TwoRavens: A Graphical, Browser-Based Statistical Interface for Data Repositories by Vito D’Orazio...

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TwoRavensA Graphical, Browser-Based Statistical Interface

for Data Repositories

Vito D’Orazio and James Honaker

Data ScienceInstitute for Quantitative Social Science

Harvard University

June 11, 2015

TwoRavens• Gesture-based web application• Explore and analyze tabular data files

I Nearly 25,000 on Harvard’s Dataverse

• Easy access to descriptive statistics• Interactive statistical modeling using the language of

directed graphs

TwoRavens• Gesture-based web application• Explore and analyze tabular data files

I Nearly 25,000 on Harvard’s Dataverse• Easy access to descriptive statistics• Interactive statistical modeling using the language of

directed graphs

TwoRavens• Gesture-based web application• Explore and analyze tabular data files

I Nearly 25,000 on Harvard’s Dataverse• Easy access to descriptive statistics• Interactive statistical modeling using the language of

directed graphs

TwoRavens• Gesture-based web application• Explore and analyze tabular data files

I Nearly 25,000 on Harvard’s Dataverse• Easy access to descriptive statistics• Interactive statistical modeling using the language of

directed graphs

Design Principles

• Browser-based, thin clientI Data are never localI Metadata are pulled client-side

I Dataverse’s DDI metadataI TwoRavens’ generated metadata

I Data are pulled server-side• Device independent and broadly accessible

I Only requires an internet connectionI Does not presuppose expert statistical knowledge or

experience with statistical software

Architecture

http://datascience.iq.harvard.edu/about-tworavens

Future Directions

1. Automated Statistical Model Selection2. Accumulation of User Results3. Interface for Curator Privacy Model

Future Directions

1. Automated Statistical Model Selection

2. Accumulation of User Results3. Interface for Curator Privacy Model

Future Directions

1. Automated Statistical Model Selection2. Accumulation of User Results

3. Interface for Curator Privacy Model

Future Directions

1. Automated Statistical Model Selection2. Accumulation of User Results3. Interface for Curator Privacy Model

PrivateData Curator

PUser

PUser

PUser

q1

p1

q2

p2

p3

q3

Figure: The curator architecture for data privacy.

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