The Great Lakes Observing System (GLOS) is hosting the Great Lakes Data Challenge in summer of 2016. As part of our 10 year anniversary, GLOS will be taking open data to the next level by using open innovation to broaden our community and create new partnerships to engage people in problem solving for the Great Lakes. GLOS is currently soliciting support for sponsors and issue experts.

GOALS

  • Inspire a wider audience to engage with Great Lakes issues
  • Use technologies, innovation and creativity to solve Great Lakes problems
  • Encourage the use of open data resources from GLOS and beyond

TIMELINE

  • Late May 2016: Launch challenge
  • June: Kick-off event(s), including IAGLR
  • August 15: Submissions due. Submissions can include an app, data “mash-up”, visualization, story, or other innovative idea for using, collecting, analyzing, visualizing, and/or communicating Great Lakes data.
  • August 15-31: Judging
  • September 15: Winners notified
  • October 12-13: Award presentation at GLOS Annual Meeting in Ann Arbor, MI

GLOS PROVIDES

  • Baseline prize money: $5,000
  • Data, technical support, and resources for developer guidelines, rules, etc.
  • Data Challenge(s) coordination

WE NEED YOU

Sponsors: by May 20 The Great Lakes Data Challenge is a unique opportunity to network the region’s
environmental, governmental and non-profit sectors with the information technology sector. Sponsors must commit by May 20 to ensure inclusion in event promotions.

Consider sponsoring the challenge at one of our suggested levels (see next page) to help support prize
money, event costs, and promotional giveaways. This is a great way to promote your business/organization to a diverse audience of environmental data and technology stakeholders.

Issue Experts: by June 1 We are looking for volunteers with expertise in areas including invasive species, nutrients and algae and boater safety, among others. You would agree to be a resource to teams who have specific questions about the topic at hand. The commitment could be flexible according to your interest and availability.

Please contact GLOS at kpaige@glos.us if you are interested in supporting the data challenge in any of these areas.

Be a part of the Great Lakes Observing System’s Data Challenge

  • SUPERIOR $5,000
    All lower level sponsorship benefits as well as…
    Top billing as Data Challenge co-sponsor in all event promotions and media releases
    Large, prominent logo on event giveaways, promotional signage, and website
  • MICHIGAN $2,500
    All lower level sponsorship benefits as well as…
    Acknowledgement as co-sponsor for a custom challenge category
    Logo on event giveaways
  • HURON $1,000
    All lower level sponsorship benefits as well as…
    Sponsorship acknowledgement at promotional events including kick-off and award presentation
    Logo on Data Challenge website and promotional signage
  • ONTARIO $500
    All lower level sponsorship benefits as well as…
    Sponsorship acknowledgement on promotional signage
    Complimentary individual (for 1 person) GLOS membership and registration to the GLOS Annual Meeting
  • ERIE $250
    Sponsorship acknowledgement and website link on Data Challenge website
    Acknowledgement in GLOS Annual Report

Link to sponsor commitment page.

UMHS – PUHSC Joint Institute 2016 Symposium: Call for Posters

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University of Michigan Health System & Peking University Health Science Center

Joint Institute for Translational and Clinical Research

2016 Symposium

Call for Posters

[Downloadable Directions]

Call for Poster Abstracts: Submission Information
Showcase your research to Peking University Health Science Center counterparts as the JI looks to expand by offering funding to non-medical school faculty for new health-related joint research projects. A great venue to meet potential collaborators, the poster session will be Thursday, Oct. 13. Details, including times, will follow poster acceptance.

How to submit
Abstracts should relate to clinical and translational research studies and should be submitted electronically in a Microsoft Word document.

Send abstracts to globalreach@umich.edu by September 9, 2016. Please include the following:
Title

  • Title should be brief but should not contain abbreviations.
  • Do not bold use letters in the title unless necessary.
  • Do not capitalize all letters in title, only the first word and key words.

Authors

  • Include all authors and their affiliations. To associate authors and their institutional affiliations, please place a number in parenthesis after each author’s name (if more than one author) and the corresponding number before each affiliated institution’s name (if more than one institution).
  • Put the submitting/presenting author’s name in bold.
  • Do not capitalize all letters in speaker information, only as appropriate.

Abstract

  • Abstracts are limited to 300 words. Use size 11 Arial or Calibri font.
  • Submit text only. Do not include tables, graphics, or charts.
  • Do not include title, authors, or author affiliations in the abstract text.
  • Abstracts may include background, methods, results, conclusions, and funding-source acknowledgements, if applicable.

Submitter contact information

  • First and last name, degrees
  • Email address

Please proofread carefully – information submitted with errors may be published as is. Use a word processing program to assist with checking for grammar and spelling errors, as well as word count.
The deadline to submit abstracts is Sept. 9, 2016. For more information, contact globalreach@umich.edu.

 

MIDAS awards first round of challenge funding in transportation and learning analytics

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Four research projects — two each in transportation and learning analytics — have been awarded funding in the first round of the Michigan Institute for Data Science Challenge Initiatives program.

The projects will each receive $1.25 million dollars from MIDAS as part of the Data Science Initiative announced in fall 2015.

U-M Dearborn also will contribute $120,000 to each of the two transportation-related projects.

The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science.

New on-campus data-science and computational research services available

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Researchers across campus now have access to several new services to help them navigate the new tools and methodologies emerging for data-intensive and computational research.

As part of the U-M Data Science Initiative announced in fall 2015, Consulting for Statistics, Computing and Analytics Research (CSCAR) is offering new and expanded services, including guidance on:

  • Research methodology for data science.
  • Large scale data processing using high performance computing systems.
  • Optimization of code and use of Flux and other advanced computing systems.
  • Advanced data management.
  • Geospatial data analyses.
  • Exploratory analysis and data visualization.
  • Obtaining licensed data from commercial sources.
  • Scraping, aggregating and integrating data from public sources.
  • Analysis of restricted data.

“With Big Data and computational simulations playing an ever-larger role in research in a variety of fields, it’s increasingly important to provide researchers with a comprehensive ecosystem of support and services that address those methodologies,” said CSCAR Director Kerby Shedden.

As part of this significant expansion of its scope, the campuswide statistical consulting service CSCAR has been renamed Consulting for Statistics, Computing and Analytics Research. It was formerly known as the Center for Statistical Consultation and Research.

For more information, see the University Record article.

Workshop: Refining the Concept of Scientific Inference When Working with Big Data — June 8-9 (webcast)

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The National Academies of Sciences, Engineering and Medicine Committee on Applied and Theoretical Statistics is holding a workshop titled “Refining the Concept of Scientific Inference When Working With Big Data” in Washington DC, June 8-9.

The workshop will bring together statisticians, data scientists and domain researchers from different biomedical disciplines to explore four key issues of scientific inference:

  • Inference about causal discoveries driven by large observational data
  • Inference about discoveries from data on large networks
  • Inference about discoveries based on integration of diverse datasets
  • Inference when regularization is used to simplify fitting of high-dimensional models

Prof. Alfred Hero, co-director of the Michigan Institute for Data Science (MIDAS) and Professor of Electrical Engineering and Computer Science, is a co-chair of the event.

More information:

 

Digging Into Data Challenge seeks data science projects in social science and humanities — June 29 deadline

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The Digging Into Data Challenge, which aims to address how “big data” changes the research landscape for the humanities and social sciences, is seeking submissions for its fourth found of funding.

Digging into Data is a grant program sponsored by several leading research funders from around the world (see each round below). Teams of researchers from at least two different participating countries send in grant applications. These applications are reviewed by an international peer review panel.

Examples of the titles of previous grant winners include:

  • Automating Data Extraction from Chinese Texts
  • Digging Archaeology Data: Image Search and Markup
  • Field Mapping: An Archival Protocol for Social Science Research Findings.

For more information on the program, see its website. For information on applying, see the Application Materials page. The deadline is June 29, 2016