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Frequently Asked Questions

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Filter Cart

The Filter Cart provides a powerful way to query and access data for which you may be interested.  

A few points related to the filter cart are important to understand with the NDA Query/Filter implementation: 

First, the filter cart is populated asyncronously.  So, when you run a query, it may take a moment to populate but this will happen in the background so you can define other queries during this time.  

When you are adding your first filter, all data associated with your query will be added to the filter cart (whether it be a collection, a concept, a study, a data structure/elment or subjects). Not all data structures or collections will necessarily be displayed.  For example, if you select the NDA imaging structure image03, and further restrict that query to scan_type fMRI, only fMRI images will appear and only the image03 structure will be shown.  To see other data structures, select "Find All Subject Data" which will query all data for those subjects. When a secord or third filter is applied, an AND condition is used.  A subject must exist in all filters.  If the subject does not appear in any one filter, that subjects data will not be included in your filter cart. If that happens, clear your filter cart, and start over.  

It is best to package more data than you need and access those data using other tools, independent of the NDA (e.g. miNDAR snapshot), to limit the data selected.  If you have any questions on data access, are interested in using avaialble web services, or need help accessing data, please contact us for assistance.  

Frequently Asked Questions

Glossary

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NDA provides a single access to de-identified autism research data. For permission to download data, you will need an NDA account with approved access to NDA or a connected repository (AGRE, IAN, or the ATP). For NDA access, you need to be a research investigator sponsored by an NIH recognized institution with federal wide assurance. See Request Access for more information.

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Data Structures with shared data
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Hungry Donkey Task

485 Shared Subjects

N/A
Clinical Assessments
Task Based
07/10/2015
hundonk01
04/12/2017
View Change History
01
Query Element Name Data Type Size Required Description Value Range Notes Aliases
subjectkey GUID Required The NDAR Global Unique Identifier (GUID) for research subject NDAR*
src_subject_id String 20 Required Subject ID how it's defined in lab/project
interview_date Date Required Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY Required field
interview_age Integer Required Age in months at the time of the interview/test/sampling/imaging. 0 :: 1260 Age is rounded to chronological month. If the research participant is 15-days-old at time of interview, the appropriate value would be 0 months. If the participant is 16-days-old, the value would be 1 month.
sex String 20 Required Sex of the subject M;F M = Male; F = Female gender
Query as_block1 Integer Recommended Number of large win, high freq loss selections - in block 1 0 :: 20
Query as_block2 Integer Recommended Number of large win, high freq loss selections - in block 2 0 :: 20
Query as_block3 Integer Recommended Number of large win, high freq loss selections - in block 3 0 :: 20
Query as_block4 Integer Recommended Number of large win, high freq loss selections - in block 4 0 :: 20
Query as_block5 Integer Recommended Number of large win, high freq loss selections - in block 5 0 :: 20
Query as_block6 Integer Recommended Number of large win, high freq loss selections - in block 6 0 :: 20
Query as_block7 Integer Recommended Number of large win, high freq loss selections - in block 7 0 :: 20
Query as_block8 Integer Recommended Number of large win, high freq loss selections - in block 8 0 :: 20
Query as_block9 Integer Recommended Number of large win, high freq loss selections - in block 9 0 :: 20
Query as_block10 Integer Recommended Number of large win, high freq loss selections - in block 10 0 :: 20
Query ss_block1 Integer Recommended Number of large win, low freq loss selections - in block 1 0 :: 20
Query ss_block2 Integer Recommended Number of large win, low freq loss selections - in block 2 0 :: 20
Query ss_block3 Integer Recommended Number of large win, low freq loss selections - in block 3 0 :: 20
Query ss_block4 Integer Recommended Number of large win, low freq loss selections - in block 4 0 :: 20
Query ss_block5 Integer Recommended Number of large win, low freq loss selections - in block 5 0 :: 20
Query ss_block6 Integer Recommended Number of large win, low freq loss selections - in block 6 0 :: 20
Query ss_block7 Integer Recommended Number of large win, low freq loss selections - in block 7 0 :: 20
Query ss_block8 Integer Recommended Number of large win, low freq loss selections - in block 8 0 :: 20
Query ss_block9 Integer Recommended Number of large win, low freq loss selections - in block 9 0 :: 20
Query ss_block10 Integer Recommended Number of large win, low freq loss selections - in block 10 0 :: 20
Query ks_block1 Integer Recommended Number of small win, high freq loss selections - in block 1 0 :: 20
Query ks_block2 Integer Recommended Number of small win, high freq loss selections - in block 2 0 :: 20
Query ks_block3 Integer Recommended Number of small win, high freq loss selections - in block 3 0 :: 20
Query ks_block4 Integer Recommended Number of small win, high freq loss selections - in block 4 0 :: 20
Query ks_block5 Integer Recommended Number of small win, high freq loss selections - in block 5 0 :: 20
Query ks_block6 Integer Recommended Number of small win, high freq loss selections - in block 6 0 :: 20
Query ks_block7 Integer Recommended Number of small win, high freq loss selections - in block 7 0 :: 20
Query ks_block8 Integer Recommended Number of small win, high freq loss selections - in block 8 0 :: 20
Query ks_block9 Integer Recommended Number of small win, high freq loss selections - in block 9 0 :: 20
Query ks_block10 Integer Recommended Number of small win, high freq loss selections - in block 10 0 :: 20
Query ls_block1 Integer Recommended Number of small win, low freq loss selections - in block 1 0 :: 20
Query ls_block2 Integer Recommended Number of small win, low freq loss selections - in block 2 0 :: 20
Query ls_block3 Integer Recommended Number of small win, low freq loss selections - in block 3 0 :: 20
Query ls_block4 Integer Recommended Number of small win, low freq loss selections - in block 4 0 :: 20
Query ls_block5 Integer Recommended Number of small win, low freq loss selections - in block 5 0 :: 20
Query ls_block6 Integer Recommended Number of small win, low freq loss selections - in block 6 0 :: 20
Query ls_block7 Integer Recommended Number of small win, low freq loss selections - in block 7 0 :: 20
Query ls_block8 Integer Recommended Number of small win, low freq loss selections - in block 8 0 :: 20
Query ls_block9 Integer Recommended Number of small win, low freq loss selections - in block 9 0 :: 20
Query ls_block10 Integer Recommended Number of small win, low freq loss selections - in block 10 0 :: 20
Query adv_block1 Integer Recommended Advantageous selections (net win) in block 1 0 :: 20
Query adv_block2 Integer Recommended Advantageous selections (net win) in block 2 0 :: 20
Query adv_block3 Integer Recommended Advantageous selections (net win) in block 3 0 :: 20
Query adv_block4 Integer Recommended Advantageous selections (net win) in block 4 0 :: 20
Query adv_block5 Integer Recommended Advantageous selections (net win) in block 5 0 :: 20
Query adv_block6 Integer Recommended Advantageous selections (net win) in block 6 0 :: 20
Query adv_block7 Integer Recommended Advantageous selections (net win) in block 7 0 :: 20
Query adv_block8 Integer Recommended Advantageous selections (net win) in block 8 0 :: 20
Query adv_block9 Integer Recommended Advantageous selections (net win) in block 9 0 :: 20; 88 88=missing
Query adv_block10 Integer Recommended Advantageous selections (net win) in block 10 0 :: 20; 88 88=missing
Query total_adv Integer Recommended Advantageous selections (net win) Total 0::500
Query disadv_block1 Integer Recommended Disadvantageous selections (net win) in block 1 0 :: 20; 88 88=missing
Query disadv_block2 Integer Recommended Disadvantageous selections (net win) in block 2 0 :: 20; 88 88=missing
Query disadv_block3 Integer Recommended Disadvantageous selections (net win) in block 3 0 :: 20; 88 88=missing
Query disadv_block4 Integer Recommended Disadvantageous selections (net win) in block 4 0 :: 20; 88 88=missing
Query disadv_block5 Integer Recommended Disadvantageous selections (net win) in block 5 0 :: 20; 88 88=missing
Query disadv_block6 Integer Recommended Disadvantageous selections (net win) in block 6 0 :: 20; 88 88=missing
Query disadv_block7 Integer Recommended Disadvantageous selections (net win) in block 7 0 :: 20; 88 88=missing
Query disadv_block8 Integer Recommended Disadvantageous selections (net win) in block 8 0 :: 20; 88 88=missing
Query disadv_block9 Integer Recommended Disadvantageous selections (net win) in block 9 0 :: 20; 88 88=missing
Query disadv_block10 Integer Recommended Disadvantageous selections (net win) in block 10 0 :: 20; 88 88=missing
Query total_disadv Integer Recommended Disadvantageous selections (net win) Total 0::500
Query total_winnings Integer Recommended Total apples won
Query total_losses Integer Recommended Total apples lost
Query net Integer Recommended Net apples won
Data Structure

This page displays the data structure defined for the measure identified in the title and structure short name. The table below displays a list of data elements in this structure (also called variables) and the following information:

  • Element Name: This is the standard element name
  • Data Type: Which type of data this element is, e.g. String, Float, File location.
  • Size: If applicable, the character limit of this element
  • Required: This column displays whether the element is Required for valid submissions, Recommended for valid submissions, Conditional on other elements, or Optional
  • Description: A basic description
  • Value Range: Which values can appear validly in this element (case sensitive for strings)
  • Notes: Expanded description or notes on coding of values
  • Aliases: A list of currently supported Aliases (alternate element names)
  • For valid elements with shared data, on the far left is a Filter button you can use to view a summary of shared data for that element and apply a query filter to your Cart based on selected value ranges

At the top of this page you can also:

  • Use the search bar to filter the elements displayed. This will not filter on the Size of Required columns
  • Download a copy of this definition in CSV format
  • Download a blank CSV submission template prepopulated with the correct structure header rows ready to fill with subject records and upload

Please email the The NDA Help Desk with any questions.