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#UMTweetCon2019

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A Conference on the Use of Twitter Data for Research and Analytics

 

#UMTweetCon2019 will connect U-M scholars across a diverse set of disciplines in an interdisciplinary exchange about common challenges and lessons learned. We further seek to facilitate new connections to help U-M scholars create opportunities for future joint research, collaborative grant writing, training and other activities. Conference attendance will be open to anyone interested in learning about the wide array of Twitter data applications in current research at the University. The conference is sponsored by the Social Science and Social Media Collaborative, the Michigan Institute for Data Science, the #Parenting Rackham Interdisciplinary Group, and coordinated by the Center for Political Studies and the Institute for Social Research.

Call for Abstracts

Do you use Twitter data in your research? Then, you are invited to submit an abstract for the first

 university wide conference at the University of Michigan (Ann Arbor, Dearborn, and Flint) on the use of Twitter data in research and analytics. #UMTweetCon2019 will connect U-M scholars across a diverse set of disciplines in an interdisciplinary exchange about common challenges and lessons learned. We further seek to facilitate new connections to help U-M scholars create opportunities for future joint research, collaborative grant writing, training and other activities. Conference attendance will be open to anyone interested in learning about the wide array of Twitter data applications in current research at the University.

To reflect the wide range of ongoing research across disciplines, we invite submissions that 1) directly examine dynamics of Tweet behavior and Twitter networks, 2) explore the representativeness and validity of Twitter data for making scientific inference, 3) develop new computational methodology for obtaining, processing, or archiving Twitter data, or 4) present applications of Twitter data for studying diverse social phenomena. During the 2-day conference, research presentations will be complemented with participatory sessions to provide participants with an opportunity to plan future activities and help create a regular user community across campuses (e.g., seminar series, computational training sessions, hackathons, regular coding meetups, etc.)

Interested U-M researchers are asked to use the form linked here to submit a short abstract of 200-300 words in length that describes their research project, along with information about participating co-authors. Submissions are due by Friday April 12, 2019.

Click here to submit an abstract for a panel or poster presentation.

Attending #UMTweetCon2019 will require a small, non-refundable registration fee from presenters and attendees alike (students/post-docs: $15 pre-conference online, $20 on-site; faculty/staff/other: $30 pre-conference online, $40 on-site). Presenters and attendees from Dearborn and Flint campuses will receive a registration discount (students/post-docs: $15, faculty/staff/other: $20). We will use the revenue from registration fees to fund best paper awards.

Real estate dataset available to researchers

By | Data, Data sets, Educational, General Interest, Happenings, News

The University of Michigan Library system and the Data Acquisition for Data Sciences program (DADS) of the U-M Data Science Initiative (DSI) have recently joined forces to license a major data resource capturing parcel-level information about the property market in the United States.  

The data were licensed from the Corelogic corporation, who have assimilated deed, tax and foreclosure information on nearly all properties in the entire US. Coverage dates vary by county, some county records go back fifty years. Coverage is more comprehensive from the 1990s to the present.

These data will support a variety of research efforts into regional economies, economic disparities, trends in land-use, housing market dynamics, and urban ecology, among many other areas.

The data are available on the Turbo Research Storage system for users of the U-M High Performance Computing infrastructure, and via the University of Michigan Library.

To access the data, researchers must first sign a MOU; contact Senior Associate Librarian Catherine Morse cmorse@umich.edu for more information, or visit https://www.lib.umich.edu/database/corelogic-parcel-level-real-estate-data.

Call for Proposals: Amazon Research Awards, deadline 9/15/17

By | Data, Educational, Funding Opportunities, News, Research

The Amazon Research Awards (ARA) program offers awards of up to $80,000 in cash and $20,000 in AWS promotional credits to faculty members at academic institutions in North America and Europe for research in these areas:

  • Computer vision
  • General AI
  • Knowledge management and data quality
  • Machine learning
  • Machine translation
  • Natural language understanding
  • Personalization
  • Robotics
  • Search and information retrieval
  • Security, privacy and abuse prevention
  • Speech

The ARA program funds projects conducted primarily by PhD students or post docs, under the supervision of the faculty member awarded the funds. To encourage collaboration and the sharing of insights, each funded proposal team is assigned an appropriate Amazon research contact. Amazon invites ARA recipients to speak at Amazon offices worldwide about their work, meet with Amazon research groups face-to-face, and encourages ARA recipients to publish their research outcome and commit related code to open-source code repositories.

Submissions are to be made online and details including rules and who may apply are located here.

New private insurance claims dataset and analytic support now available to health care researchers

By | General Interest, Happenings, HPC, News | No Comments

The Institute for Healthcare Policy and Innovation (IHPI) is partnering with Advanced Research Computing (ARC) to bring two commercial claims datasets to campus researchers.

The OptumInsight and Truven Marketscan datasets contain nearly complete insurance claims and other health data on tens of millions of people representing the US private insurance population. Within each dataset, records can be linked longitudinally for over 5 years.  

To begin working with the data, researchers should submit a brief analysis plan for review by IHPI staff, who will create extracts or grant access to primary data as appropriate.

CSCAR consultants are available to provide guidance on computational and analytic methods for a variety of research aims, including use of Flux and other UM computing infrastructure for working with these large and complex repositories.

Contact Patrick Brady (pgbrady@umich.edu) at IHPI or James Henderson (jbhender@umich.edu) at CSCAR for more information.

The data acquisition and availability was funded by IHPI and the U-M Data Science Initiative.

NSF Federal Datasets Faculty Working Group

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In response to a recent NSF solicitation (Dear Colleague Letter: Request for Input on Federal Datasets with Potential to Advance Data Science), the Michigan Institute for Data Science (MIDAS) invites Faculty to join a faculty working group to collaborate on a joint submission (deadline: March 31).

The NSF DCL working group will identify federal government data that will enhance and support the growing data science research community. We are being asked what federal data is of value for data science and machine learning that will have significant impact on science, engineering, education, and society.

If you have experience or interest in using federal datasets for your research, and would like to help shape how federal datasets can be preserved and utilized, please join this working group. We will discuss strategies for responding to NSF and potential funding (both federal and local) to support this effort. Please attend in person if possible.

RSVP

Funding available for data set acquisition

By | Funding Opportunities, General Interest, News

The new Data Acquisition for Data Science (DADS) program supports acquisition, preparation, management, and maintenance of specialized research data sets used in current and future data science-enabled research projects across U-M, with special focus on the four challenge initiative areas pursued by the Michigan Institute for Data Science (MIDAS): transportation science, health science, social science, and learning analytics.

DADS is meant to provide datasets that can be used by multiple U-M researchers and departments.

DADS is funded through the Data Science Initiative (DSI); total funding is capped at $200,000 per year for 5 years.

DADS will be managed jointly by the Library and Advanced Research Computing (ARC), with support from ARC’s Consulting for Statistics, Computing, and Analytics Research (CSCAR), MIDAS, and ARC-Technology Services (ARC-TS) units.

For more information, see arc.umich.edu/dads.