National Database for Autism
Research (NDAR)
Central Repository Access Request Procedure
What is NDAR?
• NDAR is a biomedical informatics system and central repository
developed by the NIH. NDAR provides a common platform for
data collection, retrieval and archiving, while allowing for
flexibility in data entry and analysis.
• A central function of NDAR is to store and to link together
genetic, phenotypic, images and other data derived from
individuals who participate in autism research studies.
How does one access NDAR data?
• Researchers need to apply to the NDAR Data Access
Committee (DAC) in order to be granted access to the
NDAR collection.
• The DAC approves access to data and/or images from the
NDAR Central Repository for research purposes only.
• The DAC will review the Central Repository Access
Request and the Data Use Certification (DUC) of each
Recipient requesting data and provide access based on
the expectations outlined in the NDAR policy.
These policy expectations include:
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Use the data only for the approved research;
Protect data confidentiality;
Follow appropriate data security protections;
Follow all applicable laws, regulations and local institutional
policies and procedures for handling NDAR data;
• Not attempt to identify individual participants from whom
data within a dataset were obtained;
• Not sell any of the data elements from datasets obtained
from the NIH NDAR data repository;
Expectations (cont.)
• Not share with individuals other than those listed in the request
any of the data elements from datasets obtained from the
NIH NDAR data repository;
• Agree to the listing of a summary of approved research uses
within the NIH NDAR data repository along with his or her
name and organizational affiliation;
• Agree to report, in real time, violations of the NDAR policy to the
DAC;
• Acknowledge the NDAR policy with regard to publication; and,
• Provide annual progress reports on research using NDAR data.
• In the event that requests raise concerns related to privacy and
confidentiality, risks to populations or groups, or other concerns,
the DAC will consult with other experts as appropriate.
• Recipients seeking access to data or images from NDAR are
expected to submit their Central Repository Access Request,
including a DUC, certified and co-signed by the Principal
Investigator and the designated Institutional Official(s).
• Completing this Central Repository Data Access Request is a
necessary step to access data or images from NDAR. Submission
of data to NDAR may be subject to the NDAR Central Repository
Submission Request and procedures.
Steps to Request General Access to the NDAR
Central Repository
• Read the NDAR Central Repository Data Use
Certification (DUC).
• Complete Recipient Information and Certifications
pages, including your Institution’s Federal-wide
Assurance number and a Research Use Statement:
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a brief description of your Research Project in the text box provided (this can be on a separate
piece of paper) that includes the objectives, design, and analysis plan.
Provide a statement as to whether you have/will apply for, obtain, or do not have a Certificate
of Confidentiality for the Research Project.
List all the collaborating investigators at your organization. By submitting an individual’s name
on the form, you and your Institutional Official affirm that the collaborators have read and
agreed to the terms and conditions within the Data Use Certification.
Your collaborators at different organizations must complete separate requests for the data.
Coordinated requests by collaborating organizations should all use the same title in their
request and each should reference the others in the Research Use Statement.
University of Illinois at Chicago’s Research Project Description:
Integrated Informatics and Modeling of Multiple Data Types to Predict Novel Genetic and Environmental Risk and Protective Factors for
Neuropsychiatric Disorders and Phenotypes
The Conte Center for Computational Neuropsychiatric Genomics comprises 17 project and core leaders distributed
across seven institutions, including The University of Chicago, The University of Illinois at Chicago, Northwestern
University, Stanford University, Columbia University, Harvard University, and the University of Haifa in Israel. Our
overall project goal is to mathematically model and predict phenotype-gene-environment associations using multiple
data types relevant to neuropsychiatric disorders, including autism. While numerous groups around the globe tackle
the bigger problem of tracing environmental and genetic factors influencing complex human traits, only a small
fraction of these studies is relevant to neuropsychiatry. To our knowledge, no holistic approach of mathematical
modeling and computational scrutiny of multiple threads of experimental evidence exists (e.g. pharmacogenomic and
prescription data, legacy genetic association and linkage datasets, electronic medical records, text mining of enormous
biomedical corpora). This project is designed to fill this gap. We will develop, test, and apply a drastically new
computational methodology for the analysis of more than one complex phenotype at a time. Specifically, we propose
to design and validate a battery of novel analytical tools for the inference of causal relationships among human
genomic variations, environmental factors, and more than one mental health phenotype, explicitly exploiting the
genetic and environmental non-independence of complex (multigenic) disorders. Strategically, we are counting on
extracting more and higher-quality information for multiple types of existing and newly generated data by considering
it within one unified probabilistic model - as opposed to what can be extracted from any data type and for any single
phenotype alone. This study is designed to determine whether we can detect genetic variants that influence more
than one disorder, whether we can identify environmental factors that influence risk for more than one pathological
phenotype or are very specific to a single phenotype, and whether we can infer a probability distribution (i.e., the
probability that each specific gene can harbor variations that predispose or protect an individual from disease) from a
molecular network of factors relevant to disease phenotype. To evaluate performance of our methods, we will use
cross-validation, one of the computational techniques designed for assessing the empirical validity of prediction
algorithms and commonly used by the machine learning and statistics communities. The ultimate validation of our
predictions can come only from experimental approaches including molecular, biochemical, proteomic, and animal
models that our Center is funded to perform.
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Sign the Recipient Information and Certifications page, and obtain your Institutional
Official’s signature and date.
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Provide a scanned copy of the completed DUC Recipient Information and
Certifications pages, including signatures, when requesting an account or requesting
additional access to NDAR data at http://ndarportal.nih.gov.
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Access Request Review: The DAC will review requests to access the NDAR Central
Repository. Such reviews are generally completed within 10 business days.
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The DAC will notify NDAR staff if the access request has been approved, and an
account will then be provided. The user will receive an automated notification of
their account update with any modified user name, passwords, or instructions for
accessing the NDAR Central Repository.
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Optional: NDAR System Training (if request approved): Contact NIH through
NDAR@mail.nih.gov to discuss specific training needs the user may have and
schedule the training.
• The majority of the preceding was taken directly
from NDAR’s policy statement and instructions
which can be found at:
• The policy statement for NDAR can be found at:
http://ndar.nih.gov/ndarpublicweb/Documents/NDAR_Policy.pdf
• NDAR Data Access Request Form (and instructions) can be found
at: http://tinyurl.com/NDAR-DAR
(http://ndar.nih.gov/ndarpublicweb/Documents/NDAR%20Data%20Access%20Request%2
0DUC%20FINAL.pdf)
• Once you have been approved you will have
access to the NDAR collections.
• Access to data from Pediatric MRI, ATP
Federated Clinical Assessments, ATP Brain MRI
Data and Images, IAN, and the AGRE Restricted
Access Permissions Group requires a separate
application for each of those datasets.
• Full access to SSC data are not available through
NDAR at this time; NDAR is working on getting
that data federated; the process on how to
access SSC data is outlined on the SFARI website.
• General descriptive subject information from
NIMH Genetics, Release 11, is currently available
as part of the NDAR Collections; however,
Release 12, is not currently available although it
should be soon.
• Full access to Release 12 (which includes TASC
families) may be accessed by applying directly to
NIMH Genetics.