SOCR News 2018 MNORC SOCR HAC Workshop

Revision as of 18:13, 12 October 2018 by Dinov (talk | contribs) (Logistics)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

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

This workshop will provide unique hands-on Health Data Analytics train that may be appropriate to students, postdocs, fellows, early career scholars, and junior faculty with interests in novel strategies for interrogating Big heterogeneous, time-varying, incongruent, incomplete, and multi-scale biomedical data. The workshop is divided in two complementary parts. Part one will provide didactic training in data science methods, available computational infrastructure, statistical inference techniques, and data wrangling strategies. The participants will drive all activities in the second part of the workshop where new datasets, case-studies, and scenarios brought up by all attendees will guide the demonstrations of specific predictive health analytics methods. All registered participants are encouraged to bring their laptops for try-it-now experiences.

  • Part 1, introduction to various SOCR capabilities.
  • Part 2, ad-hoc biomed and health analytics challenges presented by attendees.
  • Part 3, breakout sessions - smaller groups discuss attendees' driven analytical needs.


  • Date: Fri Oct 12, 2018
  • Place/Time: 1-5 PM, 426 N. Ingalls (SNB 1250). Lunch will be served at 12 Noon (all are welcome)
  • 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
  • Flyer: Training Event Flyer
  • Collaborative GDrive folder
  • YouTube webcast archive: Video Part 1 and Video Part 2.




  • 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. Examples of SOCR Case-studies are available on Canvas.

  • 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.

Case-Study 1: Deaths in Guatemala (2009-2016)


Translate this page:

Uk flag.gif

De flag.gif

Es flag.gif

Fr flag.gif

It flag.gif

Pt flag.gif

Jp flag.gif

Bg flag.gif

الامارات العربية المتحدة
Ae flag.gif

Fi flag.gif

इस भाषा में
In flag.gif

No flag.png

Kr flag.gif

Cn flag.gif

Cn flag.gif

Ru flag.gif

Nl flag.gif

Gr flag.gif

Hr flag.gif

Česká republika
Cz flag.gif

Dk flag.gif

Pl flag.png

Ro flag.png

Se flag.gif