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Filter Cart

Viewable at the top right of NDA pages, the Filter Cart is a temporary holder for filters and data they select. Filters are added to the Workspace first, before being submitted to The Filter Cart. Data selected by filters in the Filter Cart can be added to a Data Package or an NDA Study from the Data Packaging Page, by clicking the 'Create Data Package / Add Data to Study' button.

The filter cart supports combining multiple filters together, and depending on filter type will use "AND" or "OR"  when combining filters.

Multiple selections from the same filter type will result in those selections being applied with an ‘OR’ condition. For example, if you add an NDA Collection Filter with selections for both collections 2112 and 2563 to an empty Workspace, the subjects from NDA Collection 2112 ‘OR’ NDA Collection 2563 will be added to your Workspace even if a subject is in both NDA Collections. You can then add other NDA Collections to your Workspace which further extends the ‘OR’ condition.

If a different filter type is added to your Workspace, or a filter has already been submitted to the Filter Cart, the operation then performs a logical ‘AND’ operation. This means that given the subjects returned from the first filter, only those subjects that matched the first filter are returned by the second filter (i.e., subjects that satisfied both filters).

When combining other filters with the GUID filter, please note the GUID filter should be added last. Otherwise, preselected data may be lost. For example, a predefined filter from Featured Datasets may select a subset of data available for a subject. When combined with a GUID filter for the same subject, the filter cart will contain all data available from that subject, data structure, and dataset; this may be more data than was selected in the predefined filter for that subject. Again, you should add the GUID Filter as the last filter to your cart. This ensures 'AND' logic between filters and will limit results to the subjects, data structures, and datasets already included in your filter cart.

Note that only the subjects specific to your filter will be added to your Filter Cart and only on data shared with the research community. Other data for those same subjects may exist (i.e., within another NDA Collection, associated with a data structure that was not requested in the query, etc.). So, users should select ‘Find all Subjects Data’ to identify all data for those specific subjects. 

Additional Tips:

  • You may query the data without an account, but to gain access you will need to create an NDA user account and apply for access.  Most data access requires that you or your lab are sponsored by an NIH recognized institution with Federal Wide Assurance (FWA).  Without access, you will not be able to obtain individual-level data. 

    Once you have selected data of interest you can:
  • Create a data package - This allows you to specify format for access/download
  • Assign to Study Cohort - Associate the data to an NDA Study allowing for a DOI to be generated and the data to be linked directly to a finding, publication, or data release. 
  • Find All Subject Data - Depending on filter types being used, not all data associated with a subject will be selected.  Data may be restricted by data structure, NDA Collection, or outcome variables (e.g., NDA Study). ‘Find All Data’ expands the fliter criteria by replacing all filters in your Filter Cart with a single Query by GUID filter for all subjects selected by those filters.

    Please Note:
  • When running a query, it may take a moment to populate the Filter Cart. Queries happen in the background so you can define other queries during this time. 
  • When you add your first filter, all data associated with your query will be added to the Filter Cart (e.g., a Concept, an NDA Collection, a Data Structure/Element, etc.). As you add additional filters, they will also display in the Filter Cart. Only the name of filter will be shown in the Filter Cart, not the underlying structures. 
  • Information about the contents of the Filter Cart can be seen by clicking "Edit”.
  • Once your results appear in the Filter Cart, you can create a data package or assign subjects to a study by selecting the 'Package/Assign to Study' option. You can also 'Edit' or 'Clear' filters.
     

Frequently Asked Questions

  • The Filter Cart currently employs basic AND/OR Boolean logic. A single filter may contain multiple selections for that filter type, e.g., a single NDA Study filter might contain NDA Study 1 and NDA Study 2. A subject that is in EITHER 1 OR 2 will be returned.  Adding multiple filters to the cart, regardless of type, will AND the result of each filter.  If NDA Study 1 and NDA Study 2 are added as individual filters, data for a subject will only be selected if the subject is included in  BOTH 1 AND 2.

    When combining other filters with the GUID filter, please note the GUID filter should be added last. Otherwise, preselected data may be lost. For example, a predefined filter from Featured Datasets may select a subset of data available for a subject. When combined with a GUID filter for the same subject, the filter cart will contain all data available from that subject, data structure, and dataset; this may be more data than was selected in the predefined filter for that subject. Again, you should add the GUID Filter as the last filter to your cart. This ensures 'AND' logic between filters and will limit results to the subjects, data structures, and datasets already included in your filter cart.

  • Viewable at the top right of NDA pages, the Filter Cart is a temporary holder of data identified by the user, through querying or browsing, as being of some potential interest. The Filter Cart is where you send the data from your Workspace after it has been filtered.

  • After filters are added to the Filter Cart, users have options to ‘Create a Package’ for download, ‘Associate to Study Cohort’, or ‘Find All Subject Data’. Selecting ‘Find All Subject Data’ identifies and pulls all data for the subjects into the Filter Cart. Choosing ‘Create a Package’ allows users to package and name their query information for download. Choosing ‘Associate to Study Cohort’ gives users the opportunity to choose the Study Cohort they wish to associate this data.

Glossary

  • Once your filter cart contains the subjects of interest, select Create Data Package/Assign to Data Study which will provide options for accessing item level data and/or assigning to a study.  

  • Once queries have been added to your workspace, the next step is to Submit the Filters in the workspace to the Filter Cart.  This process runs the queries selected, saving the results within a filter cart attached to your account.  

  • The Workspace within the General Query Tool is a holding area where you can review your pending filters prior to adding them to Filter Cart. Therefore, the first step in accessing data is to select one or more items and move it into the Workspace. 

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UPPS-P for Children Short Form (ABCD-version)

11,878 Shared Subjects

Impulsivity
Clinical Assessments
Personality
12/04/2017
abcd_upps01
10/27/2020
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
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 subject at birth M;F; O; NR M = Male; F = Female; O=Other; NR = Not reported gender
eventname String 60 Required The event name for which the data was collected
Query upps6_y Integer Recommended I like to stop and think about things before I do it. 4 ; 3 ; 2 ; 1 4 = Not at all like me; 3 = Not like me; 2 = Somewhat like me; 1 = Very much like me upps_6
Query upps7_y Integer Recommended When I feel bad, I often do things I later regret in order to make myself feel better now. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_7
Query upps11_y Integer Recommended Sometimes when I feel bad, I keep doing something even though it is making me feel worse. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_11
Query upps12_y Integer Recommended I enjoy taking risks. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_12
Query upps15_y Integer Recommended I finish what I start. 4 ; 3 ; 2 ; 1 4 = Not at all like me; 3 = Not like me; 2 = Somewhat like me; 1 = Very much like me upps_15
Query upps16_y Integer Recommended I try to take a careful approach to things. 4 ; 3 ; 2 ; 1 4 = Not at all like me; 3 = Not like me; 2 = Somewhat like me; 1 = Very much like me upps_16
Query upps17_y Integer Recommended When I am upset I often act without thinking. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_17
Query upps18_y Integer Recommended I like new, thrilling things, even if they are a little scary. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_18
Query upps19_y Integer Recommended I tend to get things done on time. 4 ; 3 ; 2 ; 1 4 = Not at all like me; 3 = Not like me; 2 = Somewhat like me; 1 = Very much like me upps_19
Query upps20_y Integer Recommended When I feel rejected, I often say things that I later regret. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_20
Query upps21_y Integer Recommended I would like to learn to fly an airplane. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_21
Query upps22_y Integer Recommended I am a person who always gets the job done. 4 ; 3 ; 2 ; 1 4 = Not at all like me; 3 = Not like me; 2 = Somewhat like me; 1 = Very much like me upps_22
Query upps23_y Integer Recommended I am very careful. 4 ; 3 ; 2 ; 1 4 = Not at all like me; 3 = Not like me; 2 = Somewhat like me; 1 = Very much like me upps_23
Query upps24_y Integer Recommended I almost always finish projects that I start. 4 ; 3 ; 2 ; 1 4 = Not at all like me; 3 = Not like me; 2 = Somewhat like me; 1 = Very much like me upps_24
Query upps27_y Integer Recommended I would like to ski very fast down a high mountain slope. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_27
Query upps28_y Integer Recommended I tend to stop and think before doing things. 4 ; 3 ; 2 ; 1 4 = Not at all like me; 3 = Not like me; 2 = Somewhat like me; 1 = Very much like me upps_28
Query upps35_y Integer Recommended When I am in a great mood, I tend to do things that can cause me problems. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_35
Query upps36_y Integer Recommended I tend to act without thinking when I am very, very happy. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_36
Query upps37_y Integer Recommended When I get really happy about something, I tend to do things that can lead to trouble. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_37
Query upps39_y Integer Recommended I tend to lose control when I am in a great mood. 1 ; 2 ; 3 ; 4 1 = Not at all like me; 2 = Not like me; 3 = Somewhat like me; 4 = Very much like me upps_39
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.