Difference between revisions of "SOCR News 2018 MNORC SOCR HAC Workshop"

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* This workshop is sponsored in part by [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 NIH Grant P30 DK089503] and NSF Grants [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1734853 1734853] and [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1636840 1636840].
* This workshop is sponsored in part by NIH Grants [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 P30 DK089503], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8821268&icde=22205726 P20 NR015331], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8907508&icde=22205754 U54 EB020406], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8882615&icde=22023333 P50 NS091856], [https://projectreporter.nih.gov/project_info_description.cfm?aid=9172096&icde=30598205 P30AG053760], as well as, NSF Grants [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1734853 1734853] and [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1636840 1636840].
* SOCR Home page: http://www.socr.umich.edu
* SOCR Home page: http://www.socr.umich.edu
* [https://drive.google.com/drive/folders/1CaJptsqjSudMMv2ma-G_x5T-IMgc9Gzr GDrive]
* [https://drive.google.com/drive/folders/1CaJptsqjSudMMv2ma-G_x5T-IMgc9Gzr GDrive]

Revision as of 07:26, 18 September 2018

SOCR News & Events: MNORC-IBIC/SOCR/HAC Health Data Analytics Workshop


  • Date: Fri Oct 12, 2018
  • Place/Time: 1-5 PM, 426 N. Ingalls (SNB 1250)
  • Organizers: MNORC-IBIC, SOCR Team, HAC
  • Registration: (space is limited to 25!) Please use this link to register for the training workshop. If there is sufficient interest, we may offer a live stream via BlueJeans.
  • Format:
    • Presentations: capabilities, resources, and expertise (6 x 15-min)
    • Participant-led challenges, case-studies, template below, (20-30-min)
    • Hands-on Consulting, Try-It-Now, apply to new data (120-min)
    • Participants should bring laptops, and datasets, to try some of the resources hands-on at the training workshop




  • Provide expertise in experimental design and modeling for preclinical, clinical and translational research studies that integrate clinical, molecular, neurobehavioral and other phenotype data.
  • Provide guidance on the appropriate data architecture to enable integration and mining of data.
  • Provide guidance and training in techniques and technologies to integrate and mine investigator generated or existing data sets.
  • Assist investigators in the development of secure, Health Insurance Portability and Accountability Act (HIPAA)-compliant databases.
  • Develop and promote the use of software tools for data visualization.
  • Collaborate with other investigators, projects and centers to develop optimal data handling procedures and data housing systems, provide researcher friendly reports with suggestions for appropriate analytical tools.

Case-Study Template

Big Data is becoming ubiquitous. To examine complex health conditions, intricate biomedical phenotypes, and causal relations, advanced analytical techniques and powerful computational methods are necessary to ingest, harmonize, process, analyze and visualize large, heterogeneous, multisource, incomplete, multiscale, and incongruent datasets (DOI: 10.1186/s13742-016-0117-6). This template shows some of the characteristics that need to be provided prior to data interrogation. Each case-study should include the following components:

All Training Workshop Participants are encouraged to prepare and submit the the Workshop GDrive partition a Case-Study that represents a common data, visualization, analytical, methodological, processing, or interpretation challenge encountered in their clinical, basic or translational research.

  • Title: Brief but descriptive case-study title
  • Overview: A brief summary of the case-study
  • Driving Challenges: List a set of 3-5 questions that have clear healthcare applications that might be addressed, or at least examined by, using the dataset
  • Meta-data: Define all data elements, describe the dataset, data dictionary, data format, etc.
  • Data: Package (e.g., as ZIP and share on GDrive, M+Box, etc.) the complete dataset. No PHI! The data could represent observational, derived, or simulated data. In general, to justify use of advanced analytics, the case-study should represent a real and interesting phenomena (e.g., include at least 10 variables, one or more time-points and represent 100 + cases/subjects/instances, hopefully, hundreds or thousands of cases)
  • Provenance: Include appropriate, references, URLs, PMCIDs, comments, credits, etc. describing the provenance of these data

Examples of many case-studies are available on the SMHS Case-Studies Canvas Site.


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