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Filter Cart
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NDA Help Center

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 filter 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

  • What is a 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.
  • What do I do after filters are added to the Filter Cart?
    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.
  • Are there limitations on the amount of data a user can download?

    NDA limits the rate at which individual users can transfer data out of Amazon Web Services (AWS) S3 Object storage to non-AWS internet addresses. All users have a download limit of 20 Terabytes. This limit applies to the volume of data an individual user can transfer within a 30-day window. Only downloads to non-AWS internet addresses will be counted against the limit.

  • How does Filter Cart Boolean logic work?

    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.

Glossary

  • Workspace
    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.
  • 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 adds data using an AND condition. The opportunity to further refine data to determine what will be downloaded or sent to a miNDAR is available on the Data Packaging Page, the next step after the Filter Cart. Subsequent access to data is restricted by User Permission or Privilege; however Filter Cart use is not.
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Change History For

Frog Story Eye Tracking

46 Shared Subjects

N/A
Clinical Assessments
Eye Tracking
06/10/2019
frogstory01
07/15/2019
View Data Structure
01
Element Name Change Description Before Now When
sex DESCRIPTION DESCRIPTION: Sex of the subject DESCRIPTION: Sex of subject at birth 07/09/2021 10:38 AM
interview_date NOTES, NOTES: Required field NOTES: 01/08/2021 10:21 AM
sex VALUE_RANGE, , NOTES, VALUE_RANGE: M;F NOTES: M = Male; F = Female VALUE_RANGE: M;F; O; NR NOTES: M = Male; F = Female; O=Other; NR = Not reported 02/21/2020 13:02 PM
time_facesum_3 VALUE_RANGE, VALUE_RANGE: 100::100000 VALUE_RANGE: 07/15/2019 16:37 PM
time_animatesum VALUE_RANGE, VALUE_RANGE: 20000::140000 VALUE_RANGE: 07/15/2019 16:36 PM
time_bodiessum VALUE_RANGE, VALUE_RANGE: 20000::140000 VALUE_RANGE: 07/15/2019 16:36 PM
time_facesum VALUE_RANGE, VALUE_RANGE: 20000::140000 VALUE_RANGE: 07/15/2019 16:36 PM
time_focus_of_attnsum VALUE_RANGE, VALUE_RANGE: 20000::140000 VALUE_RANGE: 07/15/2019 16:36 PM
time_inanimatesum VALUE_RANGE, VALUE_RANGE: 20000::140000 VALUE_RANGE: 07/15/2019 16:36 PM
time_offareasum VALUE_RANGE, VALUE_RANGE: 20000::140000 VALUE_RANGE: 07/15/2019 16:36 PM
time_protagonistsum VALUE_RANGE, VALUE_RANGE: 20000::140000 VALUE_RANGE: 07/15/2019 16:36 PM
time_animatesum_2 VALUE_RANGE, VALUE_RANGE: 20000::300000 VALUE_RANGE: 07/15/2019 16:35 PM
time_bodiessum_2 VALUE_RANGE, VALUE_RANGE: 20000::300000 VALUE_RANGE: 07/15/2019 16:35 PM
time_facesum_2 VALUE_RANGE, VALUE_RANGE: 20000::300000 VALUE_RANGE: 07/15/2019 16:35 PM
time_focus_of_attnsum_2 VALUE_RANGE, VALUE_RANGE: 20000::300000 VALUE_RANGE: 07/15/2019 16:35 PM
time_inanimatesum_2 VALUE_RANGE, VALUE_RANGE: 20000::300000 VALUE_RANGE: 07/15/2019 16:35 PM
time_offareasum_2 VALUE_RANGE, VALUE_RANGE: 20000::300000 VALUE_RANGE: 07/15/2019 16:35 PM
time_protagonistsum_2 VALUE_RANGE, VALUE_RANGE: 20000::300000 VALUE_RANGE: 07/15/2019 16:35 PM
tattime_inanimate NEW_MAP_ELEMENT Added data element 'tattime_inanimate' to data structure 'frogstory01'. 07/15/2019 16:33 PM
tattime_inanimate TYPE TYPE: Integer TYPE: Float 07/15/2019 16:33 PM
fs1percentdur_focus_of_attn POSITION POSITION: 133 POSITION: 132 07/15/2019 16:33 PM
fs1percentdur_protagonist POSITION POSITION: 130 POSITION: 129 07/15/2019 16:33 PM
fs1percentfix_focus_of_attn POSITION POSITION: 126 POSITION: 125 07/15/2019 16:33 PM
fs1percentfix_inanimate POSITION POSITION: 135 POSITION: 134 07/15/2019 16:33 PM
fs1percentfix_protagonist POSITION POSITION: 123 POSITION: 122 07/15/2019 16:33 PM
percentfix_bodies POSITION POSITION: 125 POSITION: 124 07/15/2019 16:33 PM
percentfix_face POSITION POSITION: 124 POSITION: 123 07/15/2019 16:33 PM
percentfix_offarea POSITION POSITION: 127 POSITION: 126 07/15/2019 16:33 PM
tatpercentdur_animate POSITION POSITION: 128 POSITION: 127 07/15/2019 16:33 PM
tatpercentdur_bodies POSITION POSITION: 132 POSITION: 131 07/15/2019 16:33 PM
tatpercentdur_face POSITION POSITION: 131 POSITION: 130 07/15/2019 16:33 PM
tatpercentdur_inanimate POSITION POSITION: 129 POSITION: 128 07/15/2019 16:33 PM
tatpercentdur_offarea POSITION POSITION: 134 POSITION: 133 07/15/2019 16:33 PM
durationsum_2 VALUE_RANGE, VALUE_RANGE: 100::300000 VALUE_RANGE: 100::400000 07/12/2019 14:49 PM
durationsum_3 VALUE_RANGE, VALUE_RANGE: 17000::100000 VALUE_RANGE: 10000::150000 07/01/2019 17:31 PM
time_animatesum_3 VALUE_RANGE, VALUE_RANGE: 4000::90000 VALUE_RANGE: 100::100000 07/01/2019 17:31 PM
time_bodiessum_3 VALUE_RANGE, VALUE_RANGE: 4000::90000 VALUE_RANGE: 100::100000 07/01/2019 17:31 PM
time_facesum_3 VALUE_RANGE, VALUE_RANGE: 4000::90000 VALUE_RANGE: 100::100000 07/01/2019 17:31 PM
time_focus_of_attnsum_3 VALUE_RANGE, VALUE_RANGE: 4000::90000 VALUE_RANGE: 100::100000 07/01/2019 17:31 PM
time_inanimatesum_3 VALUE_RANGE, VALUE_RANGE: 4000::90000 VALUE_RANGE: 100::100000 07/01/2019 17:31 PM
time_offareasum_3 VALUE_RANGE, VALUE_RANGE: 4000::90000 VALUE_RANGE: 100::100000 07/01/2019 17:31 PM
time_protagonistsum_3 VALUE_RANGE, VALUE_RANGE: 4000::90000 VALUE_RANGE: 100::100000 07/01/2019 17:31 PM
animatesum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 0::500 07/01/2019 17:30 PM
bodiessum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 0::500 07/01/2019 17:30 PM
facesum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 0::500 07/01/2019 17:30 PM
focus_of_attnsum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 0::500 07/01/2019 17:30 PM
inanimatesum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 0::500 07/01/2019 17:30 PM
offareasum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 0::500 07/01/2019 17:30 PM
protagonistsum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 0::500 07/01/2019 17:30 PM
time_animatesum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 20000::300000 07/01/2019 17:30 PM
time_bodiessum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 20000::300000 07/01/2019 17:30 PM
time_facesum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 20000::300000 07/01/2019 17:30 PM
time_focus_of_attnsum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 20000::300000 07/01/2019 17:30 PM
time_inanimatesum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 20000::300000 07/01/2019 17:30 PM
time_offareasum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 20000::300000 07/01/2019 17:30 PM
time_protagonistsum_2 VALUE_RANGE, VALUE_RANGE: 40::500 VALUE_RANGE: 20000::300000 07/01/2019 17:30 PM
animatesum_1 VALUE_RANGE, VALUE_RANGE: 14::120 VALUE_RANGE: 0::150 07/01/2019 17:30 PM
bodiessum_1 VALUE_RANGE, VALUE_RANGE: 14::120 VALUE_RANGE: 0::150 07/01/2019 17:30 PM
facesum_1 VALUE_RANGE, VALUE_RANGE: 14::120 VALUE_RANGE: 0::150 07/01/2019 17:30 PM
focus_of_attnsum_1 VALUE_RANGE, VALUE_RANGE: 14::120 VALUE_RANGE: 0::150 07/01/2019 17:30 PM
inanimatesum_1 VALUE_RANGE, VALUE_RANGE: 14::120 VALUE_RANGE: 0::150 07/01/2019 17:30 PM
offareasum_1 VALUE_RANGE, VALUE_RANGE: 14::120 VALUE_RANGE: 0::150 07/01/2019 17:30 PM
protagonistsum_1 VALUE_RANGE, VALUE_RANGE: 14::120 VALUE_RANGE: 0::150 07/01/2019 17:30 PM
time_animatesum_1 VALUE_RANGE, VALUE_RANGE: 5000::60000 VALUE_RANGE: 100::60000 07/01/2019 17:30 PM
time_bodiessum_1 VALUE_RANGE, VALUE_RANGE: 5000::60000 VALUE_RANGE: 100::60000 07/01/2019 17:30 PM
time_facesum_1 VALUE_RANGE, VALUE_RANGE: 5000::60000 VALUE_RANGE: 100::60000 07/01/2019 17:30 PM
time_focus_of_attnsum_1 VALUE_RANGE, VALUE_RANGE: 5000::60000 VALUE_RANGE: 100::60000 07/01/2019 17:30 PM
time_inanimatesum_1 VALUE_RANGE, VALUE_RANGE: 5000::60000 VALUE_RANGE: 100::60000 07/01/2019 17:30 PM
time_offareasum_1 VALUE_RANGE, VALUE_RANGE: 5000::60000 VALUE_RANGE: 100::60000 07/01/2019 17:30 PM
time_protagonistsum_1 VALUE_RANGE, VALUE_RANGE: 5000::60000 VALUE_RANGE: 100::60000 07/01/2019 17:30 PM
fs1percentfix_inanimate NEW_MAP_ELEMENT Added data element 'fs1percentfix_inanimate' to data structure 'frogstory01'. 07/01/2019 17:28 PM
fs1percentfix_inanimate NEW_DATA_ELEMENT 07/01/2019 17:28 PM
gender PERMANENTLY ALIASED gender sex 04/22/2019 00:00 AM
Data Structure Change History

This page displays a list of all the changes that have been made to this data structure since its original definition. You can view the element name, the attribute that was changed, previous and subsequent values, and the timestamp of the change. Use the search bar to filter the displayed changes.

Please email the The NDA Help Desk with any questions.