THE DATA TRANSCRIPTION AND ANALYSIS (DTA)
TOOL. HANDS ON WORKSHOP
María Blume, Pontificia Universidad Católica del Perú,
Isabelle Barrière, Long Island University & Yeled
V'Yalda Early Childhood Center
Cristina Dye, Newcastle University, and
Ted Caldwell, Gorges
With the invaluable help of Carissa Kang and
Jonathan Masci, Cornell University
Development of Linguistic Linked Open Data (LLOD)
Resources for Collaborative Data-Intensive Research in
the Language Sciences
July 25th, 2015
This tool funded by

National Science Foundation. CI-TEAM program.
“Transforming the Primary Research Process Through
Cybertool Dissemination: An Implementation of a
Virtual Center for the study of language
acquisition”. (María Blume and Barbara Lust). NSF
OCI-0753415
Purpose and goals
Purpose



To create a culture of national
and international
collaboration among
researchers and their labs.
to create shared principles
and methods of data
documentation, management
and collaboration
to enable the practice of
these principles and methods
through the use of cybertools.
Goal

To provide a new
generation of researchers
and students, including
those with diverse
disciplinary, geographical
and cultural backgrounds,
with a solid foundation in
these principles and
methods through the use
of these new cybertools.
Purpose and goals
Purpose

To create a tool for
collaboration that can
allow for the
management,
documentation and
analysis of
crosslinguistic
language data.
Goal

To provide a resource
that allows users to
manage data across
datasets and projects,
including the ability to
reuse previously
collected data.
Virtual Center for the Study of
Language Acquisition (VCLA)
http://vcla.clal.cornell.edu/
The Virtual Center for Language
Acquisition Research



A community of researchers that are linked in their
assumption that the most fundamental questions of
language acquisition require interdisciplinary
collaboration, both theoretical and empirical
methods, and a cross-linguistic approach.
Eight founding member institutions.
One international collaborator in Peru.
The VCLA
website
 A center that unites through the web
a series of research labs across the
country and the world.
 [Its] mission is to foster collaborative
research among researchers
working in the area of language
acquisition, collaborations which are
potentially interdisciplinary, which
may be at a distance
geographically and which may
involve the comparative study of
multiple languages, and interactions
on shared data, as well as a variety
of lab methods.
The VCLA
List of projects
by VCLA
members to
give
undergraduate
and graduate
students and
other
researchers
ideas for future
research and
collaboration.
Courses
Courses

We have created a series of courses centered
on research methodology, best practices and
the intensive hands-on experience with
cybertools such as the Experiment Bank and
Web DTA.
The Web Conferences: Elluminate
Cornell and
UTEP students
meeting
during our
first course.
The Web Conferences: Elluminate
A UTEP
student
presents her
research
proposal to
peers and
faculty at
UTEP and
Pontificia
Universidad
Católica del
Perú.
The Virtual Linguistics Lab (VLL)
http://clal.cornell.edu/vll
The Virtual Linguistics Lab

The VLL portal provides structured access to the
components of a virtual linguistic lab:
 Materials
for the scientific and collaborative study of
language acquisition.
 web-based courses, integrating synchronous and
asynchronous forms of interactive information
distribution.
Meeting the Challenges through a
Virtual Linguistics Lab

The VLL includes
a series of web-based courses, integrating synchronous and
asynchronous forms of interactive information distribution,
 a web-based experiment bank and data transcription and
analysis tool, with an associated set of data collected over
20 years by the Cornell Language Acquisition Lab and other
labs across the USA.
 a series of structured audio-visual demonstrations and
related learning modules.


These materials are integrated into a universitysupported cyberinfrastructure to ensure the high
availability needs of a distance learning program
VLL Components

Laboratory methods:
 Research
methods manual.
 Standards.
 Courses
 Teaching
materials.
 Audio/visual samples (lessons, assignments, data).
 Web conferences.
 Discussion board.
VLL Portal Topics
Teaching Modules


Provide graduate and undergraduate students with a
set of interactive web-based lessons which teach them
the specific procedures of investigating language
knowledge.
These link to
Audio/video examples.
 Glossary
 The experiment bank
 The methods manual
 The Data Transcription and analysis tool.
 Published or unpublished papers
 Specific exercises/homework.

The modules



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provide students with selected excerpts of language
data to be studied and analyzed
give students a virtual experience of an interview
of a subject and real experience of analysis of the
subject's language.
allow students to learn a method to use in own
research or practice
allow students to learn how to analyze previously
collected data
A teaching module
The teaching
modules give
students access
to:
Audio/Visual
examples.
•
PowerPoint
presentations
explaining the
methods.
•
•
Readings
Interactive
assignments.
•
Audio/Visual Materials
Teaching the
procedure for
the Act Out
task.
Audio/Visual Materials
An
experimental
study showing
the Elicited
Imitation task
done with a
2-year-old in
Peru.
An interactive assignment
Elicited
Imitation
assignment
comparing
monolingual
and bilingual
children.
These
assignments
train students
to transcribe
and analyze
data, and
compare their
results to the
original
paper’s results.
An interactive assignment
A child subject
enjoys the
experiment.
These samples
give the
student a
virtual
experience of
data
collection.
Cybertools
Cybertools


Multilingualism questionnaire.
Data Transcription and Analysis Tool (DTA)
 includes
an Experiment Bank
 gives access to Libraries of comparable data.
 DTA User’s Manual.

Virtual workshops.
Cybertool access through VLL
Data quality: the opportunities

Technology can enable:
 Precision
and completeness in data-capture
procedures
 Capacity for many levels of structural
description and analysis
 Capacity to link points of data along multiple
dimensions
Why do we need the DTA tool in the
study of language acquisition and use?



Multiple languages
Multiple formats
Multiple methods of data collection



observational vs. experimental,
cross-sectional or longitudinal.
Multiple aspects of metadata



age and/or developmental/cognitive stage of speaker.
social and pragmatic context
culture.
Data management and use


Different labs practice distinct forms of data
management.
The scientific use of any single record requires
access to many levels of data, ranging from raw
(establishing provenance) to structured and
analyzed data (establishing intellectual worth).
Data Transcription and Analysis Tool
(WebDTA)
31





http://webdta.clal.cornell.edu/site/login
A primary research tool which provides the user
with a web interface which guides him/her through
steps for generating, storing and accessing data.
Users contribute data in a structured, uniform
manner.
Users access calibrated data from a shared
relational database.
Diverse data become comparable at many levels.
The Data Transcription and Analysis
Tool (WebDTA)
32


Collects all information related to a study
(experimental or observational) in the same
location.
Makes all information about the study available to
the public.
 Researchers
seeking to replicate or criticize it.
 Students studying the particular method or research
topic.

Trains researchers and students on how to organize
research data.
WebDTA Tool
33





It stores its data in a relational database on a
centralized server (other systems store flat text files).
It supports both Natural Speech and Experimental
data.
It can be used for both Research and Education
(structured teaching modules).
It is open-ended. New specialized coding screens can
be added.
It has robust query capabilities based on its relational
database structure.
Brief development history


Virtual Language Laboratory (VLL), its Data Transcription
and Analysis Tool (DTA, WebDTA) and the proprietary
methodology that supports these were developed over 30
years of personal effort by Prof. Lust and student and peer
contribution.
Several rudimentary versions of the DTA were sketched out
and crafted in old software. However, when user friendly
relational databases became common place, research and
student users were able to define a new approach. A more
powerful version of the DTA using FoxPro as the engine was
developed.

Katharina Boser, Reiko Mazuka, Julie Eisele, Paul Navarre, David
Parkinson, Shamitha Somashekar, and María Blume.
Brief development history


Cliff Crawford provoked the CLAL's development of a webbased interface for the DTA tool and has held major
responsibility for programming of the first web-based
interface, using PostgreSQL.
The current version of the DTA tool, unifying the previously
independent cybertool Experiment Bank with the DTA was
developed by Ted Caldwell and Greg Kops at Gorges,
Web Development and Internet Solutions
(http://www.gorges.us/) with María Blume and Barbara
Lust, and input from students of the Cornell Language
Acquisition Lab (Natalia Buitrago, Gabriel Clandorf,
Poornima Guna, Jennie Lin, and Jordan Whitlock and UTEP
Marina Kalashnikova and Martha Rayas).
DTA Schema
Structure



The current version of the WebDTA tool is built on
Yii, a PHP web development framework that uses
the "Model-View-Controller" pattern to structure the
application and the "Active Record" pattern to
manage records from the database.
MySQL is used for the database platform.
All are open source technologies.
External links





We are collaborating with Cornell University’s Albert Mann Library in their
current pilot program, DataStaR (Data Staging Repository) intended to help
researchers create high quality metadata in the formats required by
external repositories…”(Steinhart 2010: 1) (Funded by the National
Science Foundation (Grant No. 111-0712989)
The program adopts a semantic web approach to metadata.
At present, one VCLA dataset (Sinhala language) from more than 400
children studied in Sri Lanka has been entered in DataStaR, linking the
VCLA database to the Library staging repository, and is available for
collaborative use through this repository.
DataStaR uses RDF (Resource Description Framework (RDF)) statements and
OWL (Web Ontology Language) classes in order to integrate different
metadata frameworks across disciplines.
http://datastar.mannlib.cornell.edu/display/n6291 and
http://www.news.cornell.edu/stories/Oct11/SinhalaTools.html
Project sample



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An Experimental Project
https://webdta.clal.cornell.edu/projects/151/overview
A Natural Speech Corpus
https://webdta.clal.cornell.edu/projects/15/datasets/
40/sessions
Coding Gesture
https://webdta.clal.cornell.edu/projects/235/datasets
/249/sessions/2648/transcriptions/1669/utterances
Code-switching project
https://webdta.clal.cornell.edu/projects/230/overview
Queries https://webdta.clal.cornell.edu/queries
Accessing the DTA



Permission required due to Human Subjects Issues.
Need to contact Barbara Lust (bcl4@cornell.edu) or
María Blume (mblume@pucp.pe)
Go to https://webdta.clal.cornell.edu/ (the link is in
a doc called DTA address which we e-mailed you
along with documents containing data from children
which you can use to practice. We have removed
the identifying data.)
Acknowledgments



María Blume and Barbara Lust. 2008. Transforming the
Primary Research Process Through Cybertool
Dissemination: An Implementation of a Virtual Center
for the Study of Language Acquisition. NSF OCI0753415
Lust, Barbara. 2003. Planning Grant: A Virtual Center
for Child Language Acquisition Research. National
Science Foundation. NSF BCS-0126546
VCLA founding members:







Cornell: Marianella Casasola, Claire Cardie, James Gair, and Qi Wang.
NeuroFocus: Elise Temple
Boston College: Claire Foley
Rutgers University at New Brusnwick: Liliana Sánchez.
Rutgers University at Newark: Jennifer Austin
California State University at San Bernardino: YuChin Chien.
Southern Illinois University at Carbondale: Usha Lakshmanan.
Acknowledgments

VCLA affiliates:

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



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

City University of New Yors: Gita Martohardjono, Valerie Shafer,
and Isabelle Barrière .
Newcastle University: Cristina Dye.
Ben Gurion University at the Negev: Yarden Kedar
Tyndale University College and Seminary: Sujin Yang.
Columbia University: Joy Hirsch.
University of Texas at El Paso: Ellen Courtney and Alfredo Urzúa.
University of California at San Diego: Sarah Callahan.
Pontificia Universidad Católica Del Perú: Jorge Iván Pérez Silva
Kyungsung University: Kwee Ock Lee
Central Institute of English and Foreign Languages: R. Amritavalli
Osmania University: A. Usha Rani.
Acknowledgments




Janet McCue and Barbara Lust
2004-2006. National Science
Foundation Award: Planning
Information Infrastructure
Through a New LibraryResearch Partnership.
(SGER=Small Grant for
Exploratory Research)
American Institute for Sri
Lankan Studies, Cornell
University Einaudi Center.
Cornell University Faculty
Innovation in Teaching Awards,
Cornell Institute for Social and
Economic Research (CISER).



New York State
Hatch grant.
Our application
developers Ted
Caldwell and Greg
Kops (GORGES).
Our consultants Cliff
Crawford and
Tommy Cusick;
Our student RAs:
Darlin Alberto,
Gabriel Clandorf,
Natalia Buitrago,
References
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Berners-Lee, Tim. .3/2009. Ted Lecture. Tim Berners-Lee on the next Web.
http://en.wikipedia.org/wiki/Linked_data.
Bickel, Balthasar, Bernard Comrie, and Martin Haspelmath. 2008. Leipzig Glossing Rules:
Conventions for Interlinear Morpheme-by-Morpheme Glosses. Available online at
http://www.eva.mpg.de/lingua/resources/glossing-rules.php.
Blume, María and Barbara Lust, 2011a and in prep. Data Transcription and Analysis Tool
User’s Manual. (with the collaboration of Shamitha Somashekar, and Tina Ogden).
Blume, María and Barbara Lust. 2011b. Presentation to the National Science Foundation. CI
Team Principal Investigator’s Meeting. University of Illinois at Urbana Champaign, Ill. May 2426. Transforming the Primary Research Process Through Cybertool Dissemination: An
Implementation of a Virtual Center for the Study of Language Acquisition. NSF OCI-0753415.
Farrar, S.O. and Langendoen, D.T. 2003 A linguistic ontology for the semantic web. GLOT
International, 7(3) 97-100.
Khan, Huda, Brian Caruso, Brian Lowe, Jon Corson-Rikert, Diane Dietrich and Gail Steinhart.
2011. DataStaR: Using the Semantic Web approach for Data Curation. International Journal
of Digital Curation 2(6): 209-221.
Lowe, Brian. 2009. DataStaR: Bridging XML and OWL in Science Metadata Management.
Metadata and Semantics Research 46: 141-150.
http://www.springerlink.com/content/q0825vj78ul38712/
References
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Lust, Barbara, Suzanne Flynn, María Blume, Elaine Westbrooks, and Theresa Tobin. (2010).
Constructing Adequate Documentation for Multi-faceted Cross Linguistic Language Data: A
Case Study from a Virtual Center for Study of Language Acquisition. In Grenoble, Lenore and
Louanna Furbee, (eds.), Language Documentation: Theory, Practice and Values. pp. 127-152.
Amsterdam/Philadelphia: John Benjamins.
Open Archives Initiative (OAI), http://www.openarchives.org/ (15 Mar. 2005).
Open Language Archives Community (OLAC), http://www.language-archives.org/ (24 Feb.
2011).
Simons, G. Farrar, JS., Fitzsimons, B., Lewis, W., Langendoen, D.T. and Gonzalez, H. 2004a. The
semantics of markup: Mapping legacy markup schemas to a common semantics.
Simons, G., Fitzsimons, B., Langendoen, D.T., Lewis, Wm., Farrar, S., Lanham, A., Basham, R. and
Gonzalez H. 2004b. http://emeld.org/workshop/2004/langendoen-paper.html
Steinhart, Gail. 2010. DataStaR: A Data Staging Repository to Support the Sharing and
Publication of Research Data. 31st Annual IATUL Conference - The Evolving World of eScience: Impact and Implications for Science and Technology Libraries. June 20-24, 2010.
West Lafayette, IN. http://docs.lib.purdue.edu/iatul2010/conf/day2/8/.
DTA: Project list
DTA: Project info
Metadata on
Experimental or
Naturalistic
research.
These screens
help students and
researchers
save/access the
basic information
for a research
study and also
keep track of
publications,
presentations,
related studies,
and
bibliography
related to a
research project.
DTA Metadata: Subject info
Subject
information
that allows for
one subject’s
data to be
used in
multiple
datasets.
DTA: Research Design
These screens
help students
and
researchers
save/access
the research
study’s design.
DTA: Summary Report
This report
shows the
data at the
project level.
DTA: Summary Report
From the
project report
one can
access the
summary
reports for the
different
datasets of
the project.
DTA: Summary Report
DTA Metadata: Session info
This screen
provides info
for every time
a subject was
recorded for
a given
dataset.
DTA: Recordings
One can
include
several
“recordings”
for each
session,
including
audio, video,
and previous
transcripts.
DTA: Transcription
This screen
allows one to
transcribe,
switch
between
recordings,
and time-align
recordings
and
transcripts.
DTA: Basic Natural Speech Coding
Basic levels of
linguistic
coding to train
students.
Additional
levels of
general or
projectspecific
coding can be
created by
users.
DTA: Experimental Coding for
Grammaticality Judgment Task.
An example
of a project
specific
coding
created for an
experimental
task.
DTA: Query
A multicondition
query.
Different
queries can
be created
and saved by
users as
needed.
The Virtual Workshops
The Virtual Workshops Topics
Virtual
Workshops
teach users
how to
navigate our
cybertools.
The Virtual Workshops: The DTA
Manual
It allows users
to take notes
and has
quizzes to
check for
understanding
of the
cybertool.
The Virtual Workshops: A video
demonstration
Prof. Lust
explains the
purpose and
motivation of
the cybertool
to students
and
researchers at
Cornell,
Rutgers New
Brunswick, MIT
and UTEP.
The Virtual Workshops: A video
demonstration
The DTA tool
programmer,
Ted Caldwell,
shows students
the different
DTA screens
and their
purpose with
added
comentary by
María Blume
and Barbara
Lust.