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We envision the organization of the training as being similar to conference; depending on your interests and areas of expertise, you may take all courses in one track or you may take one from each as needed.

 

Training METHODS
Onsite Workshop (OW)
Resources (R)
Instructor Training (IT)
Tutorials/Webinars (T)
Online Course (OC)

 

 

Data Mgmt PracticesTraining (methods) 
DMPs/funder compliance

Cradle (OW, OC)

CDL (T) 

 
Data Management overview  
metadata

Cradle (OW, OC)

Essentials 4 Data Support (OC)

RDM Rose (R, T)

 
data citation/data publication  
workflow  
file management  
open data/science  
quality control  
data analysis and mining  
reproducibility, reuse  
archiving and preservation  
Institutional Repositories  
finding data  
protecting sensitive data  
   
   
   
   
   

 

 

Tools  

R (other stats packages? SPSS, Stats, etc)

DST4L (OW)
Software Carpentry (OW)
Data Carpentry (OW)
 
Python  
MATLAB  
Unix Shell/Command Line  
Git  
Github  
OpenRefine  
Mercurial  
Relational databases and SQL  
Data in spreadsheets (esp. Excel)  
data visualization  
cloud computing  
data manipulation tools  
ArcGIS, QGIS, web mapping  
   
   
   
   
   
Policies/Guidelines  
copyright  
legal issues (e.g., patents?)  
human subjects/privacy  
security (tech policy)  
ethics  
   
   
   
   
   
   
   
   
   
   
   
   
   
   

 

 

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