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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

hundonk

01

Download Definition as
Download Submission Template as
Element NameData TypeSizeRequiredDescriptionValue RangeNotesAliases
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/biospecimen 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.
sexString20RequiredSex of the subjectM;FM = Male; F = Femalegender
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_block1IntegerRecommendedAdvantageous selections (net win) in block 10 :: 20
adv_block2IntegerRecommendedAdvantageous selections (net win) in block 20 :: 20
adv_block3IntegerRecommendedAdvantageous selections (net win) in block 30 :: 20
adv_block4IntegerRecommendedAdvantageous selections (net win) in block 40 :: 20
adv_block5IntegerRecommendedAdvantageous selections (net win) in block 50 :: 20
adv_block6IntegerRecommendedAdvantageous selections (net win) in block 60 :: 20
adv_block7IntegerRecommendedAdvantageous selections (net win) in block 70 :: 20
adv_block8IntegerRecommendedAdvantageous selections (net win) in block 80 :: 20
adv_block9IntegerRecommendedAdvantageous selections (net win) in block 90 :: 20; 8888=missing
adv_block10IntegerRecommendedAdvantageous selections (net win) in block 100 :: 20; 8888=missing
total_advIntegerRecommendedAdvantageous selections (net win) Total0 :: 200
disadv_block1IntegerRecommendedDisadvantageous selections (net win) in block 10 :: 20; 8888=missing
disadv_block2IntegerRecommendedDisadvantageous selections (net win) in block 20 :: 20; 8888=missing
disadv_block3IntegerRecommendedDisadvantageous selections (net win) in block 30 :: 20; 8888=missing
disadv_block4IntegerRecommendedDisadvantageous selections (net win) in block 40 :: 20; 8888=missing
disadv_block5IntegerRecommendedDisadvantageous selections (net win) in block 50 :: 20; 8888=missing
disadv_block6IntegerRecommendedDisadvantageous selections (net win) in block 60 :: 20; 8888=missing
disadv_block7IntegerRecommendedDisadvantageous selections (net win) in block 70 :: 20; 8888=missing
disadv_block8IntegerRecommendedDisadvantageous selections (net win) in block 80 :: 20; 8888=missing
disadv_block9IntegerRecommendedDisadvantageous selections (net win) in block 90 :: 20; 8888=missing
disadv_block10IntegerRecommendedDisadvantageous selections (net win) in block 100 :: 20; 8888=missing
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.