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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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Data Structures with shared data
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ABCD Parent PhenX Community Cohesion

6,571 Shared Subjects

Community cohesion is a self-report, person-level measure about whether the people in a participant's neighborhood are close-knit, willing to help, get along with each other, share the same values and can be trusted.  
Clinical Assessments
Social Adjustment
04/09/2020
abcd_pxccp01
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
comc_phenx_select_language Integer Recommended ¿Español? 0 ; 1 0 = No; 1 = Yes comc_phenx_select_language___1, community_cohesion_phenx_select_language_p
comc_phenx_close_knit_p Integer Recommended This is a close-knit neighborhood. / Este es un vecindario muy unido. 5;4;3;2;1;999;777 5= Strongly Agree / Totalmente de acuerdo;4= Agree / De acuerdo; 3= Neither Agree Nor Disagree / Ni de acuerdo ni en desacuerdo; 2= Disagree / En desacuerdo; 1 = Strongly Disagree / Totalmente en desacuerdo; 999 = Don't Know / No sé; 777= Refused / niego a contestar community_cohesion_phenx_close_knit_p
comc_phenx_help_p Integer Recommended People around here are willing to help their neighbors. / La gente de por aquí está dispuesta a ayudar a sus vecinos. 5;4;3;2;1;999;777 5= Strongly Agree / Totalmente de acuerdo;4= Agree / De acuerdo; 3= Neither Agree Nor Disagree / Ni de acuerdo ni en desacuerdo; 2= Disagree / En desacuerdo; 1 = Strongly Disagree / Totalmente en desacuerdo; 999 = Don't Know / No sé; 777= Refused / niego a contestar community_cohesion_phenx_help_p
comc_phenx_get_along_p Integer Recommended People in this neighborhood generally don't get along with each other. / La gente en este vecindario por lo general no se lleva bien. 1;2;3;4;5;999;777 1= Strongly Agree / Totalmente de acuerdo; 2= Agree / De acuerdo; 3= Neither Agree Nor Disagree / Ni de acuerdo ni en desacuerdo; 4 =Disagree / En desacuerdo; 5= Strongly Disagree / Totalmente en desacuerdo; 999 = Don't Know / No sé; 777= Refused / niego a contestar community_cohesion_phenx_get_along_p
comc_phenx_share_values_p Integer Recommended People in this neighborhood do not share the same values. / La gente de este vecindario no comparte los mismos valores. 1= Strongly Agree / Totalmente de acuerdo; 2= Agree / De acuerdo; 3= Neither Agree Nor Disagree / Ni de acuerdo ni en desacuerdo; 4 =Disagree / En desacuerdo; 5= Strongly Disagree / Totalmente en desacuerdo; 999 = Don't Know / No sé; 777= Refused / niego a contestar community_cohesion_phenx_share_values_p
comc_phenx_trusted_p Integer Recommended People in this neighborhood can be trusted. / La gente de este vecindario es de confianza. 1;2;3;4;5;999;777 5= Strongly Agree / Totalmente de acuerdo;4= Agree / De acuerdo; 3= Neither Agree Nor Disagree / Ni de acuerdo ni en desacuerdo; 2= Disagree / En desacuerdo; 1 = Strongly Disagree / Totalmente en desacuerdo; 999 = Don't Know / No sé; 777= Refused / niego a contestar community_cohesion_phenx_trusted_p
comc_phenx_skip_p Integer Recommended If a group of neighborhood children were skipping school and hanging out on a street corner how likely is that your neighbors would do something about it? / Si un grupo de niños del vecindario estuviera faltando a la escuela y pasando el rato en una esquina ¿qué probabilidades hay de que sus vecinos hagan algo al respecto? 5;4;3;2;1;999;777 5=Very Likely / Muy probable; 4= Likely / Probable; 3 = Neither Likely Nor Unlikely / Ni probable ni improbable; 2= Unlikely / Improbable; 1= Very Unlikely / Muy improbable; 999 = Don't Know / No sé; 777 = Refused / niego a contestar community_cohesion_phenx_skip_p
comc_phenx_graffiti_p Integer Recommended If some children were spray-painting graffiti on a local building how likely is it that your neighbors would do something about it? / Si algunos niños estuvieran pintando grafiti con pintura en aerosol en un edificio local ¿qué probabilidades hay de que sus vecinos hagan algo al respecto? 5;4;3;2;1;999;777 5=Very Likely / Muy probable; 4= Likely / Probable; 3 = Neither Likely Nor Unlikely / Ni probable ni improbable; 2= Unlikely / Improbable; 1= Very Unlikely / Muy improbable; 999 = Don't Know / No sé; 777 = Refused / niego a contestar community_cohesion_phenx_graffiti_p
comc_phenx_disrespect_p Integer Recommended If a child was showing disrespect to an adult how likely is it that people in your neighborhood would scold that child? / Si un niño le estuviera faltando el respeto a un adulto ¿qué probabilidades hay de que la gente de su vecindario regañe a ese niño? 5;4;3;2;1;999;777 5=Very Likely / Muy probable; 4= Likely / Probable; 3 = Neither Likely Nor Unlikely / Ni probable ni improbable; 2= Unlikely / Improbable; 1= Very Unlikely / Muy improbable; 999 = Don't Know / No sé; 777 = Refused / niego a contestar community_cohesion_phenx_disrespect_p
comc_phenx_fight_p Integer Recommended If there was a fight in front of your house and someone was being beaten or threatened how likely is it that your neighbors would break it up? / Si hubiera una pelea enfrente de su casa y a alguien lo estuvieran golpeando o amenazando ¿qué probabilidades hay de que sus vecinos intervengan? 5;4;3;2;1;999;777 5=Very Likely / Muy probable; 4= Likely / Probable; 3 = Neither Likely Nor Unlikely / Ni probable ni improbable; 2= Unlikely / Improbable; 1= Very Unlikely / Muy improbable; 999 = Don't Know / No sé; 777 = Refused / niego a contestar community_cohesion_phenx_fight_p
comc_phenx_budget_p Integer Recommended Suppose that because of budget cuts the fire station closest to your home was going to be closed down by the city. How likely is it that neighborhood residents would organize to try to do something to keep the fire station open? / Suponga que debido a los recortes de presupuesto la municipalidad fuese a cerrar la estacion de bomberos mas cercana a su casa. ¿Qué tan probable es que los residentes de su vecindad se organicen para tratar de hacer algo para mantener la estación de bomberos abierta? 5;4;3;2;1;999;777 5=Very Likely / Muy probable; 4= Likely / Probable; 3 = Neither Likely Nor Unlikely / Ni probable ni improbable; 2= Unlikely / Improbable; 1= Very Unlikely / Muy improbable; 999 = Don't Know / No sé; 777 = Refused / niego a contestar community_cohesion_phenx_budget_p
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.