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NDA Help Center

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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Category Fluency

catflu

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/projectsubject_id
interview_dateDateRequiredDate on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYYRequired fieldassessment_date
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 = Female
completedIntegerRecommendedChecks if completed0::30=No; 1=Yes; 2=Yes, lost; 3=Unverifiedcategory_fluency_completion_status
cf_animals_timeIntegerRecommendedfluency animals time frameseconds
cf_animals_raw_scoreIntegerRecommendedfluency animals number of correct responses0 :: 77
verbal_cat_ssIntegerRecommendedVerbal Fluency, category animals, scaled scorecf_animals_scaled_score
cf_animals_percrankFloatRecommendedfluency animals percentile rank0 :: 100
cf_animals_rangeIntegerRecommendedfluency animals range1 :: 71 = Impaired; 2 = Borderline; 3 = Low Average; 4 = Average; 5 = High Average; 6 = Superior; 7 = Very Superior
cf_vegetables_timeIntegerRecommendedfluency vegetables time frameseconds
cf_vegetables_rawIntegerRecommendedfluency vegetables number of correct responses0 :: 77
cf_vegetables_scaled_scoreIntegerRecommendedfluency, category vegetables, scaled score
cf_vegetables_percrankFloatRecommendedfluency vegetables percentile rank0 :: 100
cf_vegetables_rangeIntegerRecommendedfluency vegetables range1 :: 71 = Impaired; 2 = Borderline; 3 = Low Average; 4 = Average; 5 = High Average; 6 = Superior; 7 = Very Superior
cf_birds_timeIntegerRecommendedfluency birds time frameseconds
cf_birds_rawIntegerRecommendedfluency birds number of correct responses0 :: 77
cf_birds_scaled_scoreIntegerRecommendedfluency, category birds, scaled score
cf_birds_percrankFloatRecommendedfluency birds percentile rank0 :: 100
cf_birds_rangeIntegerRecommendedfluency birds range1 :: 71 = Impaired; 2 = Borderline; 3 = Low Average; 4 = Average; 5 = High Average; 6 = Superior; 7 = Very Superior
cf_dogs_timeIntegerRecommendedfluency dogs time frameseconds
cf_dogs_rawIntegerRecommendedfluency dogs number of correct responses0 :: 77
cf_dogs_scaled_scoreIntegerRecommendedfluency, category dogs, scaled score
cf_dogs_percrankFloatRecommendedfluency dogs percentile rank0 :: 100
cf_dogs_rangeIntegerRecommendedfluency dogs range1 :: 71 = Impaired; 2 = Borderline; 3 = Low Average; 4 = Average; 5 = High Average; 6 = Superior; 7 = Very Superior
cf_houseitems_timeIntegerRecommendedfluency household items time frameseconds
cf_houseitems_rawIntegerRecommendedfluency household items number of correct responses0 :: 77
cf_houseitems_scaled_scoreIntegerRecommendedfluency, category household items, scaled score
cf_houseitems_percrankFloatRecommendedfluency household items percentile rank0 :: 100
cf_houseitems_rangeIntegerRecommendedfluency household items range1 :: 71 = Impaired; 2 = Borderline; 3 = Low Average; 4 = Average; 5 = High Average; 6 = Superior; 7 = Very Superior
cf_vehicles_timeIntegerRecommendedfluency vehicles time frameseconds
cf_vehicles_rawIntegerRecommendedfluency vehicles number of correct responses0 :: 77
cf_vehicles_scaled_scoreIntegerRecommendedfluency, category vehicles, scaled score
cf_vehicles_percrankFloatRecommendedfluency vehicles percentile rank0 :: 100
cf_vehicles_rangeIntegerRecommendedfluency vehicles range1 :: 71 = Impaired; 2 = Borderline; 3 = Low Average; 4 = Average; 5 = High Average; 6 = Superior; 7 = Very Superior
cf_boats_timeIntegerRecommendedfluency boats time frameseconds
cf_boats_rawIntegerRecommendedfluency boats number of correct responses0 :: 77
cf_boats_scaled_scoreIntegerRecommendedfluency, category boats, scaled score
cf_boats_percrankFloatRecommendedfluency boats percentile rank0 :: 100
cf_boats_rangeIntegerRecommendedfluency boats range1 :: 71 = Impaired; 2 = Borderline; 3 = Low Average; 4 = Average; 5 = High Average; 6 = Superior; 7 = Very Superior
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: catflu01 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.