Difference between revisions of "SOCR News MIWI SummerSchool 2020"

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* During the Morning session, we will review a number of free/open-access web resources.
 
* During the Morning session, we will review a number of free/open-access web resources.
 
* In the Afternoon session, teams of trainees will be exploring hands-on some of their data using the available tools and services. The mentor will provide feedback and consultation, as needed.
 
* In the Afternoon session, teams of trainees will be exploring hands-on some of their data using the available tools and services. The mentor will provide feedback and consultation, as needed.
* Each team/participant will be asked to submit a brief 2-page report illustrating (1) some challenges, (2) describing the test data they used, (3) illustrating some of the results, and (4) reflect on their experience and findings.
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* Teams/participants may choose to draft brief reports illustrating (1) some challenges, (2) describing the test data they used, (3) illustrating some of the results, and (4) reflect on their experience and findings.
  
 
== Discussion Topics ==
 
== Discussion Topics ==
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* [http://socr.umich.edu/html/Navigators.html SOCR Navigators]
 
* [http://socr.umich.edu/html/Navigators.html SOCR Navigators]
 
* [[SOCR_Data | SOCR Datasets and Challenging Case-studies]]
 
* [[SOCR_Data | SOCR Datasets and Challenging Case-studies]]
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* [https://DataSifter.org Data Sifter] (statistical obfuscation of sensitive data)
 
* [[SOCR_Books|Electronic Textbooks]]:  
 
* [[SOCR_Books|Electronic Textbooks]]:  
 
** [[EBook| Probability and Statistics EBook]]  
 
** [[EBook| Probability and Statistics EBook]]  

Latest revision as of 09:25, 25 June 2020

SOCR News & Events: Michigan Integrative Well-Being and Inequality (MIWI) Summer School

The Michigan Integrative Well-Being and Inequality (MIWI) Training Program is an interdisciplinary methods training program that prepares participating scholars to investigate the intersection of mental and physical health, with an emphasis on how this intersection relates to health disparities. The training encompasses conceptual frameworks, study designs, data collection needs, and analytic approaches necessary to conduct this innovative research. The program includes an intensive 4-day summer school, in Ann Arbor, MI, followed by ongoing collaboration with a mentorship team.

Logistics

Objectives

  • Offer a number of "open-science" resources for data science and predictive health analytics
  • Gain knowledge about some qualitative (e.g., exploratory data analysis) and quantitative (e.g., AI/ML) methods for interrogating large and complex biomedical datasets and health case-studies
  • Hands-on experience with using some of the tools and web-services; bring your own data (BYOD)
  • Provide consultation and team-science approach to tackling difficult data-driven challenges

Format

  • Prior to session, all trainees need to
    • Draft a top-3 list of problems they are facing or expect to face in the next 12-months (these should be specific, tangible, and pragmatic)
    • Select 1-2 datasets that they may be interested in experimenting with during the Summer School and during this Session. The data can be in any format, however, if a specialized format is used, we need to be able to translate the data into a common data format.
  • During the Morning session, we will review a number of free/open-access web resources.
  • In the Afternoon session, teams of trainees will be exploring hands-on some of their data using the available tools and services. The mentor will provide feedback and consultation, as needed.
  • Teams/participants may choose to draft brief reports illustrating (1) some challenges, (2) describing the test data they used, (3) illustrating some of the results, and (4) reflect on their experience and findings.

Discussion Topics

Background

Demos


References




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