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Open Educational Resources for Big Data Science

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Poster for NIH Big Data to Knowledge (BD2K) meeting, which funds this work. We have created a series of modules for biomedical big data science.

Published in: Health & Medicine

Open Educational Resources for Big Data Science

  1. 1. § How  to  scope  generic   curricula  for  different   levels  of  users   Open Educational Resources for Big Data Science Acknowledgements   This  work  is  supported  by  NIH  Grants  1R25EB020379-­‐01   and    1R25GM114820-­‐01.   Develop  open  educational  resources   (OERs)  for  use  in  courses,  programs,   workshops,  and  related  activities.   Objectives   William Hersh1, Shannon McWeeney1, Melissa Haendel1,2, Nicole Vasilevsky1,2, Bjorn Pederson1 1Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 2Ontology Development Group, Library, Oregon Health & Science University, Portland, OR Challenges   Beginning  informatics  graduate  students   Advanced  undergraduates  exploring  future  career   paths  into  data  science   Established  professionals  who  need  to  apply   BD2K  concepts  in  their  present  jobs   Established  investigators  and  senior  trainees   July 6-10th, 2015, 9am-5pm daily Modules   July 6-10th, 2015, 9am-5pm daily Modeled  after  the  successful  Of:ice  of  the   National  Coordinator  for  Health  IT  (ONC)   curriculum  materials.  The  value  of  using  the  ONC   Health  IT  Curriculum  approach  includes  an  open   format  with  both:   §  “Out  of  the  box”  content   §  Source  materials  for  that  content   Approach   http://skynet.ohsu.edu/bd2k     Target  Audience   Scope Images Style Dissemination How  to  scope  generic  curricula   for  different  levels  of  users   How  to  translate  diverse   teaching  styles  into  general   materials   How  to  incorporate  images   and  other  copyrighted   materials  into  open  resources   How  to  maximize   dissemination  while  protecting   intellectual  property    Detailed  references   Data  exercises   Learning  objectives   Narrated  lectures  and   source  slides   Modules contain: 1  |  Biomedical  Big  Data  Science   2  |  Introduction  to  Big  Data  in  Biology  and  Medicine     3  |  Ethical  Issues  in  Use  of  Big  Data     4  |  Terminology  of  Biomedical,  Clinical,  and  Translational   Research     5  |  Computing  Concepts  for  Big  Data     6  |  Clinical  Data  and  Standards  Related  to  Big  Data     7  |  Basic  Research  Data  Standards     8  |  Public  Health  and  Big  Data     9  |  Team  Science     10  |  Secondary  Use  (Reuse)  of  Clinical  Data     11  |  Publication  and  Peer  Review   12  |  Information  Retrieval     13  |  Version  control  and  identibiers     14  |  Data  annotation  and  curation     15  |  Data  and  tools  landscape     16  |  Ontologies  101     17  |  Data  modeling     18  |  Data  metadata  and  provenance     19  |  Semantic  data  interoperability     20  |  Semantic  Web  data     21  |  Context-­‐based  selection  of  data     22  |  Translating  the  Question     23  |  Implications  of  Provenance  and  Pre-­‐processing   24  |  Data  tells  a  story     25  |  Choice  of  Algorithms  and  Algorithm  Dynamics     26  |  Statistical  Signibicance,  P-­‐hacking  and  Multiple-­‐testing     27  |  Displaying  Conbidence  and  Uncertainty     28  |  Visualization  and  Interpretation     29  |  Replication,  Validation  and  the  spectrum  of   Reproducibility   30  |  Regulatory  Issues  in  Big  Data  for  Genomics  and  Health     31  |  Hosting  data  dissemination  and  data  stewardship   workshops     32  |  Guidelines  for  reporting,  publications,  and  data   sharing   ww ww

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