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NDAR provides a single access to de-identified autism research data. For permission to download data, you will need an NDAR account with approved access to NDAR or a connected repository (AGRE, IAN, or the ATP). For NDAR 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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Selected Filters
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The filters you have selected from various query interfaces will be stored here, in the 'Filter Cart'. The database will be queried using filters added to your 'Filter Cart', when multiple filters are defined, each will be executed using 'AND' logic, so with each filter that is applied the result set gets smaller.

From the 'Filter Cart' you can inspect each of the filters that have been defined, and you also have the option to remove filters. The 'Filter Cart' itself will display the number of filters applied along with the number of subjects that are identified by the combination of those filters. For example a GUID filter with two subjects, followed by a GUID filter for just one of those subjects would return only data for the subject that is in both GUID filters.

If you have a question about the filter cart, or underlying filters please contact the help desk at The NDA Help Desk

Description
Value Range
Notes
Data Structures with shared data
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Hungry Donkey Task

hundonk

01

Download Definition as
Download Submission Template as
Element NameData TypeSizeRequiredDescriptionValue RangeNotes
subjectkeyGUIDRequiredThe NDAR Global Unique Identifier (GUID) for research subjectNDAR*
src_subject_idString20RequiredSubject ID how it's defined in lab/project
interview_dateDateRequiredDate on which the interview/genetic test/sampling/imaging was completed. MM/DD/YYYYRequired field
interview_ageIntegerRequiredAge in months at the time of the interview/test/sampling/imaging.0 :: 1260Age 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.
genderString20RequiredSex of the subjectM;FM = Male; F = Female
as_block1IntegerRecommendedNumber of large win, high freq loss selections - in block 10 :: 20
as_block2IntegerRecommendedNumber of large win, high freq loss selections - in block 20 :: 20
as_block3IntegerRecommendedNumber of large win, high freq loss selections - in block 30 :: 20
as_block4IntegerRecommendedNumber of large win, high freq loss selections - in block 40 :: 20
as_block5IntegerRecommendedNumber of large win, high freq loss selections - in block 50 :: 20
as_block6IntegerRecommendedNumber of large win, high freq loss selections - in block 60 :: 20
as_block7IntegerRecommendedNumber of large win, high freq loss selections - in block 70 :: 20
as_block8IntegerRecommendedNumber of large win, high freq loss selections - in block 80 :: 20
as_block9IntegerRecommendedNumber of large win, high freq loss selections - in block 90 :: 20
as_block10IntegerRecommendedNumber of large win, high freq loss selections - in block 100 :: 20
ss_block1IntegerRecommendedNumber of large win, low freq loss selections - in block 10 :: 20
ss_block2IntegerRecommendedNumber of large win, low freq loss selections - in block 20 :: 20
ss_block3IntegerRecommendedNumber of large win, low freq loss selections - in block 30 :: 20
ss_block4IntegerRecommendedNumber of large win, low freq loss selections - in block 40 :: 20
ss_block5IntegerRecommendedNumber of large win, low freq loss selections - in block 50 :: 20
ss_block6IntegerRecommendedNumber of large win, low freq loss selections - in block 60 :: 20
ss_block7IntegerRecommendedNumber of large win, low freq loss selections - in block 70 :: 20
ss_block8IntegerRecommendedNumber of large win, low freq loss selections - in block 80 :: 20
ss_block9IntegerRecommendedNumber of large win, low freq loss selections - in block 90 :: 20
ss_block10IntegerRecommendedNumber of large win, low freq loss selections - in block 100 :: 20
ks_block1IntegerRecommendedNumber of small win, high freq loss selections - in block 10 :: 20
ks_block2IntegerRecommendedNumber of small win, high freq loss selections - in block 20 :: 20
ks_block3IntegerRecommendedNumber of small win, high freq loss selections - in block 30 :: 20
ks_block4IntegerRecommendedNumber of small win, high freq loss selections - in block 40 :: 20
ks_block5IntegerRecommendedNumber of small win, high freq loss selections - in block 50 :: 20
ks_block6IntegerRecommendedNumber of small win, high freq loss selections - in block 60 :: 20
ks_block7IntegerRecommendedNumber of small win, high freq loss selections - in block 70 :: 20
ks_block8IntegerRecommendedNumber of small win, high freq loss selections - in block 80 :: 20
ks_block9IntegerRecommendedNumber of small win, high freq loss selections - in block 90 :: 20
ks_block10IntegerRecommendedNumber of small win, high freq loss selections - in block 100 :: 20
ls_block1IntegerRecommendedNumber of small win, low freq loss selections - in block 10 :: 20
ls_block2IntegerRecommendedNumber of small win, low freq loss selections - in block 20 :: 20
ls_block3IntegerRecommendedNumber of small win, low freq loss selections - in block 30 :: 20
ls_block4IntegerRecommendedNumber of small win, low freq loss selections - in block 40 :: 20
ls_block5IntegerRecommendedNumber of small win, low freq loss selections - in block 50 :: 20
ls_block6IntegerRecommendedNumber of small win, low freq loss selections - in block 60 :: 20
ls_block7IntegerRecommendedNumber of small win, low freq loss selections - in block 70 :: 20
ls_block8IntegerRecommendedNumber of small win, low freq loss selections - in block 80 :: 20
ls_block9IntegerRecommendedNumber of small win, low freq loss selections - in block 90 :: 20
ls_block10IntegerRecommendedNumber of small win, low freq loss selections - in block 100 :: 20
adv_block1IntegerRequiredAdvantageous selections (net win) in block 10 :: 20
adv_block2IntegerRequiredAdvantageous selections (net win) in block 20 :: 20
adv_block3IntegerRequiredAdvantageous selections (net win) in block 30 :: 20
adv_block4IntegerRequiredAdvantageous selections (net win) in block 40 :: 20
adv_block5IntegerRequiredAdvantageous selections (net win) in block 50 :: 20
adv_block6IntegerRequiredAdvantageous selections (net win) in block 60 :: 20
adv_block7IntegerRequiredAdvantageous selections (net win) in block 70 :: 20
adv_block8IntegerRequiredAdvantageous selections (net win) in block 80 :: 20
adv_block9IntegerRequiredAdvantageous selections (net win) in block 90 :: 20
adv_block10IntegerRequiredAdvantageous selections (net win) in block 100 :: 20
total_advIntegerRecommendedAdvantageous selections (net win) Total0 :: 200
disadv_block1IntegerRequiredDisadvantageous selections (net win) in block 10 :: 20
disadv_block2IntegerRequiredDisadvantageous selections (net win) in block 20 :: 20
disadv_block3IntegerRequiredDisadvantageous selections (net win) in block 30 :: 20
disadv_block4IntegerRequiredDisadvantageous selections (net win) in block 40 :: 20
disadv_block5IntegerRequiredDisadvantageous selections (net win) in block 50 :: 20
disadv_block6IntegerRequiredDisadvantageous selections (net win) in block 60 :: 20
disadv_block7IntegerRequiredDisadvantageous selections (net win) in block 70 :: 20
disadv_block8IntegerRequiredDisadvantageous selections (net win) in block 80 :: 20
disadv_block9IntegerRequiredDisadvantageous selections (net win) in block 90 :: 20
disadv_block10IntegerRequiredDisadvantageous selections (net win) in block 100 :: 20
total_disadvIntegerRecommendedDisadvantageous selections (net win) Total0 :: 200
total_winningsIntegerRecommendedTotal apples won
total_lossesIntegerRecommendedTotal apples lost
netIntegerRecommendedNet 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.

Distribution for DataStructure: hundonk01 and Element:
Chart Help

Filters enable researchers to view the data shared in NDA before applying for access or for selecting specific data for download or NDA Study assignment. For those with access to NDA shared data, you may select specific values to be included by selecting an individual bar chart item or by selecting a range of values (e.g. interview_age) using the "Add Range" button. Note that not all elements have appropriately distinct values like comments and subjectkey and are not available for filtering. Additionally, item level detail is not always provided by the research community as indicated by the number of null values given.

Filters for multiple data elements within a structure are supported. Selections across multiple data structures will be supported in a future version of NDA.